Navigating the Intersection: How Traffic Control Types Affect Cyclist Right- of-Way using A Mixed Logit Model Analysis

Navigating the Intersection: How Traffic Control Types Affect Cyclist Right- of-Way using A Mixed Logit Model Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Navigating the Intersection: How Traffic Control Types Affect Cyclist Right- of-Way using A Mixed Logit Model Analysis Mousa Abushattal, Fadi Alhomaidat, Mohammad El-Yabroudi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6108064/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Cycling safety is becoming increasingly critical as both cycling popularity and vehicular traffic continue to rise. Cyclists face heightened risks at intersections and junctions; areas where conflicting traffic flows, ambiguous right-of-way rules, and inadequate signaling often lead to collisions. Enhancing safety at these key points through improved junction design, effective traffic controls, and dedicated cycling infrastructure is essential for protecting all road users. This research investigates the behavior of drivers and cyclists at junctions under various control setup (traffic signals, stop/yield signs, and no control), with a particular focus on how intersection control influences drivers’ failure to yield the right-of-way to cyclists. Utilizing ten years of Michigan police crash investigation data, we employ a Mixed Logit Model to analyze different interaction scenarios at intersections and assess the likelihood of drivers failing to yield. The experimental results indicate that most crash characteristics; such as driver age, weekday, vehicle type, and speed; consistently affect the probability of yielding failure across different intersection control types. On the other hand, driving under the influence of alcohol or drugs shows a distinct impact on crash likelihood. Additionally, the findings reveal a negative association between the probability of a driver failing to yield and the maneuver of driving straight ahead, along with significant heterogeneity in yielding failures across various intersection control types. These insights underscore the need for a deeper understanding of driver attitudes in interactions with cyclists. Strategies aimed at reducing intersection complexity; such as better coordination of bicycle facilities with intersection control types and enhanced driver education on cyclist behavior and traffic rules, should be considered to improve overall safety. Earth and environmental sciences/Environmental social sciences/Psychology and behaviour Physical sciences/Engineering/Civil engineering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION The transition from car travel to cycling in urban environments presents significant benefits for public health, environmental sustainability, and urban mobility. Studies have shown that cycling as a daily mode of transport substantially increases physical activity levels, reducing the risk of all-cause mortality, cardiovascular diseases, and obesity-related conditions​. Research on bicycle-sharing programs, such as Barcelona’s Bicing initiative, has demonstrated a positive health impact, with physical activity benefits outweighing risks related to air pollution exposure and road traffic incidents​. Moreover, shifting a portion of urban car trips to cycling not only reduces greenhouse gas emissions but also contributes to improved air quality, potentially preventing pollution-related deaths​. The integration of cycling into urban transport policies can thus serve as a dual-purpose strategy, simultaneously addressing public health concerns and mitigating environmental impacts. 1 – 3 . However, safety issues are among the largest obstacles for road users to increase the probability of using cycling for their trips. In 2020, about 938 cyclists were killed in traffic, which accounts for 2.4 percent of total traffic fatalities, and 38,886 have injured. Figure 1 shows the total numbers of injured cyclists per year for the period of 2011–2020. Figure 2 shows the total numbers of cyclists fatalities per year for the period of 2011–2020. Out of total fatalities 26% occurred at intersections, Fig. 3 show the percent of fatalities per crash locations 4 . Intersection crashes remain a critical challenge in traffic safety, particularly among older drivers and in environments with complex visual demands. Research has shown that older drivers are disproportionately involved in intersection-related collisions, often due to failures in yielding the right-of-way, misjudging gaps in traffic, or failing to detect other road users. These errors are frequently attributed to age-related declines in visual scanning, cognitive processing, and reaction time, making intersection navigation particularly hazardous for aging drivers. Additionally, studies on driver attention allocation reveal that perceptual errors play a significant role in intersection crashes. Many drivers, regardless of age, exhibit an inadequate distribution of attention, often focusing on high-traffic areas while neglecting peripheral hazards such as pedestrians or cyclists. [5], [6], [7], [8], [9], [10] This effect is particularly pronounced in lower-complexity intersections, where drivers may develop inappropriate expectations about potential hazards, leading to failures in visual search and risk assessment. Addressing these issues requires a combination of infrastructure improvements, such as roundabouts and protected left-turn lanes, and advancements in driver-assist technologies that enhance situational awareness and mitigate perceptual errors in high-risk scenarios. 5 , 6 . Therefore, the intersections are usually equipped with traffic controls such as traffic signals, signs, and markings that guide users' interactions to improve safety 7 . Despite the positive impact of traffic controls on traffic mobility and safety, they often lead to some adverse effects that may contribute to particular crash types, such as failing to yield the right-of-way 8 , 9 . Literature Review Numerous studies have investigated the contributing factors in driver-cyclist crashes, including the driver's characteristics, vehicle, environment, and infrastructure. These factors influence the likelihood and severity of crashes, with particular attention to intersection-related incidents due to their complexity. A logistic model has been developed for a bicycle route safety mode based on crash severity, incorporating variables such as lane width, vertical gradient, and highway classification 8 . In Australia, the research examined the impact of driver age and experience on driver-cyclist crashes, identifying patterns associated with novice and experienced drivers and advocating for improved cyclist safety throught novice driver training 10 , 11 . Environmental conditions such as weather, light, and road surface were analyzed using log-binomial regression on police reports between 2000 and 2014 12 . The research unveiled a substantial correlation between lighting conditions and crash severity for driver-cyclist crashes, whereas weather and road surface seemed to have a limited impact. For intersection crashes, various studies have focused on examining driver-cyclist interaction. Left-turn crashes had been found to be less severe than other crash categories based on crash reports and reviews of user behavior videos 8 . Likewise, a study was conducted in North to examine the influence of various factors on bicycle crashes at intersection and non-intersection locations 13 . The study demonstrated a significant association between contributing factors and crash location. For example, the cyclist injury severity was identified as significantly impacted by cyclists being under the influence of alcohol. In contrast, non–intersection cyclist crash injuries were affected by drivers under the influence of alcohol. Using negative binomial regression 14 analyzed the cyclist safety for 540 non-signalized intersections by classifying the crashes as whether the cyclist or driver had the right-of-way. The findings suggested the likelihood of a crash in which a cyclist has the right-of-way at an intersection is higher with some features such as two-way bicycle tracks and good marking that inversely correlated with speed calming initiatives. Given the difficulty of navigating intersections associated with high workload, understanding the interaction between drivers and cyclists is more important and requires higher attention 15 . For instance, a risk comparison between intersection and non-intersection crashes was conducted by 16 to investigate the effect of various factors on cyclist injury severity. The study revealed a difference in which factors affect bicyclist injury severity in both environments. Similarly, the cyclist crash severity had been analyzed under two built environments of intersection and non-intersection to show which factors cause cyclist injury at each location 17 . As one of the intersection elements, traffic control had a major effect on the driver and cyclist interaction. Another study analyzed the impact of various types of intersection traffic controls in Australia by conducting a logit model 16 . The analysis results indicated that cyclist injury severity is associated with more factors under stop or yield controlled intersections than signal-controlled or non-controlled intersections. Similarly, naturalistic research involving different intersection types was performed to examine the effects of road features on cyclist crash risk. The findings suggested that the risk of cyclist crashes at intersections with a traffic light is higher than at priority (Stop/ yield) or uncontrolled intersections 18 . Regarding driver–cyclist intersection crashes, the previous studies suggested a specific correlation between control type and crash pattern 8 , 19 , 20 . A simulation study was conducted on a non-signalized T-intersection and showed that when drivers attempt to turn right, they commonly ignore the cyclists who come from the right side and fail to yield the right-of-way to them 19 . The research was performed in Australia on non-signalized and signalized intersections and reported a high proportion of left-turn crashes between drivers and cyclists with no traffic control. Moreover, the research emphasized the importance of user guidance by traffic control to make their behavior predictable and understandable to each other when they interact at an intersection 8 . Another study analyzed three driver movements at a signalized intersection (through, left turning, and right turning) when engaging with a cyclist 21 . Using negative binomial models, the authors examined the collision risk between users for each interaction scenario of driver movements. A correlation was found between the crash risk for each driver's movement and particular interaction circumstances. For instance, the crash risk for through movement had been influenced positively by running red signals. At the same time, the increment of average right-turning vehicle volume would decrease the through-movement risk. Another study established a strong connection between driver-cyclist crash risk and intersection complexity under different traffic control types, with greater crash risks associated with the presence of cycle lanes and traffic lights 22 . The literature demonstrates that intersection control types play a crucial role in shaping driver behavior and crash patterns in driver-cyclist intersections. Studies have highlighted the correlation between traffic control measures and crash severity, emphasizing the importance of user guidance through signals and signage. However, while prior research has identified general trends, a comprehensive understanding of how different intersection control types influence the likelihood of drivers failing to yield to cyclists remains limited. This study aims to fill that gap by investigating how intersection traffic control design and operational characteristics affect the probability of drivers failing to yield the right-of-way to cyclists. By identifying key design and operational factors, this research will provide valuable insights for engineers and planners to improve intersection safety and reduce driver-cyclist conflicts. DATA DESCRIPTION This study examined crash records related to vehicle-bicycle collisions over a ten-year period using police reports from the State of Michigan. The crash data provides detailed insights into motor vehicle–cyclist incidents. Typically, the dataset includes various types of motorist–cyclist collisions: both those involving multiple vehicles and cyclists, and single-cyclist crashes where no motor vehicle was involved. This study focused on intersection-related crashes involving a single vehicle and a cyclist. After removing multi-vehicle cyclist crashes and cases with missing or unknown data from an initial total of 11,449 driver–cyclist intersection-related crashes, the final dataset comprised 5,084 crashes (44.4%). The analysis incorporated various attributes related to the crash, the environment, the parties involved, and the vehicle units to assess how different intersection traffic control types influence single motor vehicle drivers’ failure to yield the right-of-way to cyclists. The Michigan Department of State Police Manual categorizes hazardous actions taken by parties (drivers, cyclists, and pedestrians) into several categories which are summarized in Table 1 Table 1 Actions of different road users as categorized by Michigan Department of State Police Manual Actions by different road users Failed to yield Improper lane use Improper passing Improper backing Speed to fast Reckless driving Speed to slow Careless driving Disregard traffic control Improper signal Drove wrong way Unable to stop Drove left of center Improper turn To assess the impact of traffic control on driver behavior, crashes were classified into two categories: those resulting from a failure to yield the right-of-way and those involving other hazardous actions. Consequently, the response variables were categorized based on these hazardous actions (failure to yield vs. other hazardous actions) to analyze the effect of traffic control type. The Michigan Department of State Police Manual classifies a user's prior crash action into 38 distinct categories. For drivers, the primary movements at intersections include traveling straight ahead, turning left, and turning right. Cyclist movements, on the other hand, are categorized into three main types: moving straight (through), turning (left or right), and crossing (either at an intersection or elsewhere). Based on these classifications, nine distinct interaction scenarios were identified for each type of traffic control. Corresponding abbreviations were introduced for each scenario to streamline analysis and improve clarity. These abbreviations simplify the classification of cyclist-driver interactions, making it easier to study different traffic situations, identify potential conflict points, and develop strategies for safer intersections. The abbreviation convention consists of four letters, divided into two groups, the first group is for the Cyclist and thus it starts with ‘C’, and the second group is for the Driver and thus it starts with ‘D’. Moreover, the second letter of each group is used to abbreviate the movement (A: Ahead, T: Turn, etc.). Table 2 summarizes the interaction scenarios between drivers and cyclists under various traffic controls. Table 2 Interaction Scenarios between Driver and Cyclist Interaction scenario Abbreviation of interaction scenarios Cyclist Movement Driver Movement Straight Ahead Straight Ahead CA-DA Turning (Left or Right) Straight Ahead CT-DA Crossing Straight Ahead CC-DA Straight Ahead Turning Left CA-DL Turning (Left or Right) Turning Left CT-DL Crossing Turning Left CC-DL Straight Ahead Turning Right CA-DR Turning (Left or Right) Turning Right CT-DR Crossing Turning Right CT-DR Three types of intersection traffic control are included (traffic signal control, stop/yield sign, and no traffic control) to determine the effect of traffic type on yielding behavior. Figure 4 presents the distribution of crashes across signalized, stop/yield, and uncontrolled intersections, distinguishing between Failed-to-Yield Crashes and Other Hazardous Actions Crashes. Signalized intersections experience the highest number of crashes, primarily due to failed-to-yield incidents, suggesting driver non-compliance or misjudgment despite traffic control measures. Stop/Yield sign intersections also exhibit a high crash frequency, indicating that regulatory signage alone may not fully mitigate risks. Uncontrolled intersections show the lowest crash numbers, though this may reflect lower traffic volumes or underreporting rather than inherently safer conditions. The response predictor was measured alongside other crash characteristics such as driver age, weather, number of lanes, car type, driving while impaired, and speed limit. Table 2 presents descriptive statistics related to intersection control type and hazardous actions. There are a few things to note when looking at the data overall. First, signalized intersections accounted for about half of all collisions in all intersection control types. In the dataset of crashes, both male and female are equally represented (approximately 50 percent). Furthermore, the report reveals stable factors, such as a high proportion of crashes (around 82 percent) happening on weekdays while driving a passenger vehicle (around 85 percent). However, some characteristics differed depending on the type of intersection control and risky behavior. For example, elderly drivers make up a small proportion of driver-cyclist crashes, with 22.32 percent yield crashes at uncontrolled intersections and 21.26 percent for non-yield crashes at stop/yield sign intersections. However, compared to the other two types of intersection control, elderly driver crashes are more prevalent in signalized intersections with all types of risky behavior. Except for the signalized intersection in the yield dataset, the weather state ratios are fairly identical across intersection control types and hazardous actions (approximately 62 percent). For the yield action dataset, two-lane intersections show up in stop and uncontrolled intersections (71.51 percent and 59.82 percent, respectively), while three-lane intersections have a high proportion of signalized intersections. Non-yield crashes follow the same pattern, with 71 percent for two-lane stop signs and 59.83 percent for uncontrolled intersections, respectively, and 77.21 percent for three-lane signalized intersections. This heatmap Fig. 5 shows how different interaction scenarios contribute to crashes across various intersection control types and crash types (Failed-to-Yield vs. Other Hazardous Actions). Darker shades indicate a higher number of crashes in those scenarios, making it easier to see which driver-cyclist interactions are the most common causes of accidents. According to the type of intersection control, some scenarios are heavily represented relative to others based on prior crash behavior of drivers and bicycles. The higher percentage (28.46 percent) is notable for cyclists riding ahead with the driver turning right under signal control in the yield crash dataset. Uncontrolled intersections are more associated with scenarios with straight-ahead action (CA-DA, CA-DL, CA-DR, and CC–DA). In contrast, stop/yield signs are also more associated with scenarios with straight-ahead action (38.51 percent). The interaction where the driver and cyclist appear to ride straight ahead is well represented among the three types of intersection controls compared to other interaction scenarios, with 35.68 percent for signalized intersections, 49.25 percent for stop/yield sign intersections, and 32.48 percent for uncontrolled intersections in the non-yield crashes dataset. As a result, the distribution of interaction scenarios percentages across intersection control types illustrates the need to explore how intersection control types impact driver-cyclist interactions. Table 3 Descriptive Summary of Model Variables Variable Failed-to-Yield Crashes Dataset Count (Proportion) 3440(67.66%) Other Hazardous Actions Crashes Dataset Count (Proportion) 1644(32.34%) Signal 1727 (50.20%) Stop/Yield Sign 1490 (43.30%) Uncontrolled 223(6.50%) Signal 819(49.80%) Stop/Yield Sign 712(43.30%) Uncontrolled 113(6.90%) Elderly driver (> 60) 450(26.03%) 347(23.32%) 50(22.32%) 213(25.42%) 155(21.26%) 28(23.93%) Male Driver 872(50.43%) 755(50.74%) 127(56.70%) 422(50.36%) 390(53.50%) 57(48.72%) Weekday 1417(81.95%) 1226(82.39%) 186(83.04%) 695(82.94%) 585(80.25%) 99(84.62%) DUI 10(0.58%) 11(0.74%) 4(1.79%) 15(1.79%) 3(0.41%) 1(0.85%) Passenger Vehicle 1465(84.73%) 1227(82.46%) 196(87.50%) 726(86.63%) 591(81.07%) 100(85.47%) Clear Weather Condition 1083(62.64%) 1074(72.18%) 168(75.00%) 623(74.34%) 536(73.53%) 91(77.78%) Daylight light Condition 1393(80.57%) 1268(85.22%) 183(81.70%) 644(76.85%) 594(81.48%) 99(84.62%) Dry Road Condition 1317(76.17%) 1338(89.92%) 204(91.07%) 749(89.38%) 670(91.91%) 106(90.60%) Number of lanes One lane 17(0.98%) 53(3.56%) 9(4.02%) 8(0.95%) 26(3.57%) 5(4.27%) Two Lanes 409(23.66%) 1064(71.51%) 134(59.82%) 183(21.84%) 521(71.47%) 70(59.83%) Three or more Lanes 1304(75.42%) 370(24.87%) 80(35.71%) 647(77.21%) 182(24.97%) 41(35.04%) Interaction Scenario CA – DA 273( (15.79%) 573(38.51%) 54(24.11%) 299(35.68%) 359(49.25%) 38(32.48%) CA – DL 219(12.67%) 227(15.26%) 57(25.45%) 71(8.47%) 66(9.05%) 20(17.09%) CA – DR 492(28.46%) 283(19.02%) 37(16.52%) 155(18.50%) 138(18.93%) 25(21.37%) CT - DA 16(0.93%) 38(2.55%) 13(5.80%) 11(1.31%) 39(5.35%) 16(13.68%) CT – DL 6(0.35%) 11(0.74%) 2(0.89%) 2(0.24%) 12(1.65%) 0(0.00%) CT – DR 8(0.46%) 4(0.27%) 1(0.45%) 3(0.36%) 10(1.37%) 2(1.71%) CC- DA 180(10.41%) 173(11.63%) 34(15.18%) 166(19.81%) 63(8.64%) 11(9.40%) CC – DL 120(6.94%) 56(3.76%) 12(5.36%) 38(4.53%) 18(2.47%) 0(0.00%) CC – DR 418(24.18%) 122(6.25%) 14(6.25%) 93(11.10%) 24(3.29%) 4(3.42%) MODEL DESCRIPTION The mixed logit model, or the random-parameters logit model, is a widely used statistical approach for analyzing decision-making processes where outcomes vary across individuals or cases. Unlike traditional logit models, which assume that all observations respond uniformly to explanatory factors, the mixed logit model allows parameters to vary, making it more flexible in handling unobserved heterogeneity. This feature is particularly valuable in traffic safety research, where multiple factors, including driver behavior, roadway conditions, and environmental variables, influence crash occurrences. The mixed logit model can account for crash variability by allowing associated factors to differ between crashes, addressing the complexities of diverse crash conditions that cannot be accurately captured using a standard logit approach. The Independence from Irrelevant Alternatives (IIA) assumption states that unobserved predictors are uncorrelated with response outcomes. The mixed logit model avoids restrictive model constraints that assume parameters remain fixed across alternatives. This flexibility has led to widespread use in traffic safety studies, particularly in understanding how various factors contribute to crash risks 23 – 26 ]. The mixed logit model has been applied extensively in traffic safety research to analyze crash severity, driver behavior, and roadway conditions. It is commonly used to investigate factors contributing to different levels of crash severity, such as minor, serious, or fatal crashes, by considering variables like driver behavior, road infrastructure, and environmental conditions. The model has also been instrumental in studying driver and cyclist interactions at intersections, particularly in understanding how violations such as failing to yield or disregarding traffic signals contribute to crashes. Additionally, it has been used to assess driver compliance with traffic signals and signs, evaluate mode choice behaviors among commuters, and analyze the effectiveness of road safety policies in reducing crashes. By capturing the complexities of driving behavior and crash dynamics, the mixed logit model provides a more nuanced understanding of how different factors contribute to traffic accidents. This study uses the mixed logit model to analyze driver responsibility in crashes involving cyclists at intersections. The model accounts for variations in crash scenarios by coding driver responsibility as a binary variable, where one indicates that the driver was at fault in a crash involving a cyclist, and zero indicates that the driver was not at fault. This classification is applied to two datasets: one focusing on crashes resulting from other hazardous driving actions and another specifically analyzing failed-to-yield crashes. By integrating a mixed logit framework, the model captures variations in crash dynamics while treating factors such as driver age, day of the week, light conditions, and driver–bicycle interactions as fixed variables. This approach provides deeper insights into the underlying causes of intersection crashes and offers valuable information for improving road safety policies and traffic management strategies 9 , 25 , 27 , 28 . In contrast, seven Michigan Department of Transportation (MDOT) zones are treated as a random variable to account for discrepancies in transportation regions when drivers and cyclists interact. The mixed logit model can be represented in the same manner as the binary logistic regression model but with the inclusion of an assumption of random parameters. The function form of the mixed logit model is in Eq. 1 : $$\:{Q}_{ij}=\:{\beta\:}_{i}{X}_{ij}+{\epsilon\:}_{ij}$$ 1 where \(\:{Q}_{ij}\) is a function defining the driver fault category i (1 or 0) for crash j, \(\:{X}_{ij}\) Expresses a measurable crash vector for several factors such as crash characteristics, vehicle unit, party type factors that indicate the fault outcome for crash j, \(\:{\beta\:}_{i}\) Is a vector for estimable coefficients and the term. \(\:{\epsilon\:}_{ij}\) Indicates the error part with extreme value distribution assumption 29 . Moreover, by assuming the error ( \(\:{\epsilon\:}_{ij}\) ) is an extreme value that is distributed logistically across crashes. The equation of the Multinomial logit model defined in Eq. 2 : $$\:{P}_{n}\left(i\right)=\:\frac{{e}^{\left({\beta\:}_{i}{X}_{in}\right)}}{\sum\:{e}^{\left({\beta\:}_{i}{X}_{in}\right)}}$$ 2 where \(\:{P}_{n}\left(i\right)\) Is the observations' (n) probability classified into discrete outcomes (i). For the mixed logistic model, when unobserved parameters impact the driver's responsibility and make factors vary across crashes, the mixed logistic model provides the discrete outcome (i) with an estimable parameters vector ( \(\:{{\beta\:}}_{\text{i}}\) ). Eq. 3 express the probabilities of the model category: $$\:{P}_{n}\left(i\right)=\:\int\:\frac{{e}^{\left({\beta\:}_{i}{X}_{in}\right)}}{\sum\:{e}^{\left({\beta\:}_{i}{X}_{in}\right)}}f\left(\beta\:|\phi\:\right)d\beta\:$$ 3 Where \(\:{P}_{n}\left(i\right)\) Is a probability function for the discrete outcome of driver fault (1 or 0), \(\:\:{\beta\:}_{i}\) is a vector for regression coefficients, X is an explanatory variable, and \(\:f\left(\beta\:|\phi\:\right)\) is a density function of a random parameter in the model with \(\:\phi\:\) distribution across observations. The density function form may be a regular, lognormal, triangular, or uniform distribution. Results Tables 3 and 4 show the results for a mixed logistic model for both datasets (failed to yield action and non-yield hazardous behavior) at an intersection. The interpretation of the models' results described by the log odds ratio for being the driver at fault (was unable to yield) and not being at fault (the cyclist was at fault). Driver Crash Responsibility for Yield and Non-Yield Crashes The mixed logistic model was analyzed to examine whether intersection control types (signal, stop, and uncontrolled intersection), demographic characteristics, and environmental circumstances would affect the interaction between driver and cyclist. The results suggest that gender, weather, and road conditions did not dramatically affect driver–cyclist interaction across the intersection control type in both hazardous action categories. Based on the statistical outcomes, being an elderly driver was correlated with an increased likelihood of failing to yield the right-of-way by the driver for both hazardous action types, with odd ratios of 17 and 16 for yield and other hazardous action, respectively. Weekdays proved to be a significant contributing factor in both models compared to the weekend. However, the findings indicate a higher risk likelihood of other hazardous actions than failed to yield actions (17 versus 22 percent). Contrary to that, a driver impaired by alcohol and/or drugs has been found more responsible for failed-to-yield crashes with an increase in probability of approximately 3.91 times compared to the normal driver. In contrast, in the other hazardous actions model, they were responsible for 2.89 times the probability increase. For vehicle type, drivers who use small vehicles such as passenger cars, vans, or SUVs are more likely to be at fault by approximately 30 percent (OR = 1.29) for failed-to-yield crashes and 25 (OR = 1.25) percent in other driver hazardous actions. Furthermore, when a driver and a cyclist meet at an intersection in daylight, the model reveals that the risk of the driver being the responsible party for a crash rises by 21 percent in failed-to-yield crashes relative to other hazardous actions (25 percent). When a cyclist interacts with a car in a multi-lane intersection, the models' findings suggest a reduction in driver responsibility. Regarding yield collisions, intersections with two lanes reduce the likelihood of the driver being at fault by 38 percent (OR = 0.62) compared to one-lane intersections, and intersections with three or more lanes reduce the likelihood of the driver being at fault by 42 percent (OR = 0.58). In the case of other driver-hazardous actions, the findings reveal that on a two-lane system, drivers are less likely to be found at fault during interactions with cyclists than on a three-lane system (0.80 and 0.84, respectively). Across intersection control, the speed contribution appeared to have a small impact on probability, increasing by 1 percent. Table 4 Mixed Logistic Model Result for Yield and Non-Yield Actions Variable Yield Crash Other Hazardous Actions Odds Ratio SE Z P>|z| Odds Ratio SE Z P>|z| Driver Age 1.17 0.090 2.06 0.040 1.16 0.080 2.21 0.027 Day of Week 1.17 0.100 1.82 0.069 1.22 0.092 2.67 0.008 DUI 3.91 1.501 3.56 0.000 2.89 1.047 2.93 0.003 Vehicle Type 1.29 0.118 2.78 0.005 1.25 0.102 2.75 0.006 Light Condition 1.21 0.128 1.84 0.066 1.25 0.096 2.86 0.004 Number of Lane 2 0.62 0.136 -2.17 0.030 - - - - 3 0.58 0.128 -2.48 0.013 - - - - Speed Limit 1.01 0.004 2.46 0.014 1.01 0.003 3.14 0.002 CA – DA 1.62 0.160 4.86 0.000 0.58 0.127 -2.50 0.012 CA – DL 12.35 1.786 17.39 0.000 3.90 1.180 4.50 0.000 CA – DR 7.47 0.834 18.03 0.000 - - - - CT - DA 0.17 0.081 -3.71 0.000 0.10 0.060 -3.76 0.000 CT – DL 39.18 40.526 3.55 0.000 - - - - CT – DR 2.80 1.655 1.73 0.083 2.05 2.557 0.57 0.567 CC- DA - - - - 0.05 0.032 -4.88 0.000 CC – DL 9.19 1.900 10.74 0.000 - - - - CC – DR 13.53 2.054 17.15 0.000 - - - - Nine interaction scenarios between driver and cyclist were generated to provide insight into their interaction under three types of intersection control. According to Table 4 , most interaction scenarios were significant for both types of driver hazardous behavior (failed to yield and other hazardous action). According to the findings, a straight-ahead interaction scenario (CA–DA) for all crash parties is related to a 62 percent (OR = 1.62) rise in driver responsibility for failed to yield crashes, compared to a 42 percent (OR = 0.58) decrease in being at fault for other hazardous actions. When a cyclist rides straight ahead when a car turns left (CA-DL), the driver seems more likely to be at fault in both types of hazardous action. However, the odds suggest that the driver is more at fault in yield crashes, with a likelihood of 12.35 times compared to 3.90 times in other hazardous action scenarios. Whenever a bicycle attempted to ride straight ahead while a driver attempted to turn right at an intersection (CA-DR), the interaction increased the driver's responsibility in yield and other hazardous actions by 7.47 and 1.28 times, respectively. When the cyclist's prior action is turning, and the driver travels straight ahead (CT-DA), there is a high percentage (OR = 0.17) of decreasing in probability for the driver being at fault by yielding the right-of-way to the cyclist and around 90 percent (OR = 0.10) less likely to be at fault by any other hazardous action. In interactions where the cyclist tries to turn versus the driver's prior turning action (CT-DL), the likelihood of a driver's fault in a yield crash increases by 39.18 times for left turning and 2.80 times for right turning. No significant effect on probability was observed whenever a driver and a bicycle engaged in turning movements for other hazardous actions. In the cyclist crossing interaction scenario at intersections, the risk of yield activity did not show a significant impact when the driver went straight ahead during the interaction (CC-DA). However, the odds suggest drivers are less likely to be at fault due to other hazardous actions (95 percent, OR = 0.05). A crossing action by a cyclist versus a driver turning left (CC-DL) was associated with a 9.19 times increase in the likelihood of a driver failing to yield the right-of-way to cyclists, whereas turning right by a driver (CC-DR) was associated with a 13.53 times increase in the likelihood of a driver failing to yield the right-of-way to cyclists. Meanwhile, the involvement of other hazardous actions during the crossing interaction had little effect on the likelihood of driver error in the crossing scenario. Failure to Yield under Intersection Control Types Figure 6 depicts the frequency of drivers' hazardous acts while engaging with cyclists at an intersection. According to the results, when a driver and a bicycle collide at an intersection, the failed yield crash is the most common occurrence, i.e., 70%. As a result, a mixed logistic model was used to examine why drivers failed to yield under different intersection controls (Table 5 ). According to the model, driver gender and environmental factors do not substantially affect a driver's likelihood of failing to yield to a cyclist. Furthermore, elderly drivers were related to a 20 (OR = 1.20) percent increase in the risk of failing to yield to a cyclist at a signalized intersection, a 29 percent (OR = 1.29) increase at a stop sign controlled intersection, and a 24 percent (OR = 1.24) increase at an uncontrolled intersection. Compared to weekend interactions, signalized and stop sign intersections increase by around the same percentage (24 vs. 23 percent), whereas uncontrolled intersections increase by a higher percentage (29 percent). In addition, the table of results indicates the effects of alcohol and drugs. For instance, in signalized intersections, the likelihood of a driver failing to yield is 2.41 times. In comparison, the probability is higher for stop-controlled intersections 3.44 times and 2.56, with an increase in the probability of a driver failing to yield when intersections are not controlled. When a passenger car, van, or SUV is involved in a driver-cyclist interaction, the model findings indicate a 40 percent rise in the probability of a cyclist failing to yield the right-of-way. The speed parameter induced a small increase in the likelihood of failure to yield at an intersection, close to the general hazardous action model results. Various impacts appeared regarding the parties' actions (drivers and cyclists) under various intersection control types. For comparison, a signalized intersection reduced the probability of a driver failing to yield by 67 percent (OR = 0.33) while the driver and cyclist were going straight ahead. In comparison, a stop sign intersection reduced the likelihood by 53 percent (OR = 0.47), and uncontrolled intersections reduced the likelihood by 46 percent (OR = 0.54). When a cyclist rides straight ahead while a vehicle turns to the left, an uncontrolled intersection has a risk odd ratio of 5.98 for failing to yield to the cyclist, compared to 4.82 and 2.16 for signalized and stop-controlled intersections, respectively. When the driver tried to turn right instead of left in the same situation, the signalized interaction was only found to be significant on the failure probability, raising the likelihood by 3.53 times. A few interaction scenarios were significant when cyclists' actions were described as turning (either right or left). In signalized and stop sign-controlled intersections, the percentage of failure is almost the same for turning cyclists with straight-driving drivers (5 vs. 6 percent). Regarding turning cyclists with the right-turning driver, only signalized intersections were found to reduce the failure risk by 9 percent. Finally, when the cyclist activity was reported as crossing the intersection, the uncontrolled intersection was associated with a higher likelihood of being the cyclist who failed to yield the right-of-way with 99 percent (OR = 0.01), followed by stop-controlled and signalized intersections with 75 and 72 percent (OR = 25, OR = 28), respectively. Table 5 Mixed Logistic Model Results for Different Intersection Control Types Variable Signal Stop Uncontrolled Odds Ratio SE P>|z| Odds Ratio SE P>|z| Odds Ratio SE P>|z| Driver Age 1.20 0.115 0.060 1.29 0.118 0.005 1.24 0.111 0.017 Day of Week 1.24 0.130 0.037 1.23 0.121 0.039 1.29 0.125 0.008 DUI 2.41 1.290 0.099 3.44 1.839 0.021 2.56 1.353 0.075 Vehicle Type 1.40 0.153 0.002 1.41 0.146 0.001 1.45 0.147 0.000 Speed Limit 1.01 0.005 0.010 1.01 0.005 0.041 1.01 0.005 0.002 CA – DA 0.33 0.049 0.000 0.47 0.051 0.000 0.54 0.154 0.031 CA – DL 4.82 1.055 0.000 2.16 0.401 0.000 5.98 2.847 0.000 CA – DR 3.53 0.504 0.000 - - - - - - CT - DA 0.05 0.047 0.003 0.06 0.030 0.000 - - - CC- DA 0.28 0.049 0.000 0.25 0.043 0.000 0.01 0.013 0.000 CC – DL 5.74 1.809 0.000 - - - - - - CC – DR 6.21 1.184 0.000 1.65 0.389 0.033 - - - When a driver and a bicycle interact at a signalized intersection, the risk of driver's failure increases by 5.74 times, while other control types are insignificant. Drivers were observed raising the probability of failure to yield by 6.21 times for signalized intersections and 65 percent (OR = 1.65) for stop-controlled intersections in the last interaction scenario, where a bicycle crosses the intersection, and the driver tends to turn right. DISCUSSION For the interaction between driver and cyclist, intersections are considered crash-prone zones. This study explores the effect of intersection control types on driver-cyclist crashes based on both parties' prior behavior. The results on demographic characteristics and environmental factors align with previous studies. The models' findings indicate that elderly drivers have a greater chance of making errors in both the failure to yield and other hazardous action models 30 . The difficulty of intersection scanning, search errors, physical-joint problems, and visual problems for elderly drivers compared to younger drivers can all be seen as explanations for this result 6 , 31 , 32 . As a result, it is possible to assume that elderly drivers failed to yield the right-of-way to cyclists because they could not detect them or made a mistake due to increased cognitive workload and intersection difficulty. Elderly drivers have a problem yielding the right-of-way to cyclists at intersections because they are commonly associated with failure rather than reckless or speeding faults, illustrating the problem of turning movement for elderly drivers 31 , 33 . Elderly driver coefficients indicate an increase in fault probability for driver intersections and require more mental tasks to process from the driver than other traffic circumstances in both models 34 . The findings of two risky acts reveal that drivers are 17 percent and 22 percent more likely to be at fault on weekdays by failing to yield and other hazardous actions, respectively. These findings can be explained by the fact that people are more likely to drive and use a motor vehicle on weekdays, which raises the amount of traffic at intersections and the complexity of navigating them relative to weekends, where the number of trips decreases. People are more likely to choose to take a leisure trip and use active modes of transport 35 , 36 . As expected, an impaired driver by alcohol or drugs raises the risk of the driver being at fault by 3.91 times for collisions attributable to yield behavior, which is possibly due to drivers not being able to identify and react properly to a close-by cyclist. Driving under alcohol or drug influence also raises the risk of the driver being at fault by 2.89 times by other hazardous actions. When intoxicated drivers contact cyclists at the intersection, their actions will be riskier and more related to misjudgment and troubled behavior 35 , 37 . The presence of alcohol and drugs would restrict the mental abilities of drivers due to the high demand for tasks at intersections 37 . Therefore, relative to other actions, it is a major factor correlated with driver fault by yield actions. The study found that drivers of heavy vehicles (e.g., trucks, buses) are less likely to be at fault when engaging with cyclists at the intersection than drivers of passenger cars. This outcome concurred with 38 that large vehicle drivers are more likely to avoid serious crashes due to driving experience. In addition, 39 analysis shows that cyclists have trouble handling blind spots around large vehicles, making them more likely to be at fault. Daylight conditions indicate a rise in the risk of driver fault with the light environment in both models. One of the possible causes is that cyclists are less likely to ride at night and are typically most likely to travel in the daytime, so most drivers are involved with cyclists in daylight within complicated environments (intersections). Overall, the analysis results suggest a decline relative to a single-lane intersection system with the possibility of being the driver in the fault group at the multi-lane intersection (2 or more). Increasing the number of lanes at the intersection area offers additional freedom of movement and decreases interference between interaction units 40 . While speed is well recognized as one of the major factors leading to crash cause and crash severity 41 , 42 , a rise in the risk of driver-fault behavior was found by increasing traffic speed by one percent. This small effect may be attributed to the fact that most of the intersections were located in urban areas with low traffic speeds and may not impair the driver's actions while dealing with cyclists. Failed to yield action versus other hazardous actions The risk of the driver being held responsible for failure to yield to cyclists (Red Color) and non-yield cited hazardous actions (Blue Color) is depicted in Fig. 7 . Each point presents the odd ratio of interaction scenarios for a certain intersection traffic control, while the vertical line represents the 95 percent confidence interval. In general, the graph indicates that, as previously mentioned in the literature, drivers are more likely to fail to yield in all interactions with cyclists than to participate in other risky activities. In an interaction situation where the cyclist and driver attempt to ride straight ahead (CA-DA), the model results indicate that the driver is more likely to be responsible for failing to give the cyclist the right of way and less likely to engage in any other risky actions. In most crash dataset cases, the driver and cyclist were on different intersection approaches for this interaction situation. Due to obstacles that obstruct the driver's view from the bicycle side, such as oncoming traffic, a house, or a tree, which cause a time-to-collision reduction 43 , the driver can fail to identify cyclists on the roadway who ride straight ahead. Both hazardous actions (failing to yield action and other actions) appear to increase the probability of the driver being at fault in the case of the cyclist riding ahead while the driver turns left (CA-DL). However, the yield action poses enormous odds in these circumstances to deem the driver at fault relative to other dangerous acts. 8 , 44 results align with the driver's failure in this interaction type, such as sharing a signalized intersection in which the driver appears to accept a limited gap, and the body of some automobiles may obstruct the driver's line of sight. Furthermore, study findings suggest that when drivers turn right while cyclists travel straight (CA–DR), they are bound to be to blame, as compared with cyclists. The same example can be noticed additionally for other perilous activity results. Commonly, the vast majority of these failure-to-yield actions are related to the capacity of the driver to see, designate, and scan the cyclist on the right side, specifically when cyclists travel alongside the driver's vehicle in the blind zone 45 or because of an obstacle that blocks the view 43 . Moreover, it has been found that before carrying out a right turn maneuver, drivers were less likely to search their right-hand side relative to left turn maneuvers 20 . Inconsistent findings have been found regarding the interaction scenario in which cyclists tried to do a turning maneuver (either left or right). For example, in (CT-DA) interaction, the results show that drivers are less likely to fail and cyclists are more responsible for intersection crashes (for failure to yield and other hazardous crashes). Visual and scanning activity may not contribute to these interaction scenarios since the cyclist is positioned in the driver's field view, and drivers are more likely to grant the cyclist the right-of-way. In addition, previous findings suggest that cyclists are more likely to be at fault because they are less likely to comply with intersectional traffic laws 46 . In the (CT-DL) interaction scenario for the yield model, a noticeable impact occurred, while in other risky acts, prior driver action did not seem to have a major rule on the likelihood of driver fault during the interaction. This type of crash is often due to rule infringement for control type by the driver at intersections or due to poor negotiation with cyclists. In addition, drivers tend to focus on the pathway during the turning maneuver. They are less likely to search the side of the vehicle (particularly for the blind zone) or the surrounding area for other users (such as cyclists) 47 . Likewise, a driver's right turn action when the cyclist turns at the intersection (CT-DR) considerably increases the likelihood that the driver is at fault only in yielding. On the other hand, it revealed that the driver is less likely to be at fault when they decide to do a right turn compared to a left turn. This consequence could be due to a greater possibility of the driver violating laws relative to left turns and being more likely to merge with the cyclist instead of crossing their path by the driver during a turning right maneuver. In the case of a driver engaging with a crossing cyclist at an intersection, driver turning movements (either left or right) were found to be significant in the yield scenario. At the same time, other hazardous actions were only significant in the scenario where the driver traveled straight ahead. The findings indicate a small rise in the driver being at fault due to other hazardous actions in (CC–DA), where vehicles appear to ride straight ahead as cyclists cross the intersection. This finding can be explained by the fact that failure to yield is often associated with violating the law at the time of turning. At the same time, unsafe driver acts are more likely to be associated with other risky actions, such as speed during straight-ahead travel 48 if a driver turns left as a cyclist crosses the intersection (CC–DL), the probability of the driver failing to yield the right-of-way to the cyclist increases. The drivers should split their focus between two sides (right and left) while turning left, which creates some mental distraction for them. Drivers making a right turn (CC–DR), on the other hand, can "actively but unintentionally" ignore potential dangers on the right, such as a bicycle approaching 20 . Previously, this difference in turning attention was due to looking but being unable to see or failing to look as sensory failure was established by previous studies. Effect of traffic control types on failure to yield Figure 8 depicts the probability of a driver being at fault during driver-cyclist interactions through various intersection control types (Signal, Stop/Yield, and Uncontrolled). Across all control types, motorist drivers are less likely to fail to yield the right-of-way in the first scenario (CA–DA). The results showed that the signalized intersection had the lowest likelihood of failing to yield the right-of-way to cyclists, followed by stop sign control and uncontrolled intersection. These findings can be explained by the fact that drivers at signalized intersections are more likely to obey signal laws, and cyclists are more likely to be in the field of view where no extra attention is needed because the primary drivers' focus is usually on the signal light. Even if the cyclist is likely to be in the driver's field of view when stopping at a stop sign, extra attention is needed for arriving and departing traffic at the intersection, raising the risk of a driver failing to yield the right-of-way 5 . As one would expect from an uncontrolled intersection, there was much uncertainty in that area, and the driver was exposed to a great deal of environmental demand that needed a lot of mental capacity and attention 49 . On the other hand, (CA–DL) scenario shows an increased probability of the driver failing to yield the right-of-way with the lowest probability of stop sign control followed by signal and uncontrolled, respectively. The difficulty of signalized intersections (such as traffic patterns and right-of-way) can be clarified by sufficient scene scanning without hazard recognition or restricted event processing by selective perception and disturbances during left turn maneuvers 50 , 51 . Only the signalized intersection showed a substantial increase in failure to yield likelihood towards cyclists in the interaction scenarios where cyclists appear to ride ahead, and drivers turn right (CA–DR). The study indicates more tension between the driver and the cyclist during this type of interaction, possibly due to signal phasing and priority causing uncertainty. Similar findings have been discovered by Buch and Jensen 52 ; traffic signal designs such as a green right-turn arrow before the green phase can cause right-turn conflicts at some intersections partially because some motor vehicles continue to drive as though the right-turn arrow is still on, oblivious to the change in obligation to give way when cyclists get green. Furthermore, during a narrative analysis of crash reports, the transition period between phases seemed to be a crucial point because the cyclists assumed they had enough time to move and that the driver could see them on the lane when, in fact, the driver was only waiting for the green light and did not see the cyclist approaching. Only when the driver tends to ride ahead was the scenario found significant (CT–DA) for the second type of cyclist maneuver in which the cyclist tends to turn (either right or left). Under signal and stop sign control intersections, the driver was less likely to fail to yield the right-of-way. Unlike stop sign-controlled intersections, drivers at signalized intersections have a broader field of view. They are less likely to breach traffic laws because they have priority of travel by a traffic signal and are less confused by approaching traffic. Besides that, in a straight-ahead situation, cars are more restricted to signal obligation than cyclists, who can approach from the sidewalk side and violate the traffic signal law due to a misjudgment of time and distance gap for turning maneuvers 53 , 54 . In comparison, priority plays a major role in the conflict at stop sign-controlled intersections. Higher levels of uncertainty may have occurred due to more flexibility in driver movement and higher mental demand in the traveling ahead scenario for other intersection approaches than at signalized intersections 55 , 56 . Regarding bicycle crossing situations, it seems drivers who ride ahead are less likely to fail to yield the right-of-way at an uncontrolled intersection. Drivers are more likely to proceed through intersections at high speeds with less visual search and attention than they would at unsignalized intersections, according to 57 findings. Moreover, cyclists at uncontrolled intersections may misjudge the distance and speed of approaching vehicles 58 . In the case of a crossing cyclist and a turning driver, the results show a significant increase in the likelihood of failing to yield. In a signalized intersection scenario, the left-turning driver appeared significantly. This failure to yield could be due to a lack of correct directional checking, speed, or effective lane use at the queuing space toward oncoming crossing cyclists. Our findings are consistent with 59 who revealed that drivers are more likely to make errors when turning left at a signalized intersection. The stop sign and the signalized intersection significantly impacted the last driver-cyclist interaction scenario, with drivers tending to turn right. 20 discovered that drivers turning right focus on cars approaching from the left and fail to notice the cyclist approaching from the right in time. However, the stop sign was identified as one of the countermeasures that provide drivers with more time during a negotiation process and compel motorists to allocate time to both directions, leveraging cyclist detection by the driver. On the other hand, drivers at signalized intersections are more likely to fail to yield to crossing cyclists due to signal complexity and ineffective traffic light phasing design, which causes confusion and poor judgment between interaction parties 60 . Conclusion This study employs a mixed logit model to analyze the impact of different intersection control types (signals, stop/yield signs, and uncontrolled intersections) on drivers' failure to yield the right-of-way to cyclists at urban intersections. The model was developed using ten years of crash data involving single motor vehicle–cyclist collisions in Michigan. Key crash characteristics, including driver age, day of the week, vehicle type, lighting conditions, number of lanes, and speed limit; were found to be consistent across both hazardous action types (yield and non-yield crashes) and intersection control types. However, the driver’s responsibility for hazardous actions varied only when under the influence of alcohol or drugs. The results indicate that the risk of hazardous actions (both yield and non-yield crashes) varies within the same interaction scenario. However, drivers are more likely to be involved in or at fault for yield crashes involving bicycles than for non-yield crashes. In the Cyclist Ahead – Driver Ahead (CA–DA) interaction scenario, drivers are more likely to fail to yield to cyclists but less likely to be at fault for non-yield behavior. The findings also reveal that the likelihood of a driver failing to yield to a cyclist significantly depends on the type of intersection control in place. For instance, when interacting with crossing cyclists (CC–DL, CC–DR), vehicles making left or right turns are more likely to be at fault due to yield-related actions. Additionally, in scenarios where the driver is traveling straight ahead (CA–DA, CT–DA, and CC–DA), the risk of being at fault for a yield crash decreases. When comparing the risk of driver yield failure across different intersection control types, the effect varies depending on the interaction scenario. For instance, in scenarios where the driver is traveling straight ahead, signalized intersections have a lower failure probability than stop signs or uncontrolled intersections. Conversely, stop/yield signs are associated with a lower failure rate in left-turning vehicle scenarios. In some cases, intersections without control are linked to a higher likelihood of failure. This study highlights the significant impact of intersection control design on driver behavior, particularly yielding ability, which may be influenced by variations in required attention and cognitive demands. Although the models assess the impact of intersection control on driver-cyclist interactions, they have certain limitations, primarily due to data constraints. Analyzing interaction ratios in both crash datasets (yield and non-yield) reveals that some scenarios, such as CT–CL and CT–CR, are underrepresented compared to others. Future research should aim to provide more comprehensive data for these cases. Additionally, further investigation into driver yielding behavior; such as quantifying driver attitudes under different traffic control conditions when interacting with cyclists, would offer deeper insights and contribute to improving bicycle safety. Moreover, inconsistencies in the coding of prior crash actions, particularly for cyclists in travel-ahead and crossing scenarios, have been identified in crash records. To enhance data reliability, future studies should differentiate and clearly define specific activity types, as well as leverage unstructured data analysis for improved consistency. Enhancing communication and negotiation between road users can help reduce the likelihood of failure to yield the right-of-way. Potential countermeasures include vehicle-to-vehicle (V2V) communication and sensor-based technologies that alert drivers to the presence of nearby cyclists, particularly in turning scenarios. Another effective approach is the construction of dedicated cyclist facilities at intersections, such as separated bike lanes with enhanced safety features. Additionally, implementing dedicated cycling signal phases and ensuring sufficient signal timing can help eliminate priority confusion between drivers and cyclists, improving intersection safety. Declarations Author Contribution M. A: Conceptualization, methodology, data analysis, writing – original draft.F.A.: Methodology, data interpretation, writing – review and editing.M. E.: Data interpretation, writing – review and editing, supervision.All authors have read and approved the final manuscript. Data Availability The data supporting this study's findings are available from police reports from the State of Michigan. Still, restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are, however, available from Dr. Mousa Abushattal [email protected] upon reasonable request and with permission of the State of Michigan. References Lindsay, G., Macmillan, A. & Woodward, A. 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Application of Gap Acceptance Concept to Investigate Behaviour of Drivers at Roundabouts Under Mixed Traffic Conditions. Lecture Notes Civil Eng. 263 LNCE , 75–87 (2025). Lemonnier, S., Brémond, R. & Baccino, T. Gaze behavior when approaching an intersection: Dwell time distribution and comparison with a quantitative prediction. Transp. Res. Part. F Traffic Psychol. Behav. 35 , 60–74 (2015). Romoser, M. R. E., Pollatsek, A., Fisher, D. L. & Williams, C. C. Comparing the glance patterns of older versus younger experienced drivers: Scanning for hazards while approaching and entering the intersection. Transp. Res. Part. F Traffic Psychol. Behav. 16 , 104–116 (2013). Figliozzi, M., Wheeler, N. & Monsere, C. Methodology for estimating bicyclist acceleration and speed distributions at intersections. Transp. Res. Rec . 66–75. 10.3141/2387-08 (2013). Wang, C., Lu, L. & Lu, J. Statistical Analysis of Bicyclists’ Injury Severity at Unsignalized Intersections. Traffic Inj Prev. 16 , 507–512 (2015). Gstalter, H. & Fastenmeier, W. Reliability of drivers in urban intersections. Accid. Anal. Prev. 42 , 225–234 (2010). Young, K. L., Salmon, P. M. & Lenné, M. G. At the cross-roads: An on-road examination of driving errors at intersections. Accid. Anal. Prev. 58 , 226–234 (2013). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviewers agreed at journal 22 Mar, 2025 Reviews received at journal 10 Mar, 2025 Reviewers agreed at journal 07 Mar, 2025 Reviewers invited by journal 05 Mar, 2025 Editor assigned by journal 05 Mar, 2025 Editor invited by journal 05 Mar, 2025 Submission checks completed at journal 04 Mar, 2025 First submitted to journal 25 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6108064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":423894367,"identity":"8e3e7c5e-19ed-476e-af62-a23cbc0b04e6","order_by":0,"name":"Mousa Abushattal","email":"","orcid":"","institution":"Al-Hussein Bin Talal University","correspondingAuthor":false,"prefix":"","firstName":"Mousa","middleName":"","lastName":"Abushattal","suffix":""},{"id":423894368,"identity":"3131a8d2-3af8-4172-bc70-8bc23e1e90f1","order_by":1,"name":"Fadi Alhomaidat","email":"data:image/png;base64,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","orcid":"","institution":"Al-Hussein Bin Talal University","correspondingAuthor":true,"prefix":"","firstName":"Fadi","middleName":"","lastName":"Alhomaidat","suffix":""},{"id":423894369,"identity":"88f4750d-05d0-4f8f-8ca9-2592aec49919","order_by":2,"name":"Mohammad El-Yabroudi","email":"","orcid":"","institution":"Lawrence Technological University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"El-Yabroudi","suffix":""}],"badges":[],"createdAt":"2025-02-25 20:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6108064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6108064/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09801-6","type":"published","date":"2025-09-30T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77911514,"identity":"1121b569-34a5-4b27-a98d-db80ee03f4f7","added_by":"auto","created_at":"2025-03-06 18:01:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101562,"visible":true,"origin":"","legend":"\u003cp\u003eUSA Cyclists injured per year due to traffic crashes\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/d67a7414c975d227418edbad.jpg"},{"id":77911516,"identity":"020a21b1-a31d-4564-a866-27c30d0e6116","added_by":"auto","created_at":"2025-03-06 18:01:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106245,"visible":true,"origin":"","legend":"\u003cp\u003eCyclists fatalities due to traffic crashes\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/c470b345634aca8a563069e6.jpg"},{"id":77911527,"identity":"4e6fc553-8d76-4db3-8413-18ac7dbc4d08","added_by":"auto","created_at":"2025-03-06 18:01:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84835,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of Cyclists -vehicles crashes per location\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/668fb316c776b0c0f0fd60db.jpg"},{"id":77911522,"identity":"cf34f041-1256-4acc-8180-64ee06a5c64c","added_by":"auto","created_at":"2025-03-06 18:01:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124173,"visible":true,"origin":"","legend":"\u003cp\u003eCrashes by Intersection Control Type\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/0ef28bb9f1a4ee967f2b9bb3.jpg"},{"id":77912200,"identity":"f8612d43-44ef-484f-8b26-8783b01d7b57","added_by":"auto","created_at":"2025-03-06 18:09:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132489,"visible":true,"origin":"","legend":"\u003cp\u003eheatmap of interaction Scenarios Leading to Crashes\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/5fe0f1d9ebfe65077e29e2c6.jpg"},{"id":77911517,"identity":"8d1fda73-1b40-406f-87da-4589d00b8175","added_by":"auto","created_at":"2025-03-06 18:01:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":138766,"visible":true,"origin":"","legend":"\u003cp\u003eHazardous actions frequency for intersection crashes in Michigan\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/f2b89e7845124f87f7de932c.jpg"},{"id":77912204,"identity":"8b66344a-ef3b-4703-b367-f23a067badc1","added_by":"auto","created_at":"2025-03-06 18:09:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":23063,"visible":true,"origin":"","legend":"\u003cp\u003eOdd ratios for yield and non-yield action at intersections\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/92e465b64be98ca46ccf4456.jpg"},{"id":77911535,"identity":"e954c47d-af81-4574-8beb-eced46692460","added_by":"auto","created_at":"2025-03-06 18:01:16","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":28371,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratios for yield crashes across different intersection control types\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/0bd77c15e41ade32c66ac021.jpg"},{"id":92883793,"identity":"550daabd-f908-4ea8-85ab-546826a2e9e4","added_by":"auto","created_at":"2025-10-06 16:09:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2119151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6108064/v1/e57973a9-4f83-4b3d-9428-fc53fb4087b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Navigating the Intersection: How Traffic Control Types Affect Cyclist Right- of-Way using A Mixed Logit Model Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe transition from car travel to cycling in urban environments presents significant benefits for public health, environmental sustainability, and urban mobility. Studies have shown that cycling as a daily mode of transport substantially increases physical activity levels, reducing the risk of all-cause mortality, cardiovascular diseases, and obesity-related conditions​. Research on bicycle-sharing programs, such as Barcelona\u0026rsquo;s Bicing initiative, has demonstrated a positive health impact, with physical activity benefits outweighing risks related to air pollution exposure and road traffic incidents​. Moreover, shifting a portion of urban car trips to cycling not only reduces greenhouse gas emissions but also contributes to improved air quality, potentially preventing pollution-related deaths​. The integration of cycling into urban transport policies can thus serve as a dual-purpose strategy, simultaneously addressing public health concerns and mitigating environmental impacts.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, safety issues are among the largest obstacles for road users to increase the probability of using cycling for their trips. In 2020, about 938 cyclists were killed in traffic, which accounts for 2.4 percent of total traffic fatalities, and 38,886 have injured. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the total numbers of injured cyclists per year for the period of 2011\u0026ndash;2020. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the total numbers of cyclists fatalities per year for the period of 2011\u0026ndash;2020. Out of total fatalities 26% occurred at intersections, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the percent of fatalities per crash locations \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIntersection crashes remain a critical challenge in traffic safety, particularly among older drivers and in environments with complex visual demands. Research has shown that older drivers are disproportionately involved in intersection-related collisions, often due to failures in yielding the right-of-way, misjudging gaps in traffic, or failing to detect other road users. These errors are frequently attributed to age-related declines in visual scanning, cognitive processing, and reaction time, making intersection navigation particularly hazardous for aging drivers. Additionally, studies on driver attention allocation reveal that perceptual errors play a significant role in intersection crashes. Many drivers, regardless of age, exhibit an inadequate distribution of attention, often focusing on high-traffic areas while neglecting peripheral hazards such as pedestrians or cyclists. [5], [6], [7], [8], [9], [10]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis effect is particularly pronounced in lower-complexity intersections, where drivers may develop inappropriate expectations about potential hazards, leading to failures in visual search and risk assessment. Addressing these issues requires a combination of infrastructure improvements, such as roundabouts and protected left-turn lanes, and advancements in driver-assist technologies that enhance situational awareness and mitigate perceptual errors in high-risk scenarios.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, the intersections are usually equipped with traffic controls such as traffic signals, signs, and markings that guide users' interactions to improve safety \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Despite the positive impact of traffic controls on traffic mobility and safety, they often lead to some adverse effects that may contribute to particular crash types, such as failing to yield the right-of-way \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eNumerous studies have investigated the contributing factors in driver-cyclist crashes, including the driver's characteristics, vehicle, environment, and infrastructure. These factors influence the likelihood and severity of crashes, with particular attention to intersection-related incidents due to their complexity.\u003c/p\u003e \u003cp\u003eA logistic model has been developed for a bicycle route safety mode based on crash severity, incorporating variables such as lane width, vertical gradient, and highway classification \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In Australia, the research examined the impact of driver age and experience on driver-cyclist crashes, identifying patterns associated with novice and experienced drivers and advocating for improved cyclist safety throught novice driver training \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Environmental conditions such as weather, light, and road surface were analyzed using log-binomial regression on police reports between 2000 and 2014 \u003csup\u003e12\u003c/sup\u003e. The research unveiled a substantial correlation between lighting conditions and crash severity for driver-cyclist crashes, whereas weather and road surface seemed to have a limited impact.\u003c/p\u003e \u003cp\u003eFor intersection crashes, various studies have focused on examining driver-cyclist interaction. Left-turn crashes had been found to be less severe than other crash categories based on crash reports and reviews of user behavior videos \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Likewise, a study was conducted in North to examine the influence of various factors on bicycle crashes at intersection and non-intersection locations \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The study demonstrated a significant association between contributing factors and crash location. For example, the cyclist injury severity was identified as significantly impacted by cyclists being under the influence of alcohol. In contrast, non\u0026ndash;intersection cyclist crash injuries were affected by drivers under the influence of alcohol. Using negative binomial regression \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e analyzed the cyclist safety for 540 non-signalized intersections by classifying the crashes as whether the cyclist or driver had the right-of-way. The findings suggested the likelihood of a crash in which a cyclist has the right-of-way at an intersection is higher with some features such as two-way bicycle tracks and good marking that inversely correlated with speed calming initiatives.\u003c/p\u003e \u003cp\u003eGiven the difficulty of navigating intersections associated with high workload, understanding the interaction between drivers and cyclists is more important and requires higher attention \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For instance, a risk comparison between intersection and non-intersection crashes was conducted by \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e to investigate the effect of various factors on cyclist injury severity. The study revealed a difference in which factors affect bicyclist injury severity in both environments. Similarly, the cyclist crash severity had been analyzed under two built environments of intersection and non-intersection to show which factors cause cyclist injury at each location \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. As one of the intersection elements, traffic control had a major effect on the driver and cyclist interaction. Another study analyzed the impact of various types of intersection traffic controls in Australia by conducting a logit model \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The analysis results indicated that cyclist injury severity is associated with more factors under stop or yield controlled intersections than signal-controlled or non-controlled intersections. Similarly, naturalistic research involving different intersection types was performed to examine the effects of road features on cyclist crash risk. The findings suggested that the risk of cyclist crashes at intersections with a traffic light is higher than at priority (Stop/ yield) or uncontrolled intersections \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding driver\u0026ndash;cyclist intersection crashes, the previous studies suggested a specific correlation between control type and crash pattern \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A simulation study was conducted on a non-signalized T-intersection and showed that when drivers attempt to turn right, they commonly ignore the cyclists who come from the right side and fail to yield the right-of-way to them \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The research was performed in Australia on non-signalized and signalized intersections and reported a high proportion of left-turn crashes between drivers and cyclists with no traffic control. Moreover, the research emphasized the importance of user guidance by traffic control to make their behavior predictable and understandable to each other when they interact at an intersection \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Another study analyzed three driver movements at a signalized intersection (through, left turning, and right turning) when engaging with a cyclist \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Using negative binomial models, the authors examined the collision risk between users for each interaction scenario of driver movements. A correlation was found between the crash risk for each driver's movement and particular interaction circumstances. For instance, the crash risk for through movement had been influenced positively by running red signals. At the same time, the increment of average right-turning vehicle volume would decrease the through-movement risk. Another study established a strong connection between driver-cyclist crash risk and intersection complexity under different traffic control types, with greater crash risks associated with the presence of cycle lanes and traffic lights \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe literature demonstrates that intersection control types play a crucial role in shaping driver behavior and crash patterns in driver-cyclist intersections. Studies have highlighted the correlation between traffic control measures and crash severity, emphasizing the importance of user guidance through signals and signage. However, while prior research has identified general trends, a comprehensive understanding of how different intersection control types influence the likelihood of drivers failing to yield to cyclists remains limited. This study aims to fill that gap by investigating how intersection traffic control design and operational characteristics affect the probability of drivers failing to yield the right-of-way to cyclists. By identifying key design and operational factors, this research will provide valuable insights for engineers and planners to improve intersection safety and reduce driver-cyclist conflicts.\u003c/p\u003e"},{"header":"DATA DESCRIPTION","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cp\u003eThis study examined crash records related to vehicle-bicycle collisions over a ten-year period using police reports from the State of Michigan. The crash data provides detailed insights into motor vehicle\u0026ndash;cyclist incidents. Typically, the dataset includes various types of motorist\u0026ndash;cyclist collisions: both those involving multiple vehicles and cyclists, and single-cyclist crashes where no motor vehicle was involved. This study focused on intersection-related crashes involving a single vehicle and a cyclist. After removing multi-vehicle cyclist crashes and cases with missing or unknown data from an initial total of 11,449 driver\u0026ndash;cyclist intersection-related crashes, the final dataset comprised 5,084 crashes (44.4%). The analysis incorporated various attributes related to the crash, the environment, the parties involved, and the vehicle units to assess how different intersection traffic control types influence single motor vehicle drivers\u0026rsquo; failure to yield the right-of-way to cyclists.\u003c/p\u003e \u003cp\u003eThe Michigan Department of State Police Manual categorizes hazardous actions taken by parties (drivers, cyclists, and pedestrians) into several categories which are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActions of different road users as categorized by Michigan Department of State Police Manual\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActions by different road users\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailed to yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproper lane use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproper passing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproper backing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed to fast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReckless driving\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed to slow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCareless driving\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisregard traffic control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproper signal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrove wrong way\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnable to stop\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrove left of center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproper turn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo assess the impact of traffic control on driver behavior, crashes were classified into two categories: those resulting from a failure to yield the right-of-way and those involving other hazardous actions. Consequently, the response variables were categorized based on these hazardous actions (failure to yield vs. other hazardous actions) to analyze the effect of traffic control type.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eMichigan Department of State Police Manual\u003c/em\u003e classifies a user's prior crash action into 38 distinct categories. For drivers, the primary movements at intersections include traveling straight ahead, turning left, and turning right. Cyclist movements, on the other hand, are categorized into three main types: moving straight (through), turning (left or right), and crossing (either at an intersection or elsewhere). Based on these classifications, nine distinct interaction scenarios were identified for each type of traffic control. Corresponding abbreviations were introduced for each scenario to streamline analysis and improve clarity. These abbreviations simplify the classification of cyclist-driver interactions, making it easier to study different traffic situations, identify potential conflict points, and develop strategies for safer intersections. The abbreviation convention consists of four letters, divided into two groups, the first group is for the Cyclist and thus it starts with \u0026lsquo;C\u0026rsquo;, and the second group is for the Driver and thus it starts with \u0026lsquo;D\u0026rsquo;. Moreover, the second letter of each group is used to abbreviate the movement (A: Ahead, T: Turn, etc.). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the interaction scenarios between drivers and cyclists under various traffic controls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction Scenarios between Driver and Cyclist\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInteraction scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAbbreviation of interaction scenarios\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclist Movement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDriver Movement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStraight Ahead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight Ahead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA-DA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurning (Left or Right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight Ahead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT-DA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight Ahead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC-DA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStraight Ahead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurning Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA-DL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurning (Left or Right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurning Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT-DL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurning Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC-DL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStraight Ahead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurning Right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA-DR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurning (Left or Right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurning Right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT-DR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurning Right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT-DR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThree types of intersection traffic control are included (traffic signal control, stop/yield sign, and no traffic control) to determine the effect of traffic type on yielding behavior. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the distribution of crashes across signalized, stop/yield, and uncontrolled intersections, distinguishing between Failed-to-Yield Crashes and Other Hazardous Actions Crashes. Signalized intersections experience the highest number of crashes, primarily due to failed-to-yield incidents, suggesting driver non-compliance or misjudgment despite traffic control measures. Stop/Yield sign intersections also exhibit a high crash frequency, indicating that regulatory signage alone may not fully mitigate risks. Uncontrolled intersections show the lowest crash numbers, though this may reflect lower traffic volumes or underreporting rather than inherently safer conditions.\u003c/p\u003e \u003cp\u003eThe response predictor was measured alongside other crash characteristics such as driver age, weather, number of lanes, car type, driving while impaired, and speed limit. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics related to intersection control type and hazardous actions. There are a few things to note when looking at the data overall. First, signalized intersections accounted for about half of all collisions in all intersection control types. In the dataset of crashes, both male and female are equally represented (approximately 50 percent). Furthermore, the report reveals stable factors, such as a high proportion of crashes (around 82 percent) happening on weekdays while driving a passenger vehicle (around 85 percent). However, some characteristics differed depending on the type of intersection control and risky behavior. For example, elderly drivers make up a small proportion of driver-cyclist crashes, with 22.32 percent yield crashes at uncontrolled intersections and 21.26 percent for non-yield crashes at stop/yield sign intersections. However, compared to the other two types of intersection control, elderly driver crashes are more prevalent in signalized intersections with all types of risky behavior. Except for the signalized intersection in the yield dataset, the weather state ratios are fairly identical across intersection control types and hazardous actions (approximately 62 percent). For the yield action dataset, two-lane intersections show up in stop and uncontrolled intersections (71.51 percent and 59.82 percent, respectively), while three-lane intersections have a high proportion of signalized intersections. Non-yield crashes follow the same pattern, with 71 percent for two-lane stop signs and 59.83 percent for uncontrolled intersections, respectively, and 77.21 percent for three-lane signalized intersections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis heatmap Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows how different interaction scenarios contribute to crashes across various intersection control types and crash types (Failed-to-Yield vs. Other Hazardous Actions). Darker shades indicate a higher number of crashes in those scenarios, making it easier to see which driver-cyclist interactions are the most common causes of accidents.\u003c/p\u003e \u003cp\u003eAccording to the type of intersection control, some scenarios are heavily represented relative to others based on prior crash behavior of drivers and bicycles. The higher percentage (28.46 percent) is notable for cyclists riding ahead with the driver turning right under signal control in the yield crash dataset. Uncontrolled intersections are more associated with scenarios with straight-ahead action (CA-DA, CA-DL, CA-DR, and CC\u0026ndash;DA). In contrast, stop/yield signs are also more associated with scenarios with straight-ahead action (38.51 percent). The interaction where the driver and cyclist appear to ride straight ahead is well represented among the three types of intersection controls compared to other interaction scenarios, with 35.68 percent for signalized intersections, 49.25 percent for stop/yield sign intersections, and 32.48 percent for uncontrolled intersections in the non-yield crashes dataset. As a result, the distribution of interaction scenarios percentages across intersection control types illustrates the need to explore how intersection control types impact driver-cyclist interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Summary of Model Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eFailed-to-Yield Crashes Dataset\u003c/p\u003e \u003cp\u003eCount (Proportion)\u003c/p\u003e \u003cp\u003e3440(67.66%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eOther Hazardous Actions Crashes Dataset\u003c/p\u003e \u003cp\u003eCount (Proportion)\u003c/p\u003e \u003cp\u003e1644(32.34%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignal\u003c/p\u003e \u003cp\u003e1727 (50.20%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStop/Yield Sign\u003c/p\u003e \u003cp\u003e1490 (43.30%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUncontrolled 223(6.50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignal\u003c/p\u003e \u003cp\u003e819(49.80%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStop/Yield Sign\u003c/p\u003e \u003cp\u003e712(43.30%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUncontrolled\u003c/p\u003e \u003cp\u003e113(6.90%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eElderly driver (\u0026gt;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450(26.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347(23.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50(22.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e213(25.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e155(21.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28(23.93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale Driver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e872(50.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e755(50.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e127(56.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e422(50.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e390(53.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e57(48.72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeekday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1417(81.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1226(82.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186(83.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e695(82.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e585(80.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e99(84.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDUI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(0.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(0.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(1.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15(1.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3(0.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1(0.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePassenger Vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1465(84.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1227(82.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196(87.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e726(86.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e591(81.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e100(85.47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClear Weather Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1083(62.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1074(72.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e168(75.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e623(74.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e536(73.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91(77.78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDaylight light Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1393(80.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1268(85.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183(81.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e644(76.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e594(81.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e99(84.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDry Road Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1317(76.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1338(89.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204(91.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e749(89.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e670(91.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e106(90.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of lanes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOne lane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(0.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53(3.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9(4.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8(0.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26(3.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5(4.27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTwo Lanes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409(23.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1064(71.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134(59.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e183(21.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e521(71.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e70(59.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThree or more Lanes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1304(75.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e370(24.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80(35.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e647(77.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e182(24.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41(35.04%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInteraction Scenario\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA \u0026ndash; DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273( (15.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e573(38.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54(24.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e299(35.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e359(49.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38(32.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219(12.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227(15.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57(25.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71(8.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66(9.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20(17.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e492(28.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e283(19.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(16.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e155(18.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e138(18.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25(21.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT - DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(0.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(2.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13(5.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11(1.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39(5.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16(13.68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(0.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(0.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2(0.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2(0.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12(1.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(0.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(0.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(0.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3(0.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10(1.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2(1.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC- DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180(10.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173(11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34(15.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e166(19.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63(8.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11(9.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120(6.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56(3.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12(5.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38(4.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18(2.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e418(24.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122(6.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14(6.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93(11.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24(3.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4(3.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMODEL DESCRIPTION\u003c/h3\u003e\n\u003cp\u003eThe mixed logit model, or the random-parameters logit model, is a widely used statistical approach for analyzing decision-making processes where outcomes vary across individuals or cases. Unlike traditional logit models, which assume that all observations respond uniformly to explanatory factors, the mixed logit model allows parameters to vary, making it more flexible in handling unobserved heterogeneity. This feature is particularly valuable in traffic safety research, where multiple factors, including driver behavior, roadway conditions, and environmental variables, influence crash occurrences.\u003c/p\u003e \u003cp\u003eThe mixed logit model can account for crash variability by allowing associated factors to differ between crashes, addressing the complexities of diverse crash conditions that cannot be accurately captured using a standard logit approach. The Independence from Irrelevant Alternatives (IIA) assumption states that unobserved predictors are uncorrelated with response outcomes. The mixed logit model avoids restrictive model constraints that assume parameters remain fixed across alternatives. This flexibility has led to widespread use in traffic safety studies, particularly in understanding how various factors contribute to crash risks \u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e].\u003c/p\u003e \u003cp\u003eThe mixed logit model has been applied extensively in traffic safety research to analyze crash severity, driver behavior, and roadway conditions. It is commonly used to investigate factors contributing to different levels of crash severity, such as minor, serious, or fatal crashes, by considering variables like driver behavior, road infrastructure, and environmental conditions. The model has also been instrumental in studying driver and cyclist interactions at intersections, particularly in understanding how violations such as failing to yield or disregarding traffic signals contribute to crashes. Additionally, it has been used to assess driver compliance with traffic signals and signs, evaluate mode choice behaviors among commuters, and analyze the effectiveness of road safety policies in reducing crashes. By capturing the complexities of driving behavior and crash dynamics, the mixed logit model provides a more nuanced understanding of how different factors contribute to traffic accidents.\u003c/p\u003e \u003cp\u003eThis study uses the mixed logit model to analyze driver responsibility in crashes involving cyclists at intersections. The model accounts for variations in crash scenarios by coding driver responsibility as a binary variable, where one indicates that the driver was at fault in a crash involving a cyclist, and zero indicates that the driver was not at fault. This classification is applied to two datasets: one focusing on crashes resulting from other hazardous driving actions and another specifically analyzing failed-to-yield crashes. By integrating a mixed logit framework, the model captures variations in crash dynamics while treating factors such as driver age, day of the week, light conditions, and driver\u0026ndash;bicycle interactions as fixed variables. This approach provides deeper insights into the underlying causes of intersection crashes and offers valuable information for improving road safety policies and traffic management strategies \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eIn contrast, seven Michigan Department of Transportation (MDOT) zones are treated as a random variable to account for discrepancies in transportation regions when drivers and cyclists interact. The mixed logit model can be represented in the same manner as the binary logistic regression model but with the inclusion of an assumption of random parameters. The function form of the mixed logit model is in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{ij}=\\:{\\beta\\:}_{i}{X}_{ij}+{\\epsilon\\:}_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is a function defining the driver fault category i (1 or 0) for crash j, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ij}\\)\u003c/span\u003e\u003c/span\u003e Expresses a measurable crash vector for several factors such as crash characteristics, vehicle unit, party type factors that indicate the fault outcome for crash j, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e Is a vector for estimable coefficients and the term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e Indicates the error part with extreme value distribution assumption \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Moreover, by assuming the error ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e) is an extreme value that is distributed logistically across crashes. The equation of the Multinomial logit model defined in Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{P}_{n}\\left(i\\right)=\\:\\frac{{e}^{\\left({\\beta\\:}_{i}{X}_{in}\\right)}}{\\sum\\:{e}^{\\left({\\beta\\:}_{i}{X}_{in}\\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{n}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e Is the observations' (n) probability classified into discrete outcomes (i). For the mixed logistic model, when unobserved parameters impact the driver's responsibility and make factors vary across crashes, the mixed logistic model provides the discrete outcome (i) with an estimable parameters vector ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e express the probabilities of the model category:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{P}_{n}\\left(i\\right)=\\:\\int\\:\\frac{{e}^{\\left({\\beta\\:}_{i}{X}_{in}\\right)}}{\\sum\\:{e}^{\\left({\\beta\\:}_{i}{X}_{in}\\right)}}f\\left(\\beta\\:|\\phi\\:\\right)d\\beta\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{n}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e Is a probability function for the discrete outcome of driver fault (1 or 0),\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector for regression coefficients, X is an explanatory variable, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(\\beta\\:|\\phi\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e is a density function of a random parameter in the model with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\phi\\:\\)\u003c/span\u003e\u003c/span\u003e distribution across observations. The density function form may be a regular, lognormal, triangular, or uniform distribution.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show the results for a mixed logistic model for both datasets (failed to yield action and non-yield hazardous behavior) at an intersection. The interpretation of the models' results described by the log odds ratio for being the driver at fault (was unable to yield) and not being at fault (the cyclist was at fault).\u003c/p\u003e\n\u003ch3\u003eDriver Crash Responsibility for Yield and Non-Yield Crashes\u003c/h3\u003e\n\u003cp\u003eThe mixed logistic model was analyzed to examine whether intersection control types (signal, stop, and uncontrolled intersection), demographic characteristics, and environmental circumstances would affect the interaction between driver and cyclist. The results suggest that gender, weather, and road conditions did not dramatically affect driver\u0026ndash;cyclist interaction across the intersection control type in both hazardous action categories.\u003c/p\u003e \u003cp\u003eBased on the statistical outcomes, being an elderly driver was correlated with an increased likelihood of failing to yield the right-of-way by the driver for both hazardous action types, with odd ratios of 17 and 16 for yield and other hazardous action, respectively. Weekdays proved to be a significant contributing factor in both models compared to the weekend. However, the findings indicate a higher risk likelihood of other hazardous actions than failed to yield actions (17 versus 22 percent). Contrary to that, a driver impaired by alcohol and/or drugs has been found more responsible for failed-to-yield crashes with an increase in probability of approximately 3.91 times compared to the normal driver. In contrast, in the other hazardous actions model, they were responsible for 2.89 times the probability increase. For vehicle type, drivers who use small vehicles such as passenger cars, vans, or SUVs are more likely to be at fault by approximately 30 percent (OR\u0026thinsp;=\u0026thinsp;1.29) for failed-to-yield crashes and 25 (OR\u0026thinsp;=\u0026thinsp;1.25) percent in other driver hazardous actions. Furthermore, when a driver and a cyclist meet at an intersection in daylight, the model reveals that the risk of the driver being the responsible party for a crash rises by 21 percent in failed-to-yield crashes relative to other hazardous actions (25 percent). When a cyclist interacts with a car in a multi-lane intersection, the models' findings suggest a reduction in driver responsibility.\u003c/p\u003e \u003cp\u003eRegarding yield collisions, intersections with two lanes reduce the likelihood of the driver being at fault by 38 percent (OR\u0026thinsp;=\u0026thinsp;0.62) compared to one-lane intersections, and intersections with three or more lanes reduce the likelihood of the driver being at fault by 42 percent (OR\u0026thinsp;=\u0026thinsp;0.58). In the case of other driver-hazardous actions, the findings reveal that on a two-lane system, drivers are less likely to be found at fault during interactions with cyclists than on a three-lane system (0.80 and 0.84, respectively). Across intersection control, the speed contribution appeared to have a small impact on probability, increasing by 1 percent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed Logistic Model Result for Yield and Non-Yield Actions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eYield Crash\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eOther Hazardous Actions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriver Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay of Week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDUI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVehicle Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Lane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed Limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA \u0026ndash; DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT - DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC- DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNine interaction scenarios between driver and cyclist were generated to provide insight into their interaction under three types of intersection control. According to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, most interaction scenarios were significant for both types of driver hazardous behavior (failed to yield and other hazardous action). According to the findings, a straight-ahead interaction scenario (CA\u0026ndash;DA) for all crash parties is related to a 62 percent (OR\u0026thinsp;=\u0026thinsp;1.62) rise in driver responsibility for failed to yield crashes, compared to a 42 percent (OR\u0026thinsp;=\u0026thinsp;0.58) decrease in being at fault for other hazardous actions. When a cyclist rides straight ahead when a car turns left (CA-DL), the driver seems more likely to be at fault in both types of hazardous action. However, the odds suggest that the driver is more at fault in yield crashes, with a likelihood of 12.35 times compared to 3.90 times in other hazardous action scenarios. Whenever a bicycle attempted to ride straight ahead while a driver attempted to turn right at an intersection (CA-DR), the interaction increased the driver's responsibility in yield and other hazardous actions by 7.47 and 1.28 times, respectively. When the cyclist's prior action is turning, and the driver travels straight ahead (CT-DA), there is a high percentage (OR\u0026thinsp;=\u0026thinsp;0.17) of decreasing in probability for the driver being at fault by yielding the right-of-way to the cyclist and around 90 percent (OR\u0026thinsp;=\u0026thinsp;0.10) less likely to be at fault by any other hazardous action. In interactions where the cyclist tries to turn versus the driver's prior turning action (CT-DL), the likelihood of a driver's fault in a yield crash increases by 39.18 times for left turning and 2.80 times for right turning. No significant effect on probability was observed whenever a driver and a bicycle engaged in turning movements for other hazardous actions. In the cyclist crossing interaction scenario at intersections, the risk of yield activity did not show a significant impact when the driver went straight ahead during the interaction (CC-DA). However, the odds suggest drivers are less likely to be at fault due to other hazardous actions (95 percent, OR\u0026thinsp;=\u0026thinsp;0.05). A crossing action by a cyclist versus a driver turning left (CC-DL) was associated with a 9.19 times increase in the likelihood of a driver failing to yield the right-of-way to cyclists, whereas turning right by a driver (CC-DR) was associated with a 13.53 times increase in the likelihood of a driver failing to yield the right-of-way to cyclists. Meanwhile, the involvement of other hazardous actions during the crossing interaction had little effect on the likelihood of driver error in the crossing scenario.\u003c/p\u003e\n\u003ch3\u003eFailure to Yield under Intersection Control Types\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the frequency of drivers' hazardous acts while engaging with cyclists at an intersection. According to the results, when a driver and a bicycle collide at an intersection, the failed yield crash is the most common occurrence, i.e., 70%. As a result, a mixed logistic model was used to examine why drivers failed to yield under different intersection controls (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). According to the model, driver gender and environmental factors do not substantially affect a driver's likelihood of failing to yield to a cyclist. Furthermore, elderly drivers were related to a 20 (OR\u0026thinsp;=\u0026thinsp;1.20) percent increase in the risk of failing to yield to a cyclist at a signalized intersection, a 29 percent (OR\u0026thinsp;=\u0026thinsp;1.29) increase at a stop sign controlled intersection, and a 24 percent (OR\u0026thinsp;=\u0026thinsp;1.24) increase at an uncontrolled intersection. Compared to weekend interactions, signalized and stop sign intersections increase by around the same percentage (24 vs. 23 percent), whereas uncontrolled intersections increase by a higher percentage (29 percent). In addition, the table of results indicates the effects of alcohol and drugs. For instance, in signalized intersections, the likelihood of a driver failing to yield is 2.41 times.\u003c/p\u003e \u003cp\u003eIn comparison, the probability is higher for stop-controlled intersections 3.44 times and 2.56, with an increase in the probability of a driver failing to yield when intersections are not controlled. When a passenger car, van, or SUV is involved in a driver-cyclist interaction, the model findings indicate a 40 percent rise in the probability of a cyclist failing to yield the right-of-way. The speed parameter induced a small increase in the likelihood of failure to yield at an intersection, close to the general hazardous action model results.\u003c/p\u003e \u003cp\u003eVarious impacts appeared regarding the parties' actions (drivers and cyclists) under various intersection control types. For comparison, a signalized intersection reduced the probability of a driver failing to yield by 67 percent (OR\u0026thinsp;=\u0026thinsp;0.33) while the driver and cyclist were going straight ahead. In comparison, a stop sign intersection reduced the likelihood by 53 percent (OR\u0026thinsp;=\u0026thinsp;0.47), and uncontrolled intersections reduced the likelihood by 46 percent (OR\u0026thinsp;=\u0026thinsp;0.54). When a cyclist rides straight ahead while a vehicle turns to the left, an uncontrolled intersection has a risk odd ratio of 5.98 for failing to yield to the cyclist, compared to 4.82 and 2.16 for signalized and stop-controlled intersections, respectively. When the driver tried to turn right instead of left in the same situation, the signalized interaction was only found to be significant on the failure probability, raising the likelihood by 3.53 times.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA few interaction scenarios were significant when cyclists' actions were described as turning (either right or left). In signalized and stop sign-controlled intersections, the percentage of failure is almost the same for turning cyclists with straight-driving drivers (5 vs. 6 percent). Regarding turning cyclists with the right-turning driver, only signalized intersections were found to reduce the failure risk by 9 percent. Finally, when the cyclist activity was reported as crossing the intersection, the uncontrolled intersection was associated with a higher likelihood of being the cyclist who failed to yield the right-of-way with 99 percent (OR\u0026thinsp;=\u0026thinsp;0.01), followed by stop-controlled and signalized intersections with 75 and 72 percent (OR\u0026thinsp;=\u0026thinsp;25, OR\u0026thinsp;=\u0026thinsp;28), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed Logistic Model Results for Different Intersection Control Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eSignal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eStop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c17\" namest=\"c15\"\u003e \u003cp\u003eUncontrolled\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriver Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay of Week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDUI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e1.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVehicle Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed Limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA \u0026ndash; DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e2.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT - DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC- DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC \u0026ndash; DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC \u0026ndash; DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen a driver and a bicycle interact at a signalized intersection, the risk of driver's failure increases by 5.74 times, while other control types are insignificant. Drivers were observed raising the probability of failure to yield by 6.21 times for signalized intersections and 65 percent (OR\u0026thinsp;=\u0026thinsp;1.65) for stop-controlled intersections in the last interaction scenario, where a bicycle crosses the intersection, and the driver tends to turn right.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eFor the interaction between driver and cyclist, intersections are considered crash-prone zones. This study explores the effect of intersection control types on driver-cyclist crashes based on both parties' prior behavior. The results on demographic characteristics and environmental factors align with previous studies. The models' findings indicate that elderly drivers have a greater chance of making errors in both the failure to yield and other hazardous action models \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The difficulty of intersection scanning, search errors, physical-joint problems, and visual problems for elderly drivers compared to younger drivers can all be seen as explanations for this result \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. As a result, it is possible to assume that elderly drivers failed to yield the right-of-way to cyclists because they could not detect them or made a mistake due to increased cognitive workload and intersection difficulty. Elderly drivers have a problem yielding the right-of-way to cyclists at intersections because they are commonly associated with failure rather than reckless or speeding faults, illustrating the problem of turning movement for elderly drivers \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Elderly driver coefficients indicate an increase in fault probability for driver intersections and require more mental tasks to process from the driver than other traffic circumstances in both models \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The findings of two risky acts reveal that drivers are 17 percent and 22 percent more likely to be at fault on weekdays by failing to yield and other hazardous actions, respectively. These findings can be explained by the fact that people are more likely to drive and use a motor vehicle on weekdays, which raises the amount of traffic at intersections and the complexity of navigating them relative to weekends, where the number of trips decreases. People are more likely to choose to take a leisure trip and use active modes of transport \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. As expected, an impaired driver by alcohol or drugs raises the risk of the driver being at fault by 3.91 times for collisions attributable to yield behavior, which is possibly due to drivers not being able to identify and react properly to a close-by cyclist. Driving under alcohol or drug influence also raises the risk of the driver being at fault by 2.89 times by other hazardous actions. When intoxicated drivers contact cyclists at the intersection, their actions will be riskier and more related to misjudgment and troubled behavior \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The presence of alcohol and drugs would restrict the mental abilities of drivers due to the high demand for tasks at intersections \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Therefore, relative to other actions, it is a major factor correlated with driver fault by yield actions. The study found that drivers of heavy vehicles (e.g., trucks, buses) are less likely to be at fault when engaging with cyclists at the intersection than drivers of passenger cars. This outcome concurred with \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e that large vehicle drivers are more likely to avoid serious crashes due to driving experience. In addition, \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e analysis shows that cyclists have trouble handling blind spots around large vehicles, making them more likely to be at fault. Daylight conditions indicate a rise in the risk of driver fault with the light environment in both models. One of the possible causes is that cyclists are less likely to ride at night and are typically most likely to travel in the daytime, so most drivers are involved with cyclists in daylight within complicated environments (intersections). Overall, the analysis results suggest a decline relative to a single-lane intersection system with the possibility of being the driver in the fault group at the multi-lane intersection (2 or more). Increasing the number of lanes at the intersection area offers additional freedom of movement and decreases interference between interaction units \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. While speed is well recognized as one of the major factors leading to crash cause and crash severity \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, a rise in the risk of driver-fault behavior was found by increasing traffic speed by one percent. This small effect may be attributed to the fact that most of the intersections were located in urban areas with low traffic speeds and may not impair the driver's actions while dealing with cyclists.\u003c/p\u003e\n\u003ch3\u003eFailed to yield action versus other hazardous actions\u003c/h3\u003e\n\u003cp\u003eThe risk of the driver being held responsible for failure to yield to cyclists (Red Color) and non-yield cited hazardous actions (Blue Color) is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Each point presents the odd ratio of interaction scenarios for a certain intersection traffic control, while the vertical line represents the 95 percent confidence interval. In general, the graph indicates that, as previously mentioned in the literature, drivers are more likely to fail to yield in all interactions with cyclists than to participate in other risky activities. In an interaction situation where the cyclist and driver attempt to ride straight ahead (CA-DA), the model results indicate that the driver is more likely to be responsible for failing to give the cyclist the right of way and less likely to engage in any other risky actions. In most crash dataset cases, the driver and cyclist were on different intersection approaches for this interaction situation. Due to obstacles that obstruct the driver's view from the bicycle side, such as oncoming traffic, a house, or a tree, which cause a time-to-collision reduction \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, the driver can fail to identify cyclists on the roadway who ride straight ahead. Both hazardous actions (failing to yield action and other actions) appear to increase the probability of the driver being at fault in the case of the cyclist riding ahead while the driver turns left (CA-DL). However, the yield action poses enormous odds in these circumstances to deem the driver at fault relative to other dangerous acts. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e results align with the driver's failure in this interaction type, such as sharing a signalized intersection in which the driver appears to accept a limited gap, and the body of some automobiles may obstruct the driver's line of sight.\u003c/p\u003e \u003cp\u003eFurthermore, study findings suggest that when drivers turn right while cyclists travel straight (CA\u0026ndash;DR), they are bound to be to blame, as compared with cyclists. The same example can be noticed additionally for other perilous activity results. Commonly, the vast majority of these failure-to-yield actions are related to the capacity of the driver to see, designate, and scan the cyclist on the right side, specifically when cyclists travel alongside the driver's vehicle in the blind zone \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e or because of an obstacle that blocks the view \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Moreover, it has been found that before carrying out a right turn maneuver, drivers were less likely to search their right-hand side relative to left turn maneuvers \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInconsistent findings have been found regarding the interaction scenario in which cyclists tried to do a turning maneuver (either left or right). For example, in (CT-DA) interaction, the results show that drivers are less likely to fail and cyclists are more responsible for intersection crashes (for failure to yield and other hazardous crashes). Visual and scanning activity may not contribute to these interaction scenarios since the cyclist is positioned in the driver's field view, and drivers are more likely to grant the cyclist the right-of-way. In addition, previous findings suggest that cyclists are more likely to be at fault because they are less likely to comply with intersectional traffic laws \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In the (CT-DL) interaction scenario for the yield model, a noticeable impact occurred, while in other risky acts, prior driver action did not seem to have a major rule on the likelihood of driver fault during the interaction. This type of crash is often due to rule infringement for control type by the driver at intersections or due to poor negotiation with cyclists. In addition, drivers tend to focus on the pathway during the turning maneuver. They are less likely to search the side of the vehicle (particularly for the blind zone) or the surrounding area for other users (such as cyclists) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Likewise, a driver's right turn action when the cyclist turns at the intersection (CT-DR) considerably increases the likelihood that the driver is at fault only in yielding. On the other hand, it revealed that the driver is less likely to be at fault when they decide to do a right turn compared to a left turn. This consequence could be due to a greater possibility of the driver violating laws relative to left turns and being more likely to merge with the cyclist instead of crossing their path by the driver during a turning right maneuver.\u003c/p\u003e \u003cp\u003eIn the case of a driver engaging with a crossing cyclist at an intersection, driver turning movements (either left or right) were found to be significant in the yield scenario. At the same time, other hazardous actions were only significant in the scenario where the driver traveled straight ahead. The findings indicate a small rise in the driver being at fault due to other hazardous actions in (CC\u0026ndash;DA), where vehicles appear to ride straight ahead as cyclists cross the intersection. This finding can be explained by the fact that failure to yield is often associated with violating the law at the time of turning. At the same time, unsafe driver acts are more likely to be associated with other risky actions, such as speed during straight-ahead travel \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e if a driver turns left as a cyclist crosses the intersection (CC\u0026ndash;DL), the probability of the driver failing to yield the right-of-way to the cyclist increases. The drivers should split their focus between two sides (right and left) while turning left, which creates some mental distraction for them. Drivers making a right turn (CC\u0026ndash;DR), on the other hand, can \"actively but unintentionally\" ignore potential dangers on the right, such as a bicycle approaching \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Previously, this difference in turning attention was due to looking but being unable to see or failing to look as sensory failure was established by previous studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEffect of traffic control types on failure to yield\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the probability of a driver being at fault during driver-cyclist interactions through various intersection control types (Signal, Stop/Yield, and Uncontrolled). Across all control types, motorist drivers are less likely to fail to yield the right-of-way in the first scenario (CA\u0026ndash;DA). The results showed that the signalized intersection had the lowest likelihood of failing to yield the right-of-way to cyclists, followed by stop sign control and uncontrolled intersection. These findings can be explained by the fact that drivers at signalized intersections are more likely to obey signal laws, and cyclists are more likely to be in the field of view where no extra attention is needed because the primary drivers' focus is usually on the signal light. Even if the cyclist is likely to be in the driver's field of view when stopping at a stop sign, extra attention is needed for arriving and departing traffic at the intersection, raising the risk of a driver failing to yield the right-of-way \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As one would expect from an uncontrolled intersection, there was much uncertainty in that area, and the driver was exposed to a great deal of environmental demand that needed a lot of mental capacity and attention \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, (CA\u0026ndash;DL) scenario shows an increased probability of the driver failing to yield the right-of-way with the lowest probability of stop sign control followed by signal and uncontrolled, respectively. The difficulty of signalized intersections (such as traffic patterns and right-of-way) can be clarified by sufficient scene scanning without hazard recognition or restricted event processing by selective perception and disturbances during left turn maneuvers \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Only the signalized intersection showed a substantial increase in failure to yield likelihood towards cyclists in the interaction scenarios where cyclists appear to ride ahead, and drivers turn right (CA\u0026ndash;DR). The study indicates more tension between the driver and the cyclist during this type of interaction, possibly due to signal phasing and priority causing uncertainty. Similar findings have been discovered by Buch and Jensen \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e; traffic signal designs such as a green right-turn arrow before the green phase can cause right-turn conflicts at some intersections partially because some motor vehicles continue to drive as though the right-turn arrow is still on, oblivious to the change in obligation to give way when cyclists get green.\u003c/p\u003e \u003cp\u003eFurthermore, during a narrative analysis of crash reports, the transition period between phases seemed to be a crucial point because the cyclists assumed they had enough time to move and that the driver could see them on the lane when, in fact, the driver was only waiting for the green light and did not see the cyclist approaching. Only when the driver tends to ride ahead was the scenario found significant (CT\u0026ndash;DA) for the second type of cyclist maneuver in which the cyclist tends to turn (either right or left). Under signal and stop sign control intersections, the driver was less likely to fail to yield the right-of-way. Unlike stop sign-controlled intersections, drivers at signalized intersections have a broader field of view. They are less likely to breach traffic laws because they have priority of travel by a traffic signal and are less confused by approaching traffic. Besides that, in a straight-ahead situation, cars are more restricted to signal obligation than cyclists, who can approach from the sidewalk side and violate the traffic signal law due to a misjudgment of time and distance gap for turning maneuvers \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In comparison, priority plays a major role in the conflict at stop sign-controlled intersections. Higher levels of uncertainty may have occurred due to more flexibility in driver movement and higher mental demand in the traveling ahead scenario for other intersection approaches than at signalized intersections \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding bicycle crossing situations, it seems drivers who ride ahead are less likely to fail to yield the right-of-way at an uncontrolled intersection. Drivers are more likely to proceed through intersections at high speeds with less visual search and attention than they would at unsignalized intersections, according to \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e findings. Moreover, cyclists at uncontrolled intersections may misjudge the distance and speed of approaching vehicles \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In the case of a crossing cyclist and a turning driver, the results show a significant increase in the likelihood of failing to yield. In a signalized intersection scenario, the left-turning driver appeared significantly. This failure to yield could be due to a lack of correct directional checking, speed, or effective lane use at the queuing space toward oncoming crossing cyclists. Our findings are consistent with \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e who revealed that drivers are more likely to make errors when turning left at a signalized intersection. The stop sign and the signalized intersection significantly impacted the last driver-cyclist interaction scenario, with drivers tending to turn right. \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e discovered that drivers turning right focus on cars approaching from the left and fail to notice the cyclist approaching from the right in time. However, the stop sign was identified as one of the countermeasures that provide drivers with more time during a negotiation process and compel motorists to allocate time to both directions, leveraging cyclist detection by the driver. On the other hand, drivers at signalized intersections are more likely to fail to yield to crossing cyclists due to signal complexity and ineffective traffic light phasing design, which causes confusion and poor judgment between interaction parties \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employs a mixed logit model to analyze the impact of different intersection control types (signals, stop/yield signs, and uncontrolled intersections) on drivers' failure to yield the right-of-way to cyclists at urban intersections. The model was developed using ten years of crash data involving single motor vehicle\u0026ndash;cyclist collisions in Michigan. Key crash characteristics, including driver age, day of the week, vehicle type, lighting conditions, number of lanes, and speed limit; were found to be consistent across both hazardous action types (yield and non-yield crashes) and intersection control types. However, the driver\u0026rsquo;s responsibility for hazardous actions varied only when under the influence of alcohol or drugs.\u003c/p\u003e \u003cp\u003eThe results indicate that the risk of hazardous actions (both yield and non-yield crashes) varies within the same interaction scenario. However, drivers are more likely to be involved in or at fault for yield crashes involving bicycles than for non-yield crashes. In the Cyclist Ahead \u0026ndash; Driver Ahead (CA\u0026ndash;DA) interaction scenario, drivers are more likely to fail to yield to cyclists but less likely to be at fault for non-yield behavior. The findings also reveal that the likelihood of a driver failing to yield to a cyclist significantly depends on the type of intersection control in place. For instance, when interacting with crossing cyclists (CC\u0026ndash;DL, CC\u0026ndash;DR), vehicles making left or right turns are more likely to be at fault due to yield-related actions. Additionally, in scenarios where the driver is traveling straight ahead (CA\u0026ndash;DA, CT\u0026ndash;DA, and CC\u0026ndash;DA), the risk of being at fault for a yield crash decreases.\u003c/p\u003e \u003cp\u003eWhen comparing the risk of driver yield failure across different intersection control types, the effect varies depending on the interaction scenario. For instance, in scenarios where the driver is traveling straight ahead, signalized intersections have a lower failure probability than stop signs or uncontrolled intersections. Conversely, stop/yield signs are associated with a lower failure rate in left-turning vehicle scenarios. In some cases, intersections without control are linked to a higher likelihood of failure. This study highlights the significant impact of intersection control design on driver behavior, particularly yielding ability, which may be influenced by variations in required attention and cognitive demands.\u003c/p\u003e \u003cp\u003eAlthough the models assess the impact of intersection control on driver-cyclist interactions, they have certain limitations, primarily due to data constraints. Analyzing interaction ratios in both crash datasets (yield and non-yield) reveals that some scenarios, such as CT\u0026ndash;CL and CT\u0026ndash;CR, are underrepresented compared to others. Future research should aim to provide more comprehensive data for these cases. Additionally, further investigation into driver yielding behavior; such as quantifying driver attitudes under different traffic control conditions when interacting with cyclists, would offer deeper insights and contribute to improving bicycle safety.\u003c/p\u003e \u003cp\u003eMoreover, inconsistencies in the coding of prior crash actions, particularly for cyclists in travel-ahead and crossing scenarios, have been identified in crash records. To enhance data reliability, future studies should differentiate and clearly define specific activity types, as well as leverage unstructured data analysis for improved consistency.\u003c/p\u003e \u003cp\u003eEnhancing communication and negotiation between road users can help reduce the likelihood of failure to yield the right-of-way. Potential countermeasures include vehicle-to-vehicle (V2V) communication and sensor-based technologies that alert drivers to the presence of nearby cyclists, particularly in turning scenarios. Another effective approach is the construction of dedicated cyclist facilities at intersections, such as separated bike lanes with enhanced safety features. Additionally, implementing dedicated cycling signal phases and ensuring sufficient signal timing can help eliminate priority confusion between drivers and cyclists, improving intersection safety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM. A: Conceptualization, methodology, data analysis, writing \u0026ndash; original draft.F.A.: Methodology, data interpretation, writing \u0026ndash; review and editing.M. E.: Data interpretation, writing \u0026ndash; review and editing, supervision.All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this study's findings are available from police reports from the State of Michigan. Still, restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are, however, available from Dr. Mousa Abushattal [email protected] upon reasonable request and with permission of the State of Michigan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLindsay, G., Macmillan, A. \u0026amp; Woodward, A. Moving urban trips from cars to bicycles: Impact on health and emissions. \u003cem\u003eAust N Z. J. Public. Health\u003c/em\u003e. \u003cb\u003e35\u003c/b\u003e, 54\u0026ndash;60 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026ouml;tschi, T., Garrard, J. \u0026amp; Giles-Corti, B. Cycling as a Part of Daily Life: A Review of Health Perspectives. \u003cem\u003eTransp. Rev.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 45\u0026ndash;71 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan, R. 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Prev.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 226\u0026ndash;234 (2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6108064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6108064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCycling safety is becoming increasingly critical as both cycling popularity and vehicular traffic continue to rise. Cyclists face heightened risks at intersections and junctions; areas where conflicting traffic flows, ambiguous right-of-way rules, and inadequate signaling often lead to collisions. Enhancing safety at these key points through improved junction design, effective traffic controls, and dedicated cycling infrastructure is essential for protecting all road users. This research investigates the behavior of drivers and cyclists at junctions under various control setup (traffic signals, stop/yield signs, and no control), with a particular focus on how intersection control influences drivers\u0026rsquo; failure to yield the right-of-way to cyclists. Utilizing ten years of Michigan police crash investigation data, we employ a Mixed Logit Model to analyze different interaction scenarios at intersections and assess the likelihood of drivers failing to yield. The experimental results indicate that most crash characteristics; such as driver age, weekday, vehicle type, and speed; consistently affect the probability of yielding failure across different intersection control types. On the other hand, driving under the influence of alcohol or drugs shows a distinct impact on crash likelihood. Additionally, the findings reveal a negative association between the probability of a driver failing to yield and the maneuver of driving straight ahead, along with significant heterogeneity in yielding failures across various intersection control types. These insights underscore the need for a deeper understanding of driver attitudes in interactions with cyclists. Strategies aimed at reducing intersection complexity; such as better coordination of bicycle facilities with intersection control types and enhanced driver education on cyclist behavior and traffic rules, should be considered to improve overall safety.\u003c/p\u003e","manuscriptTitle":"Navigating the Intersection: How Traffic Control Types Affect Cyclist Right- of-Way using A Mixed Logit Model Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-06 18:01:11","doi":"10.21203/rs.3.rs-6108064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-14T08:42:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T10:30:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199639207385308517615639663825284982944","date":"2025-04-03T07:23:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T08:44:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303088501819706685468830180570429545571","date":"2025-03-22T12:44:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-10T04:18:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73071256352642286985352687213923264240","date":"2025-03-07T05:20:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-06T04:42:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-06T04:37:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-06T03:43:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-04T10:05:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-25T20:21:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03b90165-d4e9-4aa8-9ae9-0f473ce1a3da","owner":[],"postedDate":"March 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45170823,"name":"Earth and environmental sciences/Environmental social sciences/Psychology and behaviour"},{"id":45170824,"name":"Physical sciences/Engineering/Civil engineering"}],"tags":[],"updatedAt":"2025-10-06T16:03:18+00:00","versionOfRecord":{"articleIdentity":"rs-6108064","link":"https://doi.org/10.1038/s41598-025-09801-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-30 15:57:50","publishedOnDateReadable":"September 30th, 2025"},"versionCreatedAt":"2025-03-06 18:01:11","video":"","vorDoi":"10.1038/s41598-025-09801-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09801-6","workflowStages":[]},"version":"v1","identity":"rs-6108064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6108064","identity":"rs-6108064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}