Rainfall and Road Safety in Texas: A Detailed Study of Relative Crash Risk from 2006 to 2021

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Employing a matched pair methodology, the research offers insights into the multifaceted impact of precipitation on crash likelihood, leveraging extensive crash data and gridded hourly precipitation records. The findings reveal that precipitation significantly increases crash risk, with an annual minimum rise of 32% and an average increase of 38%. Interestingly, rainy conditions are associated with reduced crash severity compared to dry weather. Although the relative risk is higher for all crash types during rainy conditions, the relative risk for no-injury crashes is 40% higher compared to fatal crashes. Spatial analysis highlights a correlation between population density and crash frequency. Moreover, the study investigates the interplay among roadway types, weather conditions, and driver behavior. Precipitation intensity was associated with a 36–52% increase in crash risk, with higher increases for more intense rainfall (over 25 mm/hr). The relative risk varied by age group, with the highest risk observed in young adults (18–30 years old) and the lowest in individuals older than 65. Generally, females exhibited a lower risk, ranging from 7–13% lower depending on the age group. Temporal factors—including time of day, day of the week, and month of the year—significantly impact road safety during precipitation, with early morning hours posing the highest crash risk due to rush hour traffic and changing lighting conditions. This comprehensive study enhances our understanding of road safety dynamics, providing foundational insights to inform policy development for safer and more sustainable transportation systems. Addressing human factors, alongside advancements in vehicle safety technology and road design, holds promise for reducing crash severity and improving overall road safety outcomes. Earth and environmental sciences/Climate sciences Physical sciences/Engineering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Safety is a key aspect of transportation system sustainability due to the significant socio-economic and environmental impacts of roadway crashes [ 1 ]. A sustainable transportation system is crucial for sustainable national development and requires attention from policymakers and regulatory bodies [ 2 ]. Promoting safety in a scientific manner is critical to mitigating these crash outcomes [ 3 ]. Accordingly, researchers are responsible for conducting thorough investigations into the causes of crashes and developing effective mitigation strategies. Environmental factors, particularly meteorological conditions, have a substantial influence on road safety. Many scholarly investigations have been conducted to comprehensively assess the influence of weather on roadway safety. These studies examined the impacts of variables such as rain [ 4 , 5 , 6 , 7 , 8 ]; snow, hail, and sleet [ 5 ]; temperature [ 9 , 10 , 8 ]; humidity [ 4 ]; visibility, ice, and wind [ 11 ]; fog and smoke [ 12 , 13 ]. The studies underscore the differentiated effects of these meteorological factors on road safety, with rain being a recurrent and widespread occurrence across diverse regions. Consequently, it emerges as a pertinent focus for research aimed at improving safety within varied transportation systems. The ubiquity of precipitation, especially rain, underscores the significance of dedicated studies to advance our understanding and enhance safety countermeasures in transportation. Rainfall has a significant impact on the likelihood of traffic crashes. It reduces visibility on roads and pavement friction, increasing the risk of crashes and decreasing vehicle speed [ 14 ]. In areas with inadequate drainage systems, water collects on the streets, leading to waterlogging and lane obstruction, further increasing the risk of crashes [ 15 ]. Studies have shown that there is a statistically significant increase in crash and injury rates during rainy days, with a higher risk on days with heavier rainfall [ 16 ]. Additionally, extreme rainfall can cause hydroplaning, which reduces friction between the pavement surface and tires, increasing the risk of crashes [ 17 ]. Numerous studies exploring the impact of precipitation on crash risks have generated a spectrum of findings. While some studies underscore the significant influence of precipitation on the frequency of crashes, others consider the extent of its effect with caution. For example, a study investigated the effects of different precipitation types on crash rates and suggested that all forms of precipitation increase the relative risk of crashes [ 5 ]. In another study, the authors revealed a correlation between varying levels of rainfall intensity and a heightened frequency of crashes [ 4 ]. Another research effort confirmed that precipitation elevates the likelihood of both fatal and non-fatal crashes [ 8 ]. A study concluded that precipitation significantly increases the risk of fatal traffic crashes, regardless of its intensity [ 18 ]. Researchers conducted a study demonstrating a direct link between increased precipitation and a greater number of crashes [ 19 ]. A comprehensive investigation quantified the influence of meteorological factors on various crash types, reinforcing precipitation's role in boosting crash rates [ 20 ]. The study conducted by Eck, et al. [ 7 ] demonstrated a significant increase in crash risk during rainy periods in the states of the Carolinas, USA. However, a study conducted in Kentucky, USA, observed a decrease in the severity of crashes as precipitation levels rose, attributing this phenomenon to increased driver caution and reduced speeds under these conditions [ 21 ]. Moreover, a study suggested that precipitation-related fatalities primarily occurred on wet roadways, with snow being the main factor for winter precipitation and ice/frost for transitional winter precipitation [ 22 ]. Additionally, research indicated that decreased visibility was a factor in less severe injury crashes [ 9 , 11 ]. In snowy conditions, studies indicated a decrease in both the frequency and severity of crashes [ 23 ]. A study emphasized that some studies overlook the indirect effects of winter precipitation on crashes. The study stressed that future studies must account for these effects, as they are crucial for a better understanding of how precipitation impacts crashes [ 24 ]. Diverse methodological approaches, models, and influencing factors employed in various studies contribute to disparities in findings and conclusions. Researchers have underscored the pivotal role of sample size in crash risk models, suggesting that a larger dataset enhances the development of robust analytical models [ 25 , 26 ]. The limited availability of data for studies has raised concerns among researchers, as it can potentially impact the accuracy and reliability of study outcomes [ 27 , 28 , 29 , 30 ]. To address this challenge, the utilization of extensive data, commonly referred to as big data, becomes crucial for investigating the association between precipitation and crash risk [ 28 ]. In addition to data volume, the analysis of crash risks necessitates consideration of precise corresponding environmental data to ensure reliable outcomes. Both the temporal and spatial dimensions of precipitation and crash data hold great importance. Particularly in expansive regions, sparsely distributed weather stations can introduce uncertainty due to spatial variability in weather conditions [ 31 , 32 , 26 ]. Additionally, the analysis of traffic safety heavily relies on the temporal scale of precipitation data (monthly, daily, and sub-daily records), with daily data being the most commonly used due to its widespread availability [ 16 ]. However, daily data falls short in accounting for dry intervals between rainy periods [ 33 ], emphasizing the need for finer temporal scales. In response to these challenges, researchers propose radar data as a source of accurate precipitation information, independent of weather station availability [ 34 , 26 ]. However, a study suggested that while a daily time step provides a relative risk estimate similar to an hourly time step for most rural counties in our study area, it proves effective for fewer than half of the urban counties [ 35 ]. Gridded hourly precipitation datasets emerge as dependable resources for investigating precipitation's impact on crash risk in diverse regions and conditions. Consequently, this research employs gridded hourly precipitation data to explore the influence of precipitation on transportation safety and crash risk. This study investigates the influence of rainfall on the probability of car crashes within Texas road networks. Texas, one of the largest and most populated states in the United States, experiences significant travel activity and a diverse range of climatic conditions. The combination of these elements, along with the frequent occurrence of severe weather conditions and substantial annual precipitation in Texas, highlights the need to investigate the impact of rainfall on road safety. Texas actively collects a large amount of reliable information about road crashes. This information forms a fundamental basis for in-depth research into transportation safety. Moreover, Texas possesses extensive and trustworthy data on precipitation, offering a solid foundation for the study's objectives. The research also aligns with the safety objectives of transportation agencies worldwide, particularly the "Vision Zero" initiatives, which aim to eliminate fatal crashes on their road networks. Employing a matched pair methodology, the study spans a 16-year period from 2006 to 2021, utilizing big data analysis to overcome previous limitations in data size. The results of this study offer valuable insights into the impact of precipitation on crashes across Texas' roadways, contributing to the enhancement of safety measures and the overall management of the transportation system. 2. Data Description 2.1. Crash Dataset This study acquired a crash dataset spanning from 2006 to 2021 from the Crash Records Information System [CRIS, 36]. The Texas Department of Transportation curates the CRIS data. CRIS consists of three primary sub-databases: crash, vehicle, and primary person. The crash sub-database contains comprehensive details about each incident, including the speed limit, weather conditions, road type and alignment, surface conditions, traffic control devices, the initial harmful event, manner of collision, date and time of the crash, location, total injuries, day of the week, and the severity level of the incident. The vehicle sub-database provides specifics on each vehicle involved, such as vehicle type, contributing factors to the crash, the vehicle's actions, and model year. The primary person file stores information about individuals involved in crashes, including details on the type of person (driver, passenger, etc.), injury severity, age, gender, ethnicity, ejection from the vehicle, use of restraints, deployment of airbags, involvement of alcohol or drugs, and license details. The Texas Department of Transportation (TXDOT https://cris.txdot.gov ) supplied the detailed crash data used to support the findings of this study under a data sharing agreement. 2.2. Precipitation Dataset This study utilized precipitation data from NEXRAD Stage-IV. The United States National Centers for Environmental Prediction (NCEP) creates the Stage-IV product based on the Stage-III data, compiled by the 12 River Forecast Centers of the National Weather Service (NWS). The Stage-IV product integrates multiple Stage-III radar outputs through the NEXRAD Precipitation Processing Systems. This product is widely used, particularly for driving hydrological models across the contiguous U.S. Initially recorded at 5–6 minute intervals, the data is processed and consolidated into a gridded format with a 1-hour temporal resolution. The high-resolution grid covers the entire contiguous U.S., with a spatial resolution of about 4 km by 4 km, resulting in over 40,000 grid points just in Texas. The dataset spans from January 1, 2002, to the present. Public access to the data is available via the National Center for Atmospheric Research’s (NCAR) FTP servers upon request. For further details, visit their website: https:‌//‌‌www.emc.ncep.noaa.gov/‌mmb/ylin/pcpanl/stage4/ . Similar to other tools for measuring precipitation, weather radar presents both advantages and drawbacks [ 37 ]. Notable advantages include the bias-adjusted, near-real-time nature of its estimates, ease of transferability and processing, higher spatial resolution due to the gridded binary (GRIB) format, reliable performance in regions with limited rain gauge coverage and minimal elevation variations, and accurate estimation of high rainfall rates [ 38 ]. Conversely, the disadvantages of radar estimates encompass potential data discontinuities in regions beyond the radar's effective range and in overlapping areas covered by multiple radar stations. Furthermore, the automated quality control process at the hourly scale contributes to their lower accuracy compared to previous versions and presents challenges in identifying flawed rain gauge data [ 26 ]. The rainfall distribution in Texas is highly variable, ranging from less than 250 mm/year in the western areas of the state to over 1,300 mm/year in the east (Fig. 1 ). Influencing factors include topography, elevation, and proximity to the Gulf of Mexico. West Texas receives less rainfall due to the rain shadow effect of nearby mountains, which causes air to rise and release moisture on the windward side, leaving the area dry. In contrast, East Texas receives higher rainfall because of its proximity to the Gulf of Mexico, which contributes to increased moisture in the air. Furthermore, in El Niño periods, higher rainfall is generally observed in months outside of the summer season [ 39 ]. Conversely, La Niña is a primary catalyst for tropical cyclone precipitation (TCP) in Texas. It induces a greater frequency of precipitating storms, amplifying TCP's overall contribution to total precipitation [ 40 ]. The driest year during the study period, 2011 (average annual total of 345 mm), was influenced by La Niña, causing cooler sea surface temperatures and weakening the jet stream, while the wettest year, 2015 (average annual total of 1112 mm), was influenced by El Niño, causing warmer sea surface temperatures and strengthening the jet stream, directing storms towards Texas. 3. Methods 3.1. Match-pair application and time-step selection This study employs the Matched Pairs Analysis (MPA) method to investigate the impact of precipitation on crashes. MPA stands as a robust approach for constructing statistical models and scrutinizing how precipitation influences crash risk. It has gained widespread adoption among researchers for assessing the effects of adverse weather conditions on both the frequency and severity of crashes [ 34 , 32 , 16 , 26 , 41 , 5 ]. MPA effectively addresses a major challenge in crash analysis related to uncertainties arising from time-dependent factors such as traffic conditions and driver behaviors, well-recognized for their significant impact on the frequency and severity of crashes. These factors exhibit significant variations across different times and locations. MPA mitigates this uncertainty by utilizing control periods corresponding to the actual crash events, thereby removing the temporal and spatial factors' impact [ 26 ]. A fundamental assumption in MPA is that traffic patterns and volumes remain relatively consistent during the same hour on the same day of the week, excluding holidays and special events. Therefore, in MPA, time intervals are typically separated by one week. Moreover, the choice of time intervals profoundly affects the analysis's accuracy [ 7 ]. Many studies opt for daily intervals due to data limitations and the complexities involved in hourly. However, only a limited number of studies utilize smaller time intervals such as hourly [ 26 ], three-hourly [ 34 ], six-hourly [ 42 ], or flexible intervals extending up to twelve-hour intervals [ 43 ]. The drawback of daily analysis is that crashes occurring during non-precipitation times are sometimes categorized as precipitation-related because they occurred on days with recorded precipitation [ 26 , 44 ]. For time intervals greater than an hour, there should be a cumulative precipitation threshold within the interval to classify it as a "wet period." However, with an hourly time interval, each hour with any amount of precipitation is regarded as a "wet hour," as there is no differentiation between precipitation intensity and cumulative amount at this temporal resolution [ 26 ]. This study uses an hourly analysis, pairing each hour of precipitation with a control hour, either one week before or after the event. This approach controls for factors such as daily traffic volume, light conditions, and other time-sensitive factors. In this study, a specific hour within a particular geographic location (grid cell) is classified as either a wet period (with precipitation) or a dry period (without precipitation). Subsequently, an hour within a precise one-week interval at the same location is designated as its matched pair. We initially check the subsequent week; if no corresponding hour is found, we proceed to examine the previous week. If no matching hour is discovered within this two-week timeframe, the hour in question is excluded from the analysis. This matching procedure is conducted independently of the crash data. After successfully identifying matched pairs, crashes are categorized into two types: "wet crashes" during hours with precipitation and "dry crashes" during hours without precipitation. All matched hour pairs for a specific radar grid are considered, and the corresponding crashes during each of these matched hours are counted. It is assumed that traffic volume remains unchanged between wet and dry periods. The traditional application of the MPA methodology poses a significant challenge due to the vast road network in Texas. Identifying matching pairs for all Stage IV precipitation grids and hours across multiple years was an extensive undertaking. Consequently, a modification to the method was introduced to streamline the process. Step 1 involved linking crashes to their corresponding Stage IV precipitation grids. In Step 2, Stage IV precipitation grids without associated crashes were removed. Step 3 meticulously sought and identified match hours for each wet (with precipitation) or dry crash hour. Finally, Step 4 encompassed the development of crash matching pairs, utilizing the methodologies depicted in Fig. 2 . It is important to emphasize that, given the relatively low frequency of precipitation, a minority of dry hours with crashes were successfully paired with matching wet hours. Conversely, the majority of wet hours with crashes found corresponding dry hour matches. 3.2. Relative Risk Factor (RRF) Computation The Relative Risk Factor (RRF) calculation is the underlying method used to estimate relative risk for each methodological change assessed in this study. Once crashes, injuries, and fatalities were tabulated for events and controls, estimates of the relative risk of crash, injury, and fatality, along with their 95% confidence intervals, were calculated using the RRF approach. The RRF (or approximate relative risk) represents the odds of a crash, injury, or fatality during an event period compared to a control period. This is estimated using the following equation: $$\:RRF=\frac{{A}_{i}/C}{({B}_{i}/D)}$$ where A i is the number of collisions, injuries, or fatalities during an hour with rainfall, and B i is the number during the matched control hour, while C and D are the number of safe outcomes during the rainfall hour and dry hour, respectively. The values of A i and B i are from traffic data. Although C and D are very large, their exact values do not significantly impact the RRF calculation and are, therefore, not explicitly set. After calculating the RRF for each event-control pair, it is log-transformed to approximate a normal distribution. The RRF is assigned a weight inversely proportional to its variance. The variance of the logarithm of the RRF is: $$\:{v}_{i}=\frac{1}{{A}_{i}}+\frac{1}{{B}_{i}}+\frac{1}{C}+\frac{1}{D}$$ When A i is zero, a continuity correction factor of 0.5 is added. The statistical weight of each event-control pair is: $$\:{w}_{i}=\frac{1}{{v}_{i}}$$ The weighted mean RRF is calculated as: $$\:{y}_{i}=\text{e}\text{x}\text{p}\left(\frac{{\sum\:}_{i=1}^{g}{w}_{i}{y}_{i}}{\sum\:_{i=1}^{g}{w}_{i}}\right)$$ Where y i is the logarithm of the RRF. The 95% confidence intervals are: $$\:95\text{\%}\:\text{C}\text{I}=\text{exp}\left(\frac{{\sum\:}_{i=1}^{g}{w}_{i}{y}_{i}}{\sum\:_{i=1}^{g}{w}_{i}}\right)\pm\:\frac{1.96}{\sqrt{\sum\:_{i=1}^{g}{w}_{i}}}$$ Values of relative risk greater than 1 indicate an increased risk of crashes during rainfall, while values less than 1 indicate a decreased risk. The RRF calculations involved a variety factors, including age, gender, hour of the day, day of the week, month, road type, road part, and crash severity. These parameters were considered to assess their individual and combined effects on crash risks under rainfall conditions, determining which groups or times are more hazardous. This comprehensive approach provides valuable insights into the factors influencing weather-related traffic crashes and offers a foundation for developing effective road safety interventions. 3.3. MPA Limitations In the context of MPA, there is a widespread assumption that rainfall does not significantly affect traffic volume and patterns, a notion adopted by various studies [ 42 , 33 , 34 , 32 , 26 ]. However, some research suggests that precipitation may, in fact, have an impact on various traffic aspects, potentially resulting in a reduction in the number of crashes during rainy conditions [ 45 , 16 ]. These effects could manifest as a decrease in traffic volume [ 16 ] and/or the speed at which traffic moves [ 46 ]. Nevertheless, it is crucial to acknowledge that the majority of studies examining the interplay between precipitation and traffic volumes have focused on daily analyses. Consequently, their findings may not be directly applicable when scrutinizing shorter time periods, such as hourly intervals, due to the possibility of precipitation having a distinct impact on traffic volumes at such finer time scales. We believe that analyzing data at an hourly time scale can reduce the impact of decreased traffic speeds and volumes during rain on our estimates, although not entirely. In any case, this problem could potentially impact the results by making the estimates of relative risk a little more conservative [ 47 ]. 4. Results and Discussion In this section, we present and discuss the results of the MPA as wells as the different factors that affect the estimated RRF. The annual variability of RRF and the state-averaged total are shown in Table 1 . Overall, it is very clear that precipitation significantly increases the likelihood of crash occurrence. Precipitation elevated the crash risk by a minimum of 32% over the study period. On average, rainy conditions increase the likelihood of a crash by a drastic 38% over the study period. This considerable increase underscores the role of adverse weather conditions in the region, emphasizing the urgent need for mitigative measures to enhance roadway safety. Table 1 – Annual relative risk factor and rainfall over the study period. Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Overall RRF 1.41 1.37 1.41 1.44 1.45 1.48 1.45 1.42 1.36 1.32 1.38 1.33 1.35 1.36 1.44 1.33 1.38 Lower limit 1.38 1.35 1.38 1.41 1.43 1.44 1.42 1.39 1.33 1.30 1.36 1.30 1.33 1.34 1.42 1.30 1.37 Upper limit 1.44 1.40 1.44 1.47 1.48 1.53 1.49 1.45 1.38 1.34 1.40 1.35 1.37 1.39 1.47 1.35 1.39 Annual Rainfall (mm) 556 821 571 683 675 345 592 667 702 1112 906 833 914 756 665 755 722 (345–1112) It is noteworthy that the highest RRF was observed during the driest year, 2011, while the wettest year, 2015, recorded the lowest RRF. This suggests that the observed increase in annual precipitation may have played a role in the apparent decline of annual RRF. In fact, the correlation between annual rainfall and relative risk is -0.78, indicating a complex relationship between weather conditions and traffic crashes. This complexity is influenced by various factors, including driving behaviors, preparedness and expectancy, and roadway maintenance and drainage [ 48 , 49 ]. Figure 3 provides a visual representation of crash data spanning the years 2006 to 2021 on a map of Texas, with each red dot pinpointing the precise location of a crash. The visual insight suggests that crashes have occurred throughout the state, with a notable concentration in east Texas, where a substantial proportion of the population resides. When separated, crashes during wet and dry weather conditions exhibit similar spatial patterns. Figure 3 (b) shows the major highways of Texas. Figure 3 reveals a noticeable concentration of crashes in and around Texas's major urban centers. In Fig. 3 (a), crash clustering follows the population distribution, with approximately 86% of the total Texas population located in counties along and east of the I-35 interstate highway [ 50 ]. A larger population corresponds to higher vehicle traffic volumes, inherently elevating the potential for crash incidents. While it underscores the influence of population density on crash frequency, the clustering of crashes is more complex. Factors such as complex road networks, diverse traffic conditions, and the interaction of multiple transportation modes, which collectively heighten the risk of collisions, also contribute to this clustering in urban areas. Additionally, there is a possibility that urban regions have higher crash reporting rates, as incidents in rural areas may be less documented. This distinction may partially account for the observed geographic disparities in crash prevalence. 4.1. Rainfall Impact on Crash Severity Figure 4 displays the annual relative risk factors corresponding to different injury types averaged over the study period, with error bars showing the annual variability. The results demonstrated that precipitation increases the relative risk factors for all crash severity levels, with a negative correlation between relative risk and crash severity level. The consistent decline in relative risk factors as crash severity increases suggests that drivers may adapt their behavior during wet weather, specifically by exercising caution and curbing high-risk activities. These observations underscore the critical role of driver behavior and adaptation in response to adverse weather conditions. It is plausible that drivers adopt safer behaviors, such as reducing speed, maintaining a safe following distance, and making fewer sudden maneuvers during wet conditions [ 51 , 52 ]. While these observations are reasonable, they should be interpreted with caution. Further empirical support from future research is critical. Additionally, involvement with alcohol or drugs, no-restraint use, and speeding were less likely to co-occur with fatalities during adverse weather conditions [ 53 ]. 4.2. Impact on Different Roadway Types Texas' extensive network of roadways is systematically divided into seven distinct categories: tollways, interstate highways, US and state highways, county roadways, city streets, farm-to-market roadways, and others, which is a miscellaneous classification encompassing roadways not included in the preceding categories. This categorization facilitates a more in-depth exploration of how different types of roadways interact with weather conditions. Figure 5 shows the average relative risk factors over the study period across these various types. For all roadway classifications, the relative risk factors are higher than 1, signifying that precipitation significantly heightens the risk of crashes on all roadway types. However, the degree of this effect varies substantially across the types. Notably, tollways and interstate highways have the most pronounced relative risk factors, whereas city streets report more moderate risk factors. The elevated risk on tollways and interstate highways can be attributed to their high-speed nature, where drivers confront amplified challenges and reduced reaction times during inclement weather [ 54 ]. On the other hand, drivers may associate lower relative risk factors with more manageable driving speeds on city streets and county roadways. These findings offer a perspective on the complex interaction of roadway classifications, meteorological conditions, and driver actions. By understanding these elements, policymakers and safety advocates can pinpoint high-risk areas where intervention measures are most required. 4.3. Impact of Precipitation Intensities Figure 6 shows the average relative risk factors over the study period, corresponding to different levels of precipitation intensity. First, the results show that all relative risk factors exceed 1 for intensity classes. Additionally, there seems to be a direct correlation between precipitation intensity and crash risk. The reduced roadway visibility during heavy precipitation is a critical contributor. Intense precipitation events often lead to decreased visibility, making it challenging for drivers to recognize their surroundings. Poor visibility can also lead to delayed reaction times, reduced ability to identify hazards, and, in some cases, complete loss of visual contact with the road. Another important factor is the increased reduction of tire-road friction with precipitation, which impacts the vehicle's ability to maintain control. Higher precipitation intensities demand more cautious driving behaviors, but it seems drivers don't always adapt adequately to the increased risk. Furthermore, insufficient drainage systems and poorly maintained roads can contribute to the accumulation of standing water during intense events, increasing the risk of hydroplaning and other weather-related crashes. 4.4. Effect of Driver’s Age and Gender Figure 7 offers an insight into the proportion of male and female drivers involved in all crashes, examining the relationship between RFF, driver age, and gender. Across the range of age groups, female drivers consistently exhibit lower crash risk in comparison to their male counterparts. Moreover, these results indicate that drivers aged between 18 and 30, regardless of their gender, are particularly vulnerable to heightened crash risks during precipitation. This susceptibility decreases for older drivers, suggesting that older drivers adopt more cautious driving practices or drive on low-traffic roadways only during adverse weather conditions. Furthermore, the analysis incorporates varying levels of exposure among different genders and age groups, acknowledging potential behavioral differences. It is possible that certain drivers, based on age and gender, may opt to refrain from driving during precipitation events [ 55 ]. It is well known that females make shorter work trips, use public transit more frequently, and drive fewer miles per year compared to their male counterparts [ 56 , 57 ], and studies have shown that females have a greater likelihood of self-regulating driving, which includes avoiding driving in inclement weather [ 58 ]. 4.5. Temporal Variability of RRF To evaluate the temporal variability of the relative risk factors, the study investigated three aspects of time: the time of day, the day of the week, and the month of the year. We scrutinize each of these temporal factors to assess the combined influence of adverse weather conditions and time on crash risk. Figure 8 presents the average relative risk factors categorized by the time of day, providing an overall average across all years. The RRF is consistently above 1 throughout the day, indicating the influence of precipitation on crash risk. The results reveal a distinct pattern that underscores the significance of the time of day. Early morning hours, particularly between 4 AM and 8 AM, exhibit the highest relative risk factors. This heightened risk during these hours can be attributed to several interrelated factors. Lower light conditions often occur during morning peak hours, and the combination of rainfall can significantly reduce visibility, making it more difficult for drivers to see the road, other vehicles, and pedestrians, thereby increasing the risk of crashes [ 59 ]. Additionally, driver alertness may be lower during early morning commutes. This, combined with potentially lower visibility due to the transition from night to day, could further elevate crash risk [ 60 ]. In contrast, afternoon commutes often experience a more dispersed traffic pattern [ 61 ]. This, along with potentially improved visibility compared to early mornings, might contribute to a lower crash risk during this period. While these assertions appear logically sound, it is imperative that they undergo rigorous empirical examination to ascertain their validity and quantitatively assess their implications for traffic safety. Such a study would provide invaluable insights for the scientific community and policymakers alike, aiding in the formulation of evidence-based interventions to mitigate traffic-related risks during peak hours. When investigating the day of the week's influence, as depicted in Fig. 9 , the results indicate that there is no specific visible pattern. The influence of the day of the week on relative risk factors appears to be minor, with slightly lower risk during the middle of the week. The difference in travel patterns across different days of the week may explain this variability. Regarding the impact of the month of the year, Fig. 10 illustrates a significantly lower risk during the late spring and summer months. The impact of rainfall on crash risk is lower in the summer compared to other seasons. This can be due to warmer temperatures, higher evaporation, longer daylight hours, improved road maintenance, and more dispersed traffic volume [ 62 , 60 ]. Additionally, in winter, rainfall on cooler road surfaces can have a higher effect than in the summer [ 14 ]. Furthermore, during the summer months, most schools are closed, reducing the impact of the morning peak hours. 5. Summary and Conclusion This extensive research thoroughly examines the intricate relationship between precipitation and its influence on crash occurrences over a 16-year period in Texas. The comprehensive analysis of crash data and precipitation records reveals valuable insights across multiple dimensions, including temporal, spatial, roadway types, driver characteristics, and precipitation intensities, all of which significantly contribute to road safety during adverse weather conditions. These findings prompt consideration of interventions and strategies to mitigate crash occurrences. The study unequivocally establishes that precipitation substantially elevates crash risk, with a minimum increase of 32% and an average of 38% during rainy conditions. While there has been a gradual decline in risk values over the years, this underscores the imperative of maintaining a focus on enhancing roadway safety during adverse weather. Precipitation's multifaceted impact on safety is manifested in slippery road surfaces, diminished visibility, hydroplaning, and impaired driver concentration. The spatial distribution of crashes highlights a clear correlation between population density and crash frequency, indicating the need for targeted safety interventions in densely populated areas. Interestingly, during wet conditions, a higher proportion of crashes result in 'no injury,' suggesting that drivers may adopt more cautious behaviors, which can reduce crash severity. However, this trend of caution is less evident as crash severity increases.. Also, precipitation consistently increases relative risk across all crash categories, emphasizing the importance of driver behavior in adverse weather and the need for safety strategies. Driver behavior, along with advancements in vehicle safety technology and road design, can significantly contribute to a reduction in the severity of crashes. This reiterates the critical importance of addressing human factors in addition to vehicle safety enhancements. The study underscores the intricate connection between various roadway types, weather conditions, and driver behavior. Notably, tollways and interstate highways exhibit the highest relative risk factors (2.20, compared to 1.20 for city streets), emphasizing the importance of behavioral interventions in conjunction with vehicle safety improvements. Precipitation intensities were associated with an increase in crash risk of 36–52%, depending on rainfall intensity, with higher increases for more intense rainfall (over 25 mm/hr). This can be attributed to reduced visibility, compromised tire-road friction, and driver behavior. The necessity for comprehensive safety measures becomes particularly evident during intense precipitation events. The relative risk varies by age group, with the highest risk observed in young adults (18–30 years old) and the lowest in individuals older than 65. Generally, females have a lower risk, ranging from 7–13% lower depending on the age group. These findings can inform targeted safety strategies. The temporal dimension, encapsulating the time of day, day of the week, and month of the year, is a critical factor influencing road safety during precipitation. The early morning hours are notably characterized by the highest crash risk, attributed to factors such as rush hour traffic and changing lighting conditions. In contrast, the afternoon rush hours do not seem be associated with higher relative risks. Given the correlation between population density and crash rates, the report highlights the need for targeted safety measures in densely populated areas. Potential solutions include enhancing road design and maintenance, promoting public awareness campaigns, and investing in advanced traffic management systems. Exploring the link between crash distributions and healthcare access could also improve emergency response systems. Transportation safety during inclement weather can also be enhanced through advanced vehicle technologies, infrastructure improvements, real-time weather information dissemination, public awareness campaigns, and research into weather-adaptive traffic management systems. It is worth mentioning that this study has certain limitations, including its exclusive focus on Texas, potentially restricting the generalizability of its findings to regions with diverse climates and road infrastructures. Additionally, the analysis overlooks crucial variables such as driver experience, vehicle type, and road conditions, all of which can significantly influence crash occurrences. The study does not delve into the effects of alcohol or substance use during adverse weather conditions, and it does not consider data on the utilization of safety features in vehicles, such as antilock brakes or electronic stability control. Furthermore, socioeconomic factors and road maintenance practices are not examined in relation to crash occurrences, presenting opportunities for further investigation. The number of crashes (wet/dry pairs) is significantly lower in west Texas than in east Texas due to the west-east gradient of precipitation and population as well as gaps in radar coverage in that region. This has implications for the number of potential matches throughout the state and, consequently, the number of crashes during matched wet/dry hours. However, the process of weighting each matched pair as outlined above in the methodology largely accounts for this [ 35 ]. Suggestions for future research include conducting similar studies in diverse regions to assess the transferability of findings and exploring the roles of driver experience, vehicle safety features, and road conditions in crash occurrences during precipitation. Investigating the influence of alcohol or substance use on crash risk during adverse weather conditions and examining the impact of socioeconomic factors and road maintenance practices on crash occurrences are also recommended. Furthermore, additional research is needed to assess the effectiveness of specific safety measures, such as real-time weather information distribution to drivers or the development of advanced weather-responsive technologies in vehicles. Evaluating the impact of climate change on the frequency and intensity of precipitation events and its implications for road safety is crucial, given the increasing trend in severe precipitation events, necessitating proactive safety measures to address the growing challenges posed by intensified precipitation and its potential significant impact on roadway safety. Declarations Declaration of Competing Interest None. Author Contribution Hatim Sharif guided this research and contributed significantly to preparing the manuscript for publication and developed the research methodology in collaboration with Samira Tafazzol. Dawit Ghebreyesus, Khondoker Billah, and Chad Furl downloaded and processed the rainfall and crash dat. Dawit Ghebreyesus and Chad Furl. developed the scripts used in the analysis. Samira Tafazzol, Hatim Sharif, and Mohammadreza Gholikhani performed the statistical analysis. Samira Tafazzol and Mohammadreza Gholikhani prepared the first draft. Samira Tafazzol, Hatim Sharif, and Chad Furl performed the final overall proofreading of the manuscript. Data Availability The crash data used to support the findings of this study were supplied by the Texas Department of Transportation (TXDOT) under a data sharing and so cannot be made freely available. Requests for access to these data should be made to [https://cris.txdot.gov].Public access to the data is available via the National Center for Atmospheric Research’s (NCAR) FTP servers upon request. For further details, visit their website: https://water.noaa.gov/about/precipitation-data-access (Access date: May 30, 2025). References Wunderlich, R. C., & Shipp, E. M. (2012). Perspectives on road safety through the lens of traffic crashes in the United States. In Advances in Transportation and Health (pp. 35-58). Elsevier. Peden, M. M., & Puvanachandra, P. (2019). Looking back on 10 years of global road safety. International health , 11 (5), 327-330. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 16 Jul, 2025 Editor invited by journal 08 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 01 Jul, 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. 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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-6992902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486797753,"identity":"4973c29f-3f44-43ac-9849-a5a80b48c2dc","order_by":0,"name":"Samira Tafazzol","email":"","orcid":"","institution":"University of Texas at San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Tafazzol","suffix":""},{"id":486797754,"identity":"1b72c86d-6b1a-4877-8105-7b11f87980d0","order_by":1,"name":"Hatim Sharif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACAyA+AMQJDAyMDR8+QEUl8GthhmtpnDkDbAQRWhggWhgYZ/MQo8Wc/fzBQzcY6vL4Zx9ubLZt+2PP38B88DYPHi2WPckMh3MYDhdLnEtsbM5tM0iccYAt2RqfFoMDYC0HEhvOMLY/BmpJYDjAYyaNV8v5xyAtdYnzzzA2Nlu2GdjLH+D/hl/LDbAtzIkbQFoY2wwYNxzgYSOg5bHB4RyDw4kbgVoae84ZJ248zGZsOQevwxIff86pqEucd4b9YcOPMjl7uePND2+8waMFqhGZw0xQ+SgYBaNgFIwCQgAAKqFQ31CXFsAAAAAASUVORK5CYII=","orcid":"","institution":"University of Texas at San Antonio","correspondingAuthor":true,"prefix":"","firstName":"Hatim","middleName":"","lastName":"Sharif","suffix":""},{"id":486797755,"identity":"0b6dbed1-79d2-4266-8b89-839035dec17e","order_by":2,"name":"Mohammadreza Gholikhani","email":"","orcid":"","institution":"University of Texas at San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Gholikhani","suffix":""},{"id":486797756,"identity":"a75e5e57-5bd1-4ee3-b546-402e4981be05","order_by":3,"name":"Dawit Ghebreyesus","email":"","orcid":"","institution":"Bridgefarmer \u0026 Associates, Inc","correspondingAuthor":false,"prefix":"","firstName":"Dawit","middleName":"","lastName":"Ghebreyesus","suffix":""},{"id":486797757,"identity":"5e8dde6e-ebc8-488a-a9dc-07b16016596f","order_by":4,"name":"Khondoker Billah","email":"","orcid":"","institution":"East West University","correspondingAuthor":false,"prefix":"","firstName":"Khondoker","middleName":"","lastName":"Billah","suffix":""},{"id":486797759,"identity":"180e5fde-89fe-4805-8452-4a38ecf00aa4","order_by":5,"name":"Chad Furl","email":"","orcid":"","institution":"Edwards Aquifer Authority","correspondingAuthor":false,"prefix":"","firstName":"Chad","middleName":"","lastName":"Furl","suffix":""}],"badges":[],"createdAt":"2025-06-27 15:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6992902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6992902/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-20760-w","type":"published","date":"2025-10-21T16:17:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87190578,"identity":"a31b7b6f-9701-42c7-8835-bb1be3b04bde","added_by":"auto","created_at":"2025-07-21 11:11:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":432811,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of hourly rainfall: (a) the average over the study period; (b) the driest year, 2011; (c) and the wettest year, 2015.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/cad8b6fc0dc5250c8f5ae5f9.png"},{"id":87190905,"identity":"d4e72c8a-1578-4915-8a11-753bdc31581b","added_by":"auto","created_at":"2025-07-21 11:19:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43583,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for pair matching of wet and dry crashes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/9e606ece67f57260821273a4.png"},{"id":87190593,"identity":"3285b0b7-f539-4158-8489-ca0569c5fea0","added_by":"auto","created_at":"2025-07-21 11:11:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":349881,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Rain crashes between 2006 and 2021; (b) major highways in Texas; \u0026nbsp;\u0026nbsp;(c) Rain crashes between 2006 and 2021 and major highways in Texas\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/19df88d8a1ad93f16cee6db1.png"},{"id":87190907,"identity":"d87b0f0a-e077-4342-93f7-a551319f83b2","added_by":"auto","created_at":"2025-07-21 11:19:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50904,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Relative Risk Factor corresponding to different types of injuries averaged over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/a238fdf8edc82d8b24fba660.png"},{"id":87191793,"identity":"393f0002-270d-4d6d-9e16-6b1c79837fe6","added_by":"auto","created_at":"2025-07-21 11:27:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57462,"visible":true,"origin":"","legend":"\u003cp\u003eAverage relative risk factors for different roadway types averaged over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/3b7c1aaefb811e1217678789.png"},{"id":87191967,"identity":"5ca05460-d39a-4093-945d-dc05a68c623a","added_by":"auto","created_at":"2025-07-21 11:35:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41129,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Relative Risk Factor estimated for different rainfall intensities averaged over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/bd5b599ff20af28c946c4dae.png"},{"id":87190582,"identity":"c32efeba-18e2-4375-9dc4-ba1c4a3c3013","added_by":"auto","created_at":"2025-07-21 11:11:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":65669,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction between the relative risk factor and the drivers’ age and gender over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/ed082aebb2cae9bce77648a4.png"},{"id":87191797,"identity":"25dbf859-1c0c-4de0-b072-5dc7ee922035","added_by":"auto","created_at":"2025-07-21 11:27:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":93409,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Relative Risk Factor by hour of the day averaged over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/415385925e19115c24d45250.png"},{"id":87191968,"identity":"f09b9413-d598-4e27-85ec-42e76579674d","added_by":"auto","created_at":"2025-07-21 11:35:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":61681,"visible":true,"origin":"","legend":"\u003cp\u003eAverage Relative Risk Factor by day of the week averaged over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/736d947ce0a52809b4509d97.png"},{"id":87190588,"identity":"d7c5c3ee-2f25-40b4-ad66-7ea4a97a9212","added_by":"auto","created_at":"2025-07-21 11:11:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":63142,"visible":true,"origin":"","legend":"\u003cp\u003eAverage relative risk factor by month averaged over the study period (error bars show the 95% confidence intervals).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/14832d2b16e34e75d1b2d67f.png"},{"id":94490563,"identity":"d43ff1ad-4295-4e3b-8c4f-d4e3285a12d4","added_by":"auto","created_at":"2025-10-27 17:12:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2058176,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6992902/v1/a2c7e2cb-aa7f-4738-a3a6-a84310dc77f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rainfall and Road Safety in Texas: A Detailed Study of Relative Crash Risk from 2006 to 2021","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSafety is a key aspect of transportation system sustainability due to the significant socio-economic and environmental impacts of roadway crashes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A sustainable transportation system is crucial for sustainable national development and requires attention from policymakers and regulatory bodies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Promoting safety in a scientific manner is critical to mitigating these crash outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accordingly, researchers are responsible for conducting thorough investigations into the causes of crashes and developing effective mitigation strategies.\u003c/p\u003e\u003cp\u003eEnvironmental factors, particularly meteorological conditions, have a substantial influence on road safety. Many scholarly investigations have been conducted to comprehensively assess the influence of weather on roadway safety. These studies examined the impacts of variables such as rain [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; snow, hail, and sleet [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; temperature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; humidity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; visibility, ice, and wind [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; fog and smoke [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The studies underscore the differentiated effects of these meteorological factors on road safety, with rain being a recurrent and widespread occurrence across diverse regions. Consequently, it emerges as a pertinent focus for research aimed at improving safety within varied transportation systems. The ubiquity of precipitation, especially rain, underscores the significance of dedicated studies to advance our understanding and enhance safety countermeasures in transportation.\u003c/p\u003e\u003cp\u003eRainfall has a significant impact on the likelihood of traffic crashes. It reduces visibility on roads and pavement friction, increasing the risk of crashes and decreasing vehicle speed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In areas with inadequate drainage systems, water collects on the streets, leading to waterlogging and lane obstruction, further increasing the risk of crashes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have shown that there is a statistically significant increase in crash and injury rates during rainy days, with a higher risk on days with heavier rainfall [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, extreme rainfall can cause hydroplaning, which reduces friction between the pavement surface and tires, increasing the risk of crashes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous studies exploring the impact of precipitation on crash risks have generated a spectrum of findings. While some studies underscore the significant influence of precipitation on the frequency of crashes, others consider the extent of its effect with caution. For example, a study investigated the effects of different precipitation types on crash rates and suggested that all forms of precipitation increase the relative risk of crashes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In another study, the authors revealed a correlation between varying levels of rainfall intensity and a heightened frequency of crashes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Another research effort confirmed that precipitation elevates the likelihood of both fatal and non-fatal crashes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A study concluded that precipitation significantly increases the risk of fatal traffic crashes, regardless of its intensity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Researchers conducted a study demonstrating a direct link between increased precipitation and a greater number of crashes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A comprehensive investigation quantified the influence of meteorological factors on various crash types, reinforcing precipitation's role in boosting crash rates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The study conducted by Eck, et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] demonstrated a significant increase in crash risk during rainy periods in the states of the Carolinas, USA. However, a study conducted in Kentucky, USA, observed a decrease in the severity of crashes as precipitation levels rose, attributing this phenomenon to increased driver caution and reduced speeds under these conditions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, a study suggested that precipitation-related fatalities primarily occurred on wet roadways, with snow being the main factor for winter precipitation and ice/frost for transitional winter precipitation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, research indicated that decreased visibility was a factor in less severe injury crashes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In snowy conditions, studies indicated a decrease in both the frequency and severity of crashes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A study emphasized that some studies overlook the indirect effects of winter precipitation on crashes. The study stressed that future studies must account for these effects, as they are crucial for a better understanding of how precipitation impacts crashes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDiverse methodological approaches, models, and influencing factors employed in various studies contribute to disparities in findings and conclusions. Researchers have underscored the pivotal role of sample size in crash risk models, suggesting that a larger dataset enhances the development of robust analytical models [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The limited availability of data for studies has raised concerns among researchers, as it can potentially impact the accuracy and reliability of study outcomes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To address this challenge, the utilization of extensive data, commonly referred to as big data, becomes crucial for investigating the association between precipitation and crash risk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition to data volume, the analysis of crash risks necessitates consideration of precise corresponding environmental data to ensure reliable outcomes.\u003c/p\u003e\u003cp\u003eBoth the temporal and spatial dimensions of precipitation and crash data hold great importance. Particularly in expansive regions, sparsely distributed weather stations can introduce uncertainty due to spatial variability in weather conditions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the analysis of traffic safety heavily relies on the temporal scale of precipitation data (monthly, daily, and sub-daily records), with daily data being the most commonly used due to its widespread availability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, daily data falls short in accounting for dry intervals between rainy periods [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], emphasizing the need for finer temporal scales. In response to these challenges, researchers propose radar data as a source of accurate precipitation information, independent of weather station availability [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, a study suggested that while a daily time step provides a relative risk estimate similar to an hourly time step for most rural counties in our study area, it proves effective for fewer than half of the urban counties [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Gridded hourly precipitation datasets emerge as dependable resources for investigating precipitation's impact on crash risk in diverse regions and conditions. Consequently, this research employs gridded hourly precipitation data to explore the influence of precipitation on transportation safety and crash risk.\u003c/p\u003e\u003cp\u003eThis study investigates the influence of rainfall on the probability of car crashes within Texas road networks. Texas, one of the largest and most populated states in the United States, experiences significant travel activity and a diverse range of climatic conditions. The combination of these elements, along with the frequent occurrence of severe weather conditions and substantial annual precipitation in Texas, highlights the need to investigate the impact of rainfall on road safety. Texas actively collects a large amount of reliable information about road crashes. This information forms a fundamental basis for in-depth research into transportation safety. Moreover, Texas possesses extensive and trustworthy data on precipitation, offering a solid foundation for the study's objectives. The research also aligns with the safety objectives of transportation agencies worldwide, particularly the \"Vision Zero\" initiatives, which aim to eliminate fatal crashes on their road networks. Employing a matched pair methodology, the study spans a 16-year period from 2006 to 2021, utilizing big data analysis to overcome previous limitations in data size. The results of this study offer valuable insights into the impact of precipitation on crashes across Texas' roadways, contributing to the enhancement of safety measures and the overall management of the transportation system.\u003c/p\u003e"},{"header":"2. Data Description","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Crash Dataset\u003c/h2\u003e\u003cp\u003eThis study acquired a crash dataset spanning from 2006 to 2021 from the Crash Records Information System [CRIS, 36]. The Texas Department of Transportation curates the CRIS data. CRIS consists of three primary sub-databases: crash, vehicle, and primary person. The crash sub-database contains comprehensive details about each incident, including the speed limit, weather conditions, road type and alignment, surface conditions, traffic control devices, the initial harmful event, manner of collision, date and time of the crash, location, total injuries, day of the week, and the severity level of the incident.\u003c/p\u003e\u003cp\u003eThe vehicle sub-database provides specifics on each vehicle involved, such as vehicle type, contributing factors to the crash, the vehicle's actions, and model year. The primary person file stores information about individuals involved in crashes, including details on the type of person (driver, passenger, etc.), injury severity, age, gender, ethnicity, ejection from the vehicle, use of restraints, deployment of airbags, involvement of alcohol or drugs, and license details. The Texas Department of Transportation (TXDOT \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cris.txdot.gov\u003c/span\u003e\u003cspan address=\"https://cris.txdot.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) supplied the detailed crash data used to support the findings of this study under a data sharing agreement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Precipitation Dataset\u003c/h2\u003e\u003cp\u003eThis study utilized precipitation data from NEXRAD Stage-IV. The United States National Centers for Environmental Prediction (NCEP) creates the Stage-IV product based on the Stage-III data, compiled by the 12 River Forecast Centers of the National Weather Service (NWS). The Stage-IV product integrates multiple Stage-III radar outputs through the NEXRAD Precipitation Processing Systems. This product is widely used, particularly for driving hydrological models across the contiguous U.S. Initially recorded at 5\u0026ndash;6 minute intervals, the data is processed and consolidated into a gridded format with a 1-hour temporal resolution. The high-resolution grid covers the entire contiguous U.S., with a spatial resolution of about 4 km by 4 km, resulting in over 40,000 grid points just in Texas. The dataset spans from January 1, 2002, to the present. Public access to the data is available via the National Center for Atmospheric Research\u0026rsquo;s (NCAR) FTP servers upon request. For further details, visit their website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps:\u0026zwnj;//\u0026zwnj;\u0026zwnj;www.emc.ncep.noaa.gov/\u0026zwnj;mmb/ylin/pcpanl/stage4/\u003c/span\u003e\u003cspan address=\"https:\u0026zwnj;//\u0026zwnj;\u0026zwnj;www.emc.ncep.noaa.gov/\u0026zwnj;mmb/ylin/pcpanl/stage4/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eSimilar to other tools for measuring precipitation, weather radar presents both advantages and drawbacks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Notable advantages include the bias-adjusted, near-real-time nature of its estimates, ease of transferability and processing, higher spatial resolution due to the gridded binary (GRIB) format, reliable performance in regions with limited rain gauge coverage and minimal elevation variations, and accurate estimation of high rainfall rates [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Conversely, the disadvantages of radar estimates encompass potential data discontinuities in regions beyond the radar's effective range and in overlapping areas covered by multiple radar stations. Furthermore, the automated quality control process at the hourly scale contributes to their lower accuracy compared to previous versions and presents challenges in identifying flawed rain gauge data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe rainfall distribution in Texas is highly variable, ranging from less than 250 mm/year in the western areas of the state to over 1,300 mm/year in the east (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Influencing factors include topography, elevation, and proximity to the Gulf of Mexico. West Texas receives less rainfall due to the rain shadow effect of nearby mountains, which causes air to rise and release moisture on the windward side, leaving the area dry. In contrast, East Texas receives higher rainfall because of its proximity to the Gulf of Mexico, which contributes to increased moisture in the air. Furthermore, in El Ni\u0026ntilde;o periods, higher rainfall is generally observed in months outside of the summer season [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Conversely, La Ni\u0026ntilde;a is a primary catalyst for tropical cyclone precipitation (TCP) in Texas. It induces a greater frequency of precipitating storms, amplifying TCP's overall contribution to total precipitation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The driest year during the study period, 2011 (average annual total of 345 mm), was influenced by La Ni\u0026ntilde;a, causing cooler sea surface temperatures and weakening the jet stream, while the wettest year, 2015 (average annual total of 1112 mm), was influenced by El Ni\u0026ntilde;o, causing warmer sea surface temperatures and strengthening the jet stream, directing storms towards Texas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Match-pair application and time-step selection\u003c/h2\u003e\u003cp\u003eThis study employs the Matched Pairs Analysis (MPA) method to investigate the impact of precipitation on crashes. MPA stands as a robust approach for constructing statistical models and scrutinizing how precipitation influences crash risk. It has gained widespread adoption among researchers for assessing the effects of adverse weather conditions on both the frequency and severity of crashes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. MPA effectively addresses a major challenge in crash analysis related to uncertainties arising from time-dependent factors such as traffic conditions and driver behaviors, well-recognized for their significant impact on the frequency and severity of crashes. These factors exhibit significant variations across different times and locations. MPA mitigates this uncertainty by utilizing control periods corresponding to the actual crash events, thereby removing the temporal and spatial factors' impact [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA fundamental assumption in MPA is that traffic patterns and volumes remain relatively consistent during the same hour on the same day of the week, excluding holidays and special events. Therefore, in MPA, time intervals are typically separated by one week. Moreover, the choice of time intervals profoundly affects the analysis's accuracy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many studies opt for daily intervals due to data limitations and the complexities involved in hourly. However, only a limited number of studies utilize smaller time intervals such as hourly [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], three-hourly [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], six-hourly [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], or flexible intervals extending up to twelve-hour intervals [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The drawback of daily analysis is that crashes occurring during non-precipitation times are sometimes categorized as precipitation-related because they occurred on days with recorded precipitation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. For time intervals greater than an hour, there should be a cumulative precipitation threshold within the interval to classify it as a \"wet period.\" However, with an hourly time interval, each hour with any amount of precipitation is regarded as a \"wet hour,\" as there is no differentiation between precipitation intensity and cumulative amount at this temporal resolution [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study uses an hourly analysis, pairing each hour of precipitation with a control hour, either one week before or after the event. This approach controls for factors such as daily traffic volume, light conditions, and other time-sensitive factors. In this study, a specific hour within a particular geographic location (grid cell) is classified as either a wet period (with precipitation) or a dry period (without precipitation). Subsequently, an hour within a precise one-week interval at the same location is designated as its matched pair. We initially check the subsequent week; if no corresponding hour is found, we proceed to examine the previous week. If no matching hour is discovered within this two-week timeframe, the hour in question is excluded from the analysis. This matching procedure is conducted independently of the crash data.\u003c/p\u003e\u003cp\u003eAfter successfully identifying matched pairs, crashes are categorized into two types: \"wet crashes\" during hours with precipitation and \"dry crashes\" during hours without precipitation. All matched hour pairs for a specific radar grid are considered, and the corresponding crashes during each of these matched hours are counted. It is assumed that traffic volume remains unchanged between wet and dry periods.\u003c/p\u003e\u003cp\u003eThe traditional application of the MPA methodology poses a significant challenge due to the vast road network in Texas. Identifying matching pairs for all Stage IV precipitation grids and hours across multiple years was an extensive undertaking. Consequently, a modification to the method was introduced to streamline the process. Step 1 involved linking crashes to their corresponding Stage IV precipitation grids. In Step 2, Stage IV precipitation grids without associated crashes were removed. Step 3 meticulously sought and identified match hours for each wet (with precipitation) or dry crash hour. Finally, Step 4 encompassed the development of crash matching pairs, utilizing the methodologies depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It is important to emphasize that, given the relatively low frequency of precipitation, a minority of dry hours with crashes were successfully paired with matching wet hours. Conversely, the majority of wet hours with crashes found corresponding dry hour matches.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Relative Risk Factor (RRF) Computation\u003c/h2\u003e\u003cp\u003eThe Relative Risk Factor (RRF) calculation is the underlying method used to estimate relative risk for each methodological change assessed in this study. Once crashes, injuries, and fatalities were tabulated for events and controls, estimates of the relative risk of crash, injury, and fatality, along with their 95% confidence intervals, were calculated using the RRF approach. The RRF (or approximate relative risk) represents the odds of a crash, injury, or fatality during an event period compared to a control period. This is estimated using the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:RRF=\\frac{{A}_{i}/C}{({B}_{i}/D)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere A\u003csub\u003ei\u003c/sub\u003e is the number of collisions, injuries, or fatalities during an hour with rainfall, and B\u003csub\u003ei\u003c/sub\u003e is the number during the matched control hour, while C and D are the number of safe outcomes during the rainfall hour and dry hour, respectively. The values of A\u003csub\u003ei\u003c/sub\u003e and B\u003csub\u003ei\u003c/sub\u003e are from traffic data. Although C and D are very large, their exact values do not significantly impact the RRF calculation and are, therefore, not explicitly set.\u003c/p\u003e\u003cp\u003eAfter calculating the RRF for each event-control pair, it is log-transformed to approximate a normal distribution. The RRF is assigned a weight inversely proportional to its variance. The variance of the logarithm of the RRF is:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{v}_{i}=\\frac{1}{{A}_{i}}+\\frac{1}{{B}_{i}}+\\frac{1}{C}+\\frac{1}{D}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen A\u003csub\u003ei\u003c/sub\u003e is zero, a continuity correction factor of 0.5 is added. The statistical weight of each event-control pair is:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{w}_{i}=\\frac{1}{{v}_{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe weighted mean RRF is calculated as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}=\\text{e}\\text{x}\\text{p}\\left(\\frac{{\\sum\\:}_{i=1}^{g}{w}_{i}{y}_{i}}{\\sum\\:_{i=1}^{g}{w}_{i}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere y\u003csub\u003ei\u003c/sub\u003e is the logarithm of the RRF. The 95% confidence intervals are:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:95\\text{\\%}\\:\\text{C}\\text{I}=\\text{exp}\\left(\\frac{{\\sum\\:}_{i=1}^{g}{w}_{i}{y}_{i}}{\\sum\\:_{i=1}^{g}{w}_{i}}\\right)\\pm\\:\\frac{1.96}{\\sqrt{\\sum\\:_{i=1}^{g}{w}_{i}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eValues of relative risk greater than 1 indicate an increased risk of crashes during rainfall, while values less than 1 indicate a decreased risk.\u003c/p\u003e\u003cp\u003eThe RRF calculations involved a variety factors, including age, gender, hour of the day, day of the week, month, road type, road part, and crash severity. These parameters were considered to assess their individual and combined effects on crash risks under rainfall conditions, determining which groups or times are more hazardous. This comprehensive approach provides valuable insights into the factors influencing weather-related traffic crashes and offers a foundation for developing effective road safety interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3. MPA Limitations\u003c/h2\u003e\u003cp\u003eIn the context of MPA, there is a widespread assumption that rainfall does not significantly affect traffic volume and patterns, a notion adopted by various studies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, some research suggests that precipitation may, in fact, have an impact on various traffic aspects, potentially resulting in a reduction in the number of crashes during rainy conditions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These effects could manifest as a decrease in traffic volume [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and/or the speed at which traffic moves [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Nevertheless, it is crucial to acknowledge that the majority of studies examining the interplay between precipitation and traffic volumes have focused on daily analyses. Consequently, their findings may not be directly applicable when scrutinizing shorter time periods, such as hourly intervals, due to the possibility of precipitation having a distinct impact on traffic volumes at such finer time scales. We believe that analyzing data at an hourly time scale can reduce the impact of decreased traffic speeds and volumes during rain on our estimates, although not entirely. In any case, this problem could potentially impact the results by making the estimates of relative risk a little more conservative [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eIn this section, we present and discuss the results of the MPA as wells as the different factors that affect the estimated RRF. The annual variability of RRF and the state-averaged total are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall, it is very clear that precipitation significantly increases the likelihood of crash occurrence. Precipitation elevated the crash risk by a minimum of 32% over the study period. On average, rainy conditions increase the likelihood of a crash by a drastic 38% over the study period. This considerable increase underscores the role of adverse weather conditions in the region, emphasizing the urgent need for mitigative measures to enhance roadway safety.\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\u003e\u003cb\u003e\u0026ndash;\u003c/b\u003e Annual relative risk factor and rainfall over the study period.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"18\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2006\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2007\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRRF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLower limit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUpper limit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Rainfall (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e722 (345\u0026ndash;1112)\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\u003eIt is noteworthy that the highest RRF was observed during the driest year, 2011, while the wettest year, 2015, recorded the lowest RRF. This suggests that the observed increase in annual precipitation may have played a role in the apparent decline of annual RRF. In fact, the correlation between annual rainfall and relative risk is -0.78, indicating a complex relationship between weather conditions and traffic crashes. This complexity is influenced by various factors, including driving behaviors, preparedness and expectancy, and roadway maintenance and drainage [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a visual representation of crash data spanning the years 2006 to 2021 on a map of Texas, with each red dot pinpointing the precise location of a crash. The visual insight suggests that crashes have occurred throughout the state, with a notable concentration in east Texas, where a substantial proportion of the population resides. When separated, crashes during wet and dry weather conditions exhibit similar spatial patterns. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b) shows the major highways of Texas. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals a noticeable concentration of crashes in and around Texas's major urban centers. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a), crash clustering follows the population distribution, with approximately 86% of the total Texas population located in counties along and east of the I-35 interstate highway [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. A larger population corresponds to higher vehicle traffic volumes, inherently elevating the potential for crash incidents. While it underscores the influence of population density on crash frequency, the clustering of crashes is more complex. Factors such as complex road networks, diverse traffic conditions, and the interaction of multiple transportation modes, which collectively heighten the risk of collisions, also contribute to this clustering in urban areas. Additionally, there is a possibility that urban regions have higher crash reporting rates, as incidents in rural areas may be less documented. This distinction may partially account for the observed geographic disparities in crash prevalence.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Rainfall Impact on Crash Severity\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the annual relative risk factors corresponding to different injury types averaged over the study period, with error bars showing the annual variability. The results demonstrated that precipitation increases the relative risk factors for all crash severity levels, with a negative correlation between relative risk and crash severity level. The consistent decline in relative risk factors as crash severity increases suggests that drivers may adapt their behavior during wet weather, specifically by exercising caution and curbing high-risk activities. These observations underscore the critical role of driver behavior and adaptation in response to adverse weather conditions. It is plausible that drivers adopt safer behaviors, such as reducing speed, maintaining a safe following distance, and making fewer sudden maneuvers during wet conditions [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. While these observations are reasonable, they should be interpreted with caution. Further empirical support from future research is critical. Additionally, involvement with alcohol or drugs, no-restraint use, and speeding were less likely to co-occur with fatalities during adverse weather conditions [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Impact on Different Roadway Types\u003c/h2\u003e\u003cp\u003eTexas' extensive network of roadways is systematically divided into seven distinct categories: tollways, interstate highways, US and state highways, county roadways, city streets, farm-to-market roadways, and others, which is a miscellaneous classification encompassing roadways not included in the preceding categories. This categorization facilitates a more in-depth exploration of how different types of roadways interact with weather conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the average relative risk factors over the study period across these various types. For all roadway classifications, the relative risk factors are higher than 1, signifying that precipitation significantly heightens the risk of crashes on all roadway types. However, the degree of this effect varies substantially across the types. Notably, tollways and interstate highways have the most pronounced relative risk factors, whereas city streets report more moderate risk factors. The elevated risk on tollways and interstate highways can be attributed to their high-speed nature, where drivers confront amplified challenges and reduced reaction times during inclement weather [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. On the other hand, drivers may associate lower relative risk factors with more manageable driving speeds on city streets and county roadways. These findings offer a perspective on the complex interaction of roadway classifications, meteorological conditions, and driver actions. By understanding these elements, policymakers and safety advocates can pinpoint high-risk areas where intervention measures are most required.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Impact of Precipitation Intensities\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the average relative risk factors over the study period, corresponding to different levels of precipitation intensity. First, the results show that all relative risk factors exceed 1 for intensity classes. Additionally, there seems to be a direct correlation between precipitation intensity and crash risk. The reduced roadway visibility during heavy precipitation is a critical contributor. Intense precipitation events often lead to decreased visibility, making it challenging for drivers to recognize their surroundings. Poor visibility can also lead to delayed reaction times, reduced ability to identify hazards, and, in some cases, complete loss of visual contact with the road. Another important factor is the increased reduction of tire-road friction with precipitation, which impacts the vehicle's ability to maintain control. Higher precipitation intensities demand more cautious driving behaviors, but it seems drivers don't always adapt adequately to the increased risk. Furthermore, insufficient drainage systems and poorly maintained roads can contribute to the accumulation of standing water during intense events, increasing the risk of hydroplaning and other weather-related crashes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Effect of Driver\u0026rsquo;s Age and Gender\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e offers an insight into the proportion of male and female drivers involved in all crashes, examining the relationship between RFF, driver age, and gender. Across the range of age groups, female drivers consistently exhibit lower crash risk in comparison to their male counterparts. Moreover, these results indicate that drivers aged between 18 and 30, regardless of their gender, are particularly vulnerable to heightened crash risks during precipitation. This susceptibility decreases for older drivers, suggesting that older drivers adopt more cautious driving practices or drive on low-traffic roadways only during adverse weather conditions. Furthermore, the analysis incorporates varying levels of exposure among different genders and age groups, acknowledging potential behavioral differences. It is possible that certain drivers, based on age and gender, may opt to refrain from driving during precipitation events [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. It is well known that females make shorter work trips, use public transit more frequently, and drive fewer miles per year compared to their male counterparts [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and studies have shown that females have a greater likelihood of self-regulating driving, which includes avoiding driving in inclement weather [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Temporal Variability of RRF\u003c/h2\u003e\u003cp\u003eTo evaluate the temporal variability of the relative risk factors, the study investigated three aspects of time: the time of day, the day of the week, and the month of the year. We scrutinize each of these temporal factors to assess the combined influence of adverse weather conditions and time on crash risk. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the average relative risk factors categorized by the time of day, providing an overall average across all years. The RRF is consistently above 1 throughout the day, indicating the influence of precipitation on crash risk. The results reveal a distinct pattern that underscores the significance of the time of day. Early morning hours, particularly between 4 AM and 8 AM, exhibit the highest relative risk factors. This heightened risk during these hours can be attributed to several interrelated factors. Lower light conditions often occur during morning peak hours, and the combination of rainfall can significantly reduce visibility, making it more difficult for drivers to see the road, other vehicles, and pedestrians, thereby increasing the risk of crashes [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Additionally, driver alertness may be lower during early morning commutes. This, combined with potentially lower visibility due to the transition from night to day, could further elevate crash risk [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In contrast, afternoon commutes often experience a more dispersed traffic pattern [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This, along with potentially improved visibility compared to early mornings, might contribute to a lower crash risk during this period. While these assertions appear logically sound, it is imperative that they undergo rigorous empirical examination to ascertain their validity and quantitatively assess their implications for traffic safety. Such a study would provide invaluable insights for the scientific community and policymakers alike, aiding in the formulation of evidence-based interventions to mitigate traffic-related risks during peak hours.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen investigating the day of the week's influence, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the results indicate that there is no specific visible pattern. The influence of the day of the week on relative risk factors appears to be minor, with slightly lower risk during the middle of the week. The difference in travel patterns across different days of the week may explain this variability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding the impact of the month of the year, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates a significantly lower risk during the late spring and summer months. The impact of rainfall on crash risk is lower in the summer compared to other seasons. This can be due to warmer temperatures, higher evaporation, longer daylight hours, improved road maintenance, and more dispersed traffic volume [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, in winter, rainfall on cooler road surfaces can have a higher effect than in the summer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, during the summer months, most schools are closed, reducing the impact of the morning peak hours.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Summary and Conclusion","content":"\u003cp\u003eThis extensive research thoroughly examines the intricate relationship between precipitation and its influence on crash occurrences over a 16-year period in Texas. The comprehensive analysis of crash data and precipitation records reveals valuable insights across multiple dimensions, including temporal, spatial, roadway types, driver characteristics, and precipitation intensities, all of which significantly contribute to road safety during adverse weather conditions. These findings prompt consideration of interventions and strategies to mitigate crash occurrences.\u003c/p\u003e\u003cp\u003eThe study unequivocally establishes that precipitation substantially elevates crash risk, with a minimum increase of 32% and an average of 38% during rainy conditions. While there has been a gradual decline in risk values over the years, this underscores the imperative of maintaining a focus on enhancing roadway safety during adverse weather. Precipitation's multifaceted impact on safety is manifested in slippery road surfaces, diminished visibility, hydroplaning, and impaired driver concentration. The spatial distribution of crashes highlights a clear correlation between population density and crash frequency, indicating the need for targeted safety interventions in densely populated areas.\u003c/p\u003e\u003cp\u003eInterestingly, during wet conditions, a higher proportion of crashes result in 'no injury,' suggesting that drivers may adopt more cautious behaviors, which can reduce crash severity. However, this trend of caution is less evident as crash severity increases.. Also, precipitation consistently increases relative risk across all crash categories, emphasizing the importance of driver behavior in adverse weather and the need for safety strategies. Driver behavior, along with advancements in vehicle safety technology and road design, can significantly contribute to a reduction in the severity of crashes. This reiterates the critical importance of addressing human factors in addition to vehicle safety enhancements.\u003c/p\u003e\u003cp\u003eThe study underscores the intricate connection between various roadway types, weather conditions, and driver behavior. Notably, tollways and interstate highways exhibit the highest relative risk factors (2.20, compared to 1.20 for city streets), emphasizing the importance of behavioral interventions in conjunction with vehicle safety improvements.\u003c/p\u003e\u003cp\u003ePrecipitation intensities were associated with an increase in crash risk of 36\u0026ndash;52%, depending on rainfall intensity, with higher increases for more intense rainfall (over 25 mm/hr). This can be attributed to reduced visibility, compromised tire-road friction, and driver behavior. The necessity for comprehensive safety measures becomes particularly evident during intense precipitation events.\u003c/p\u003e\u003cp\u003eThe relative risk varies by age group, with the highest risk observed in young adults (18\u0026ndash;30 years old) and the lowest in individuals older than 65. Generally, females have a lower risk, ranging from 7\u0026ndash;13% lower depending on the age group. These findings can inform targeted safety strategies.\u003c/p\u003e\u003cp\u003eThe temporal dimension, encapsulating the time of day, day of the week, and month of the year, is a critical factor influencing road safety during precipitation. The early morning hours are notably characterized by the highest crash risk, attributed to factors such as rush hour traffic and changing lighting conditions. In contrast, the afternoon rush hours do not seem be associated with higher relative risks.\u003c/p\u003e\u003cp\u003eGiven the correlation between population density and crash rates, the report highlights the need for targeted safety measures in densely populated areas. Potential solutions include enhancing road design and maintenance, promoting public awareness campaigns, and investing in advanced traffic management systems. Exploring the link between crash distributions and healthcare access could also improve emergency response systems. Transportation safety during inclement weather can also be enhanced through advanced vehicle technologies, infrastructure improvements, real-time weather information dissemination, public awareness campaigns, and research into weather-adaptive traffic management systems.\u003c/p\u003e\u003cp\u003eIt is worth mentioning that this study has certain limitations, including its exclusive focus on Texas, potentially restricting the generalizability of its findings to regions with diverse climates and road infrastructures. Additionally, the analysis overlooks crucial variables such as driver experience, vehicle type, and road conditions, all of which can significantly influence crash occurrences. The study does not delve into the effects of alcohol or substance use during adverse weather conditions, and it does not consider data on the utilization of safety features in vehicles, such as antilock brakes or electronic stability control. Furthermore, socioeconomic factors and road maintenance practices are not examined in relation to crash occurrences, presenting opportunities for further investigation.\u003c/p\u003e\u003cp\u003eThe number of crashes (wet/dry pairs) is significantly lower in west Texas than in east Texas due to the west-east gradient of precipitation and population as well as gaps in radar coverage in that region. This has implications for the number of potential matches throughout the state and, consequently, the number of crashes during matched wet/dry hours. However, the process of weighting each matched pair as outlined above in the methodology largely accounts for this [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSuggestions for future research include conducting similar studies in diverse regions to assess the transferability of findings and exploring the roles of driver experience, vehicle safety features, and road conditions in crash occurrences during precipitation. Investigating the influence of alcohol or substance use on crash risk during adverse weather conditions and examining the impact of socioeconomic factors and road maintenance practices on crash occurrences are also recommended. Furthermore, additional research is needed to assess the effectiveness of specific safety measures, such as real-time weather information distribution to drivers or the development of advanced weather-responsive technologies in vehicles. Evaluating the impact of climate change on the frequency and intensity of precipitation events and its implications for road safety is crucial, given the increasing trend in severe precipitation events, necessitating proactive safety measures to address the growing challenges posed by intensified precipitation and its potential significant impact on roadway safety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHatim Sharif guided this research and contributed significantly to preparing the manuscript for publication and developed the research methodology in collaboration with Samira Tafazzol. Dawit Ghebreyesus, Khondoker Billah, and Chad Furl downloaded and processed the rainfall and crash dat. Dawit Ghebreyesus and Chad Furl. developed the scripts used in the analysis. Samira Tafazzol, Hatim Sharif, and Mohammadreza Gholikhani performed the statistical analysis. Samira Tafazzol and Mohammadreza Gholikhani prepared the first draft. Samira Tafazzol, Hatim Sharif, and Chad Furl performed the final overall proofreading of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe crash data used to support the findings of this study were supplied by the Texas Department of Transportation (TXDOT) under a data sharing and so cannot be made freely available. Requests for access to these data should be made to [https://cris.txdot.gov].Public access to the data is available via the National Center for Atmospheric Research\u0026rsquo;s (NCAR) FTP servers upon request. For further details, visit their website: https://water.noaa.gov/about/precipitation-data-access (Access date: May 30, 2025).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWunderlich, R. C., \u0026amp; Shipp, E. M. (2012). 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Examining the effect of adverse weather on road transportation using weather and traffic sensors. \u003cem\u003ePlos one\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(10), e0205409. \u003c/li\u003e\n\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-6992902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6992902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the relationship between precipitation and crashes on Texas roadways, spanning from 2006 to 2021. Employing a matched pair methodology, the research offers insights into the multifaceted impact of precipitation on crash likelihood, leveraging extensive crash data and gridded hourly precipitation records. The findings reveal that precipitation significantly increases crash risk, with an annual minimum rise of 32% and an average increase of 38%. Interestingly, rainy conditions are associated with reduced crash severity compared to dry weather. Although the relative risk is higher for all crash types during rainy conditions, the relative risk for no-injury crashes is 40% higher compared to fatal crashes. Spatial analysis highlights a correlation between population density and crash frequency. Moreover, the study investigates the interplay among roadway types, weather conditions, and driver behavior. Precipitation intensity was associated with a 36–52% increase in crash risk, with higher increases for more intense rainfall (over 25 mm/hr). The relative risk varied by age group, with the highest risk observed in young adults (18–30 years old) and the lowest in individuals older than 65. Generally, females exhibited a lower risk, ranging from 7–13% lower depending on the age group. Temporal factors—including time of day, day of the week, and month of the year—significantly impact road safety during precipitation, with early morning hours posing the highest crash risk due to rush hour traffic and changing lighting conditions. This comprehensive study enhances our understanding of road safety dynamics, providing foundational insights to inform policy development for safer and more sustainable transportation systems. Addressing human factors, alongside advancements in vehicle safety technology and road design, holds promise for reducing crash severity and improving overall road safety outcomes.\u003c/p\u003e","manuscriptTitle":"Rainfall and Road Safety in Texas: A Detailed Study of Relative Crash Risk from 2006 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 11:11:49","doi":"10.21203/rs.3.rs-6992902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-08T09:10:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T22:48:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T04:07:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T19:57:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22026686057757702893209298532809128765","date":"2025-07-17T07:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39448999308677707428816177386131337906","date":"2025-07-17T03:16:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127674724092569027976473219794852946503","date":"2025-07-17T03:13:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53573741222039208522378561675437588415","date":"2025-07-17T03:12:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T03:06:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T03:02:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-08T04:14:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-01T12:18:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-01T12:14:48+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":"bb0e7494-cc75-47f7-a631-f3d75c10c25a","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51681029,"name":"Earth and environmental sciences/Climate sciences"},{"id":51681030,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-10-27T16:27:11+00:00","versionOfRecord":{"articleIdentity":"rs-6992902","link":"https://doi.org/10.1038/s41598-025-20760-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-21 16:17:04","publishedOnDateReadable":"October 21st, 2025"},"versionCreatedAt":"2025-07-21 11:11:49","video":"","vorDoi":"10.1038/s41598-025-20760-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-20760-w","workflowStages":[]},"version":"v1","identity":"rs-6992902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6992902","identity":"rs-6992902","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}