Pub Date : 2025-12-27DOI: 10.1016/j.trf.2025.103497
Frank Westerhuis, Bastiaan Sporrel, Arjan Stuiver, Dick de Waard
The number of accidents with bicycles has increased over the past years, and a relationship with infrastructure and rider characteristics has been demonstrated. In particular, older cyclists are relatively often reported to be involved in accidents. Therefore, cycling behaviour of 21 younger (average age 21, range 18–24) and 22 older cyclists (average age 69, range 60–80 years) riding conventional bicycles was compared on three types of cycling infrastructure: a cycle lane, a bi- and unidirectional cycle path. These tests were performed on the road. Additionally, performance on neuropsychological tests and the relation with on-road performance was assessed to study whether performance on these tests correlates with actual cycling behaviour and whether test performance could possibly be used to predict behaviour. While older participants cycled at a significantly lower speed, cycling behaviour in terms of lane control did not differ between younger and older participants. The cycling infrastructure had a clear effect on performance and there is a relation with space: more space coincides with lower speed and more distance to the kerb. Effects of external events on lane position were also found, the presence of a person on the adjacent pavement or being overtaken by another cyclist both affect lateral position. Apart from a link between cycling speed and performance on the Trail Making Test, there were no relations found between scores on neuropsychological tests and cycling performance. Neuropsychological tests should therefore not be taken as sole predictors of cycling performance. A final observation was that older participants in this type of on-road study are likely to be more fit than the average older cyclist. This should be taken into account when generalising results.
{"title":"Cycling behaviour of older and younger adults: Differences in performance and the relation with infrastructure and neuropsychological test performance","authors":"Frank Westerhuis, Bastiaan Sporrel, Arjan Stuiver, Dick de Waard","doi":"10.1016/j.trf.2025.103497","DOIUrl":"10.1016/j.trf.2025.103497","url":null,"abstract":"<div><div>The number of accidents with bicycles has increased over the past years, and a relationship with infrastructure and rider characteristics has been demonstrated. In particular, older cyclists are relatively often reported to be involved in accidents. Therefore, cycling behaviour of 21 younger (average age 21, range 18–24) and 22 older cyclists (average age 69, range 60–80 years) riding conventional bicycles was compared on three types of cycling infrastructure: a cycle lane, a bi- and unidirectional cycle path. These tests were performed on the road. Additionally, performance on neuropsychological tests and the relation with on-road performance was assessed to study whether performance on these tests correlates with actual cycling behaviour and whether test performance could possibly be used to predict behaviour. While older participants cycled at a significantly lower speed, cycling behaviour in terms of lane control did not differ between younger and older participants. The cycling infrastructure had a clear effect on performance and there is a relation with space: more space coincides with lower speed and more distance to the kerb. Effects of external events on lane position were also found, the presence of a person on the adjacent pavement or being overtaken by another cyclist both affect lateral position. Apart from a link between cycling speed and performance on the Trail Making Test, there were no relations found between scores on neuropsychological tests and cycling performance. Neuropsychological tests should therefore not be taken as sole predictors of cycling performance. A final observation was that older participants in this type of on-road study are likely to be more fit than the average older cyclist. This should be taken into account when generalising results.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103497"},"PeriodicalIF":4.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-27DOI: 10.1016/j.trf.2025.103504
Kongjin Zhu, Yiting Qian, Ning Guo
Sufficient charging infrastructures are essential for the widespread adoption of electric vehicles (EVs). To improve availability and accessibility, many governments have advanced policy initiatives to implement EV charging network expansion and infrastructure upgrades. On-street charging, which provides flexible recharging services for EVs via deploying charging infrastructures on roadside, are emerging in many countries and regions. However, the public behavioral intention to accept and adopt this new charging pattern remains unclear. Understanding consumer acceptance is essential to facilitate its widespread adoption, as it ultimately determines the success of on-street charging whenever it becomes widely available. This study, therefore, introduces an extended unified theory of acceptance and use of technology (UTAUT) model that incorporates two additional latent factors, perceived risk and government support. A field survey was conducted to gather data from 478 valid respondents. The regression analysis method and moderation analysis are employed to validate the model, identify the key determinants, and investigate the impacts of moderating variables such as gender, age, education, monthly income, driving experience, EV ownership and on-street parking frequency. The results indicate that performance expectancy and government support are the key determinants of the public acceptance of on-street charging. Furthermore, social influence has a positive impact on acceptance, while perceived risk has a negative impact on acceptance, and effort expectancy has little effect on acceptance. Gender, age, education, driving experience and EV ownership serve as significant moderating variables. The findings suggest recommendations for policymakers and providers in devising effective adoption strategies. It highlights the importance of substantial government support through not only efficient policy instruments but also infrastructure investment to foster on-street charging adoption.
{"title":"Public acceptance of on-street charging for electric vehicles in China: An extension of the UTAUT model","authors":"Kongjin Zhu, Yiting Qian, Ning Guo","doi":"10.1016/j.trf.2025.103504","DOIUrl":"10.1016/j.trf.2025.103504","url":null,"abstract":"<div><div>Sufficient charging infrastructures are essential for the widespread adoption of electric vehicles (EVs). To improve availability and accessibility, many governments have advanced policy initiatives to implement EV charging network expansion and infrastructure upgrades. On-street charging, which provides flexible recharging services for EVs via deploying charging infrastructures on roadside, are emerging in many countries and regions. However, the public behavioral intention to accept and adopt this new charging pattern remains unclear. Understanding consumer acceptance is essential to facilitate its widespread adoption, as it ultimately determines the success of on-street charging whenever it becomes widely available. This study, therefore, introduces an extended unified theory of acceptance and use of technology (UTAUT) model that incorporates two additional latent factors, perceived risk and government support. A field survey was conducted to gather data from 478 valid respondents. The regression analysis method and moderation analysis are employed to validate the model, identify the key determinants, and investigate the impacts of moderating variables such as gender, age, education, monthly income, driving experience, EV ownership and on-street parking frequency. The results indicate that performance expectancy and government support are the key determinants of the public acceptance of on-street charging. Furthermore, social influence has a positive impact on acceptance, while perceived risk has a negative impact on acceptance, and effort expectancy has little effect on acceptance. Gender, age, education, driving experience and EV ownership serve as significant moderating variables. The findings suggest recommendations for policymakers and providers in devising effective adoption strategies. It highlights the importance of substantial government support through not only efficient policy instruments but also infrastructure investment to foster on-street charging adoption.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103504"},"PeriodicalIF":4.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-27DOI: 10.1016/j.trf.2025.103494
Xiaohua Zhao , Yuejia Wang , Sen Luan , Yibo Dai , Tingquan He
The static risk of the bridge foundation and road structure, together with the dynamic risk of the traffic operation state and external environment, aggravates the risk on the bridge. The superposition of dynamic and static risk factors is the main factor of bridge risk in the process of a vehicle driving from an ordinary highway section to a foggy bridge section. However, the transformation of driving behavior characteristics in this process is unclear. This study aims to elucidate the changes in driving behavior characteristics and the effects of scene and driver attributes on driving behavior in the process of dynamic and static risk superposition. On the basis of the East Hubei Yangtze River Bridge case study, three driving scenarios (ordinary, bridge, and foggy bridge sections) were established, within which car-following events were designed. On the basis of a driving simulation system platform, thirty-eight participants were recruited to conduct driving simulation experiments and to obtain drivers' behavior data on the three road sections. A total of thirteen microscopic parameters, including speed, acceleration, car-following distance, and Wiedemann 99 model parameters, were extracted to compare and analyze the driving behavior characteristics of the three sections. The Wiedemann 99 car-following model parameters were taken as the dependent variables, while road sections and individual driver attributes (age, gender, and driving experience) were taken as the independent variables; thus, a generalized mixed effect model was constructed. The results show that the individual speed difference of the drivers in the bridge section is greater. The time headway (CC1) and standstill distance (CC0) significantly increased, indicating that the driver was more cautious in the bridge environment after the addition of static risk. On the basis of the bridge section, the dynamic risk factors of the external environment on foggy days are added. The results show that individual differences in drivers' following distance are greater in foggy bridge sections, CC0 is significantly reduced, and the oscillatory acceleration magnitude (CC7) is significantly increased. These findings indicate that the drivers exhibit poor vehicle control stability in environments of superimposed dynamic and static risks. On the basis of the results of the generalized mixed effect model, road scene factors significantly affect time headway (CC1), following distance variation (CC2), deceleration onset threshold (CC3), and oscillatory acceleration magnitude (CC7). The interaction effect of gender and age has a significant influence on CC3. According to the CC2 parameter, the fog bridge scenario has the greatest influence. The results have important theoretical and practical guiding significance for formulating safety prevention and control strategies for bridge sections on foggy days.
{"title":"Analysis of car-following behavior preferences and influencing factors on foggy bridge under the superimposed dynamic and static risks","authors":"Xiaohua Zhao , Yuejia Wang , Sen Luan , Yibo Dai , Tingquan He","doi":"10.1016/j.trf.2025.103494","DOIUrl":"10.1016/j.trf.2025.103494","url":null,"abstract":"<div><div>The static risk of the bridge foundation and road structure, together with the dynamic risk of the traffic operation state and external environment, aggravates the risk on the bridge. The superposition of dynamic and static risk factors is the main factor of bridge risk in the process of a vehicle driving from an ordinary highway section to a foggy bridge section. However, the transformation of driving behavior characteristics in this process is unclear. This study aims to elucidate the changes in driving behavior characteristics and the effects of scene and driver attributes on driving behavior in the process of dynamic and static risk superposition. On the basis of the East Hubei Yangtze River Bridge case study, three driving scenarios (ordinary, bridge, and foggy bridge sections) were established, within which car-following events were designed. On the basis of a driving simulation system platform, thirty-eight participants were recruited to conduct driving simulation experiments and to obtain drivers' behavior data on the three road sections. A total of thirteen microscopic parameters, including speed, acceleration, car-following distance, and Wiedemann 99 model parameters, were extracted to compare and analyze the driving behavior characteristics of the three sections. The Wiedemann 99 car-following model parameters were taken as the dependent variables, while road sections and individual driver attributes (age, gender, and driving experience) were taken as the independent variables; thus, a generalized mixed effect model was constructed. The results show that the individual speed difference of the drivers in the bridge section is greater. The time headway (CC1) and standstill distance (CC0) significantly increased, indicating that the driver was more cautious in the bridge environment after the addition of static risk. On the basis of the bridge section, the dynamic risk factors of the external environment on foggy days are added. The results show that individual differences in drivers' following distance are greater in foggy bridge sections, CC0 is significantly reduced, and the oscillatory acceleration magnitude (CC7) is significantly increased. These findings indicate that the drivers exhibit poor vehicle control stability in environments of superimposed dynamic and static risks. On the basis of the results of the generalized mixed effect model, road scene factors significantly affect time headway (CC1), following distance variation (CC2), deceleration onset threshold (CC3), and oscillatory acceleration magnitude (CC7). The interaction effect of gender and age has a significant influence on CC3. According to the CC2 parameter, the fog bridge scenario has the greatest influence. The results have important theoretical and practical guiding significance for formulating safety prevention and control strategies for bridge sections on foggy days.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103494"},"PeriodicalIF":4.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-27DOI: 10.1016/j.trf.2025.103492
Nguyen Thi Nhu , Hiroaki Nishiuchi , Nguyen Thi Hong Mai , Tu Sy Sua , An Minh Ngoc , Doanh Nguyen-Ngoc
Freight transport is a major contributor to greenhouse gas emissions, and transitioning to electric trucks is crucial for achieving sustainable logistics, particularly in developing countries. However, the fragmentation of the truck market, especially the dominance of owner-operators who own a small number of trucks or even a single truck, presents unique challenges for electric truck adoption. This study investigates the acceptance and adoption behavior of electric trucks among truck owner-operators in Vietnam, utilizing an extended Technology Acceptance Model (TAM). A structured questionnaire was distributed to 400 owner-operators in Hanoi, resulting in 219 valid responses. Structural Equation Modeling (SEM) was employed to examine the effects of psychological factors on adoption intentions. The findings reveal that Perceived Risk has the most substantial negative influence, while Financial Policies, Attitude, and Knowledge positively affect intention to adopt electric trucks. Notably, the extended TAM, which includes Perceived Risk, Knowledge, and Financial Incentives, explains 80.22 % of the variance in adoption intention, an improvement over the original model. The study contributes new insights into electric truck adoption in nascent markets, highlighting that owner-operators are pragmatic decision-makers influenced not only by environmental benefits but also by financial and operational factors. These findings provide actionable implications for policymakers and manufacturers seeking to accelerate the adoption of electric trucks through tailored incentives and infrastructure improvements.
{"title":"Towards the adoption of green freight transport practices in developing countries: Analysis of owner-operators' behavior on electric trucks","authors":"Nguyen Thi Nhu , Hiroaki Nishiuchi , Nguyen Thi Hong Mai , Tu Sy Sua , An Minh Ngoc , Doanh Nguyen-Ngoc","doi":"10.1016/j.trf.2025.103492","DOIUrl":"10.1016/j.trf.2025.103492","url":null,"abstract":"<div><div>Freight transport is a major contributor to greenhouse gas emissions, and transitioning to electric trucks is crucial for achieving sustainable logistics, particularly in developing countries. However, the fragmentation of the truck market, especially the dominance of owner-operators who own a small number of trucks or even a single truck, presents unique challenges for electric truck adoption. This study investigates the acceptance and adoption behavior of electric trucks among truck owner-operators in Vietnam, utilizing an extended Technology Acceptance Model (TAM). A structured questionnaire was distributed to 400 owner-operators in Hanoi, resulting in 219 valid responses. Structural Equation Modeling (SEM) was employed to examine the effects of psychological factors on adoption intentions. The findings reveal that Perceived Risk has the most substantial negative influence, while Financial Policies, Attitude, and Knowledge positively affect intention to adopt electric trucks. Notably, the extended TAM, which includes Perceived Risk, Knowledge, and Financial Incentives, explains 80.22 % of the variance in adoption intention, an improvement over the original model. The study contributes new insights into electric truck adoption in nascent markets, highlighting that owner-operators are pragmatic decision-makers influenced not only by environmental benefits but also by financial and operational factors. These findings provide actionable implications for policymakers and manufacturers seeking to accelerate the adoption of electric trucks through tailored incentives and infrastructure improvements.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103492"},"PeriodicalIF":4.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-27DOI: 10.1016/j.trf.2025.103499
Byungju Kim , Yanbin Wu , Ken Kihara , Yuji Takeda , Takatsune Kumada
Age-related changes in driving performance have long been a critical issue in traffic safety. However, it remains unclear whether such differences occur during curve negotiation, particularly in the approach phase preceding curve entry. This study examined age-related changes that emerge in this context and investigated whether driver assistance systems can mitigate these differences. Thirty-six participants (mean age = 44.3 years, SD = 17.1) completed a 30-min simulated drive under three conditions: Manual, Lane Centering (LC), and Adaptive Cruise Control (ACC). In the Manual condition, participants maintained a speed of 60 km/h and kept the vehicle centered in the lane. In the LC condition, lateral support was provided, ACC controlled speed, and participants managed the remaining operations. Results showed that, under the Manual condition, increasing age was associated with initiating curve preparation closer to the entry point and with greater variability and magnitude of steering. Under ACC, curve preparation began earlier, as indicated by an earlier Start Point of Steering Wheel Angle (SPSWA), although age-related steering patterns remained unchanged. No significant age-related differences were found in accelerator pedal use under the Manual condition, and neither performance differences nor age-related patterns were observed in the LC condition. These findings indicate that age-related changes are evident in steering. ACC promotes earlier curve entry preparation but does not compensate for age-related steering differences. LC showed limited effectiveness across age groups. Trial-level analyses confirmed that automation did not differentially affect age-related patterns in driver control behavior. Overall, the findings underscore the need for driver assistance systems that more effectively support age-related changes in lateral control during curve negotiation.
{"title":"Age-related changes in driving performance under driver assistance systems during curve negotiation","authors":"Byungju Kim , Yanbin Wu , Ken Kihara , Yuji Takeda , Takatsune Kumada","doi":"10.1016/j.trf.2025.103499","DOIUrl":"10.1016/j.trf.2025.103499","url":null,"abstract":"<div><div>Age-related changes in driving performance have long been a critical issue in traffic safety. However, it remains unclear whether such differences occur during curve negotiation, particularly in the approach phase preceding curve entry. This study examined age-related changes that emerge in this context and investigated whether driver assistance systems can mitigate these differences. Thirty-six participants (mean age = 44.3 years, SD = 17.1) completed a 30-min simulated drive under three conditions: Manual, Lane Centering (LC), and Adaptive Cruise Control (ACC). In the Manual condition, participants maintained a speed of 60 km/h and kept the vehicle centered in the lane. In the LC condition, lateral support was provided, ACC controlled speed, and participants managed the remaining operations. Results showed that, under the Manual condition, increasing age was associated with initiating curve preparation closer to the entry point and with greater variability and magnitude of steering. Under ACC, curve preparation began earlier, as indicated by an earlier Start Point of Steering Wheel Angle (SPSWA), although age-related steering patterns remained unchanged. No significant age-related differences were found in accelerator pedal use under the Manual condition, and neither performance differences nor age-related patterns were observed in the LC condition. These findings indicate that age-related changes are evident in steering. ACC promotes earlier curve entry preparation but does not compensate for age-related steering differences. LC showed limited effectiveness across age groups. Trial-level analyses confirmed that automation did not differentially affect age-related patterns in driver control behavior. Overall, the findings underscore the need for driver assistance systems that more effectively support age-related changes in lateral control during curve negotiation.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103499"},"PeriodicalIF":4.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-27DOI: 10.1016/j.trf.2025.103488
Amira Hammami, Attila Borsos
The introduction of autonomous vehicles (AVs) in urban environments where cycling activity is present has raised the need to investigate potential modifications to urban roads, not only from the perspective of AVs but also from the perspective of cyclists. This study aims to investigate the effect of different road design characteristics and varying traffic penetration rates of AVs, using a bicycle simulator study. 50 participants assessed their perceived level of safety, comfort and stress in 11 randomized scenarios. The scenarios involve a design with sharrows and four designs with separated cycling lanes (with two different cycling lane width and two pavement painting options), with 3 AV traffic penetration rates (TPR): 0 %, 50 %, and 100 %. A series of cumulative link mixed models (CLMM) was estimated to analyze the impact of design characteristics and TPRs on cyclist perceptions. The results revealed that the implementation of AVs in shared road scenarios did not improve cyclist safety. On the contrary, it has significantly reduced the perceived level of comfort and has significantly increased the perceived level of stress. However, in separated cycling lane designs, the presence of AVs was found to positively affect cyclist perceptions, although this impact was not significant. Furthermore, the study revealed that the most important factor that affects perceptions of safety, comfort, and stress is the separation between traffic and cycling lanes.
{"title":"Optimizing urban road cross-section’s design to accommodate safe autonomous vehicle-cyclist interactions: A bicycle simulator study","authors":"Amira Hammami, Attila Borsos","doi":"10.1016/j.trf.2025.103488","DOIUrl":"10.1016/j.trf.2025.103488","url":null,"abstract":"<div><div>The introduction of autonomous vehicles (AVs) in urban environments where cycling activity is present has raised the need to investigate potential modifications to urban roads, not only from the perspective of AVs but also from the perspective of cyclists. This study aims to investigate the effect of different road design characteristics and varying traffic penetration rates of AVs, using a bicycle simulator study. 50 participants assessed their perceived level of safety, comfort and stress in 11 randomized scenarios. The scenarios involve a design with sharrows and four designs with separated cycling lanes (with two different cycling lane width and two pavement painting options), with 3 AV traffic penetration rates (TPR): 0 %, 50 %, and 100 %. A series of cumulative link mixed models (CLMM) was estimated to analyze the impact of design characteristics and TPRs on cyclist perceptions. The results revealed that the implementation of AVs in shared road scenarios did not improve cyclist safety. On the contrary, it has significantly reduced the perceived level of comfort and has significantly increased the perceived level of stress. However, in separated cycling lane designs, the presence of AVs was found to positively affect cyclist perceptions, although this impact was not significant. Furthermore, the study revealed that the most important factor that affects perceptions of safety, comfort, and stress is the separation between traffic and cycling lanes.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103488"},"PeriodicalIF":4.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-26DOI: 10.1016/j.trf.2025.103503
Duc Trung Luu , Tiju Baby , Jieun Lee , Tatsuru Daimon , Seul Chan Lee
Crossing strategy refers to pedestrian objectives in choosing crossing patterns and their pace. To the best of our knowledge, there remains a deficiency in literature regarding pedestrian crossing strategies when interacting with autonomous vehicles (AVs). This study investigated the effect of various physical road infrastructures on pedestrian crossing strategies while interacting with multiple AVs. The effects of three infrastructures in a typical local street on the crossing strategies of pedestrians were identified using a structural equation model based on a stimulus–organism–response (SOR) framework. The stimulus included sidewalk, crosswalk, and/or legal on-street parking designed virtually in a four-lane local intersection, where frequent daily pedestrian–AV interactions occurred in a mixed neighborhood. The organism dimension of pedestrians was measured in terms of situation awareness (SA) and perceived risk (PR) when interacting with multiple AVs. Pedestrian crossing strategies, including their intended crossing patterns and speeds, were identified in the response dimension. Based on experimental data from 82 university students, the findings revealed that sidewalk and legal on-street parking significantly affected SA with coefficients of 0.155 and − 0.079, respectively, whereas the crosswalk had a remarkable association with PR by a coefficient of −0.098. In addition, there was a trade-off relationship between pedestrian patterns and speed (coefficient of −0.197) when interacting with multiple AVs. Our research on pedestrians interacting with multiple AVs provides novel insights to enhance the understanding of pedestrian crossing strategies and establish a comparison with the realities of crowded local roadways. These insights expand our knowledge of actual pedestrian crossing behaviors in the AV environment and support the production of safer street design guidelines.
{"title":"Pedestrian crossing strategy while interacting with multiple autonomous vehicles in local street intersections: Effects of road infrastructure","authors":"Duc Trung Luu , Tiju Baby , Jieun Lee , Tatsuru Daimon , Seul Chan Lee","doi":"10.1016/j.trf.2025.103503","DOIUrl":"10.1016/j.trf.2025.103503","url":null,"abstract":"<div><div>Crossing strategy refers to pedestrian objectives in choosing crossing patterns and their pace. To the best of our knowledge, there remains a deficiency in literature regarding pedestrian crossing strategies when interacting with autonomous vehicles (AVs). This study investigated the effect of various physical road infrastructures on pedestrian crossing strategies while interacting with multiple AVs. The effects of three infrastructures in a typical local street on the crossing strategies of pedestrians were identified using a structural equation model based on a stimulus–organism–response (SOR) framework. The stimulus included sidewalk, crosswalk, and/or legal on-street parking designed virtually in a four-lane local intersection, where frequent daily pedestrian–AV interactions occurred in a mixed neighborhood. The organism dimension of pedestrians was measured in terms of situation awareness (SA) and perceived risk (PR) when interacting with multiple AVs. Pedestrian crossing strategies, including their intended crossing patterns and speeds, were identified in the response dimension. Based on experimental data from 82 university students, the findings revealed that sidewalk and legal on-street parking significantly affected SA with coefficients of 0.155 and − 0.079, respectively, whereas the crosswalk had a remarkable association with PR by a coefficient of −0.098. In addition, there was a trade-off relationship between pedestrian patterns and speed (coefficient of −0.197) when interacting with multiple AVs. Our research on pedestrians interacting with multiple AVs provides novel insights to enhance the understanding of pedestrian crossing strategies and establish a comparison with the realities of crowded local roadways. These insights expand our knowledge of actual pedestrian crossing behaviors in the AV environment and support the production of safer street design guidelines.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103503"},"PeriodicalIF":4.4,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wrong-way riding (WWR) among e-bike riders, as a typical traffic violation, has become a significant contributor to road crashes. However, most prior studies have focused separately on sociodemographic and environmental factors of WWR among e-bike riders, leaving the psychosocial factors and cognitive decision-making processes largely underexplored. To fill this gap, this study adopted the theory of planned behavior (TPB), the prototype willingness model (PWM), and the integrated model of the TPB and the PWM to investigate WWR behavior among e-bike riders. Structural equation modeling (SEM) was employed to compare the explanatory power of the three models and to identify the key psychological predictors and cognitive decision-making pathways associated with WWR behavior. Data were collected via an online survey of 709 e-bike users in China. The results showed that the TPB, the PWM, and the integrated model all effectively explained WWR behavior among e-bike riders, with the integrated model demonstrating the strongest explanatory power. Both behavioral intention and behavioral willingness significantly influenced WWR behavior, with behavioral willingness exerting a stronger effect. These findings suggest that WWR behavior is predominantly driven by social reactive decision-making rather than reasoned decision-making. Descriptive norms were the strongest predictor of both behavioral intention and behavioral willingness. Attitudes, perceived behavioral control, and prototype perception were also significant predictors of WWR behavior among e-bike riders. Finally, based on the findings of this study, specific intervention strategies were proposed to reduce the incidence of WWR among e-bike riders, aiming to enhance e-bike traffic safety.
{"title":"Wrong-way riding behavior among e-bike riders: The roles of willingness and intention","authors":"Hongjun Cui , Mingzheng Zhang , Minqing Zhu , Xiaotao Yuan","doi":"10.1016/j.trf.2025.103500","DOIUrl":"10.1016/j.trf.2025.103500","url":null,"abstract":"<div><div>Wrong-way riding (WWR) among e-bike riders, as a typical traffic violation, has become a significant contributor to road crashes. However, most prior studies have focused separately on sociodemographic and environmental factors of WWR among e-bike riders, leaving the psychosocial factors and cognitive decision-making processes largely underexplored. To fill this gap, this study adopted the theory of planned behavior (TPB), the prototype willingness model (PWM), and the integrated model of the TPB and the PWM to investigate WWR behavior among e-bike riders. Structural equation modeling (SEM) was employed to compare the explanatory power of the three models and to identify the key psychological predictors and cognitive decision-making pathways associated with WWR behavior. Data were collected via an online survey of 709 e-bike users in China. The results showed that the TPB, the PWM, and the integrated model all effectively explained WWR behavior among e-bike riders, with the integrated model demonstrating the strongest explanatory power. Both behavioral intention and behavioral willingness significantly influenced WWR behavior, with behavioral willingness exerting a stronger effect. These findings suggest that WWR behavior is predominantly driven by social reactive decision-making rather than reasoned decision-making. Descriptive norms were the strongest predictor of both behavioral intention and behavioral willingness. Attitudes, perceived behavioral control, and prototype perception were also significant predictors of WWR behavior among e-bike riders. Finally, based on the findings of this study, specific intervention strategies were proposed to reduce the incidence of WWR among e-bike riders, aiming to enhance e-bike traffic safety.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103500"},"PeriodicalIF":4.4,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-26DOI: 10.1016/j.trf.2025.103501
Emma Tivesten, Thomas Streubel, Mikael Ljung Aust
Advanced driver assistance systems that simultaneously perform lateral and longitudinal control reduce the need for driver input, potentially leading to driver disengagement. As these systems become more capable of performing most of the operational control, drivers tend to increase eyes off road, hands off wheel, and they may be unprepared to act in situations that exceed the system's capabilities.
In this study, we analyzed the behaviors of 54 participants using a level 2 system on a test track. Drivers were considered disengaged if they had a late or absent response to a conflict at the end of the drive, resulting in a crash or near-crash. Several behaviors were associated with increased risk of disengagement, including long off-path glances, frequent visual time-sharing, gaze concentration, lack of driver steering input, and hands off wheel during uneventful driving. In contrast, continuous engagement in steering appeared to promote driver engagement, even among participants who exhibited suboptimal gaze behavior.
These findings suggest that combining metrics of steering activity and gaze behavior provides a more comprehensive assessment of driver engagement. This insight can inform the design of driver monitoring and engagement strategies in level 2 driving systems.
{"title":"Eye, steering, and hands on wheel behaviors indicating driver engagement in level 2 driving","authors":"Emma Tivesten, Thomas Streubel, Mikael Ljung Aust","doi":"10.1016/j.trf.2025.103501","DOIUrl":"10.1016/j.trf.2025.103501","url":null,"abstract":"<div><div>Advanced driver assistance systems that simultaneously perform lateral and longitudinal control reduce the need for driver input, potentially leading to driver disengagement. As these systems become more capable of performing most of the operational control, drivers tend to increase eyes off road, hands off wheel, and they may be unprepared to act in situations that exceed the system's capabilities.</div><div>In this study, we analyzed the behaviors of 54 participants using a level 2 system on a test track. Drivers were considered disengaged if they had a late or absent response to a conflict at the end of the drive, resulting in a crash or near-crash. Several behaviors were associated with increased risk of disengagement, including long off-path glances, frequent visual time-sharing, gaze concentration, lack of driver steering input, and hands off wheel during uneventful driving. In contrast, continuous engagement in steering appeared to promote driver engagement, even among participants who exhibited suboptimal gaze behavior.</div><div>These findings suggest that combining metrics of steering activity and gaze behavior provides a more comprehensive assessment of driver engagement. This insight can inform the design of driver monitoring and engagement strategies in level 2 driving systems.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103501"},"PeriodicalIF":4.4,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-25DOI: 10.1016/j.trf.2025.103487
Kun Wang , Rensu Zhou , Shuo Yang , Cheng Wang , Jing Liu , Lu Wei , LiangXu
Pedestrians' crossing the street while running a red light are a significant factor contributing to traffic accidents at intersections. Traditional models fail to capture the complex, multifactorial nonlinearities and interactions involved in this behavior due to their limited linear analytical power, while machine learning models suffer from interpretability issues. To address this, an analytical framework that combines data-driven machine learning algorithms with emerging interpretability techniques was proposed, aiming to reveal the complex, nonlinear effects and relative importance of factors influencing pedestrian red-light-crossing behavior. Empirical video data from five signalized intersections in Hefei, China were used to compare the modeling and prediction performance of four methods: logistic regression, K-nearest neighbors, support vector machine, and extreme gradient boosting (XGBoost). Shapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE) were employed to evaluate the key factors influencing pedestrians' decisions to cross at a red light. The results show that the XGBoost model outperforms the other algorithms in capturing the complex relationships among influencing factors and accurately identifying red-light-running behavior. Quantitative analysis of feature importance reveals that traffic volume is the most influential predictor, followed by pedestrian walking speed, red-light duration, conformity behavior, and age. This study overcomes the linear constraints of traditional regression models and provides a theoretical foundation for optimizing traffic management and developing intelligent law enforcement strategies.
{"title":"Identification of factors influencing pedestrians' red-light crossing behavior based on interpretable machine learning","authors":"Kun Wang , Rensu Zhou , Shuo Yang , Cheng Wang , Jing Liu , Lu Wei , LiangXu","doi":"10.1016/j.trf.2025.103487","DOIUrl":"10.1016/j.trf.2025.103487","url":null,"abstract":"<div><div>Pedestrians' crossing the street while running a red light are a significant factor contributing to traffic accidents at intersections. Traditional models fail to capture the complex, multifactorial nonlinearities and interactions involved in this behavior due to their limited linear analytical power, while machine learning models suffer from interpretability issues. To address this, an analytical framework that combines data-driven machine learning algorithms with emerging interpretability techniques was proposed, aiming to reveal the complex, nonlinear effects and relative importance of factors influencing pedestrian red-light-crossing behavior. Empirical video data from five signalized intersections in Hefei, China were used to compare the modeling and prediction performance of four methods: logistic regression, K-nearest neighbors, support vector machine, and extreme gradient boosting (XGBoost). Shapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE) were employed to evaluate the key factors influencing pedestrians' decisions to cross at a red light. The results show that the XGBoost model outperforms the other algorithms in capturing the complex relationships among influencing factors and accurately identifying red-light-running behavior. Quantitative analysis of feature importance reveals that traffic volume is the most influential predictor, followed by pedestrian walking speed, red-light duration, conformity behavior, and age. This study overcomes the linear constraints of traditional regression models and provides a theoretical foundation for optimizing traffic management and developing intelligent law enforcement strategies.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103487"},"PeriodicalIF":4.4,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号