Investigation of Drivers' Visual Attributes in Highway Entrance Zones Utilizing Self-Organizing Mapping Neural Network

Investigation of Drivers' Visual Attributes in Highway Entrance Zones Utilizing Self-Organizing Mapping Neural Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigation of Drivers' Visual Attributes in Highway Entrance Zones Utilizing Self-Organizing Mapping Neural Network Wenpin Xu, Yanyan Liu, Fangtong Jiao, Ziying Fu, Peipei Guo, Huaqing Ai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7487366/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract With increasing highway traffic, safety concerns at ramp entrances and confluence areas have intensified. This study analyzes drivers' eye movements in real-world settings, focusing on visual behavior in entrance curve section, merging observation section, ramp merging section, and freeway section. Metrics such as saccade time, angle, and speed were assessed alongside the complexity and task load of each section. Results show that the merging observation section exhibits the highest visual task load, with peak saccade time, angle, and speed at 100.78 ms, 35.09°, and 1.93 deg/ms, respectively, exceeding those in other sections. Through Self-Organizing Map (SOM) neural network clustering analysis, saccade time and angle were categorized into five groups: short-time small-angle, short-time wide-angle, medium-time small-angle, medium-time wide-angle, and long-time small-angle. Driver visual behavior varies distinctly across different road sections. Notably, in the merging observation section, the average eye movement-speed matching index (EMSMI) peaks at 0.1249 deg/ms, with a marked increase in categories A2 and A5. This section involves complex driving tasks, requiring frequent visual adjustments to navigate dynamic traffic conditions. In contrast, the entrance curve and freeway section predominantly exhibit categories A1 and A3, with lower EMSMI values of 0.0410 deg/ms and 0.0408 deg/ms, respectively. These sections show fewer outliers and a more concentrated distribution, indicating reduced cognitive load. This study quantitatively elucidates the link between drivers' visual behavior and road complexity, highlighting the heightened cognitive demands of complex traffic environments and their effects on visual behavior. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Expressway Merge area Saccadic behavior Self-Organizing Map Neural Network (SOM) Traffic safety Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Urbanization and technological advancements have spurred unprecedented growth in transportation infrastructure, notably the road network. By the end of 2022, national road mileage surpassed 5.3548 million kilometers, with expressways extending to 177,300 kilometers [1]. These expressways, known for their efficiency, convenience, cost-effectiveness, and safety, are integral to the transportation system. The annual expressway traffic volume reached 9.532 billion vehicle-trips, with small passenger cars (9 seats or fewer) comprising 73.16%, underscoring their crucial role in modern travel [2]. With the rapid increase in expressway traffic, safety concerns have intensified. Official statistics reveal that in 2022, China experienced 256,409 traffic accidents, with motor vehicle incidents comprising 93% of these cases [3]. Notably, driver-related factors directly caused approximately 65% of accidents, while 93% were associated with driver factors [4]. These figures underscore the pivotal role of drivers in accident prevention. As both interpreters of traffic information and vehicle operators, drivers are crucial to the safe functioning of the driver-pedestrian-vehicle-road system [5]. During vehicle operation, drivers perceive traffic information primarily through vision (80%), followed by hearing (14%), and touch, taste, and smell each accounting for 2% [6]. Among various traffic safety challenges, the area from the expressway ramp entrance to the merging section is particularly problematic and demands urgent attention, as depicted in Figure 1. Navigating the curved sections of highway ramps leading to merging areas poses unique challenges for drivers. Drivers must possess heightened visual perception and operational skills to adapt to the rapid changes in road alignment [7]. Similarly, the acceleration lanes of merging areas present significant speed differentials between vehicles, complicating the merging process. Drivers must quickly assess the main-line traffic flow, predict the dynamics of surrounding vehicles, and make decisions within the limited space and time to safely and efficiently merge [8]. Consequently, examining the visual characteristics of drivers on highway ramps and in merging areas, in the context of their respective driving environments, is crucial for enhancing the overall traffic safety of highways. Previous research underscores the critical role of drivers' visual perception in traffic safety. Jo et al. [9] demonstrated that high-speed driving narrows the driver's field of vision, creating a "visual tunnel effect" that strains their ability to process visual information. Namgung et al. [10] analyzed visual data from drivers on circular interchange ramps, revealing that deceleration upon entry alters visual search patterns. Deshpande et al. [11] proposed a traffic sign recognition method utilizing visual characteristics. Wang et al. [12] identified a strong link between driving behavior and visual perception in tunnel environments. Jang et al. [13] developed a lane-changing intention recognition system based on pupil size variations. Zhang et al. [14] investigated expressway overtaking behavior, finding that the type of leading vehicle significantly influences the following driver's fixation and visual search patterns. Recent studies have investigated the visual behavior of drivers on expressways and merging areas using diverse research methods. Lyu et al. [15] examined drivers' visual scanning and mental load at intersections on grassland highways through real-vehicle tests, highlighting how intersection types and priority rules affect attention allocation. Meng et al. [16] applied an enhanced visual sensitive area method to assess drivers' visual behavior on mountainous expressways, identifying the impact of various road-section environments on visual sensitivity distribution. Qin et al. [17] developed a visual attractiveness model for highway landscapes based on dynamic perception, quantitatively analyzing landscape elements' attractiveness to drivers. Xu et al. [18] studied drivers' visual search behavior on high-density overpasses, demonstrating how different road sections influence fixation behavior. Zhang et al. [19] created a visual workload model via real-vehicle tests to assess drivers' visual workload in merging areas of high-density overpasses. Current research on the visual characteristics of highway ramps leading to merging areas remains incomplete, lacking systematic findings to inform highway design, optimization, and management. This study addresses these gaps by examining drivers' visual behaviors across various highway sections through real-vehicle testing, with the aim of enhancing driving safety. The research focuses on: a) identifying differences in drivers' visual behaviors from the expressway ramp entrance to the merging area; b) assessing how these visual characteristics impact driving safety; c) quantifying the alignment between drivers' visual behaviors and speeds using the Eye Movement-Speed Matching Index (EMSMI) to explore how speed variations influence visual scanning strategies. Experimental Procedure Section Division A real-vehicle experiment was conducted on a city's expressway to examine vehicle speed variations under different traffic conditions. The experiment took place during lowflow periods, specifically from 9:00 to 11:00 a.m. and 3:00 to 5:00 p.m., Monday through Friday. These times were chosen to minimize interference from surrounding ve-hicles during congested periods, thereby reducing impacts on drivers' fixation and saccade behaviors. To maintain consistency, the experiment was suspended during adverse weather, such as rain. The experiment's data collection protocol mandates the timely exportation of equipment-recorded information during the driver's 10-minute respite, ensuring the integrity and accuracy of the acquired data. All experimental data and video footage captured by the driving recorder will be systematically numbered to facilitate subsequent pairing during data processing and analysis. The relevant sections, encompassing the highway entrance to the merging area, are depicted in Figure 2 as the specific experimental segments under investigation. Considering environmental factors like alignment and spatial layout of highway ramps, and applying criteria for curves and intersections of main and secondary roads, the area from the highway entrance to the confluence zone is divided into four sections. Section 1, the entrance curve section, is situated at the ramp's curve. Due to its tight curvature, drivers must reduce speed to maintain stability and focus on steering requirements. Section 2, the merging observation section, follows the entrance curve section. Here, driv-ers must monitor main road traffic and adjust their speed and headway in preparation for merging. Section 3, the ramp merging section, is crucial for merging with the main road. In this section, drivers assess speed differences and traffic conditions to merge safely and smoothly. Section 4, the highway main road area, lies beyond the ramp merging section and serves as the primary driving zone. Real vehicle test method Before commencing the driving test, participants must don the Dikablis Glass 3 eye-tracking device, connecting it to a laptop for calibration to ensure data accuracy. This device offers synchronous binocular data acquisition, with a sampling frequency of 60Hz and a pupil tracking accuracy of 0.1°. Participants spend 3 to 5 minutes observing inside the vehicle to ensure device comfort and stability. Upon completing the test route, experimenters halt data recording and securely store the data. The study involves 34 healthy adults, aged 25 to 44, all with highway ramp driving experience. After excluding outliers to ensure data accuracy, conducted one-way ANOVA on eye movement data across different sections. The analysis revealed significant differences in saccade duration (p=3.92×10⁻¹⁵) and saccade angle (p=3.11×10⁻³³), highlighting notable changes in drivers' visual behaviors across road segments. These findings form the basis for further analysis of visual features. Self-Organizing Map Network This study utilizes the Self-Organizing Map (SOM) to systematically cluster drivers' saccade characteristics across various highway sections, from entrances to merging areas, to investigate differences in visual characteristics over continuous time series. The Self-Organizing Map (SOM) is an unsupervised neural network algorithm notable for its superior nonlinear modeling capabilities and adaptability compared to traditional clustering methods. It effectively manages complex and high-dimensional data distributions by creating a low-dimensional topological representation of the input space. During training, SOM adjusts neuron weight vectors within a grid to facilitate Self-Organization. Employing competitive learning, each data sample is matched to its most similar neuron, mapping it to a specific grid cell. This process, through iterative weight updates, clusters similar data points in neighboring cells. The network under consideration is of size N × N , where each node i is associated with a weight vector w i ∈R2. The initial weight vectors are typically assigned random values. For a given input vector x ∈R2, the node with the weight vector closest to x , as determined by the Euclidean distance metric shown in Equation (1), is identified as the Best-Matching Unit ( BMU ). r c denotes the position of the nearest node, r i denotes the position of node i , and σ(t) represents the neighborhood width, which generally diminishes with time. The function of the neighborhood is to enhance the impact on nodes proximal to the Best Matching Unit ( BMU ) during weight updates, facilitating data aggregation and the emergence of a topological structure. The Self-Organizing Map (SOM) utilizes competitive learning to transform high-dimensional input data into a condensed low-dimensional topological representation while maintaining both local similarities and global structures. This approach facilitates cluster analysis of drivers' visual attributes, offering a foundational framework for comprehensive investigations into driving behaviors from highway entrances to merging zones. Results Drivers' saccadic behaviors In driver visual behavior analysis, saccade time and saccade speed serve as crucial evaluation metrics, primarily chosen for their fundamental involvement in the visual search process and their heightened responsiveness to fluctuations in the traffic environment. Saccade time pertains to the duration an individual requires to transition from one fixation point to another while engaged in a visual search task. This metric not only mirrors the speed at which the visual system handles information but is also notably impacted by various factors including information density, complexity, and relevance within the prevailing visual context [20]. The saccade angle, a crucial metric for assessing the extent of eye movement in visual search, is delineated into horizontal and vertical saccade amplitudes. This metric demonstrates a notable association with the quantity of information acquired and analyzed in the preceding fixation phase [21]. This study delves into drivers' visual information processing strategies by focusing on saccade time and saccade angle as key indicators. A detailed analysis is conducted using statistical data presented in Table 1 . Table 1 Mathematical statistics of saccade angle and saccade time in each section. entrance curve section merging observation section ramp merging section freeway section Saccade time MAX 69.01 100.78 97.68 95.14 MIN 18.46 12.46 15.45 15.09 MEAN 37.38 39.42 39.81 60.47 SD 13.36 18.47 18.19 17.48 Saccade angles MAX 29.14 35.09 25.54 30.79 MIN 0.19 2.37 1.06 0.04 MEAN 5.49 15.98 10.66 10.21 SD 4.90 8.29 6.33 6.31 Saccade speed MAX 1.38 1.93 1.18 1.00 MIN 0.00 0.03 0.02 0.00 MEAN 0.16 0.50 0.29 0.18 SD 0.17 0.36 0.20 0.14 In the merging observation section, the maximum saccade time is 100.78 ms, exceeding that of the entrance curve section 69.01 ms, the ramp merging section 97.68 ms and the highway main section 95.14 ms. Concurrently, the average saccade time in the merging observation section is 39.42 ms, comparable to the entrance curve section 37.38 ms and the ramp merging section 39.81 ms, but notably greater than that in the highway main section 60.47 ms. The merging observation section demonstrates significantly higher saccade angles compared to other sections, with an average of 15.98 ° and a maximum of 35.09°, in contrast to the entrance curve section (average: 5.49°, maximum: 29.14°), ramp merging section 10.66°, and freeway section 10.21°. Moreover, the merging observation section exhibits the largest fluctuations in both saccade time (18.47 ms) and saccade angle (8.29°), whereas the entrance curve section displays the least variability, with values of 13.36 ms and 4.90°, respectively. In the merging observation section, the average saccade speed is 0.50 deg/ms, reaching a maximum of 1.93 deg/ms, surpassing the speeds observed in the entrance curve section (average:0.16 deg/ms, maximum: 1.38 deg/ms), ramp merging section (average: 0.29 deg/ms, maximum: 1.18 deg/ms), and freeway section (average:0.18 deg/ms, maximum: 1.00 deg/ms). The visual workload is most pronounced in the merging observation section, with notable individual variability. Conversely, the high-speed main road segment exhibits longer saccade times but lower saccade speeds, indicating a relatively smoother visual engagement. The entrance curve section presents a comparatively straightforward visual task, with drivers demonstrating the highest level of visual behavior uniformity. The rising complexity of road environments has led to a notable increase in both the visual task load of drivers and the variability of their saccadic behaviors, particularly evident in areas such as merging observation section. Here, drivers are required to adjust their focal points and velocities more frequently to adapt to the constantly shifting traffic conditions. These observations offer a crucial theoretical foundation for enhancing highway design and promoting driving safety. Results of SOM clustering The study identified the optimal number of clusters for clustering as 5 through the computation of the silhouette coefficient. Setting the number of clusters to 5 yielded the most favorable clustering outcome, effectively capturing distinct variations in drivers' visual behaviors across different segments. Leveraging this finding, a Self-Organizing Map (SOM) Network was employed to categorize drivers' saccade time and saccade angle across various segments along the continuous time series spanning from the highway entrance to the merging area. The precise clustering outcomes for saccade time and saccade angle are depicted in Fig. 4 . The clustering analysis depicted in Fig. 4 reveals distinct patterns in the saccadic behaviors of drivers concerning time and angle dimensions. Cluster A1, characterized by short-duration and small-angle saccades, predominantly occupies a region with brief saccade durations of around 20–40 ms and small saccade angles of 0–10°. This pattern suggests that drivers engage in focused and rapid observations during tasks involving singular or uncomplicated driving conditions. In contrast, Cluster A2, typified by short-duration and wide-angle saccades, is situated in a zone with comparable short saccade durations but larger saccade angles ranging from 10–30°. This distribution implies that drivers necessitate scanning a broader field within a limited timeframe, typically observed in intricate or dynamically evolving traffic scenarios. A3 and A4 denote the responses observed during moderate saccade durations, with A3 representing small-angle movements and A4 representing wide-angle movements. The small-angle movements, occurring typically within a saccade duration of 40–60 ms and a range of 0–15°, indicate the driver's focus on close-range observations in moderately complex environments. Conversely, wide-angle movements during moderate saccade durations span a broader range of approximately 15–30°, reflecting the driver's engagement in extensive information gathering in more intricate traffic scenarios. The distribution of A5 exhibits prolonged saccadic behavior lasting approximately 60–100 ms, characterized by a notable increase in saccade duration. This behavior encompasses two categories: small-angle saccades, ranging from 0–15°, and wide-angle saccades, spanning 15–30°. Extended duration in small-angle saccades typically indicates the driver's concentration on a particular target, whereas prolonged wide-angle saccades are commonly observed in scenarios necessitating a broader visual scope to navigate intricate traffic conditions. Various types of saccades, including short-duration small-angle, short-duration wide-angle, small-angle with medium saccade duration, and wide-angle with medium saccade duration, as well as long-duration wide-angle saccades, demonstrate distinct patterns in both duration and angle among drivers navigating different traffic environments. These findings underscore the intricate nature of driving tasks and the flexible visual strategies employed by drivers. Discussion Drivers' saccadic behaviors in different sections In the road traffic setting, drivers demonstrate notable adaptability in their visual information processing strategies. When faced with intricate traffic scenarios containing a wealth of information, drivers tend to reduce their saccade duration to promptly focus on and analyze critical visual cues, thereby enhancing visual search efficiency [20]. Conversely, in less complex environments with reduced information, drivers opt to prolong their saccade duration to obtain a more comprehensive grasp of the prevailing traffic conditions. These temporal adjustments in strategies signify the dynamic optimization of drivers' visual information processing approaches across varying traffic environments. The saccade amplitude of drivers varies according to the complexity of the traffic environment. In complex settings, drivers tend to increase their saccade amplitude to capture crucial information more thoroughly and efficiently [21]. Conversely, in simpler traffic conditions, drivers decrease their saccade amplitude to enhance the precision and effectiveness of visual search. Thus, fluctuations in saccade time and amplitude not only indicate drivers' information processing capabilities during visual search but also demonstrate their heightened sensitivity and adaptability to diverse traffic environments [22]. To delve deeper into this adaptability, we conducted a statistical analysis on the distribution of five types of visual behaviors exhibited by drivers, namely short-duration small-angle, short-duration wide-angle, small-angle and wide-angle with medium saccade time, and long-duration wide-angle, across various sections including the entrance curve, merging observation section, ramp merging section, and freeway section. The distribution of each category in different sections from the expressway entrance to the confluence area is depicted in Fig. 5 . In the entrance curve segment, the predominant category is short-time small-angle (A1), constituting the largest share at 45.21%. This suggests that drivers predominantly engage in brief and limited eye scans in this road segment, aligning with the relatively straightforward driving demands of the entrance curve. The medium-time small-angle (A3) category accounts for 31.91%, indicating that drivers need to observe specific local areas within a moderate timeframe, typically associated with steering tasks. Conversely, the proportions of short-time wide-angle (A2) and medium-time wide-angle (A4) are relatively minimal at 4.26% and 18.62%, respectively, suggesting infrequent utilization of wide-angle eye scans by drivers in this context. In the observation of traffic behavior at the confluence, there was a notable increase in the occurrence of short-duration wide-angle (A2) behavior, reaching 43.92% and emerging as the predominant pattern. This shift underscores the necessity for drivers in confluence settings to swiftly survey an extensive array of traffic cues to promptly discern and react to alterations in traffic flow. Additionally, medium-duration wide-angle (A4) and short-duration small-angle (A1) behaviors accounted for 20.90% and 14.81%, respectively, indicating that aside from wide-angle observations, drivers must also engage in close-range monitoring at suitable intervals to holistically evaluate traffic conditions. The occurrence of long-duration small-angle (A5) behavior stood at 5.03%, indicating infrequent prolonged visual scanning by drivers in this specific setting. This underscores the intricate dynamism and complexity of the confluence environment, necessitating drivers to adeptly modify their visual strategies to effectively respond to evolving traffic conditions. Figure 6 illustrates the clustering outcomes of saccade time and saccade angle across various segments spanning from the highway entrance to the merging area. The distribution of the ramp merging section closely resembles that of the merging observation section, albeit exhibiting greater dispersion overall. In this context, short-duration small angles and medium-duration small angles constitute 31.23% and 33.63%, respectively, while short-duration wide angles and medium-duration wide angles make up 16.52% and 10.21%, respectively-both proportions are lower than those observed in the merging observation section. Despite the relatively intricate visual demands of the ramp merging section, the complexity of its visual tasks is marginally reduced compared to the merging observation section. Notably, the combined proportion of long-duration small angles and wide angles stands at 8.41%, suggesting that certain drivers necessitate extended observation periods in this segment to adapt to environmental changes. In the high-speed main road segment, medium-duration wide-angle and long-duration small-angle maneuvers constitute the predominant behavioral patterns, accounting for 33.50% and 32.28% of the total, respectively. Drivers in the secondary lane-changing segment necessitate moderate to extended periods for observing lane information displayed on ahead lane indicator signs to facilitate optimal lane selection and execution of lane-changing maneuvers. Additionally, the occurrence of medium-duration small-angle behavior stands at 23.06%, underscoring the diverse and intricate visual demands within this segment. Conversely, the occurrences of short-duration small-angle and short-duration wide-angle behaviors are relatively minimal, at 7.28% and 3.88%, respectively. Short-duration saccadic movements are infrequent in this context, with drivers predominantly relying on prolonged observation periods to ensure the seamless execution of lane-changing maneuvers. Table 2 Mathematical statistics of saccade angles and saccade times for each classification. Type A1 A2 A3 A4 A5 saccade time MAX 35.95 48.50 52.65 71.04 100.78 MIN 13.47 12.46 34.19 50.94 50.15 MEAN 25.57 29.23 42.78 60.32 72.93 SD 5.41 7.21 5.22 5.44 11.83 saccade angle MAX 15.77 35.55 24.76 33.00 33.73 MIN 0.04 12.88 0.14 0.19 0.22 MEAN 5.00 21.09 7.76 10.17 12.31 SD 3.24 4.68 4.73 6.67 6.77 saccade speed MAX 1.43 1.93 0.49 0.59 0.45 MIN 0.00 0.38 0.00 0.00 0.00 MEAN 0.21 0.76 0.18 0.17 0.17 SD 0.16 0.26 0.11 0.12 0.09 Type A1 behavior exhibits the shortest saccade time among the five segments, with a maximum of 35.95 ms, an average of 25.50 ms, and a standard deviation of 5.41 ms. This indicates that drivers can efficiently perform visual tasks in simple or low-complexity scenarios. Conversely, Type A5 behavior displays the longest saccade time, peaking at 100.78 ms, with an average of 72.93 ms and a standard deviation of 11.83 ms. This suggests that drivers require more time for intricate visual searches in complex or high-load situations. Behavior type A2 demonstrates a pronounced preference for wide-angle observation based on saccade angle metrics. Specifically, it exhibits a maximum saccade angle of 35.55°, an average of 21.09°, and a standard deviation of 4.68°. This behavior pattern is commonly observed in intricate traffic scenarios, necessitating rapid scanning of a broader visual field to gather pertinent traffic data. In contrast, behavior type A1 is characterized by the smallest saccade angle, with an average value of 5.00°, suggesting that in uncomplicated settings, drivers tend to have a more restricted visual focus. Behavior type A2 exhibits the highest saccade speed among the observed types, reaching a maximum of 1.93 deg/ms, with an average of 0.76 deg/ms and a standard deviation of 0.26 deg/ms. This finding implies a notable increase in saccade speed for individuals needing to swiftly survey extensive visual information. In contrast, behavior types A3, A4, and A5 demonstrate comparatively slower saccade speeds, averaging 0.18 deg/ms, 0.17 deg/ms, and 0.17 deg/ms, respectively. These results suggest that visual search behaviors characterized by medium- to long-term durations tend to be consistent and leisurely. The features of saccadic behavior are intricately linked to the intricacy of the driving context. In uncomplicated and less complex settings, drivers typically employ saccadic patterns characterized by shorter durations and limited ranges. Conversely, in intricate and high-demand settings, drivers adjust to environmental dynamics by enhancing the efficacy of visual information processing through prolonged durations and broader-angle saccades. Eye movement - speed matching index The association between driving speed and saccade speed plays a pivotal role in the dynamic interplay between drivers' visual conduct and the roadway setting. This correlation underscores drivers' adaptation of visual scanning tactics in response to variations in driving speed and task intricacy. Particularly in scenarios involving high and low driving speeds, notable alterations manifest in drivers' visual attention, information processing strategies, and response mechanisms, consequently influencing the nature and effectiveness of their saccadic behavior. To comprehensively investigate the correspondence between drivers' visual behaviors and driving task demands across varying speed conditions, we introduced the Eye Movement-Speed Matching Index (EMSMI). This index offers a holistic assessment of the correlation between drivers' visual information processing efficiency (as indicated by saccade speed) and driving task requirements (as indicated by driving speed) by evaluating the ratio of saccade angle and saccade time to driving speed. The EMSMI serves as a novel quantitative metric for evaluating the alignment of drivers' visual behaviors with driving speed across diverse speeds and traffic scenarios. The calculation formula for EMSMI is depicted in Eq. ( 4 ), and the outcomes are detailed in Table 3 . EMSMI oi represents the eye movement speed matching index of the driver at the o-th section and i-th time, measured in deg/ms. θ oi denotes the saccade angle of the driver at the o-th section and i-th time, measured in degrees. t oi s ignifies the saccade time of the driver at the o-th section and i-th time, measured in milliseconds. Lastly, v oi indicates the driving speed of the driver at the o-th section and i-th time, measured in km/h. Table 3 Mathematical statistics of EMSMI for each road section. EMSMI MAX MIN MEAN SD entrance curve section 0.3490 0.0008 0.0410 0.0424 merging observation section 0.4790 0.0081 0.1249 0.0881 ramp merging section 0.2765 0.0042 0.0688 0.0462 freeway section 0.2234 0.0003 0.0408 0.0320 In the entrance curve segment, the mean EMSMI value is 0.041 deg/ms with a standard deviation of 0.0424 deg/ms, suggesting a modest level of visual-speed correspondence in this area. Despite a peak value of 0.349 deg/ms, the visual demands placed on drivers in this roadway segment are uncomplicated, with low environmental intricacy. In the merging observation section, the EMSMI value is notably higher at 0.1249 deg/ms on average, with a standard deviation of 0.0881 deg/ms, compared to the entrance curve section. This segment of the road exhibits a pronounced alignment between drivers' visual information processing and driving speed, with a maximum value of 0.4790 deg/ms. Here, drivers exhibit excessively rapid saccade speed in relation to their driving speed, leading to inadequate visual information processing. This mismatched correlation between visual saccades and driving speed escalates drivers' cognitive load, consequently heightening the likelihood of traffic accidents. At the ramp merging section, the mean EMSMI value is 0.0688 deg/ms with a standard deviation of 0.0462 deg/ms, indicating a moderate level of visual-speed matching. Despite a maximum value of 0.2765 deg/ms, it suggests that drivers can adeptly calibrate the correlation between visual scanning and driving speed on an individual basis. In the high-speed main road segment, the EMSMI value is low, averaging 0.0408 deg/ms, with a standard deviation of 0.0320 deg/ms, a maximum of 0.2234 deg/ms, and a minimum of 0.0003 deg/ms. The relationship between driver visual behavior and driving speed in this segment is straightforward, characterized by low variability. Consistency in the alignment of driver visual behavior and driving speed minimizes the need for frequent adjustments in this segment. Figure 7 illustrates notable variations in the Eye Movement - Speed Matching Index (EMSMI) across distinct categories. Notably, Category A2 exhibits the highest mean value of 0 .1866 deg/ms, characterized by a broad distribution range and numerous outliers. Within these road segments, the synchronization between drivers' eye movements and driving velocities displays substantial oscillations, leading to delayed environmental perception, heightened reaction times, and consequent impacts on decision-making precision and driving safety. Particularly on intricate road stretches, this phenomenon escalates the likelihood of accidents. Classes A1 and A5 exhibit lower EMSMI values, displaying a more tightly clustered distribution with fewer outliers, indicating a consistent alignment between drivers' visual behaviors and driving speeds. Conversely, classes A3 and A4 demonstrate a transitional phase characterized by a gradual reestablishment of alignment between visual behaviors and speeds following encounters with complex driving tasks and environments. The figure illustrates distinct variations in the distribution characteristics of the Eye Movement - Speed Matching Index (EMSMI) along various segments of the roadway, particularly from the highway entrance to the merging zone. Notably, the merging observation section exhibits the highest average EMSMI at 0.1249 deg/ms, featuring a broad distribution range with notably more outliers compared to other roadway segments. This disparity suggests heightened complexity in driving tasks within this specific segment. Drivers are compelled to maintain safe driving practices while vigilantly monitoring the dynamic traffic patterns on the primary road, consequently leading to compromised coordination between visual information processing and driving speed, alongside a marked escalation in cognitive workload. In contrast, the mean EMSMI values in the entrance curve section and the high-speed main section are notably low, at 0.0410 deg/ms and 0.0408 deg/ms, respectively, with relatively tight distributions and minimal outliers. This suggests that the driving tasks in these segments are comparatively straightforward, the alignment between drivers' visual behaviors and vehicle speeds is relatively consistent, and the cognitive burden is relatively light. The mean EMSMI value in the ramp merging section, at 0.0688 deg/ms, falls between those of intricate and uncomplicated sections, signifying a shift from the intricate conditions of the merging observation segment to a relatively stable driving scenario. In order to investigate the correlation between drivers' eye movement-speed matching index (EMSMI) on varied road segments and driving behavior categories, this study employs classification labels A1 to A5 to depict the distribution characteristics of drivers exhibiting different behaviors. The analysis unveils the distribution patterns of each behavior category across diverse road sections, tracks the variations in EMSMI, and scrutinizes drivers' cognitive load, visual scanning strategies, and driving speed alignment in intricate traffic settings. The findings are depicted in Fig. 9 . The figure illustrates notable variations in the Eye Movement - Speed Matching Index (EMSMI) across different segments, elucidating the impact of driving task complexity on visual behavior. Specifically, in the entrance curve and high-speed main road segments, categories A1 and A3 predominate, characterized by lower EMSMI values and clustered distributions. These findings suggest a high level of stability and consistency in drivers' visual behavior in these segments, indicating lower cognitive load, simpler driving tasks, and stable alignment between visual attention and driving speed. In the merging observation section, there was a notable increase in the distribution of A2, with the EMSMI value peaking. Additionally, there was a rise in the proportion of A5. These findings suggest that drivers must engage in frequent wide-angle saccadic movements and sustain prolonged visual monitoring in intricate, dynamic traffic settings to execute maneuvers like merging or changing lanes. The elevated EMSMI value and the substantial prevalence of wide-angle behaviors in the merging observation section signify a marked escalation in cognitive resource demands on drivers, signaling the pinnacle of task complexity and identifying it as the primary area imposing the highest cognitive burden on drivers. The ramp merging section serves as a transitional zone where the distribution ratios of A1 and A3 increase, while that of A2 notably decreases, accompanied by a gradual drop in EMSMI value. This trend suggests a gradual return to visual stability among drivers, a decrease in cognitive load, and a progressive stabilization of the driving task in this segment. The distribution of EMSMI values and categories across various segments demonstrates the significant impact of driving task complexity on visual-speed coordination. Specifically, in the merging observation section, the intricate task demands necessitate frequent adjustments in drivers' visual strategies and attention distribution, resulting in a notable rise in cognitive burden. Consequently, this segment emerges as a pivotal focal point for ensuring driving safety. Conclusions Actual vehicle tests were conducted from the entrance ramp to the merging area of the expressway. Analysis of eye movement data from four sections - the entrance curve section, merging observation section, ramp merging section, and main expressway section - enabled an assessment of driving visual characteristics and safety. Four conclusions were derived from this analysis: 1. In the section focusing on merging observation section, the drivers exhibited a saccade time of 100.78 ms, a saccade angle of 35.09°, and a saccade speed of 1.93 deg/ms, all of which were notably elevated compared to those recorded on other road segments. The visual task load was most pronounced in the merging observation section, necessitating a greater allocation of visual resources to navigate the intricate traffic conditions. 2. Cluster analysis was conducted on saccade time and saccade angle utilizing the Self-Organizing Map neural network (SOM). The data were categorized into five groups: short-time small-angle, short-time wide-angle, medium-time small-angle, medium-time wide-angle, and long-time small-angle. 3. In the entrance curve segment, the predominant category is short-time small-angle (A1), representing 45.21% of cases. Conversely, in the merging observation section, the short-time wide-angle (A2) category predominates, comprising 43.92% of instances. These findings suggest that drivers engage in more pronounced saccades in intricate settings to adapt to fluctuations and potential hazards in the dynamic traffic milieu. 4. In the merging observation section, the average EMSMI value peaks at 0.1249 deg/ms, indicating heightened visual adaptation to dynamic traffic conditions. This necessitates increased synchronization of visual processing with driving speed. Conversely, the main expressway section exhibits the lowest average EMSMI value at 0.0408 deg/ms, suggesting reduced visual adjustments post-confluence completion, leading to stabilized visual behaviors and decreased task demands. The research results establish a theoretical foundation for the strategic placement of traffic safety and guidance facilities in expressway ramp-to-merging sections, offering substantial practical utility, particularly in augmenting driver safety on intricate road segments. Subsequent investigations will concentrate on analyzing driving behaviors, refining road designs, and implementing intelligent transportation systems to deliver enhanced solutions and advance driving safety and convenience. Declarations Data Availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Author contributions W.X.: Conceptualization, methodology, validation, Writing—review & editing, funding. Y.L.: software, investigation, validation, formal analysis, visualization, writing—original draft. J.F.: validation, formal analysis, writing—original draft. Z.F.: supervision, methodology, project administration, funding. P.G.: formal analysis, resources. H.A.: visualization, funding. Z.S.: validation, resources. Q.L.: project administration, supervision. All authors have read and agreed to the published version of the manuscript. Funding This study was funded by Science Research Project of Hebei Education Department (Grant No. QN2024082), the Science and Technology Program Projects of Shandong Provincial Department of Transportation (Grant No. 2024B28) and Self-funded Cangzhou Science and Technology Plan Project (Grant No. 222001010), Special Fund for Basic Scientific Research Operations of Hebei University of Water Resources and Electric Engineering (Grant No. SYKY2206). Competing interests The authors declare no competing interests. Institutional Review Board Statement: The study all methods were carried out in accordance with relevant guidelines and regulations, and all experimental protocols were approved by Ethics Committee of Cangzhou Normal University (2, July,2025). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Cai, Y., Zang, Z., Zuo, X., Liu, F., Li, L., Lu, K., & Huang, Q. (2023). Research on the Performance of Calcium Carbide Slag–Fly Ash Stabilized Soil. Advances in Civil Engineering, 2023(1), 4571162. Wan, X., Jin, P. J., Gu, H., Chen, X., & Ran, B. (2017). Modeling freeway merging in a weaving section as a sequential decision-making process. Journal of Transportation Engineering, Part A: Systems, 143(5), 05017002. Liang, Y., Yuan, H., Wang, Z., Wan, Z., Liu, T., Wu, B., ... & Tang, X. (2024). Nonlinear effects of traffic statuses and road geometries on highway traffic accident severity: A machine learning approach. PloS one, 19(11), e0314133. Wang, X., Liu, Q., Guo, F., Xu, X., & Chen, X. (2022). Causation analysis of crashes and near crashes using naturalistic driving data. Accident Analysis & Prevention, 177, 106821. Wang, S., Du, Z., Zheng, H., Han, L., Xia, X., & He, S. (2024). Improving driving safety in freeway tunnels: A field study of linear visual guiding facilities. Tunnelling and Underground Space Technology, 143, 105489. Li, W., Huang, J., Xie, G., Karray, F., & Li, R. (2021). A survey on vision-based driver distraction analysis. Journal of Systems Architecture, 121, 102319. Fu, Z., Zhang, J., Pan, B., Wu, S., & Yang, H. (2024). Calculation Model for the Exit Decision Sight Distance of Right-Turn Ramps on the Left at Interchange. Applied Sciences, 14(14), 6205. Li, Y., Zhang, H., Wang, Q., Wang, Z., & Yao, X. (2024). Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions. Journal of Advanced Transportation, 2024(1), 7766164. Jo D, Lee S, Lee Y. (2014). The Effect of Driving Speed on Driver’s Visual Attention: Experimental Investigation. In 11th International Conference on Engineering Psychology and Cognitive Ergonomics-Volume, 8532, 174–182. Namgung, M., & Sung, S. L. (2016). Analysis on Characteristics of Driver’s Visual Change on the Roundabout. Journal of the Korea Entertainment Industry Association, 10(3), 293. Deshpande, A. V., & Subashini, M. M. (2017). A new approach to traffic sign recognition through primary visual characteristics. In 2017 Innovations in Power and Advanced Computing Technologies, 1–7. Wang, S., Du, Z., Chen, G., Zheng, H., Tang, Z., & Jiao, F. (2021). Drivers’ visual characteristics in small-radius optically long tunnels on rural roads. Tunnelling and Underground Space Technology, 113, 103969. Jang Y M, Mallipeddi R, Lee M. (2014). Driver’s Lane-change Intent Identification Based on Pupillary Variation. In 2014 IEEE International Conference on Consumer Electronics, 197–198. Zhang, W., Dai, J., Pei, Y., Li, P., Yan, Y., & Chen, X. (2016). Drivers’ visual search patterns during overtaking maneuvers on freeway. International journal of environmental research and public health, 13(11), 1159. Lyu, Z., Qi, C., Zhu, S., & Wang, H. (2022). The visual scanning behavior and mental workload of drivers at prairie highway intersections with different characteristics. IEEE Access, 10, 123043–123056. Meng, Y., Cai, H., & Qing, G. (2021). Preliminary quantitative research on characteristics of driving visual sensitive region in mountainous highway environment. Int J Intell Inf Manag Sci, 10(6), 184–188. Qin, X., Fang, M., Yang, D., & Wangari, V. W. (2023). Quantitative evaluation of attraction intensity of highway landscape visual elements based on dynamic perception. Environmental Impact Assessment Review, 100, 107081. Jin, X., Ziqiu, S., Siqi, W., & Zimiao, Y. (2022). Characteristics of driver's visual search behavior in exit ramp of high-density interchanges. Journal of Southeast University. Dongnan Daxue Xuebao, 52(6). Zhang, Y., Jiang, P., Wang, S., Cheng, S., Xu, J., & Liu, Y. (2024). Study on the Driver Visual Workload in High-Density Interchange-Merging Areas Based on a Field Driving Test. Sensors, 24(19), 6247. Jiao, F., Du, Z., Wang, S., Ni, Y., & He, R. (2020). Drivers’ saccade characteristics in curves of extra-long urban underwater tunnels. Transportation research record, 2674(2), 102–111. Han, L., & Du, Z. (2024). Evaluation of eye-catching effect in highway tunnel entrance area based on saccade behavior. Traffic injury prevention, 1–9. Gené-Sampedro, A., Alonso, F., Gene-Morales, J., Monteiro, P. L., & Useche, S. A. (2024). Could driving help us to “see better”? A comparative assessment of saccadic efficiency, visual speed, and attention. BMC ophthalmology, 24(1), 90 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 23 Sep, 2025 Editor invited by journal 10 Sep, 2025 Submission checks completed at journal 06 Sep, 2025 First submitted to journal 06 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7487366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524967439,"identity":"36e77d6d-c8d5-43bf-a202-877e3aafffe5","order_by":0,"name":"Wenpin Xu","email":"","orcid":"","institution":"Hebei University of Water Resources and Electric Engineering","correspondingAuthor":false,"prefix":"","firstName":"Wenpin","middleName":"","lastName":"Xu","suffix":""},{"id":524967440,"identity":"2dbcdf2c-49dc-4cd3-ad2a-2e83063ee07a","order_by":1,"name":"Yanyan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACPmYYi72HDcI4QEALG1wLzxlitcBZEjnEamHnMZP4ueOwvLnk22OPeSoY5PhuJDB+LsDrMB4zyd4zhw13zs5LN+Y5w2AseSOBWXoGAS0SvG2HGTfczjGTzm1jSNxwIwEoSMiWv22H7TfcPAPU8o+hnigt0kBbgIYDGbkNDAkGhLWwFVvLtqUnbziTY27855iE4cwzD5ul8Wnh5z+88ebbNmvbDcfPmD2cUWMjz3c8+eBnfFqAgEWCgaEZxgGyGRgb8GtgYGD+wMBQR0jRKBgFo2AUjGQAAB2iRXnJeOl4AAAAAElFTkSuQmCC","orcid":"","institution":"Hebei University of Water Resources and Electric Engineering","correspondingAuthor":true,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Liu","suffix":""},{"id":524967441,"identity":"21938edc-945e-4735-a235-69bbd1946814","order_by":2,"name":"Fangtong Jiao","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Fangtong","middleName":"","lastName":"Jiao","suffix":""},{"id":524967442,"identity":"bd49b782-c4e7-4753-a159-4e15521f6e37","order_by":3,"name":"Ziying Fu","email":"","orcid":"","institution":"Cangzhou Road and Bridge Construction Group Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Ziying","middleName":"","lastName":"Fu","suffix":""},{"id":524967443,"identity":"36b94383-ab71-4fb3-b75f-c3efe5848707","order_by":4,"name":"Peipei Guo","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Guo","suffix":""},{"id":524967445,"identity":"1635e92d-a29d-40b4-a137-0802a0d7130b","order_by":5,"name":"Huaqing Ai","email":"","orcid":"","institution":"Cangzhou Road and Bridge Construction Group Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Huaqing","middleName":"","lastName":"Ai","suffix":""},{"id":524967448,"identity":"9c001733-1967-46a3-a655-11ef1aa161ad","order_by":6,"name":"Zhenwei Shi","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhenwei","middleName":"","lastName":"Shi","suffix":""},{"id":524967449,"identity":"6ea32bc0-59c2-4b95-a1cc-a34bfa005395","order_by":7,"name":"Qing Lan","email":"","orcid":"","institution":"Hebei University of Water Resources and Electric Engineering","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Lan","suffix":""}],"badges":[],"createdAt":"2025-08-29 09:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7487366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7487366/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-26920-2","type":"published","date":"2025-12-01T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92849902,"identity":"68ddc5e3-9347-407d-aaf8-eef6499d3d3c","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1852263,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/859d96379deadf211c6f6c6f.docx"},{"id":92850127,"identity":"d99ef62a-a335-4e11-81fe-d6612b589231","added_by":"auto","created_at":"2025-10-06 10:33:20","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9225,"visible":true,"origin":"","legend":"","description":"","filename":"74caa1d3285d40c1a97e0d9cf0b11dd2.json","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/0233de17567b43395283b953.json"},{"id":92849905,"identity":"3f811202-4f79-4523-b7a3-e7e4dd655775","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91290,"visible":true,"origin":"","legend":"","description":"","filename":"74caa1d3285d40c1a97e0d9cf0b11dd21enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/0b1ab3045b2473872774593d.xml"},{"id":92850128,"identity":"ebc40791-a6eb-4b4a-808e-f204dc4d8d0d","added_by":"auto","created_at":"2025-10-06 10:33:20","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":433128,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/d80d28a1fa7459f6dc5e727a.jpeg"},{"id":92851182,"identity":"5c96f211-f678-403b-873e-7dcf1ed1a649","added_by":"auto","created_at":"2025-10-06 10:41:20","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":347413,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/839868e4e25293ce84e094b3.png"},{"id":92849910,"identity":"a47fae97-158e-4ec4-af31-df2a3afe515e","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":455602,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/49065edbaa1c513fa85c4235.jpeg"},{"id":92850133,"identity":"bd2c931b-2a95-4919-81f7-2080b8ea69bb","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":251197,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/f5b6ab68b69649e56d3c6a20.png"},{"id":92850132,"identity":"64bfc5d0-3005-4e98-8957-9f6dc234ffc2","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89581,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/ffb81e767176e77675cd7fe6.png"},{"id":92851184,"identity":"93759a2a-a1ee-4e43-98a1-9e9462f02097","added_by":"auto","created_at":"2025-10-06 10:41:21","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":620669,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/25b371927129a9c6e52e7998.jpeg"},{"id":92849909,"identity":"3a941c16-f93f-446a-b868-06f90d6e4b5d","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76778,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/5ade2eb2d2e9462169eb71ce.png"},{"id":92849911,"identity":"893e54e5-c08a-46e0-b90a-058f2c1338b2","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92303,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/99e2f73254799ce1a78e20b2.png"},{"id":92850136,"identity":"5dfe9435-d335-4d35-8bfd-eb94b8055e0e","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216509,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/c36f915ebc122e593a0d09df.jpeg"},{"id":92849926,"identity":"73808da7-f5f8-499d-81a9-a17c0568ccda","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"wmf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2782,"visible":true,"origin":"","legend":"","description":"","filename":"image10.wmf","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/4c64ec602fc20cebc9fd73e9.wmf"},{"id":92850131,"identity":"9630efa4-e3d4-43f2-8244-c10a05a1d93f","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"wmf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1330,"visible":true,"origin":"","legend":"","description":"","filename":"image4.wmf","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/c5654550d4df534fcf2a0053.wmf"},{"id":92849917,"identity":"5404ff0e-56af-42ba-b3f3-495b82b5800f","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"wmf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3080,"visible":true,"origin":"","legend":"","description":"","filename":"image5.wmf","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/cfd97de9b03fde8492897701.wmf"},{"id":92849914,"identity":"45878d7c-7bee-40e2-b1aa-a5b607eacb29","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"wmf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2326,"visible":true,"origin":"","legend":"","description":"","filename":"image6.wmf","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/ad92a22083d26029b1283c48.wmf"},{"id":92849916,"identity":"316c2cc4-de1e-4e92-998e-9a1a6005e2a8","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174215,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/69b4c212d6cbd62a5712fad6.png"},{"id":92849913,"identity":"0565fb4e-6712-43b1-be99-5a332df75931","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39199,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/63b79c3e6f03ac659636b6e3.png"},{"id":92849925,"identity":"d491a269-7904-4870-9252-56c388caed21","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159699,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/733cffb293a4da2adb460cd0.png"},{"id":92849934,"identity":"7c5be1e7-84fc-4dc1-a43f-9dc58b661a1e","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":239472,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/f8d35d000b2dd2b19aabb68e.png"},{"id":92849929,"identity":"f6260b95-2821-419c-84f8-4b4bf50aa7d5","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29226,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/9a40b36dbce6d607158f9ac6.png"},{"id":92849928,"identity":"870345fc-9248-473a-a321-8578e40d3e4d","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118860,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/56c36a0ab1b391a93bf4043e.png"},{"id":92849920,"identity":"80dd9853-ab78-4632-91aa-5e190f1eeac8","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68663,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/308bd30d50a75b27fc79343f.png"},{"id":92849919,"identity":"1ced0177-832d-4fde-b9bd-63f261a32684","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":81891,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/533bb889505627703c121b79.png"},{"id":92850138,"identity":"a851d6f8-371c-4344-bbbc-2428f1d65345","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179403,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/adfcb417a104fa27eab31cf5.png"},{"id":92850134,"identity":"f0e91bd2-4534-4fc8-b9fa-9681d0baf582","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2543,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/87b807f8d43b6dda98db8683.png"},{"id":92850137,"identity":"e474d065-062a-4427-be8a-0e1a8a27ea6a","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1253,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/1e5741447901dfa56fad4a98.png"},{"id":92849931,"identity":"27a8df10-404b-4be2-abbb-153a54e4c861","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2036,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/b5ed505b70d237732d34364b.png"},{"id":92849927,"identity":"8a2d821b-fb69-4b82-9c3a-161d176ec11b","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1136,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/2ddb75ce09cd9df6a47d4b77.png"},{"id":92849923,"identity":"2dca7234-fbfe-4e28-8b81-7d7b43ab7f25","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89932,"visible":true,"origin":"","legend":"","description":"","filename":"74caa1d3285d40c1a97e0d9cf0b11dd21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/4c0879cb11778158edc401ae.xml"},{"id":92849935,"identity":"9c8468dc-be17-4dde-b2e8-874b13502a3c","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98522,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/6b46b7425275463677731e6e.html"},{"id":92849897,"identity":"a2e44a91-b30d-41be-ab0d-3e23e4c3e907","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTypical traffic accidents in highway merging areas\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/9ad1101dc06243f95569524c.png"},{"id":92849899,"identity":"cfb20066-fd54-46b9-bdc0-448c95d99bf4","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183594,"visible":true,"origin":"","legend":"\u003cp\u003eReal-scene images of each section in the ramp area of the test expressway.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/79906e4a04f0146f920f85ed.png"},{"id":92849901,"identity":"72bef8f4-fb8d-460c-8d91-6348c48671f5","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254753,"visible":true,"origin":"","legend":"\u003cp\u003eReal vehicle test and main equipment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/d76246e1da6011441ebcb6a0.png"},{"id":92849898,"identity":"d9dfb8f7-2213-472a-b471-d22d91c46ab3","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":230922,"visible":true,"origin":"","legend":"\u003cp\u003edisplays the clustering outcomes pertaining to both the time taken for saccades and the angles of saccades observed from the highway entrance to the merging area.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/20e580b625911d1150db286d.png"},{"id":92849904,"identity":"9ab92b22-318b-4486-aa85-d45ff8d6abfa","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54229,"visible":true,"origin":"","legend":"\u003cp\u003eDisplays the distribution of classification results for each segment spanning from the highway entrance to the merging area.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/81990a77e2be9aab387638f1.png"},{"id":92849918,"identity":"674b23a5-321e-42d7-bdd4-90e3f8389d26","added_by":"auto","created_at":"2025-10-06 10:25:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":169004,"visible":true,"origin":"","legend":"\u003cp\u003eDisplays the clustering outcomes pertaining to saccade time and saccade angle across various segments spanning from the highway entrance to the merging zone: (a)entrance curved section;(b) merging observation section; (c)ramp merging section; (d)freeway section.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/bc719d48cba5ee262610994c.png"},{"id":92850135,"identity":"566bc51e-c9a3-41df-970b-f5d565aa85ec","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49570,"visible":true,"origin":"","legend":"\u003cp\u003eEMSMI under different categories from the highway entrance to the merging area.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/5e753947a65bed23e52ebfe5.png"},{"id":92849907,"identity":"c45f185e-8a8c-4b05-b2aa-3816ac556bce","added_by":"auto","created_at":"2025-10-06 10:25:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":64746,"visible":true,"origin":"","legend":"\u003cp\u003eEMSMI in different sections from the highway entrance to the merge area.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/f9df61799f28e738e04c5fe5.png"},{"id":92850130,"identity":"79e02b26-30be-4289-85ca-c84f22467258","added_by":"auto","created_at":"2025-10-06 10:33:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":169223,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates EMSMI and its associated categories across various sections ranging from the highway entrance to the merging area.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/aedeb1e93b392f6dd292daed.png"},{"id":97724036,"identity":"54de83f6-478c-4527-9f69-3c009adb95b5","added_by":"auto","created_at":"2025-12-08 16:11:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2290734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7487366/v1/eaff0a95-7d12-464c-9b37-482f4e3a98c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of Drivers' Visual Attributes in Highway Entrance Zones Utilizing Self-Organizing Mapping Neural Network","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization and technological advancements have spurred unprecedented growth in transportation infrastructure, notably the road network. By the end of 2022, national road mileage surpassed 5.3548 million kilometers, with expressways extending to 177,300 kilometers [1]. These expressways, known for their efficiency, convenience, cost-effectiveness, and safety, are integral to the transportation system. The annual expressway traffic volume reached 9.532 billion vehicle-trips, with small passenger cars (9 seats or fewer) comprising 73.16%, underscoring their crucial role in modern travel [2].\u003c/p\u003e\n\u003cp\u003eWith the rapid increase in expressway traffic, safety concerns have intensified. Official statistics reveal that in 2022, China experienced 256,409 traffic accidents, with motor vehicle incidents comprising 93% of these cases [3]. Notably, driver-related factors directly caused approximately 65% of accidents, while 93% were associated with driver factors [4]. These figures underscore the pivotal role of drivers in accident prevention. As both interpreters of traffic information and vehicle operators, drivers are crucial to the safe functioning of the driver-pedestrian-vehicle-road system [5]. During vehicle operation, drivers perceive traffic information primarily through vision (80%), followed by hearing (14%), and touch, taste, and smell each accounting for 2% [6]. Among various traffic safety challenges, the area from the expressway ramp entrance to the merging section is particularly problematic and demands urgent attention, as depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003eNavigating the curved sections of highway ramps leading to merging areas poses unique challenges for drivers. Drivers must possess heightened visual perception and operational skills to adapt to the rapid changes in road alignment [7]. Similarly, the acceleration lanes of merging areas present significant speed differentials between vehicles, complicating the merging process. Drivers must quickly assess the main-line traffic flow, predict the dynamics of surrounding vehicles, and make decisions within the limited space and time to safely and efficiently merge [8]. Consequently, examining the visual characteristics of drivers on highway ramps and in merging areas, in the context of their respective driving environments, is crucial for enhancing the overall traffic safety of highways.\u003c/p\u003e\n\u003cp\u003ePrevious research underscores the critical role of drivers\u0026apos; visual perception in traffic safety. Jo et al. [9] demonstrated that high-speed driving narrows the driver\u0026apos;s field of vision, creating a \u0026quot;visual tunnel effect\u0026quot; that strains their ability to process visual information. Namgung et al. [10] analyzed visual data from drivers on circular interchange ramps, revealing that deceleration upon entry alters visual search patterns. Deshpande et al. [11] proposed a traffic sign recognition method utilizing visual characteristics. Wang et al. [12] identified a strong link between driving behavior and visual perception in tunnel environments. Jang et al. [13] developed a lane-changing intention recognition system based on pupil size variations. Zhang et al. [14] investigated expressway overtaking behavior, finding that the type of leading vehicle significantly influences the following driver\u0026apos;s fixation and visual search patterns.\u003c/p\u003e\n\u003cp\u003eRecent studies have investigated the visual behavior of drivers on expressways and merging areas using diverse research methods. Lyu et al. [15] examined drivers\u0026apos; visual scanning and mental load at intersections on grassland highways through real-vehicle tests, highlighting how intersection types and priority rules affect attention allocation. Meng et al. [16] applied an enhanced visual sensitive area method to assess drivers\u0026apos; visual behavior on mountainous expressways, identifying the impact of various road-section environments on visual sensitivity distribution. Qin et al. [17] developed a visual attractiveness model for highway landscapes based on dynamic perception, quantitatively analyzing landscape elements\u0026apos; attractiveness to drivers. Xu et al. [18] studied drivers\u0026apos; visual search behavior on high-density overpasses, demonstrating how different road sections influence fixation behavior. Zhang et al. [19] created a visual workload model via real-vehicle tests to assess drivers\u0026apos; visual workload in merging areas of high-density overpasses.\u003c/p\u003e\n\u003cp\u003eCurrent research on the visual characteristics of highway ramps leading to merging areas remains incomplete, lacking systematic findings to inform highway design, optimization, and management. This study addresses these gaps by examining drivers\u0026apos; visual behaviors across various highway sections through real-vehicle testing, with the aim of enhancing driving safety. The research focuses on:\u003c/p\u003e\n\u003cp\u003ea) identifying differences in drivers\u0026apos; visual behaviors from the expressway ramp entrance to the merging area;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eb) assessing how these visual characteristics impact driving safety;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ec) quantifying the alignment between drivers\u0026apos; visual behaviors and speeds using the Eye Movement-Speed Matching Index (EMSMI) to explore how speed variations influence visual scanning strategies.\u003c/p\u003e"},{"header":"Experimental Procedure","content":"\u003cp\u003e\u003cstrong\u003eSection Division\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA real-vehicle experiment was conducted on a city\u0026apos;s expressway to examine vehicle speed variations under different traffic conditions. The experiment took place during lowflow periods, specifically from 9:00 to 11:00 a.m. and 3:00 to 5:00 p.m., Monday through Friday. These times were chosen to minimize interference from surrounding ve-hicles during congested periods, thereby reducing impacts on drivers\u0026apos; fixation and saccade behaviors. To maintain consistency, the experiment was suspended during adverse weather, such as rain.\u003c/p\u003e\n\u003cp\u003eThe experiment\u0026apos;s data collection protocol mandates the timely exportation of equipment-recorded information during the driver\u0026apos;s 10-minute respite, ensuring the integrity and accuracy of the acquired data. All experimental data and video footage captured by the driving recorder will be systematically numbered to facilitate subsequent pairing during data processing and analysis. The relevant sections, encompassing the highway entrance to the merging area, are depicted in Figure 2 as the specific experimental segments under investigation.\u003c/p\u003e\n\u003cp\u003eConsidering environmental factors like alignment and spatial layout of highway ramps, and applying criteria for curves and intersections of main and secondary roads, the area from the highway entrance to the confluence zone is divided into four sections. Section 1, the entrance curve section, is situated at the ramp\u0026apos;s curve. Due to its tight curvature, drivers must reduce speed to maintain stability and focus on steering requirements. Section 2, the merging observation section, follows the entrance curve section. Here, driv-ers must monitor main road traffic and adjust their speed and headway in preparation for merging. Section 3, the ramp merging section, is crucial for merging with the main road. In this section, drivers assess speed differences and traffic conditions to merge safely and smoothly. Section 4, the highway main road area, lies beyond the ramp merging section and serves as the primary driving zone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal vehicle test method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore commencing the driving test, participants must don the Dikablis Glass 3 eye-tracking device, connecting it to a laptop for calibration to ensure data accuracy. This device offers synchronous binocular data acquisition, with a sampling frequency of 60Hz and a pupil tracking accuracy of 0.1\u0026deg;. Participants spend 3 to 5 minutes observing inside the vehicle to ensure device comfort and stability. Upon completing the test route, experimenters halt data recording and securely store the data. The study involves 34 healthy adults, aged 25 to 44, all with highway ramp driving experience.\u003c/p\u003e\n\u003cp\u003eAfter excluding outliers to ensure data accuracy, conducted one-way ANOVA on eye movement data across different sections. The analysis revealed significant differences in saccade duration (p=3.92\u0026times;10⁻\u0026sup1;⁵) and saccade angle (p=3.11\u0026times;10⁻\u0026sup3;\u0026sup3;), highlighting notable changes in drivers\u0026apos; visual behaviors across road segments. These findings form the basis for further analysis of visual features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Organizing Map Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilizes the Self-Organizing Map (SOM) to systematically cluster drivers\u0026apos; saccade characteristics across various highway sections, from entrances to merging areas, to investigate differences in visual characteristics over continuous time series.\u003c/p\u003e\n\u003cp\u003eThe Self-Organizing Map (SOM) is an unsupervised neural network algorithm notable for its superior nonlinear modeling capabilities and adaptability compared to traditional clustering methods. It effectively manages complex and high-dimensional data distributions by creating a low-dimensional topological representation of the input space. During training, SOM adjusts neuron weight vectors within a grid to facilitate Self-Organization. Employing competitive learning, each data sample is matched to its most similar neuron, mapping it to a specific grid cell. This process, through iterative weight updates, clusters similar data points in neighboring cells.\u003c/p\u003e\n\u003cp\u003eThe network under consideration is of size \u003cem\u003eN\u003c/em\u003e\u003cem\u003e\u0026times;\u003c/em\u003e\u003cem\u003eN\u003c/em\u003e, where each node \u003cem\u003ei\u003c/em\u003e is associated with a weight vector \u003cem\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u0026isin;R2. The initial weight vectors are typically assigned random values. For a given input vector \u003cem\u003ex\u003c/em\u003e\u0026isin;R2, the node with the weight vector closest to\u003cem\u003e\u0026nbsp;x\u003c/em\u003e, as determined by the Euclidean distance metric shown in Equation (1), is identified as the Best-Matching Unit (\u003cem\u003eBMU\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003er\u003csub\u003ec\u003c/sub\u003e\u0026nbsp;\u003c/em\u003edenotes the position of the nearest node, \u003cem\u003er\u003csub\u003ei\u003c/sub\u003e\u0026nbsp;\u003c/em\u003edenotes the position of node \u003cem\u003ei\u003c/em\u003e, and \u003cem\u003e\u0026sigma;(t)\u0026nbsp;\u003c/em\u003erepresents the neighborhood width, which generally diminishes with time. The function of the neighborhood is to enhance the impact on nodes proximal to the Best Matching Unit (\u003cem\u003eBMU\u003c/em\u003e) during weight updates, facilitating data aggregation and the emergence of a topological structure.\u003c/p\u003e\n\u003cp\u003eThe Self-Organizing Map (SOM) utilizes competitive learning to transform high-dimensional input data into a condensed low-dimensional topological representation while maintaining both local similarities and global structures. This approach facilitates cluster analysis of drivers\u0026apos; visual attributes, offering a foundational framework for comprehensive investigations into driving behaviors from highway entrances to merging zones.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eDrivers' saccadic behaviors\u003c/h2\u003e\u003cp\u003eIn driver visual behavior analysis, saccade time and saccade speed serve as crucial evaluation metrics, primarily chosen for their fundamental involvement in the visual search process and their heightened responsiveness to fluctuations in the traffic environment. Saccade time pertains to the duration an individual requires to transition from one fixation point to another while engaged in a visual search task. This metric not only mirrors the speed at which the visual system handles information but is also notably impacted by various factors including information density, complexity, and relevance within the prevailing visual context [20].\u003c/p\u003e\u003cp\u003eThe saccade angle, a crucial metric for assessing the extent of eye movement in visual search, is delineated into horizontal and vertical saccade amplitudes. This metric demonstrates a notable association with the quantity of information acquired and analyzed in the preceding fixation phase [21].\u003c/p\u003e\u003cp\u003eThis study delves into drivers' visual information processing strategies by focusing on saccade time and saccade angle as key indicators. A detailed analysis is conducted using statistical data presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMathematical statistics of saccade angle and saccade time in each section.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eentrance curve section\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emerging\u003c/p\u003e\u003cp\u003eobservation section\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eramp merging section\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003efreeway\u003c/p\u003e\u003cp\u003esection\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSaccade time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e97.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSaccade\u003c/p\u003e\u003cp\u003eangles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSaccade speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.14\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\u003eIn the merging observation section, the maximum saccade time is 100.78 ms, exceeding that of the entrance curve section 69.01 ms, the ramp merging section 97.68 ms and the highway main section 95.14 ms. Concurrently, the average saccade time in the merging observation section is 39.42 ms, comparable to the entrance curve section 37.38 ms and the ramp merging section 39.81 ms, but notably greater than that in the highway main section 60.47 ms.\u003c/p\u003e\u003cp\u003eThe merging observation section demonstrates significantly higher saccade angles compared to other sections, with an average of 15.98 \u0026deg; and a maximum of 35.09\u0026deg;, in contrast to the entrance curve section (average: 5.49\u0026deg;, maximum: 29.14\u0026deg;), ramp merging section 10.66\u0026deg;, and freeway section 10.21\u0026deg;. Moreover, the merging observation section exhibits the largest fluctuations in both saccade time (18.47 ms) and saccade angle (8.29\u0026deg;), whereas the entrance curve section displays the least variability, with values of 13.36 ms and 4.90\u0026deg;, respectively.\u003c/p\u003e\u003cp\u003eIn the merging observation section, the average saccade speed is 0.50 deg/ms, reaching a maximum of 1.93 deg/ms, surpassing the speeds observed in the entrance curve section (average:0.16 deg/ms, maximum: 1.38 deg/ms), ramp merging section (average: 0.29 deg/ms, maximum: 1.18 deg/ms), and freeway section (average:0.18 deg/ms, maximum: 1.00 deg/ms).\u003c/p\u003e\u003cp\u003eThe visual workload is most pronounced in the merging observation section, with notable individual variability. Conversely, the high-speed main road segment exhibits longer saccade times but lower saccade speeds, indicating a relatively smoother visual engagement. The entrance curve section presents a comparatively straightforward visual task, with drivers demonstrating the highest level of visual behavior uniformity.\u003c/p\u003e\u003cp\u003eThe rising complexity of road environments has led to a notable increase in both the visual task load of drivers and the variability of their saccadic behaviors, particularly evident in areas such as merging observation section. Here, drivers are required to adjust their focal points and velocities more frequently to adapt to the constantly shifting traffic conditions. These observations offer a crucial theoretical foundation for enhancing highway design and promoting driving safety.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eResults of SOM clustering\u003c/h2\u003e\u003cp\u003eThe study identified the optimal number of clusters for clustering as 5 through the computation of the silhouette coefficient. Setting the number of clusters to 5 yielded the most favorable clustering outcome, effectively capturing distinct variations in drivers' visual behaviors across different segments. Leveraging this finding, a Self-Organizing Map (SOM) Network was employed to categorize drivers' saccade time and saccade angle across various segments along the continuous time series spanning from the highway entrance to the merging area. The precise clustering outcomes for saccade time and saccade angle are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe clustering analysis depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals distinct patterns in the saccadic behaviors of drivers concerning time and angle dimensions. Cluster A1, characterized by short-duration and small-angle saccades, predominantly occupies a region with brief saccade durations of around 20\u0026ndash;40 ms and small saccade angles of 0\u0026ndash;10\u0026deg;. This pattern suggests that drivers engage in focused and rapid observations during tasks involving singular or uncomplicated driving conditions. In contrast, Cluster A2, typified by short-duration and wide-angle saccades, is situated in a zone with comparable short saccade durations but larger saccade angles ranging from 10\u0026ndash;30\u0026deg;. This distribution implies that drivers necessitate scanning a broader field within a limited timeframe, typically observed in intricate or dynamically evolving traffic scenarios.\u003c/p\u003e\u003cp\u003eA3 and A4 denote the responses observed during moderate saccade durations, with A3 representing small-angle movements and A4 representing wide-angle movements. The small-angle movements, occurring typically within a saccade duration of 40\u0026ndash;60 ms and a range of 0\u0026ndash;15\u0026deg;, indicate the driver's focus on close-range observations in moderately complex environments. Conversely, wide-angle movements during moderate saccade durations span a broader range of approximately 15\u0026ndash;30\u0026deg;, reflecting the driver's engagement in extensive information gathering in more intricate traffic scenarios.\u003c/p\u003e\u003cp\u003eThe distribution of A5 exhibits prolonged saccadic behavior lasting approximately 60\u0026ndash;100 ms, characterized by a notable increase in saccade duration. This behavior encompasses two categories: small-angle saccades, ranging from 0\u0026ndash;15\u0026deg;, and wide-angle saccades, spanning 15\u0026ndash;30\u0026deg;. Extended duration in small-angle saccades typically indicates the driver's concentration on a particular target, whereas prolonged wide-angle saccades are commonly observed in scenarios necessitating a broader visual scope to navigate intricate traffic conditions.\u003c/p\u003e\u003cp\u003eVarious types of saccades, including short-duration small-angle, short-duration wide-angle, small-angle with medium saccade duration, and wide-angle with medium saccade duration, as well as long-duration wide-angle saccades, demonstrate distinct patterns in both duration and angle among drivers navigating different traffic environments. These findings underscore the intricate nature of driving tasks and the flexible visual strategies employed by drivers.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDrivers\u0026apos; saccadic behaviors in different sections\u003c/h2\u003e\n \u003cp\u003eIn the road traffic setting, drivers demonstrate notable adaptability in their visual information processing strategies. When faced with intricate traffic scenarios containing a wealth of information, drivers tend to reduce their saccade duration to promptly focus on and analyze critical visual cues, thereby enhancing visual search efficiency [20]. Conversely, in less complex environments with reduced information, drivers opt to prolong their saccade duration to obtain a more comprehensive grasp of the prevailing traffic conditions. These temporal adjustments in strategies signify the dynamic optimization of drivers\u0026apos; visual information processing approaches across varying traffic environments.\u003c/p\u003e\n \u003cp\u003eThe saccade amplitude of drivers varies according to the complexity of the traffic environment. In complex settings, drivers tend to increase their saccade amplitude to capture crucial information more thoroughly and efficiently [21]. Conversely, in simpler traffic conditions, drivers decrease their saccade amplitude to enhance the precision and effectiveness of visual search. Thus, fluctuations in saccade time and amplitude not only indicate drivers\u0026apos; information processing capabilities during visual search but also demonstrate their heightened sensitivity and adaptability to diverse traffic environments [22].\u003c/p\u003e\n \u003cp\u003eTo delve deeper into this adaptability, we conducted a statistical analysis on the distribution of five types of visual behaviors exhibited by drivers, namely short-duration small-angle, short-duration wide-angle, small-angle and wide-angle with medium saccade time, and long-duration wide-angle, across various sections including the entrance curve, merging observation section, ramp merging section, and freeway section. The distribution of each category in different sections from the expressway entrance to the confluence area is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eIn the entrance curve segment, the predominant category is short-time small-angle (A1), constituting the largest share at 45.21%. This suggests that drivers predominantly engage in brief and limited eye scans in this road segment, aligning with the relatively straightforward driving demands of the entrance curve. The medium-time small-angle (A3) category accounts for 31.91%, indicating that drivers need to observe specific local areas within a moderate timeframe, typically associated with steering tasks. Conversely, the proportions of short-time wide-angle (A2) and medium-time wide-angle (A4) are relatively minimal at 4.26% and 18.62%, respectively, suggesting infrequent utilization of wide-angle eye scans by drivers in this context.\u003c/p\u003e\n \u003cp\u003eIn the observation of traffic behavior at the confluence, there was a notable increase in the occurrence of short-duration wide-angle (A2) behavior, reaching 43.92% and emerging as the predominant pattern. This shift underscores the necessity for drivers in confluence settings to swiftly survey an extensive array of traffic cues to promptly discern and react to alterations in traffic flow. Additionally, medium-duration wide-angle (A4) and short-duration small-angle (A1) behaviors accounted for 20.90% and 14.81%, respectively, indicating that aside from wide-angle observations, drivers must also engage in close-range monitoring at suitable intervals to holistically evaluate traffic conditions. The occurrence of long-duration small-angle (A5) behavior stood at 5.03%, indicating infrequent prolonged visual scanning by drivers in this specific setting.\u003c/p\u003e\n \u003cp\u003eThis underscores the intricate dynamism and complexity of the confluence environment, necessitating drivers to adeptly modify their visual strategies to effectively respond to evolving traffic conditions. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the clustering outcomes of saccade time and saccade angle across various segments spanning from the highway entrance to the merging area.\u003c/p\u003e\n \u003cp\u003eThe distribution of the ramp merging section closely resembles that of the merging observation section, albeit exhibiting greater dispersion overall. In this context, short-duration small angles and medium-duration small angles constitute 31.23% and 33.63%, respectively, while short-duration wide angles and medium-duration wide angles make up 16.52% and 10.21%, respectively-both proportions are lower than those observed in the merging observation section. Despite the relatively intricate visual demands of the ramp merging section, the complexity of its visual tasks is marginally reduced compared to the merging observation section. Notably, the combined proportion of long-duration small angles and wide angles stands at 8.41%, suggesting that certain drivers necessitate extended observation periods in this segment to adapt to environmental changes.\u003c/p\u003e\n \u003cp\u003eIn the high-speed main road segment, medium-duration wide-angle and long-duration small-angle maneuvers constitute the predominant behavioral patterns, accounting for 33.50% and 32.28% of the total, respectively. Drivers in the secondary lane-changing segment necessitate moderate to extended periods for observing lane information displayed on ahead lane indicator signs to facilitate optimal lane selection and execution of lane-changing maneuvers. Additionally, the occurrence of medium-duration small-angle behavior stands at 23.06%, underscoring the diverse and intricate visual demands within this segment. Conversely, the occurrences of short-duration small-angle and short-duration wide-angle behaviors are relatively minimal, at 7.28% and 3.88%, respectively. Short-duration saccadic movements are infrequent in this context, with drivers predominantly relying on prolonged observation periods to ensure the seamless execution of lane-changing maneuvers.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMathematical statistics of saccade angles and saccade times for each classification.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003esaccade time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003esaccade angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003esaccade speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eType A1 behavior exhibits the shortest saccade time among the five segments, with a maximum of 35.95 ms, an average of 25.50 ms, and a standard deviation of 5.41 ms. This indicates that drivers can efficiently perform visual tasks in simple or low-complexity scenarios. Conversely, Type A5 behavior displays the longest saccade time, peaking at 100.78 ms, with an average of 72.93 ms and a standard deviation of 11.83 ms. This suggests that drivers require more time for intricate visual searches in complex or high-load situations.\u003c/p\u003e\n \u003cp\u003eBehavior type A2 demonstrates a pronounced preference for wide-angle observation based on saccade angle metrics. Specifically, it exhibits a maximum saccade angle of 35.55\u0026deg;, an average of 21.09\u0026deg;, and a standard deviation of 4.68\u0026deg;. This behavior pattern is commonly observed in intricate traffic scenarios, necessitating rapid scanning of a broader visual field to gather pertinent traffic data. In contrast, behavior type A1 is characterized by the smallest saccade angle, with an average value of 5.00\u0026deg;, suggesting that in uncomplicated settings, drivers tend to have a more restricted visual focus.\u003c/p\u003e\n \u003cp\u003eBehavior type A2 exhibits the highest saccade speed among the observed types, reaching a maximum of 1.93 deg/ms, with an average of 0.76 deg/ms and a standard deviation of 0.26 deg/ms. This finding implies a notable increase in saccade speed for individuals needing to swiftly survey extensive visual information. In contrast, behavior types A3, A4, and A5 demonstrate comparatively slower saccade speeds, averaging 0.18 deg/ms, 0.17 deg/ms, and 0.17 deg/ms, respectively. These results suggest that visual search behaviors characterized by medium- to long-term durations tend to be consistent and leisurely.\u003c/p\u003e\n \u003cp\u003eThe features of saccadic behavior are intricately linked to the intricacy of the driving context. In uncomplicated and less complex settings, drivers typically employ saccadic patterns characterized by shorter durations and limited ranges. Conversely, in intricate and high-demand settings, drivers adjust to environmental dynamics by enhancing the efficacy of visual information processing through prolonged durations and broader-angle saccades.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEye movement - speed matching index\u003c/h2\u003e\n \u003cp\u003eThe association between driving speed and saccade speed plays a pivotal role in the dynamic interplay between drivers\u0026apos; visual conduct and the roadway setting. This correlation underscores drivers\u0026apos; adaptation of visual scanning tactics in response to variations in driving speed and task intricacy. Particularly in scenarios involving high and low driving speeds, notable alterations manifest in drivers\u0026apos; visual attention, information processing strategies, and response mechanisms, consequently influencing the nature and effectiveness of their saccadic behavior.\u003c/p\u003e\n \u003cp\u003eTo comprehensively investigate the correspondence between drivers\u0026apos; visual behaviors and driving task demands across varying speed conditions, we introduced the Eye Movement-Speed Matching Index (EMSMI). This index offers a holistic assessment of the correlation between drivers\u0026apos; visual information processing efficiency (as indicated by saccade speed) and driving task requirements (as indicated by driving speed) by evaluating the ratio of saccade angle and saccade time to driving speed. The EMSMI serves as a novel quantitative metric for evaluating the alignment of drivers\u0026apos; visual behaviors with driving speed across diverse speeds and traffic scenarios. The calculation formula for EMSMI is depicted in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and the outcomes are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eEMSMI\u003c/em\u003e\u003csub\u003e\u003cem\u003eoi\u003c/em\u003e\u003c/sub\u003e represents the eye movement speed matching index of the driver at the \u003cem\u003eo-th\u003c/em\u003e section and \u003cem\u003ei-th\u003c/em\u003e time, measured in deg/ms. \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003e\u003cem\u003eoi\u003c/em\u003e\u003c/sub\u003e denotes the saccade angle of the driver at the \u003cem\u003eo-th\u003c/em\u003e section and \u003cem\u003ei-th\u003c/em\u003e time, measured in degrees. \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003eoi\u003c/em\u003e\u003c/sub\u003e \u003cem\u003es\u003c/em\u003eignifies the saccade time of the driver at the \u003cem\u003eo-th\u003c/em\u003e section and \u003cem\u003ei-th\u003c/em\u003e time, measured in milliseconds. Lastly, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eoi\u003c/em\u003e\u003c/sub\u003e indicates the driving speed of the driver at the \u003cem\u003eo-th\u003c/em\u003e section and \u003cem\u003ei-th\u003c/em\u003e time, measured in km/h.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMathematical statistics of EMSMI for each road section.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEMSMI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eentrance curve section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emerging observation section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eramp merging section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efreeway section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the entrance curve segment, the mean EMSMI value is 0.041 deg/ms with a standard deviation of 0.0424 deg/ms, suggesting a modest level of visual-speed correspondence in this area. Despite a peak value of 0.349 deg/ms, the visual demands placed on drivers in this roadway segment are uncomplicated, with low environmental intricacy.\u003c/p\u003e\n \u003cp\u003eIn the merging observation section, the EMSMI value is notably higher at 0.1249 deg/ms on average, with a standard deviation of 0.0881 deg/ms, compared to the entrance curve section. This segment of the road exhibits a pronounced alignment between drivers\u0026apos; visual information processing and driving speed, with a maximum value of 0.4790 deg/ms. Here, drivers exhibit excessively rapid saccade speed in relation to their driving speed, leading to inadequate visual information processing. This mismatched correlation between visual saccades and driving speed escalates drivers\u0026apos; cognitive load, consequently heightening the likelihood of traffic accidents.\u003c/p\u003e\n \u003cp\u003eAt the ramp merging section, the mean EMSMI value is 0.0688 deg/ms with a standard deviation of 0.0462 deg/ms, indicating a moderate level of visual-speed matching. Despite a maximum value of 0.2765 deg/ms, it suggests that drivers can adeptly calibrate the correlation between visual scanning and driving speed on an individual basis.\u003c/p\u003e\n \u003cp\u003eIn the high-speed main road segment, the EMSMI value is low, averaging 0.0408 deg/ms, with a standard deviation of 0.0320 deg/ms, a maximum of 0.2234 deg/ms, and a minimum of 0.0003 deg/ms. The relationship between driver visual behavior and driving speed in this segment is straightforward, characterized by low variability. Consistency in the alignment of driver visual behavior and driving speed minimizes the need for frequent adjustments in this segment.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates notable variations in the Eye Movement - Speed Matching Index (EMSMI) across distinct categories. Notably, Category A2 exhibits the highest mean value of 0 .1866 deg/ms, characterized by a broad distribution range and numerous outliers. Within these road segments, the synchronization between drivers\u0026apos; eye movements and driving velocities displays substantial oscillations, leading to delayed environmental perception, heightened reaction times, and consequent impacts on decision-making precision and driving safety. Particularly on intricate road stretches, this phenomenon escalates the likelihood of accidents.\u003c/p\u003e\n \u003cp\u003eClasses A1 and A5 exhibit lower EMSMI values, displaying a more tightly clustered distribution with fewer outliers, indicating a consistent alignment between drivers\u0026apos; visual behaviors and driving speeds. Conversely, classes A3 and A4 demonstrate a transitional phase characterized by a gradual reestablishment of alignment between visual behaviors and speeds following encounters with complex driving tasks and environments.\u003c/p\u003e\n \u003cp\u003eThe figure illustrates distinct variations in the distribution characteristics of the Eye Movement - Speed Matching Index (EMSMI) along various segments of the roadway, particularly from the highway entrance to the merging zone. Notably, the merging observation section exhibits the highest average EMSMI at 0.1249 deg/ms, featuring a broad distribution range with notably more outliers compared to other roadway segments. This disparity suggests heightened complexity in driving tasks within this specific segment. Drivers are compelled to maintain safe driving practices while vigilantly monitoring the dynamic traffic patterns on the primary road, consequently leading to compromised coordination between visual information processing and driving speed, alongside a marked escalation in cognitive workload.\u003c/p\u003e\n \u003cp\u003eIn contrast, the mean EMSMI values in the entrance curve section and the high-speed main section are notably low, at 0.0410 deg/ms and 0.0408 deg/ms, respectively, with relatively tight distributions and minimal outliers. This suggests that the driving tasks in these segments are comparatively straightforward, the alignment between drivers\u0026apos; visual behaviors and vehicle speeds is relatively consistent, and the cognitive burden is relatively light. The mean EMSMI value in the ramp merging section, at 0.0688 deg/ms, falls between those of intricate and uncomplicated sections, signifying a shift from the intricate conditions of the merging observation segment to a relatively stable driving scenario.\u003c/p\u003e\n \u003cp\u003eIn order to investigate the correlation between drivers\u0026apos; eye movement-speed matching index (EMSMI) on varied road segments and driving behavior categories, this study employs classification labels A1 to A5 to depict the distribution characteristics of drivers exhibiting different behaviors. The analysis unveils the distribution patterns of each behavior category across diverse road sections, tracks the variations in EMSMI, and scrutinizes drivers\u0026apos; cognitive load, visual scanning strategies, and driving speed alignment in intricate traffic settings. The findings are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe figure illustrates notable variations in the Eye Movement - Speed Matching Index (EMSMI) across different segments, elucidating the impact of driving task complexity on visual behavior. Specifically, in the entrance curve and high-speed main road segments, categories A1 and A3 predominate, characterized by lower EMSMI values and clustered distributions. These findings suggest a high level of stability and consistency in drivers\u0026apos; visual behavior in these segments, indicating lower cognitive load, simpler driving tasks, and stable alignment between visual attention and driving speed.\u003c/p\u003e\n \u003cp\u003eIn the merging observation section, there was a notable increase in the distribution of A2, with the EMSMI value peaking. Additionally, there was a rise in the proportion of A5. These findings suggest that drivers must engage in frequent wide-angle saccadic movements and sustain prolonged visual monitoring in intricate, dynamic traffic settings to execute maneuvers like merging or changing lanes. The elevated EMSMI value and the substantial prevalence of wide-angle behaviors in the merging observation section signify a marked escalation in cognitive resource demands on drivers, signaling the pinnacle of task complexity and identifying it as the primary area imposing the highest cognitive burden on drivers.\u003c/p\u003e\n \u003cp\u003eThe ramp merging section serves as a transitional zone where the distribution ratios of A1 and A3 increase, while that of A2 notably decreases, accompanied by a gradual drop in EMSMI value. This trend suggests a gradual return to visual stability among drivers, a decrease in cognitive load, and a progressive stabilization of the driving task in this segment.\u003c/p\u003e\n \u003cp\u003eThe distribution of EMSMI values and categories across various segments demonstrates the significant impact of driving task complexity on visual-speed coordination. Specifically, in the merging observation section, the intricate task demands necessitate frequent adjustments in drivers\u0026apos; visual strategies and attention distribution, resulting in a notable rise in cognitive burden. Consequently, this segment emerges as a pivotal focal point for ensuring driving safety.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eActual vehicle tests were conducted from the entrance ramp to the merging area of the expressway. Analysis of eye movement data from four sections - the entrance curve section, merging observation section, ramp merging section, and main expressway section - enabled an assessment of driving visual characteristics and safety. Four conclusions were derived from this analysis:\u003c/p\u003e\n\u003cp\u003e1. In the section focusing on merging observation section, the drivers exhibited a saccade time of 100.78 ms, a saccade angle of 35.09°, and a saccade speed of 1.93 deg/ms, all of which were notably elevated compared to those recorded on other road segments. The visual task load was most pronounced in the merging observation section, necessitating a greater allocation of visual resources to navigate the intricate traffic conditions.\u003c/p\u003e\n\u003cp\u003e2. Cluster analysis was conducted on saccade time and saccade angle utilizing the Self-Organizing Map neural network (SOM). The data were categorized into five groups: short-time small-angle, short-time wide-angle, medium-time small-angle, medium-time wide-angle, and long-time small-angle.\u003c/p\u003e\n\u003cp\u003e3. In the entrance curve segment, the predominant category is short-time small-angle (A1), representing 45.21% of cases. Conversely, in the merging observation section, the short-time wide-angle (A2) category predominates, comprising 43.92% of instances. These findings suggest that drivers engage in more pronounced saccades in intricate settings to adapt to fluctuations and potential hazards in the dynamic traffic milieu.\u003c/p\u003e\n\u003cp\u003e4. In the merging observation section, the average EMSMI value peaks at 0.1249 deg/ms, indicating heightened visual adaptation to dynamic traffic conditions. This necessitates increased synchronization of visual processing with driving speed. Conversely, the main expressway section exhibits the lowest average EMSMI value at 0.0408 deg/ms, suggesting reduced visual adjustments post-confluence completion, leading to stabilized visual behaviors and decreased task demands.\u003c/p\u003e\n\u003cp\u003eThe research results establish a theoretical foundation for the strategic placement of traffic safety and guidance facilities in expressway ramp-to-merging sections, offering substantial practical utility, particularly in augmenting driver safety on intricate road segments. Subsequent investigations will concentrate on analyzing driving behaviors, refining road designs, and implementing intelligent transportation systems to deliver enhanced solutions and advance driving safety and convenience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.X.: Conceptualization, methodology, validation, Writing—review \u0026amp; editing, funding. Y.L.: software, investigation, validation, formal analysis, visualization, writing—original draft. J.F.: validation, formal analysis, writing—original draft. Z.F.: supervision, methodology, project administration, funding. P.G.: formal analysis, resources. H.A.: visualization, funding. Z.S.: validation, resources. Q.L.: project administration, supervision. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Science Research Project of Hebei Education Department (Grant No. QN2024082), the Science and Technology Program Projects of Shandong Provincial Department of Transportation (Grant No. 2024B28) and Self-funded Cangzhou Science and Technology Plan Project (Grant No. 222001010), Special Fund for Basic Scientific Research Operations of Hebei University of Water Resources and Electric Engineering (Grant No. SYKY2206).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study all methods were carried out in accordance with relevant guidelines and regulations, and all experimental protocols were approved by Ethics Committee of Cangzhou Normal University (2, July,2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCai, Y., Zang, Z., Zuo, X., Liu, F., Li, L., Lu, K., \u0026amp; Huang, Q. (2023). Research on the Performance of Calcium Carbide Slag\u0026ndash;Fly Ash Stabilized Soil. Advances in Civil Engineering, 2023(1), 4571162.\u003c/li\u003e\n\u003cli\u003eWan, X., Jin, P. J., Gu, H., Chen, X., \u0026amp; Ran, B. (2017). Modeling freeway merging in a weaving section as a sequential decision-making process. Journal of Transportation Engineering, Part A: Systems, 143(5), 05017002.\u003c/li\u003e\n\u003cli\u003eLiang, Y., Yuan, H., Wang, Z., Wan, Z., Liu, T., Wu, B., ... \u0026amp; Tang, X. (2024). Nonlinear effects of traffic statuses and road geometries on highway traffic accident severity: A machine learning approach. PloS one, 19(11), e0314133.\u003c/li\u003e\n\u003cli\u003eWang, X., Liu, Q., Guo, F., Xu, X., \u0026amp; Chen, X. (2022). Causation analysis of crashes and near crashes using naturalistic driving data. Accident Analysis \u0026amp; Prevention, 177, 106821.\u003c/li\u003e\n\u003cli\u003eWang, S., Du, Z., Zheng, H., Han, L., Xia, X., \u0026amp; He, S. (2024). Improving driving safety in freeway tunnels: A field study of linear visual guiding facilities. Tunnelling and Underground Space Technology, 143, 105489.\u003c/li\u003e\n\u003cli\u003eLi, W., Huang, J., Xie, G., Karray, F., \u0026amp; Li, R. (2021). A survey on vision-based driver distraction analysis. Journal of Systems Architecture, 121, 102319.\u003c/li\u003e\n\u003cli\u003eFu, Z., Zhang, J., Pan, B., Wu, S., \u0026amp; Yang, H. (2024). Calculation Model for the Exit Decision Sight Distance of Right-Turn Ramps on the Left at Interchange. Applied Sciences, 14(14), 6205.\u003c/li\u003e\n\u003cli\u003eLi, Y., Zhang, H., Wang, Q., Wang, Z., \u0026amp; Yao, X. (2024). Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions. Journal of Advanced Transportation, 2024(1), 7766164.\u003c/li\u003e\n\u003cli\u003eJo D, Lee S, Lee Y. (2014). The Effect of Driving Speed on Driver\u0026rsquo;s Visual Attention: Experimental Investigation. In 11th International Conference on Engineering Psychology and Cognitive Ergonomics-Volume, 8532, 174\u0026ndash;182.\u003c/li\u003e\n\u003cli\u003eNamgung, M., \u0026amp; Sung, S. L. (2016). Analysis on Characteristics of Driver\u0026rsquo;s Visual Change on the Roundabout. Journal of the Korea Entertainment Industry Association, 10(3), 293.\u003c/li\u003e\n\u003cli\u003eDeshpande, A. V., \u0026amp; Subashini, M. M. (2017). A new approach to traffic sign recognition through primary visual characteristics. In 2017 Innovations in Power and Advanced Computing Technologies, 1\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eWang, S., Du, Z., Chen, G., Zheng, H., Tang, Z., \u0026amp; Jiao, F. (2021). Drivers\u0026rsquo; visual characteristics in small-radius optically long tunnels on rural roads. Tunnelling and Underground Space Technology, 113, 103969.\u003c/li\u003e\n\u003cli\u003eJang Y M, Mallipeddi R, Lee M. (2014). Driver\u0026rsquo;s Lane-change Intent Identification Based on Pupillary Variation. In 2014 IEEE International Conference on Consumer Electronics, 197\u0026ndash;198.\u003c/li\u003e\n\u003cli\u003eZhang, W., Dai, J., Pei, Y., Li, P., Yan, Y., \u0026amp; Chen, X. (2016). Drivers\u0026rsquo; visual search patterns during overtaking maneuvers on freeway. International journal of environmental research and public health, 13(11), 1159.\u003c/li\u003e\n\u003cli\u003eLyu, Z., Qi, C., Zhu, S., \u0026amp; Wang, H. (2022). The visual scanning behavior and mental workload of drivers at prairie highway intersections with different characteristics. IEEE Access, 10, 123043\u0026ndash;123056.\u003c/li\u003e\n\u003cli\u003eMeng, Y., Cai, H., \u0026amp; Qing, G. (2021). Preliminary quantitative research on characteristics of driving visual sensitive region in mountainous highway environment. Int J Intell Inf Manag Sci, 10(6), 184\u0026ndash;188.\u003c/li\u003e\n\u003cli\u003eQin, X., Fang, M., Yang, D., \u0026amp; Wangari, V. W. (2023). Quantitative evaluation of attraction intensity of highway landscape visual elements based on dynamic perception. Environmental Impact Assessment Review, 100, 107081.\u003c/li\u003e\n\u003cli\u003eJin, X., Ziqiu, S., Siqi, W., \u0026amp; Zimiao, Y. (2022). Characteristics of driver\u0026apos;s visual search behavior in exit ramp of high-density interchanges. Journal of Southeast University. Dongnan Daxue Xuebao, 52(6).\u003c/li\u003e\n\u003cli\u003eZhang, Y., Jiang, P., Wang, S., Cheng, S., Xu, J., \u0026amp; Liu, Y. (2024). Study on the Driver Visual Workload in High-Density Interchange-Merging Areas Based on a Field Driving Test. Sensors, 24(19), 6247.\u003c/li\u003e\n\u003cli\u003eJiao, F., Du, Z., Wang, S., Ni, Y., \u0026amp; He, R. (2020). Drivers\u0026rsquo; saccade characteristics in curves of extra-long urban underwater tunnels. Transportation research record, 2674(2), 102\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eHan, L., \u0026amp; Du, Z. (2024). Evaluation of eye-catching effect in highway tunnel entrance area based on saccade behavior. Traffic injury prevention, 1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGen\u0026eacute;-Sampedro, A., Alonso, F., Gene-Morales, J., Monteiro, P. L., \u0026amp; Useche, S. A. (2024). Could driving help us to \u0026ldquo;see better\u0026rdquo;? A comparative assessment of saccadic efficiency, visual speed, and attention. BMC ophthalmology, 24(1), 90\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":"Expressway, Merge area, Saccadic behavior, Self-Organizing Map Neural Network (SOM), Traffic safety","lastPublishedDoi":"10.21203/rs.3.rs-7487366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7487366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith increasing highway traffic, safety concerns at ramp entrances and confluence areas have intensified. This study analyzes drivers' eye movements in real-world settings, focusing on visual behavior in entrance curve section, merging observation section, ramp merging section, and freeway section. Metrics such as saccade time, angle, and speed were assessed alongside the complexity and task load of each section. Results show that the merging observation section exhibits the highest visual task load, with peak saccade time, angle, and speed at 100.78 ms, 35.09\u0026deg;, and 1.93 deg/ms, respectively, exceeding those in other sections. Through Self-Organizing Map (SOM) neural network clustering analysis, saccade time and angle were categorized into five groups: short-time small-angle, short-time wide-angle, medium-time small-angle, medium-time wide-angle, and long-time small-angle. Driver visual behavior varies distinctly across different road sections. Notably, in the merging observation section, the average eye movement-speed matching index (EMSMI) peaks at 0.1249 deg/ms, with a marked increase in categories A2 and A5. This section involves complex driving tasks, requiring frequent visual adjustments to navigate dynamic traffic conditions. In contrast, the entrance curve and freeway section predominantly exhibit categories A1 and A3, with lower EMSMI values of 0.0410 deg/ms and 0.0408 deg/ms, respectively. These sections show fewer outliers and a more concentrated distribution, indicating reduced cognitive load. This study quantitatively elucidates the link between drivers' visual behavior and road complexity, highlighting the heightened cognitive demands of complex traffic environments and their effects on visual behavior.\u003c/p\u003e","manuscriptTitle":"Investigation of Drivers' Visual Attributes in Highway Entrance Zones Utilizing Self-Organizing Mapping Neural Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 10:25:16","doi":"10.21203/rs.3.rs-7487366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T05:22:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T16:02:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T17:47:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62352118950642789613505584835752032068","date":"2025-10-02T11:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105256611636600862562578689626295091282","date":"2025-10-01T16:32:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190371734823002952598033927127464589300","date":"2025-09-26T12:02:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T03:05:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T03:02:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T08:18:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-06T08:57:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-06T08:54:24+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":"72eee7fc-ce5f-410d-94cb-9fb0d04bfcb0","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55793247,"name":"Biological sciences/Neuroscience"},{"id":55793248,"name":"Biological sciences/Psychology"},{"id":55793249,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-08T16:04:51+00:00","versionOfRecord":{"articleIdentity":"rs-7487366","link":"https://doi.org/10.1038/s41598-025-26920-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-01 15:57:31","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-10-06 10:25:16","video":"","vorDoi":"10.1038/s41598-025-26920-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-26920-2","workflowStages":[]},"version":"v1","identity":"rs-7487366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7487366","identity":"rs-7487366","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}