Pub Date : 2026-03-01Epub Date: 2026-02-09DOI: 10.1016/j.trf.2026.103542
Uibeom Chun, Mohamed Abdel-Aty, Zijin Wang
Urban environments involve complex traffic, roadside features, and varying speed limits that shape drivers' visual attention. Driving speed influences how attention allocation and surrounding stimuli are perceived. Notably, visual distraction, defined as gaze diverted from driving, and speeding co-occur. Yet previous studies rarely addressed heterogeneity by relative speed ratio based on speed limit and condition under which these behaviors coincide. This study employs naturalistic driving study data and applies a Bayesian hierarchical probit model to assess heterogeneity in visual distraction across speed ratio levels, followed by a conditional Bayesian regularized horseshoe (RHS) probit model to identify the conditions under which visual distraction and speeding co-occur. The results show that visual distraction varies across speed ratio levels, with the effects of traffic density and rainfall differing, and visual distraction becoming less likely as the speed ratio increases. The conditional Bayesian RHS probit model indicates that, under speeding conditions, visual distraction is less likely on multilane roadways with high traffic density and in areas characterized by complex land-use combined with higher posted speed limits. Under conditions of visual distraction, speeding is more likely with longer headways and less likely when driving in the rightmost lane adjacent to sidewalks where pedestrians are present. Speeding under visual distraction is more predictable than visual distraction under speeding, indicating that the lower predictability of the latter limits context-based explanations of visual distraction under speeding. This study reveals visual distraction across relative speeds and provides evidence on its co-occurrence with speeding, offering new insights into their relationship.
{"title":"Understanding driver visual distraction and its relationship with speeding: Insights from naturalistic driving data","authors":"Uibeom Chun, Mohamed Abdel-Aty, Zijin Wang","doi":"10.1016/j.trf.2026.103542","DOIUrl":"10.1016/j.trf.2026.103542","url":null,"abstract":"<div><div>Urban environments involve complex traffic, roadside features, and varying speed limits that shape drivers' visual attention. Driving speed influences how attention allocation and surrounding stimuli are perceived. Notably, visual distraction, defined as gaze diverted from driving, and speeding co-occur. Yet previous studies rarely addressed heterogeneity by relative speed ratio based on speed limit and condition under which these behaviors coincide. This study employs naturalistic driving study data and applies a Bayesian hierarchical probit model to assess heterogeneity in visual distraction across speed ratio levels, followed by a conditional Bayesian regularized horseshoe (RHS) probit model to identify the conditions under which visual distraction and speeding co-occur. The results show that visual distraction varies across speed ratio levels, with the effects of traffic density and rainfall differing, and visual distraction becoming less likely as the speed ratio increases. The conditional Bayesian RHS probit model indicates that, under speeding conditions, visual distraction is less likely on multilane roadways with high traffic density and in areas characterized by complex land-use combined with higher posted speed limits. Under conditions of visual distraction, speeding is more likely with longer headways and less likely when driving in the rightmost lane adjacent to sidewalks where pedestrians are present. Speeding under visual distraction is more predictable than visual distraction under speeding, indicating that the lower predictability of the latter limits context-based explanations of visual distraction under speeding. This study reveals visual distraction across relative speeds and provides evidence on its co-occurrence with speeding, offering new insights into their relationship.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103542"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187905","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 : 2026-03-01Epub 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":"2026-03-01","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 : 2026-03-01Epub Date: 2026-02-25DOI: 10.1016/j.trf.2026.103565
Ye Eun Ko , Min Jae Park
This study investigates how cognitive, contextual, and experiential factors jointly shape public acceptance of autonomous vehicles (AVs). Drawing on survey data from 400 residents across two South Korean AV pilot areas—a dense urban environment and a high-tech industrial cluster—we examine the effects of perceived usefulness (PU) and perceived risk (PR) on acceptance intention, and test whether these relationships are moderated by area context and direct user experience. Hierarchical regression results show that PU positively predicts acceptance, whereas PR exerts a negative influence. Area context amplifies the PU–acceptance link, and direct experience attenuates the negative impact of PR. A significant three-way interaction indicates that PU's effect is strongest among participants in high-tech contexts who have also experienced an AV ride. Robustness checks using alternative dependent variables, standardized predictors, propensity score weighting, and disaggregated risk dimensions confirm the stability of findings. Theoretically, the study extends technology acceptance research by integrating geographic and experiential moderators; practically, it offers context-specific guidance for AV deployment strategies that combine benefit communication, risk mitigation, and trial opportunities.
{"title":"How area context and user experience shape public acceptance of autonomous vehicles: Evidence from South Korean pilot areas","authors":"Ye Eun Ko , Min Jae Park","doi":"10.1016/j.trf.2026.103565","DOIUrl":"10.1016/j.trf.2026.103565","url":null,"abstract":"<div><div>This study investigates how cognitive, contextual, and experiential factors jointly shape public acceptance of autonomous vehicles (AVs). Drawing on survey data from 400 residents across two South Korean AV pilot areas—a dense urban environment and a high-tech industrial cluster—we examine the effects of perceived usefulness (PU) and perceived risk (PR) on acceptance intention, and test whether these relationships are moderated by area context and direct user experience. Hierarchical regression results show that PU positively predicts acceptance, whereas PR exerts a negative influence. Area context amplifies the PU–acceptance link, and direct experience attenuates the negative impact of PR. A significant three-way interaction indicates that PU's effect is strongest among participants in high-tech contexts who have also experienced an AV ride. Robustness checks using alternative dependent variables, standardized predictors, propensity score weighting, and disaggregated risk dimensions confirm the stability of findings. Theoretically, the study extends technology acceptance research by integrating geographic and experiential moderators; practically, it offers context-specific guidance for AV deployment strategies that combine benefit communication, risk mitigation, and trial opportunities.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103565"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397365","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 : 2026-03-01Epub Date: 2026-02-24DOI: 10.1016/j.trf.2026.103559
Tien-Hsueh Chen, Yun-Ju Lee
Pedestrian safety at intersections is a critical issue in urban traffic, where decisions are often made under time pressure and uncertainty. To better understand psychological and demographic influences on crossing behavior, this study examined the joint effects of personality traits and gender on pedestrian behavioral intention. Utilizing the Big Five personality framework and K-means clustering, participants were categorized into three distinct personality clusters. The Optimistic-Extraverted type was characterized by high Extraversion and low Neuroticism, the Anxious-Sensitive type was characterized by high Neuroticism and Openness with low Extraversion, and the Stability-Focused type was characterized by low Openness with moderate scores on other traits. Extraversion was the most significant factor distinguishing behavioral responses, with Optimistic-Extraverted individuals consistently reporting higher crossing intentions than the Anxious-Sensitive group, while the Stability-Focused type showed minimal differences. Gender also influenced crossing intentions, with males generally scoring higher than females; however, the magnitude of gender differences varied across personality types. Overall, the findings indicate that personality traits and gender play distinct yet interrelated roles in pedestrian decision-making: personality shapes overarching behavioral tendencies, while gender modifies how these tendencies manifest in specific contexts. The importance of integrating psychological and demographic factors into behavioral interventions in traffic safety is significant.
{"title":"Personality traits and gender influence pedestrian behavioral intention in intersection crossing","authors":"Tien-Hsueh Chen, Yun-Ju Lee","doi":"10.1016/j.trf.2026.103559","DOIUrl":"10.1016/j.trf.2026.103559","url":null,"abstract":"<div><div>Pedestrian safety at intersections is a critical issue in urban traffic, where decisions are often made under time pressure and uncertainty. To better understand psychological and demographic influences on crossing behavior, this study examined the joint effects of personality traits and gender on pedestrian behavioral intention. Utilizing the Big Five personality framework and K-means clustering, participants were categorized into three distinct personality clusters. The Optimistic-Extraverted type was characterized by high Extraversion and low Neuroticism, the Anxious-Sensitive type was characterized by high Neuroticism and Openness with low Extraversion, and the Stability-Focused type was characterized by low Openness with moderate scores on other traits. Extraversion was the most significant factor distinguishing behavioral responses, with Optimistic-Extraverted individuals consistently reporting higher crossing intentions than the Anxious-Sensitive group, while the Stability-Focused type showed minimal differences. Gender also influenced crossing intentions, with males generally scoring higher than females; however, the magnitude of gender differences varied across personality types. Overall, the findings indicate that personality traits and gender play distinct yet interrelated roles in pedestrian decision-making: personality shapes overarching behavioral tendencies, while gender modifies how these tendencies manifest in specific contexts. The importance of integrating psychological and demographic factors into behavioral interventions in traffic safety is significant.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103559"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397368","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 : 2026-03-01Epub Date: 2026-01-31DOI: 10.1016/j.trf.2026.103531
Yingyi Li, Xiaoyi Wang, Calvin Or
Emotion-induced traffic accidents often arise from a temporary depletion in emotional awareness management (EAM). Technology-assisted EAM systems (TEAMS), which combine real-time emotion recognition with timely regulation strategies, offer a promising approach to mitigating emotion-related driving risks. This systematic review synthesizes current research on emotion recognition and emotion regulation in driving contexts. We conducted a structured search across ACM Digital Library, IEEE Xplore, Scopus, and Web of Science, followed by screening based on predefined criteria. A snowballing search was also used to identify studies on emotion regulation. Ultimately, 134 peer-reviewed studies were included. The results revealed an isolated, unbalanced development of emotion recognition and emotion regulation, with systems that integrate both remaining scarce. Existing emotion recognition research focused predominantly on facial and physiological cues, followed by speech and driving features. Most studies emphasized algorithmic performance and relied on datasets outside driving contexts, which limited ecological validity and generalizability. Research on emotion regulation remained nascent, with studies exploring regulatory strategies such as auditory, visual, and combined feedback. Most of these studies were conducted in laboratory settings, and evaluation approaches varied, often relying on questionnaires or physiological measures. This review suggests the need for future efforts to develop unified, adaptive, and human-centered TEAMS. It also recommends creating diverse, accessible multimodal driving datasets and establishing comprehensive evaluation frameworks that cover objective and subjective measures. Human-centered TEAMS may reduce emotion-induced accidents and enhance safety and interaction during transitions to higher levels of driving automation, thus supporting the development of future intelligent transportation systems.
情绪引发的交通事故往往是由于情绪意识管理（EAM）的暂时缺失引起的。技术辅助的EAM系统（TEAMS）将实时情绪识别与及时调节策略相结合，为降低与情绪相关的驾驶风险提供了一种很有前景的方法。本文对驾驶环境下情绪识别和情绪调节的研究现状进行了系统综述。我们在ACM数字图书馆、IEEE explore、Scopus和Web of Science上进行了结构化搜索，然后根据预定义的标准进行筛选。滚雪球搜索也被用于识别情绪调节的研究。最终纳入了134项同行评议的研究。结果显示，情绪识别和情绪调节的发展是孤立的、不平衡的，整合两者的系统仍然稀缺。现有的情绪识别研究主要集中在面部和生理线索，其次是语音和驾驶特征。大多数研究强调算法性能并依赖于驾驶环境之外的数据集，这限制了生态有效性和可泛化性。关于情绪调节的研究仍处于起步阶段，研究探索了诸如听觉、视觉和综合反馈等调节策略。这些研究大多是在实验室环境中进行的，评估方法各不相同，通常依赖于问卷调查或生理测量。这一综述表明，未来需要努力发展统一的、适应性强的、以人为本的团队。它还建议创建多样化、可访问的多模式驾驶数据集，并建立涵盖客观和主观测量的综合评估框架。以人为本的TEAMS可以减少情绪引发的事故，并在向更高水平的驾驶自动化过渡期间增强安全性和交互性，从而支持未来智能交通系统的发展。
{"title":"Technologies and strategies for recognizing and regulating drivers’ emotions in driving: state-of-the-art review and future directions","authors":"Yingyi Li, Xiaoyi Wang, Calvin Or","doi":"10.1016/j.trf.2026.103531","DOIUrl":"10.1016/j.trf.2026.103531","url":null,"abstract":"<div><div>Emotion-induced traffic accidents often arise from a temporary depletion in emotional awareness management (EAM). Technology-assisted EAM systems (TEAMS), which combine real-time emotion recognition with timely regulation strategies, offer a promising approach to mitigating emotion-related driving risks. This systematic review synthesizes current research on emotion recognition and emotion regulation in driving contexts. We conducted a structured search across ACM Digital Library, IEEE Xplore, Scopus, and Web of Science, followed by screening based on predefined criteria. A snowballing search was also used to identify studies on emotion regulation. Ultimately, 134 peer-reviewed studies were included. The results revealed an isolated, unbalanced development of emotion recognition and emotion regulation, with systems that integrate both remaining scarce. Existing emotion recognition research focused predominantly on facial and physiological cues, followed by speech and driving features. Most studies emphasized algorithmic performance and relied on datasets outside driving contexts, which limited ecological validity and generalizability. Research on emotion regulation remained nascent, with studies exploring regulatory strategies such as auditory, visual, and combined feedback. Most of these studies were conducted in laboratory settings, and evaluation approaches varied, often relying on questionnaires or physiological measures. This review suggests the need for future efforts to develop unified, adaptive, and human-centered TEAMS. It also recommends creating diverse, accessible multimodal driving datasets and establishing comprehensive evaluation frameworks that cover objective and subjective measures. Human-centered TEAMS may reduce emotion-induced accidents and enhance safety and interaction during transitions to higher levels of driving automation, thus supporting the development of future intelligent transportation systems.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103531"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078144","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 : 2026-03-01Epub Date: 2026-01-28DOI: 10.1016/j.trf.2026.103508
Sarah Schwindt-Drews, Bettina Abendroth
This study examines the development of drivers' general mental models during their first real-world experience with the SAE Level 3 conditionally automated driving system (CADS) Drive Pilot. While previous research has primarily investigated mental model formation in simulators or on test tracks, little is known about how accuracy and completeness evolve during initial use in naturalistic traffic. Twenty-nine participants without prior CADS experience completed a within-subject on-road study with three measurement points: before receiving any information about the CADS (t1), after a short instructional video (t2), and after a real-world drive on a German motorway (t3). Mental models were assessed with a system-specific self-report questionnaire designed to evaluate both accuracy and completeness. Qualitative and statistical analyses showed high initial accuracy for core functions, alongside considerable misconceptions and knowledge gaps regarding limitations and operational aspects. The instructional video improved both accuracy and completeness, including for some limitations not explicitly covered. Real-world driving further increased accuracy across categories. However, completeness declined, particularly for limitations not encountered during the drive. Statistical analyses confirmed significant improvements in accuracy from t1 to t2, t1 to t3 and t2 to t3. Findings suggest that short, targeted instructions combined with immediate real-world exposure can effectively enhance the accuracy of drivers' mental models. However, knowledge about seldom-encountered limitations decays rapidly without reinforcement, highlighting the need for specific instruction and in-vehicle systems that sustain awareness of rare but safety-critical constraints over time.
{"title":"Mental model evolvement during drivers' first experience with conditionally automated driving systems in real-world traffic","authors":"Sarah Schwindt-Drews, Bettina Abendroth","doi":"10.1016/j.trf.2026.103508","DOIUrl":"10.1016/j.trf.2026.103508","url":null,"abstract":"<div><div>This study examines the development of drivers' general mental models during their first real-world experience with the SAE Level 3 conditionally automated driving system (CADS) Drive Pilot. While previous research has primarily investigated mental model formation in simulators or on test tracks, little is known about how accuracy and completeness evolve during initial use in naturalistic traffic. Twenty-nine participants without prior CADS experience completed a within-subject on-road study with three measurement points: before receiving any information about the CADS (t<sub>1</sub>), after a short instructional video (t<sub>2</sub>), and after a real-world drive on a German motorway (t<sub>3</sub>). Mental models were assessed with a system-specific self-report questionnaire designed to evaluate both accuracy and completeness. Qualitative and statistical analyses showed high initial accuracy for core functions, alongside considerable misconceptions and knowledge gaps regarding limitations and operational aspects. The instructional video improved both accuracy and completeness, including for some limitations not explicitly covered. Real-world driving further increased accuracy across categories. However, completeness declined, particularly for limitations not encountered during the drive. Statistical analyses confirmed significant improvements in accuracy from t<sub>1</sub> to t<sub>2</sub>, t<sub>1</sub> to t<sub>3</sub> and t<sub>2</sub> to t<sub>3</sub>. Findings suggest that short, targeted instructions combined with immediate real-world exposure can effectively enhance the accuracy of drivers' mental models. However, knowledge about seldom-encountered limitations decays rapidly without reinforcement, highlighting the need for specific instruction and in-vehicle systems that sustain awareness of rare but safety-critical constraints over time.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103508"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078143","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 : 2026-03-01Epub Date: 2026-01-06DOI: 10.1016/j.trf.2025.103466
Thomas R. Arkell , Llewellyn Mills , Jonathon C. Arnold , Anastasia Suraev , Sarah V. Abelev , Cilla Zhou , Nicholas Lintzeris , Iain S. McGregor
As access to medical cannabis continues to expand, understanding how patients perceive and respond to driving-related risks is important for road safety. We conducted a cross-sectional online survey of Australians using cannabis for a medical condition between December 2022 and April 2023. In addition to collecting demographic and clinical information, we assessed self-reported driving under the influence of cannabis (DUIC, defined here as ‘driving while high’), driving-related behaviours, and beliefs about impairment. Binary logistic regression was used to identify predictors of past-year DUIC. Of the 2,609 respondents who had driven in the past 12 months, 73 % (N = 1905) were accessing prescribed medicinal cannabis and 28.3 % (N = 750) reported DUIC. Several factors were associated with significantly increased odds of DUIC, including more frequent medical cannabis use, being male, using illicit and smoked cannabis, and believing that cannabis does not impair driving. The most common reason for DUIC was respondents thinking they were unimpaired (N = 518, 69.1 %). While 69 % (N = 1,790) reported that roadside drug testing deterred them from driving after cannabis use, 51 % (N = 1,340) also indicated it influenced their treatment decisions. These findings reaffirm trends identified in earlier CAMS studies and align with international literature demonstrating that perceived risk and enforcement significantly influence DUIC behaviour. Efforts to reduce DUIC among medical cannabis users need to account for the nuances of therapeutic use, noting that high-visibility enforcement strategies like roadside drug testing can reduce risky behaviours but may also restrict treatment choices. Policymakers must strike a balance between road safety and equitable access to medical cannabis.
{"title":"Medical cannabis and driving in Australia: Results from the cannabis as medicine survey 2022–2023 (CAMS-22)","authors":"Thomas R. Arkell , Llewellyn Mills , Jonathon C. Arnold , Anastasia Suraev , Sarah V. Abelev , Cilla Zhou , Nicholas Lintzeris , Iain S. McGregor","doi":"10.1016/j.trf.2025.103466","DOIUrl":"10.1016/j.trf.2025.103466","url":null,"abstract":"<div><div>As access to medical cannabis continues to expand, understanding how patients perceive and respond to driving-related risks is important for road safety. We conducted a cross-sectional online survey of Australians using cannabis for a medical condition between December 2022 and April 2023. In addition to collecting demographic and clinical information, we assessed self-reported driving under the influence of cannabis (DUIC, defined here as ‘driving while high’), driving-related behaviours, and beliefs about impairment. Binary logistic regression was used to identify predictors of past-year DUIC. Of the 2,609 respondents who had driven in the past 12 months, 73 % (N = 1905) were accessing prescribed medicinal cannabis and 28.3 % (N = 750) reported DUIC. Several factors were associated with significantly increased odds of DUIC, including more frequent medical cannabis use, being male, using illicit and smoked cannabis, and believing that cannabis does not impair driving. The most common reason for DUIC was respondents thinking they were unimpaired (N = 518, 69.1 %). While 69 % (N = 1,790) reported that roadside drug testing deterred them from driving after cannabis use, 51 % (N = 1,340) also indicated it influenced their treatment decisions. These findings reaffirm trends identified in earlier CAMS studies and align with international literature demonstrating that perceived risk and enforcement significantly influence DUIC behaviour. Efforts to reduce DUIC among medical cannabis users need to account for the nuances of therapeutic use, noting that high-visibility enforcement strategies like roadside drug testing can reduce risky behaviours but may also restrict treatment choices. Policymakers must strike a balance between road safety and equitable access to medical cannabis.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103466"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927489","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 : 2026-03-01Epub Date: 2026-01-06DOI: 10.1016/j.trf.2026.103507
Dong Liu , Taotao Liu , Sangbum Son , Muwen Wang
As autonomous vehicle (AV) technologies continue to advance, users' adoption intentions have become a critical factor in promoting the societal diffusion and commercial success of AVs. This study integrates the Theory of Planned Behavior (TPB) with affective motivation theory to develop a comprehensive framework encompassing affect, interaction, cognition, and behavior. It systematically examines how hedonic motivation and perceived interaction quality influence the three core TPB components (attitude, subjective norm, and perceived behavioral control) and subsequently shape behavioral intention toward AV use. A video-based scenario experiment and a two-stage survey yielded 428 valid responses. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships and mediation effects. The results show that hedonic motivation significantly enhances perceived interaction quality. In turn, interaction quality positively affects user attitude and perceived behavioral control, with a marginal influence on subjective norm. The impact of hedonic motivation on behavioral intention is fully mediated through the sequential path of interaction quality and TPB-related cognitions. Multi-group analysis further reveals that both users' hedonic orientation and perceived interaction level significantly moderate the structural pathways. Theoretically, this research extends the emotional dimension of TPB by highlighting the mediating role of interaction quality in the adoption mechanism of AVs. Practically, the findings offer empirical support and actionable insights for the design of affect-aware human–machine interfaces (HMIs), optimization of user experience, and segmentation strategies based on affective preferences in intelligent vehicle systems.
{"title":"When driving becomes enjoyable: the role of hedonic motivation and interaction quality in the adoption of autonomous vehicles","authors":"Dong Liu , Taotao Liu , Sangbum Son , Muwen Wang","doi":"10.1016/j.trf.2026.103507","DOIUrl":"10.1016/j.trf.2026.103507","url":null,"abstract":"<div><div>As autonomous vehicle (AV) technologies continue to advance, users' adoption intentions have become a critical factor in promoting the societal diffusion and commercial success of AVs. This study integrates the Theory of Planned Behavior (TPB) with affective motivation theory to develop a comprehensive framework encompassing affect, interaction, cognition, and behavior. It systematically examines how hedonic motivation and perceived interaction quality influence the three core TPB components (attitude, subjective norm, and perceived behavioral control) and subsequently shape behavioral intention toward AV use. A video-based scenario experiment and a two-stage survey yielded 428 valid responses. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships and mediation effects. The results show that hedonic motivation significantly enhances perceived interaction quality. In turn, interaction quality positively affects user attitude and perceived behavioral control, with a marginal influence on subjective norm. The impact of hedonic motivation on behavioral intention is fully mediated through the sequential path of interaction quality and TPB-related cognitions. Multi-group analysis further reveals that both users' hedonic orientation and perceived interaction level significantly moderate the structural pathways. Theoretically, this research extends the emotional dimension of TPB by highlighting the mediating role of interaction quality in the adoption mechanism of AVs. Practically, the findings offer empirical support and actionable insights for the design of affect-aware human–machine interfaces (HMIs), optimization of user experience, and segmentation strategies based on affective preferences in intelligent vehicle systems.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103507"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927606","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 : 2026-03-01Epub Date: 2026-02-09DOI: 10.1016/j.trf.2026.103540
Zhaohui Wang, Enjian Yao, Yang Yang
Enhancing residents’ well-being is a core objective of transportation planning. Commuting, as an important part of daily life, significantly affects well-being. While this relationship has been widely examined in transportation research, existing studies often present a paradox: commuting can generate travel-related stress and diminish well-being, yet it may also support career advancement and expand social opportunities, thereby enhancing well-being. This duality indicates that commuting’s impact on well-being extends beyond subjective feelings to encompass multiple dimensions and involves a complex interplay of positive and negative effects. However, few studies have systematically investigated the relationship between commuting and multidimensional well-being, and even fewer have explored the underlying mechanisms. To address these gaps, the study proposed a multidimensional well-being framework (subjective, psychological, social well-being) and examined how commuting affects well-being through a chained mediation pathway involving social networks and travel satisfaction. Using survey data and mediation models, the study finds that commuting time indirectly reduces well-being through lowering travel satisfaction. Commuting distance exhibits a threshold effect. Specifically, moderate distances enhance well-being through solitude and career opportunities; longer distances initially reduce well-being due to fatigue; yet at very long distances (over 60 km), the expansion of social networks compensates for fatigue and ultimately improves well-being. Car commuting is associated with higher well-being than public transport use, largely because it facilitates broader social network engagement. The findings provide targeted insights for policy, including optimizing urban spatial structure, improving transportation service quality, and leveraging social network functions to strengthen positive effects.
{"title":"Impact analysis of commuting on multidimensional well-being: The mediation effects of social network and travel satisfaction","authors":"Zhaohui Wang, Enjian Yao, Yang Yang","doi":"10.1016/j.trf.2026.103540","DOIUrl":"10.1016/j.trf.2026.103540","url":null,"abstract":"<div><div>Enhancing residents’ well-being is a core objective of transportation planning. Commuting, as an important part of daily life, significantly affects well-being. While this relationship has been widely examined in transportation research, existing studies often present a paradox: commuting can generate travel-related stress and diminish well-being, yet it may also support career advancement and expand social opportunities, thereby enhancing well-being. This duality indicates that commuting’s impact on well-being extends beyond subjective feelings to encompass multiple dimensions and involves a complex interplay of positive and negative effects. However, few studies have systematically investigated the relationship between commuting and multidimensional well-being, and even fewer have explored the underlying mechanisms. To address these gaps, the study proposed a multidimensional well-being framework (subjective, psychological, social well-being) and examined how commuting affects well-being through a chained mediation pathway involving social networks and travel satisfaction. Using survey data and mediation models, the study finds that commuting time indirectly reduces well-being through lowering travel satisfaction. Commuting distance exhibits a threshold effect. Specifically, moderate distances enhance well-being through solitude and career opportunities; longer distances initially reduce well-being due to fatigue; yet at very long distances (over 60 km), the expansion of social networks compensates for fatigue and ultimately improves well-being. Car commuting is associated with higher well-being than public transport use, largely because it facilitates broader social network engagement. The findings provide targeted insights for policy, including optimizing urban spatial structure, improving transportation service quality, and leveraging social network functions to strengthen positive effects.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103540"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189149","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 : 2026-03-01Epub Date: 2025-12-20DOI: 10.1016/j.trf.2025.103493
Weiwei Wang , Zhiqiang Wen , Qizhao Peng , Zihao Zhang , Congge Shi , Ting Wei
Young novice drivers are prone to negative emotions in high-risk situations. These emotions consume limited cognitive resources and raise crash risk. Existing research has not systematically clarified the relationships among driving scenarios, emotion types, emotion intensity, and multi-channel cognitive workload. Accordingly, this study used a driving-simulator experiment to analyze these linkages. We recruited 144 Chinese young novice drivers and used pre-validated video clips to induce neutral, anger, fear, anxiety. Data were collected via the Self-Assessment Manikin (SAM), the Visual-Auditory-Cognitive-Psychomotor (VACP) workload model, and semi-structured interviews. The results showed that: (1) Negative emotions significantly increased cognitive workload in young novice drivers. Anger and fear causd significant instantaneous workload fluctuations, whereas anxiety yielded the highest mean workload. (2) Distinct negative emotions were triggered by specific driving scenarios, which have different stressors (such as security threat, time pressure and environmental complexity). The potential outcome brought by these situational stressors affect the intensity of emotion. (3) Emotion intensity was positively associated with workload level. High-arousal emotions more likely to increase demands on visual, cognitive, and psychomotor resources. Within a unified paradigm, this study delineates the pathway linking driving scenarios, emotion types, emotion intensity, and multi-channel workload. The findings provide evidence for in-vehicle emotion monitoring and environmental-adaptive interventions.
{"title":"Investigating the negative emotional intensity and cognitive workload levels of young Novice drivers in different high-risk driving scenarios: A simulated driving study","authors":"Weiwei Wang , Zhiqiang Wen , Qizhao Peng , Zihao Zhang , Congge Shi , Ting Wei","doi":"10.1016/j.trf.2025.103493","DOIUrl":"10.1016/j.trf.2025.103493","url":null,"abstract":"<div><div>Young novice drivers are prone to negative emotions in high-risk situations. These emotions consume limited cognitive resources and raise crash risk. Existing research has not systematically clarified the relationships among driving scenarios, emotion types, emotion intensity, and multi-channel cognitive workload. Accordingly, this study used a driving-simulator experiment to analyze these linkages. We recruited 144 Chinese young novice drivers and used pre-validated video clips to induce neutral, anger, fear, anxiety. Data were collected <em>via</em> the Self-Assessment Manikin (SAM), the Visual-Auditory-Cognitive-Psychomotor (VACP) workload model, and semi-structured interviews. The results showed that: (1) Negative emotions significantly increased cognitive workload in young novice drivers. Anger and fear causd significant instantaneous workload fluctuations, whereas anxiety yielded the highest mean workload. (2) Distinct negative emotions were triggered by specific driving scenarios, which have different stressors (such as security threat, time pressure and environmental complexity). The potential outcome brought by these situational stressors affect the intensity of emotion. (3) Emotion intensity was positively associated with workload level. High-arousal emotions more likely to increase demands on visual, cognitive, and psychomotor resources. Within a unified paradigm, this study delineates the pathway linking driving scenarios, emotion types, emotion intensity, and multi-channel workload. The findings provide evidence for in-vehicle emotion monitoring and environmental-adaptive interventions.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103493"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799802","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