Pub Date : 2025-03-05DOI: 10.1016/j.aap.2025.107988
Manman Zhu , Daniel J. Graham , Nan Zhang , Zijin Wang , N.N. Sze
Reckless crossing behaviour is one of the major contributing factors to pedestrian crashes and injuries. The relationship between perceived risk and actual behaviour of pedestrians was examined. However, influences of weather conditions, which is a significant crash contributory factor, on the pedestrian safety perception are less studied. In this study, pedestrian safety perception in adverse weather and low visibility conditions like rain and fog is examined using immersive Cave Automatic Virtual Environment (CAVE) experiment. For instance, the 3D virtual reality model of a mid-block crossing in Hong Kong is developed. Factors including pedestrian socio-demographics, vehicle speed, gap size and weather condition are considered in the experiments. The propensity score method is adopted to estimate the causal inferences of weather conditions on pedestrian safety perception. Moreover, effects of multilevel data for multiple treatments are accounted using inverse probability of treatment weighting. Results indicate that perceived risk of pedestrians are higher in rainy and foggy conditions. Also, adverse impact of rainy condition is more significant in the dusk time, compared to daytime. Findings should shed light on effective remedial measures like traffic management and control, and street lighting that can mitigate the risk of pedestrian crash at the mid-block crossing.
{"title":"Influences of weather on pedestrian safety perception at mid-block crossing: A CAVE-based study","authors":"Manman Zhu , Daniel J. Graham , Nan Zhang , Zijin Wang , N.N. Sze","doi":"10.1016/j.aap.2025.107988","DOIUrl":"10.1016/j.aap.2025.107988","url":null,"abstract":"<div><div>Reckless crossing behaviour is one of the major contributing factors to pedestrian crashes and injuries. The relationship between perceived risk and actual behaviour of pedestrians was examined. However, influences of weather conditions, which is a significant crash contributory factor, on the pedestrian safety perception are less studied. In this study, pedestrian safety perception in adverse weather and low visibility conditions like rain and fog is examined using immersive Cave Automatic Virtual Environment (CAVE) experiment. For instance, the 3D virtual reality model of a mid-block crossing in Hong Kong is developed. Factors including pedestrian socio-demographics, vehicle speed, gap size and weather condition are considered in the experiments. The propensity score method is adopted to estimate the causal inferences of weather conditions on pedestrian safety perception. Moreover, effects of multilevel data for multiple treatments are accounted using inverse probability of treatment weighting. Results indicate that perceived risk of pedestrians are higher in rainy and foggy conditions. Also, adverse impact of rainy condition is more significant in the dusk time, compared to daytime. Findings should shed light on effective remedial measures like traffic management and control, and street lighting that can mitigate the risk of pedestrian crash at the mid-block crossing.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 107988"},"PeriodicalIF":5.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1016/j.aap.2025.107984
Qingwen Pu , Kun Xie , Hongyu Guo , Yuan Zhu
Interactions between vehicle–pedestrian at intersections often lead to safety–critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the development of collision avoidance systems and safety-aware traffic simulations. Unmanned aerial vehicles were leveraged to collect high-resolution trajectory data of vehicle–pedestrian at urban intersections. A new surrogate safety measure, curvilinear time-to-collision (CurvTTC), was employed to identify vehicle–pedestrian near-miss scenarios. CurvTTC takes into account the curved trajectories of road users instead of assuming straight-line future trajectories, making it particularly suitable for safety analysis at intersections, where turning vehicles usually follow curved paths. An effective algorithm considering predicted trajectories and collision types was designed to compute CurvTTC. When CurvTTC was applied to capture vehicle–pedestrian conflicts at intersections, it demonstrated superior performance in identifying risks more accurately compared to other surrogate safety measures, emphasizing the importance of considering the curved trajectories of road users. Further, a novel deep deterministic policy gradient based on the Mamba network (Mamba-DDPG) approach was used to model vehicles’ crash avoidance behaviors during the vehicle–pedestrian conflicts captured. Results revealed that the Mamba-DDPG approach effectively learned the vehicle behaviors sequentially in both lateral and longitudinal dimensions during near-miss scenarios with pedestrians. The Mamba-DDPG approach achieved superior predictive accuracy by utilizing Mamba’s dynamic data reweighting, which prioritizes critical states. This resulted in better performance compared to both the standard DDPG and the Transformer-enhanced DDPG (Transformer-DDPG) methods. The Mamba-DDPG approach was employed to reconstruct evasive trajectories of vehicles when approaching pedestrians and its effectiveness in capturing the underlying policy of crash avoidance behaviors was validated.
{"title":"Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning","authors":"Qingwen Pu , Kun Xie , Hongyu Guo , Yuan Zhu","doi":"10.1016/j.aap.2025.107984","DOIUrl":"10.1016/j.aap.2025.107984","url":null,"abstract":"<div><div>Interactions between vehicle–pedestrian at intersections often lead to safety–critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the development of collision avoidance systems and safety-aware traffic simulations. Unmanned aerial vehicles were leveraged to collect high-resolution trajectory data of vehicle–pedestrian at urban intersections. A new surrogate safety measure, curvilinear time-to-collision (CurvTTC), was employed to identify vehicle–pedestrian near-miss scenarios. CurvTTC takes into account the curved trajectories of road users instead of assuming straight-line future trajectories, making it particularly suitable for safety analysis at intersections, where turning vehicles usually follow curved paths. An effective algorithm considering predicted trajectories and collision types was designed to compute CurvTTC. When CurvTTC was applied to capture vehicle–pedestrian conflicts at intersections, it demonstrated superior performance in identifying risks more accurately compared to other surrogate safety measures, emphasizing the importance of considering the curved trajectories of road users. Further, a novel deep deterministic policy gradient based on the Mamba network (Mamba-DDPG) approach was used to model vehicles’ crash avoidance behaviors during the vehicle–pedestrian conflicts captured. Results revealed that the Mamba-DDPG approach effectively learned the vehicle behaviors sequentially in both lateral and longitudinal dimensions during near-miss scenarios with pedestrians. The Mamba-DDPG approach achieved superior predictive accuracy by utilizing Mamba’s dynamic data reweighting, which prioritizes critical states. This resulted in better performance compared to both the standard DDPG and the Transformer-enhanced DDPG (Transformer-DDPG) methods. The Mamba-DDPG approach was employed to reconstruct evasive trajectories of vehicles when approaching pedestrians and its effectiveness in capturing the underlying policy of crash avoidance behaviors was validated.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107984"},"PeriodicalIF":5.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-03DOI: 10.1016/j.aap.2025.107985
Mahsa Jafari, Bhagwant Persaud
Identifying the complex relationships contributing to crash severity is vital for effective road safety strategies but can be challenging. This study explores a hybrid Structural Equation Modeling/Fuzzy-set Qualitative Comparative Analysis (SEM-FsQCA) technique to analyze these relationships, including moderation effects. By integrating SEM and FsQCA to offer a more comprehensive analysis, it overcomes a key challenge of traditional methods—the inability to simultaneously address complex causal relationships and interaction effects. Also investigated was the potential of the Synthesizing Minority Oversampling Technique (SMOTE) for addressing the inherently imbalanced nature of the crash severity and other data used for the analysis. Utilizing a database of Ohio collector roads as a case study, a multigroup analysis was also implemented to analyze factors in lower and higher-income neighbourhoods, which were characterized by imbalanced samples, and assess how combinations of road and environmental variables affect crash severity on roads adjacent to these two neighbourhoods. The SEM results indicated that, regardless of the neighbourhood income level, age, percentage of grade, the proportion of the population having a diploma or higher, horizontal curve, and speed limit all significantly affect crash severity. Those results did indicate that the effects of independent and moderating variables are significantly different for the two neighbourhoods. Using FsQCA, the causal configurations leading to higher crash severity were explored for the two neighbourhood categories. The results of the case study revealed that crash prevention measures could be more effectively developed for crashes based on the income level of neighbourhoods adjacent to the collector roads investigated.
{"title":"Application of a novel hybrid multigroup statistical approach to investigate the factors affecting crash severity","authors":"Mahsa Jafari, Bhagwant Persaud","doi":"10.1016/j.aap.2025.107985","DOIUrl":"10.1016/j.aap.2025.107985","url":null,"abstract":"<div><div>Identifying the complex relationships contributing to crash severity is vital for effective road safety strategies but can be challenging. This study explores a hybrid Structural Equation Modeling/Fuzzy-set Qualitative Comparative Analysis (SEM-FsQCA) technique to analyze these relationships, including moderation effects. By integrating SEM and FsQCA to offer a more comprehensive analysis, it overcomes a key challenge of traditional methods—the inability to simultaneously address complex causal relationships and interaction effects. Also investigated was the potential of the Synthesizing Minority Oversampling Technique (SMOTE) for addressing the inherently imbalanced nature of the crash severity and other data used for the analysis. Utilizing a database of Ohio collector roads as a case study, a multigroup analysis was also implemented to analyze factors in lower and higher-income neighbourhoods, which were characterized by imbalanced samples, and assess how combinations of road and environmental variables affect crash severity on roads adjacent to these two neighbourhoods. The SEM results indicated that, regardless of the neighbourhood income level, age, percentage of grade, the proportion of the population having a diploma or higher, horizontal curve, and speed limit all significantly affect crash severity. Those results did indicate that the effects of independent and moderating variables are significantly different for the two neighbourhoods. Using FsQCA, the causal configurations leading to higher crash severity were explored for the two neighbourhood categories. The results of the case study revealed that crash prevention measures could be more effectively developed for crashes based on the income level of neighbourhoods adjacent to the collector roads investigated.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107985"},"PeriodicalIF":5.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.aap.2025.107970
Sergio A. Useche , Rodrigo Mora , Francisco Alonso , Oscar Oviedo-Trespalacios
While young cyclists remain overrepresented in cycling crash figures, effective actions to mitigate their risks remain understudied and underapplied, especially in regions with low cycling tradition and weak or fragmented governance, as is the case in most Hispanic countries. One key emerging issue is the potential influence of personality traits such as sensation seeking (SS) on young cyclists’ behavior and safety outcomes. This study aimed to assess the relationships among SS, cycling behavior, and safety-related outcomes among a sample of young cyclists. Data were collected from 945 cyclists aged 18–25 from five Hispanic countries, who responded to an electronic survey on personality traits and cycling-related topics. Significant associations were found between sensation seeking and risk-related cycling behaviors, as well as gender differences in SS, risky cycling behavior, and self-reported cycling crash rates, with males exhibiting higher values in all categories. Path analyses suggest that SS predicts self-reported crashes through the full mediation of both deliberate (traffic violations) and unintentional (errors) risky road behaviors, with the former having a greater explanatory effect on young cyclists’ self-reported crash figures. The findings of this study highlight the need to address under-researched issues such as sensation seeking (SS) and risk-taking behavior through evidence-based interventions aimed at improving the safety of young cyclists. This is particularly relevant in countries with similar demographic characteristics and further nascent cycling cultures.
{"title":"Sensation seeking and crashes among young cyclists","authors":"Sergio A. Useche , Rodrigo Mora , Francisco Alonso , Oscar Oviedo-Trespalacios","doi":"10.1016/j.aap.2025.107970","DOIUrl":"10.1016/j.aap.2025.107970","url":null,"abstract":"<div><div>While young cyclists remain overrepresented in cycling crash figures, effective actions to mitigate their risks remain understudied and underapplied, especially in regions with low cycling tradition and weak or fragmented governance, as is the case in most Hispanic countries. One key emerging issue is the potential influence of personality traits such as sensation seeking (SS) on young cyclists’ behavior and safety outcomes. This study aimed to assess the relationships among SS, cycling behavior, and safety-related outcomes among a sample of young cyclists. Data were collected from 945 cyclists aged 18–25 from five Hispanic countries, who responded to an electronic survey on personality traits and cycling-related topics. Significant associations were found between sensation seeking and risk-related cycling behaviors, as well as gender differences in SS, risky cycling behavior, and self-reported cycling crash rates, with males exhibiting higher values in all categories. Path analyses suggest that SS predicts self-reported crashes through the full mediation of both deliberate (traffic violations) and unintentional (errors) risky road behaviors, with the former having a greater explanatory effect on young cyclists’ self-reported crash figures. The findings of this study highlight the need to address under-researched issues such as sensation seeking (SS) and risk-taking behavior through evidence-based interventions aimed at improving the safety of young cyclists. This is particularly relevant in countries with similar demographic characteristics and further nascent cycling cultures.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107970"},"PeriodicalIF":5.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.aap.2025.107967
Hailin Shi, Feng Chen, Haotian Du, Ting Zhang, Chen Li
Unsignalized intersections are accident-prone locations due to numerous conflict points and unclear right-of-way. This issue is exacerbated at night when road markings become less visible, leading to increased accident rates. Self-luminous road markings, a new type of proactive traffic safety control facility, have garnered increasing attention and are being gradually promoted due to their intelligent, stable brightness and variable characteristics. To explore the warning effect of self-luminous road markings at unsignalized intersections at night, this study designed three types of warning schemes: continuous-illuminating pedestrian crosswalk advance warning marking (CPWM), continuous-illuminating yield text advance warning marking (CYWM), and transition-illuminating pedestrian crosswalk advance warning marking (TPWM). Based on previous research, nine indicators were selected for comprehensive evaluation from the perspectives of driver’s operating, visual characteristics, and psychology. Finally, an entropy-based matter-element model was constructed to comprehensively evaluate the warning effect. The results show that the CYWM and CPWM schemes can help drivers effectively reduce the mean speed and potential lateral conflict risk at intersections, enhancing drivers’ awareness of road environment information. However, the TPWM scheme had adverse effects due to greater visual stimulation. The effectiveness of the three schemes ranks from high to low as CYWM, CPWM, and TPWM. This study confirms that self-luminous road markings are effective at intersections, providing new insights for infrastructure upgrades and offering a general framework for evaluating the effectiveness of traffic safety facilities.
{"title":"Evaluating the impact of self-luminous road markings on driver behavior at unsignalized intersections: A simulator study","authors":"Hailin Shi, Feng Chen, Haotian Du, Ting Zhang, Chen Li","doi":"10.1016/j.aap.2025.107967","DOIUrl":"10.1016/j.aap.2025.107967","url":null,"abstract":"<div><div>Unsignalized intersections are accident-prone locations due to numerous conflict points and unclear right-of-way. This issue is exacerbated at night when road markings become less visible, leading to increased accident rates. Self-luminous road markings, a new type of proactive traffic safety control facility, have garnered increasing attention and are being gradually promoted due to their intelligent, stable brightness and variable characteristics. To explore the warning effect of self-luminous road markings at unsignalized intersections at night, this study designed three types of warning schemes: continuous-illuminating pedestrian crosswalk advance warning marking (CPWM), continuous-illuminating yield text advance warning marking (CYWM), and transition-illuminating pedestrian crosswalk advance warning marking (TPWM). Based on previous research, nine indicators were selected for comprehensive evaluation from the perspectives of driver’s operating, visual characteristics, and psychology. Finally, an entropy-based matter-element model was constructed to comprehensively evaluate the warning effect. The results show that the CYWM and CPWM schemes can help drivers effectively reduce the mean speed and potential lateral conflict risk at intersections, enhancing drivers’ awareness of road environment information. However, the TPWM scheme had adverse effects due to greater visual stimulation. The effectiveness of the three schemes ranks from high to low as CYWM, CPWM, and TPWM. This study confirms that self-luminous road markings are effective at intersections, providing new insights for infrastructure upgrades and offering a general framework for evaluating the effectiveness of traffic safety facilities.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107967"},"PeriodicalIF":5.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.aap.2025.107983
Lei Han , Zhigang Du , Shoushuo Wang
This study examined the impact of the combined configuration of spiral tunnel length and radius on drivers’ heart rate variability (HRV) and stress perception through a naturalistic driving experiment with 30 participants. Three spiral tunnels varying in both length and radius were evaluated, and the effects of uphill and downhill driving directions were also considered. The results revealed that the combined configuration of tunnel length and radius significantly influenced drivers’ physiological and psychological states. Specifically, longer tunnels with smaller radii were associated with increased average heart rate (HR), decreased standard deviation of normal-to-normal intervals (SDNN), elevated low frequency to high frequency ratio (LF/HF), and reduced sample entropy (SampEn), all indicating heightened stress responses. Uphill driving consistently led to higher average HR, lower SDNN, and higher LF/HF ratio compared to downhill driving, reflecting increased stress due to greater physical and mental demands. These findings offer invaluable insights for the design and management of spiral tunnels, with the ultimate goal of enhancing driver safety and comfort. By optimizing tunnel characteristics and implementing appropriate traffic management strategies, it is possible to create a more favorable driving environment that mitigates the negative impacts on drivers and promotes overall well-being.
{"title":"The impact of spiral tunnel characteristics on driver HRV and stress perception: A naturalistic driving experiment","authors":"Lei Han , Zhigang Du , Shoushuo Wang","doi":"10.1016/j.aap.2025.107983","DOIUrl":"10.1016/j.aap.2025.107983","url":null,"abstract":"<div><div>This study examined the impact of the combined configuration of spiral tunnel length and radius on drivers’ heart rate variability (HRV) and stress perception through a naturalistic driving experiment with 30 participants. Three spiral tunnels varying in both length and radius were evaluated, and the effects of uphill and downhill driving directions were also considered. The results revealed that the combined configuration of tunnel length and radius significantly influenced drivers’ physiological and psychological states. Specifically, longer tunnels with smaller radii were associated with increased average heart rate (HR), decreased standard deviation of normal-to-normal intervals (SDNN), elevated low frequency to high frequency ratio (LF/HF), and reduced sample entropy (SampEn), all indicating heightened stress responses. Uphill driving consistently led to higher average HR, lower SDNN, and higher LF/HF ratio compared to downhill driving, reflecting increased stress due to greater physical and mental demands. These findings offer invaluable insights for the design and management of spiral tunnels, with the ultimate goal of enhancing driver safety and comfort. By optimizing tunnel characteristics and implementing appropriate traffic management strategies, it is possible to create a more favorable driving environment that mitigates the negative impacts on drivers and promotes overall well-being.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107983"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.aap.2025.107982
Lihua Li , Chuang Zhou , Jiaping Huang , Zhizhen Liu , Jintao Xie , Zhe Tan
This paper is to study the effect of icy and snowy weather on the car-following (CF) safety of autonomous vehicle (AV). The influence of weather is abstracted as mathematical model parameters, and the CF model and risk decision equation of AV under icy and snowy weather are constructed. Comparing the influence of various climates on the CF of AV and the potential safety hazards, the CF parameters of AV in icy and snowy weather are designed based on Intelligent Driver Model (IDM). The road friction coefficient is matched by the maximum acceleration and comfortable deceleration of the vehicle, and the perception error coefficient is identified by space headway and speed of the vehicle. The Waymo dataset is used as the basic data source, and the safe value interval of icy and snowy parameters is calculated by combining the CF equation and the dataset characteristics. The rationality and stability of the parameters are verified by the root mean square error (RMSE) method and the Wilson model. Using the SUMO platform, single and multiple factors scenes are designed for simulation experiments, and a safety field strength model is constructed to carry out CF risk assessment. It is found that the severity of icy and snowy weather significantly affects the road friction and perception error coefficient, and has strong safety disturbance to the driving speed and real-time headway of AV. The accelerated and decelerated CF will cause oscillation and change of autonomous driving traffic flow, and the fluctuation range and risk degree of the queue caused by decelerated CF is more pronounced than that caused by accelerated CF. The safety effects of CF vary with different icy and snowy coefficients, and the vehicle speed perception error is more likely to induce safety risks. This study further enriches CF methods in special scenes, providing the theoretical basis for AV winter travel.
{"title":"Car-following safety modeling and risk assessment of autonomous vehicle in icy and snowy weather","authors":"Lihua Li , Chuang Zhou , Jiaping Huang , Zhizhen Liu , Jintao Xie , Zhe Tan","doi":"10.1016/j.aap.2025.107982","DOIUrl":"10.1016/j.aap.2025.107982","url":null,"abstract":"<div><div>This paper is to study the effect of icy and snowy weather on the car-following (CF) safety of autonomous vehicle (AV). The influence of weather is abstracted as mathematical model parameters, and the CF model and risk decision equation of AV under icy and snowy weather are constructed. Comparing the influence of various climates on the CF of AV and the potential safety hazards, the CF parameters of AV in icy and snowy weather are designed based on Intelligent Driver Model (IDM). The road friction coefficient is matched by the maximum acceleration and comfortable deceleration of the vehicle, and the perception error coefficient is identified by space headway and speed of the vehicle. The Waymo dataset is used as the basic data source, and the safe value interval of icy and snowy parameters is calculated by combining the CF equation and the dataset characteristics. The rationality and stability of the parameters are verified by the root mean square error (RMSE) method and the Wilson model. Using the SUMO platform, single and multiple factors scenes are designed for simulation experiments, and a safety field strength model is constructed to carry out CF risk assessment. It is found that the severity of icy and snowy weather significantly affects the road friction and perception error coefficient, and has strong safety disturbance to the driving speed and real-time headway of AV. The accelerated and decelerated CF will cause oscillation and change of autonomous driving traffic flow, and the fluctuation range and risk degree of the queue caused by decelerated CF is more pronounced than that caused by accelerated CF. The safety effects of CF vary with different icy and snowy coefficients, and the vehicle speed perception error is more likely to induce safety risks. This study further enriches CF methods in special scenes, providing the theoretical basis for AV winter travel.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107982"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1016/j.aap.2025.107968
Ahmed Sajid Hasan , Md.Arifuzzaman Nayeem , Deep Patel, Omar Al-Sheikh, Mohammad Jalayer
Seat belt non-compliance remains a critical global issue, significantly increasing the risk of severe injuries and fatalities in traffic collisions. Despite widespread awareness campaigns and safety advancements, a substantial number of drivers and passengers continue to travel unrestrained. Addressing this issue requires a comprehensive understanding of the seat belt compliance trend across the globe, the factors influencing seat belt use, the ongoing practices to collect and analyze seat belt compliance data, and effective strategies for improving compliance. This study seeks to synthesize existing research on global seat belt compliance behavior by examining contributing factors, advanced data collection methods, analytical techniques, and safety countermeasures. The goal is to identify research gaps and propose strategies to improve compliance and enhance road safety. 75 studies and relevant technical reports published between 2001 and 2023, sourced from databases such as Google Scholar, TRID, ScienceDirect, PubMed, Scopus, and Web of Science, were reviewed using a robust search and selection process. The review highlights that seat belt use is influenced by driver demographics, roadway design, trip features, and temporal factors. It highlights the methods used to collect and analyze seat belt use data, including observational surveys, roadside and in-vehicle cameras, and advanced machine learning techniques such as Convolutional Neural Networks. The analysis also emphasizes the effectiveness of the “3 E’s” approach—engineering, education, and enforcement. The findings demonstrated that the compliance rate across geographic regions varies because of the robustness of the 3E policy. This study identifies gaps in current research and offers actionable strategies to improve seat belt compliance through innovative data collection, analysis, and targeted interventions aimed at enhancing global traffic safety.
{"title":"Seat belt compliance behavior of drivers and passengers: A review of data collection, analysis, contributing factors and safety countermeasures","authors":"Ahmed Sajid Hasan , Md.Arifuzzaman Nayeem , Deep Patel, Omar Al-Sheikh, Mohammad Jalayer","doi":"10.1016/j.aap.2025.107968","DOIUrl":"10.1016/j.aap.2025.107968","url":null,"abstract":"<div><div>Seat belt non-compliance remains a critical global issue, significantly increasing the risk of severe injuries and fatalities in traffic collisions. Despite widespread awareness campaigns and safety advancements, a substantial number of drivers and passengers continue to travel unrestrained. Addressing this issue requires a comprehensive understanding of the seat belt compliance trend across the globe, the factors influencing seat belt use, the ongoing practices to collect and analyze seat belt compliance data, and effective strategies for improving compliance. This study seeks to synthesize existing research on global seat belt compliance behavior by examining contributing factors, advanced data collection methods, analytical techniques, and safety countermeasures. The goal is to identify research gaps and propose strategies to improve compliance and enhance road safety. 75 studies and relevant technical reports published between 2001 and 2023, sourced from databases such as Google Scholar, TRID, ScienceDirect, PubMed, Scopus, and Web of Science, were reviewed using a robust search and selection process. The review highlights that seat belt use is influenced by driver demographics, roadway design, trip features, and temporal factors. It highlights the methods used to collect and analyze seat belt use data, including observational surveys, roadside and in-vehicle cameras, and advanced machine learning techniques such as Convolutional Neural Networks. The analysis also emphasizes the effectiveness of the “3 E’s” approach—engineering, education, and enforcement. The findings demonstrated that the compliance rate across geographic regions varies because of the robustness of the 3E policy. This study identifies gaps in current research and offers actionable strategies to improve seat belt compliance through innovative data collection, analysis, and targeted interventions aimed at enhancing global traffic safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107968"},"PeriodicalIF":5.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-22DOI: 10.1016/j.aap.2025.107971
Xiaoqi Zhai , N.N. Sze , Jaeyoung Jay Lee , Pengpeng Xu , Helai Huang
Traffic safety has increasingly become an important concern in developing long-term transportation planning strategies. Since transportation planning steps always involve some kinds of geographic entity, predicting crashes for those entities is not only a mere avenue of analytic methods in safety research, but also influential to practical application in road infrastructure design and management. However, the analyses using different spatial units are subjected to the modifiable areal unit problem (MAUP), which refers to the issue of inconsistent statistical results when dealing with geographic data of different aggregation configurations. Especially, a high-level of spatial aggregation of data could bring about the loss of detailed spatial information, also known as the scale effect. In this study, we propose Bayesian multi-scale models that are capable of accounting for the scale effect due to the high-level spatial aggregation of traffic and crash data. The performances of proposed models were assessed, as compared to the conventional (independent) model, using the crash data of two geographical scales, i.e. block groups (lower level) and census tracts (higher level) in Hillsborough County of Florida. The results indicate that the proposed multi-scale models could address the scale effects and enhance the model performance at the highly aggregated spatial units such as census tracts. This study sheds light on exploring the nature of scale effect in the macroscopic crash analysis.
{"title":"Multi-scale approaches to cope with scale effect issues in macroscopic crash analysis","authors":"Xiaoqi Zhai , N.N. Sze , Jaeyoung Jay Lee , Pengpeng Xu , Helai Huang","doi":"10.1016/j.aap.2025.107971","DOIUrl":"10.1016/j.aap.2025.107971","url":null,"abstract":"<div><div>Traffic safety has increasingly become an important concern in developing long-term transportation planning strategies. Since transportation planning steps always involve some kinds of geographic entity, predicting crashes for those entities is not only a mere avenue of analytic methods in safety research, but also influential to practical application in road infrastructure design and management. However, the analyses using different spatial units are subjected to the modifiable areal unit problem (MAUP), which refers to the issue of inconsistent statistical results when dealing with geographic data of different aggregation configurations. Especially, a high-level of spatial aggregation of data could bring about the loss of detailed spatial information, also known as the scale effect. In this study, we propose Bayesian multi-scale models that are capable of accounting for the scale effect due to the high-level spatial aggregation of traffic and crash data. The performances of proposed models were assessed, as compared to the conventional (independent) model, using the crash data of two geographical scales, i.e. block groups (lower level) and census tracts (higher level) in Hillsborough County of Florida. The results indicate that the proposed multi-scale models could address the scale effects and enhance the model performance at the highly aggregated spatial units such as census tracts. This study sheds light on exploring the nature of scale effect in the macroscopic crash analysis.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107971"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21DOI: 10.1016/j.aap.2025.107972
Yunjie Ju, Feng Chen, Xiaonan Li, Hailin Shi
The in-vehicle HMI systems regulate driving behavior by providing advisory or warning information to the driver, contributing to improved safety, reduced fuel consumption, and lower emissions. Although the issue of driver performance changes caused by HMI systems has received substantial recent attention, the implications on drivers’ workload has not received enough attention. Additionally, most previous studies provided classic visual, auditory or concurrent visual-audio feedback information but failed to determine whether the additional information resulted in workload overload, and lacked the quantitative analysis of response performance in various conflict environments. Toward to this end, this paper conducted a driving simulator experiment to examine the response performance and workload differences in the unsignalized intersection-approach process of drivers with various HMI system and conflict conditions. More precisely, an effect analysis on the drivers’ workload (response time of the DRT, DRT accuracy, and the NASA-TLX) was conducted, the Weibull AFT model with gamma heterogeneity and rANOVA method were applied. The Weibull AFT model estimation revealed the mixed effects of HMI system conditions in the drivers’ response time. In conflict situations, drivers with comprehensive visual-audio information responded earlier to DRT and lower workload. In addition, the variables for personal characteristics, safe driving history, and experience and willingness to use HMI system significantly influenced the response time of driver, among which female driver group performed longer response time. The results of DRT accuracy and NASA-TLX, drivers with comprehensive visual-audio information have excellent situation awareness when approaching and passing the unsignalized intersections; drivers believed they can accomplish performance level with less effort than others; they felt less time pressure and the driving pace was pace slow and relatively leisurely; there were fewer negative emotions such as insecure, discouraged, irritated, stressed, and annoyed. Thus, the key is whether a comprehensive situation awareness can be established for the driver, rather than purely reducing or adding additional information. The findings of this paper provide a theoretical basis for the human–machine interaction interface design and development of in-vehicle decision-making assistance systems for unsignalized intersections.
{"title":"Does visual-audio feedback impair response performance and increase workload? using detection response task and NASA-TLX to examine the effect of the HMI information on driver performance at unsignalized intersections","authors":"Yunjie Ju, Feng Chen, Xiaonan Li, Hailin Shi","doi":"10.1016/j.aap.2025.107972","DOIUrl":"10.1016/j.aap.2025.107972","url":null,"abstract":"<div><div>The in-vehicle HMI systems regulate driving behavior by providing advisory or warning information to the driver, contributing to improved safety, reduced fuel consumption, and lower emissions. Although the issue of driver performance changes caused by HMI systems has received substantial recent attention, the implications on drivers’ workload has not received enough attention. Additionally, most previous studies provided classic visual, auditory or concurrent visual-audio feedback information but failed to determine whether the additional information resulted in workload overload, and lacked the quantitative analysis of response performance in various conflict environments. Toward to this end, this paper conducted a driving simulator experiment to examine the response performance and workload differences in the unsignalized intersection-approach process of drivers with various HMI system and conflict conditions. More precisely, an effect analysis on the drivers’ workload (response time of the DRT, DRT accuracy, and the NASA-TLX) was conducted, the Weibull AFT model with gamma heterogeneity and rANOVA method were applied. The Weibull AFT model estimation revealed the mixed effects of HMI system conditions in the drivers’ response time. In conflict situations, drivers with comprehensive visual-audio information responded earlier to DRT and lower workload. In addition, the variables for personal characteristics, safe driving history, and experience and willingness to use HMI system significantly influenced the response time of driver, among which female driver group performed longer response time. The results of DRT accuracy and NASA-TLX, drivers with comprehensive visual-audio information have excellent situation awareness when approaching and passing the unsignalized intersections; drivers believed they can accomplish performance level with less effort than others; they felt less time pressure and the driving pace was pace slow and relatively leisurely; there were fewer negative emotions such as insecure, discouraged, irritated, stressed, and annoyed. Thus, the key is whether a comprehensive situation awareness can be established for the driver, rather than purely reducing or adding additional information. The findings of this paper provide a theoretical basis for the human–machine interaction interface design and development of in-vehicle decision-making assistance systems for unsignalized intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107972"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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