Pub Date : 2026-01-24DOI: 10.1016/j.trf.2026.103534
Shuxiang Lin , Hanwen Deng , Chaojie Fan , Honggang Wang , Kui Wang , Yong Peng
In partially automated driving systems, drivers must take over when the system reaches the limits of its operational design domain. Identifying the determinants of takeover performance is essential for improving safety. This study modeled the complete Stimulus-Organism-Response (S-O-R) chain using multimodal data from a simulator experiment with 30 licensed drivers. The takeover time budget (TOTB) and environmental complexity were manipulated, and relationships were estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that task-demand factors influence performance through distinct internal workload pathways. A shorter TOTB worsened safety mainly by increasing self-reported workload, whereas its effect on cognitive load was not significant. Higher environmental complexity impaired performance primarily by increasing cognitive load and, to a lesser extent, self-reported workload. The model also supports a sequential path from cognitive load to self-reported workload, revealing a staged internal process. Mediation tests confirmed that workload transmitted the effects of task demand to collision risk and control abruptness. These findings suggest that safer automated systems should manage both cognitive demand and perceived time pressure to maintain stable driver control during takeover.
{"title":"Multimodal causal modeling of the driver takeover process: The mediating role of driver workload","authors":"Shuxiang Lin , Hanwen Deng , Chaojie Fan , Honggang Wang , Kui Wang , Yong Peng","doi":"10.1016/j.trf.2026.103534","DOIUrl":"10.1016/j.trf.2026.103534","url":null,"abstract":"<div><div>In partially automated driving systems, drivers must take over when the system reaches the limits of its operational design domain. Identifying the determinants of takeover performance is essential for improving safety. This study modeled the complete Stimulus-Organism-Response (S-O-R) chain using multimodal data from a simulator experiment with 30 licensed drivers. The takeover time budget (TOTB) and environmental complexity were manipulated, and relationships were estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that task-demand factors influence performance through distinct internal workload pathways. A shorter TOTB worsened safety mainly by increasing self-reported workload, whereas its effect on cognitive load was not significant. Higher environmental complexity impaired performance primarily by increasing cognitive load and, to a lesser extent, self-reported workload. The model also supports a sequential path from cognitive load to self-reported workload, revealing a staged internal process. Mediation tests confirmed that workload transmitted the effects of task demand to collision risk and control abruptness. These findings suggest that safer automated systems should manage both cognitive demand and perceived time pressure to maintain stable driver control during takeover.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103534"},"PeriodicalIF":4.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038216","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 : 2026-01-22DOI: 10.1016/j.trf.2026.103529
Jiayi Yi , Woojoo Kim , Dengbo He , Chunxi Huang
Drivers' mental models of advanced driver assistance systems (ADAS) are their internal representations of how ADAS operate, encompassing an understanding of system capabilities, limitations, and contextual constraints. Well-calibrated mental models of ADAS are essential for safe and effective use, especially in critical situations that approach the operational boundaries. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review synthesizes findings from 71 empirical studies to examine three primary aspects: the conceptualization and measurement of drivers' ADAS mental models; the factors associated with variations in drivers' ADAS mental models; and the interventions proposed to enhance drivers' system understanding. The review reveals considerable heterogeneity in both the terminology and methodologies employed across studies. While mental model accuracy consistently correlates with visual attention metrics, its relationship with vehicle control performance is more variable. Individual differences (e.g., demographics, subjective perceptions, prior ADAS exposure, and information sources) contribute to variation in mental models, although effects are sometimes inconsistent. Training strategies are broadly classified into three categories: expository, interactive, and hands-on. Each of these has demonstrated effectiveness under particular conditions, with integrated methods often proving more beneficial. Human-machine interfaces that support the dynamic updating of mental models are also reviewed. Findings underscore the need for clearer distinctions between general and applied mental models to improve conceptual clarity and methodological comparability. They also highlight the importance of longitudinal research in evaluating the durability of training effects. A further need is identified for standardized frameworks in the design of experimental scenarios and outcome assessments.
{"title":"Drivers' mental models of advanced driver assistance systems: A systematic review of conceptualization, associated factors, and intervention strategies","authors":"Jiayi Yi , Woojoo Kim , Dengbo He , Chunxi Huang","doi":"10.1016/j.trf.2026.103529","DOIUrl":"10.1016/j.trf.2026.103529","url":null,"abstract":"<div><div>Drivers' mental models of advanced driver assistance systems (ADAS) are their internal representations of how ADAS operate, encompassing an understanding of system capabilities, limitations, and contextual constraints. Well-calibrated mental models of ADAS are essential for safe and effective use, especially in critical situations that approach the operational boundaries. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review synthesizes findings from 71 empirical studies to examine three primary aspects: the conceptualization and measurement of drivers' ADAS mental models; the factors associated with variations in drivers' ADAS mental models; and the interventions proposed to enhance drivers' system understanding. The review reveals considerable heterogeneity in both the terminology and methodologies employed across studies. While mental model accuracy consistently correlates with visual attention metrics, its relationship with vehicle control performance is more variable. Individual differences (e.g., demographics, subjective perceptions, prior ADAS exposure, and information sources) contribute to variation in mental models, although effects are sometimes inconsistent. Training strategies are broadly classified into three categories: expository, interactive, and hands-on. Each of these has demonstrated effectiveness under particular conditions, with integrated methods often proving more beneficial. Human-machine interfaces that support the dynamic updating of mental models are also reviewed. Findings underscore the need for clearer distinctions between general and applied mental models to improve conceptual clarity and methodological comparability. They also highlight the importance of longitudinal research in evaluating the durability of training effects. A further need is identified for standardized frameworks in the design of experimental scenarios and outcome assessments.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103529"},"PeriodicalIF":4.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038214","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 : 2026-01-20DOI: 10.1016/j.trf.2026.103514
Yashasvi Rachakonda , Digvijay S. Pawar
Unsignalized intersections are considered one of the most hazardous road locations, where drivers must carefully process visual information to make safe decisions, as improper attention allocation or lack of information on approaching traffic can lead to crashes. Intersection Conflict Warning System (ICWS) has been identified as a potential solution, however its influence on drivers' visual performance remains unexplored. This study aims to investigate the effect of ICWS on drivers' visual performance at unsignalized intersections using a driving simulator and eye tracking system. Forty-six licensed drivers participated in this study, and drivers eye movement behaviour towards ICWS was analyzed under various warning and intersection visibility conditions. Additionally, the effect of education about ICWS was also examined. Experimental results showed that at the restricted-view intersections, drivers had 46% longer fixation durations and 34% more fixations on warning signboards compared to clear-view intersections. Under ICWS activated conditions, drivers exhibited significantly longer fixation duration, and a higher proportion (72%) reacted after gazing at the signboard compared to the non-activated ICWS conditions (39%). Furthermore, middle aged drivers demonstrated a shorter time to first fixation on the signboard than younger drivers under ICWS activated conditions. The findings highlight that ICWS enables drivers to notice warning signboards promptly, initiate earlier visual searches for conflicting vehicles, and respond more quickly to potential conflicts, supporting its application as an effective countermeasure for enhancing safety at unsignalized intersections.
{"title":"Analyzing drivers visual attention towards intersection conflict warning system: A study using driving simulator and eye tracking system","authors":"Yashasvi Rachakonda , Digvijay S. Pawar","doi":"10.1016/j.trf.2026.103514","DOIUrl":"10.1016/j.trf.2026.103514","url":null,"abstract":"<div><div>Unsignalized intersections are considered one of the most hazardous road locations, where drivers must carefully process visual information to make safe decisions, as improper attention allocation or lack of information on approaching traffic can lead to crashes. Intersection Conflict Warning System (ICWS) has been identified as a potential solution, however its influence on drivers' visual performance remains unexplored. This study aims to investigate the effect of ICWS on drivers' visual performance at unsignalized intersections using a driving simulator and eye tracking system. Forty-six licensed drivers participated in this study, and drivers eye movement behaviour towards ICWS was analyzed under various warning and intersection visibility conditions. Additionally, the effect of education about ICWS was also examined. Experimental results showed that at the restricted-view intersections, drivers had 46% longer fixation durations and 34% more fixations on warning signboards compared to clear-view intersections. Under ICWS activated conditions, drivers exhibited significantly longer fixation duration, and a higher proportion (72%) reacted after gazing at the signboard compared to the non-activated ICWS conditions (39%). Furthermore, middle aged drivers demonstrated a shorter time to first fixation on the signboard than younger drivers under ICWS activated conditions. The findings highlight that ICWS enables drivers to notice warning signboards promptly, initiate earlier visual searches for conflicting vehicles, and respond more quickly to potential conflicts, supporting its application as an effective countermeasure for enhancing safety at unsignalized intersections.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103514"},"PeriodicalIF":4.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037680","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 : 2026-01-20DOI: 10.1016/j.trf.2026.103527
Xiaofeng Pan , Ling Jin
The development of electric vehicle (EV) still suffers from the lack of sufficient charging facilities. Therefore, the idea of private charging pile sharing (PCPS) of EVs is proposed. To encourage people to participate in such projects, this paper aims to identify the factors influencing people's willingness of sharing their private charging piles using a modified UTAUT modeling framework, where a key modification lies in re-conceptualizing Facilitating Conditions as a foundational construct that shapes other constructs. A case study was carried out in Shanghai, China, in which 361 valid observations were collected and both single-group and multiple-group analyses were conducted. The findings are summarized as follows. First, the hypotheses proposed in the modified UTAUT framework are generally supported. Second, among the considered constructs, Facilitation Conditions has the largest total effect on behavioral intention, although this effect is entirely indirect. The second largest effect comes from Social Influence, which is also the strongest direct determinant. Conversely, the effect of Effort Expectancy is insignificant. Third, there are heterogeneous effects across different socio-demographic groups. Specifically, females' intention is more strongly driven by Performance Expectancy, whereas males' intention is solely affected by Social Influence. Charging frequency also moderates the formation of Performance Expectancy. Based on these conclusions, tailored policy implications were proposed for different stakeholders and user groups.
{"title":"Are you willing to share your charging piles of electric vehicles? A case study in Shanghai using a modified UTAUT framework","authors":"Xiaofeng Pan , Ling Jin","doi":"10.1016/j.trf.2026.103527","DOIUrl":"10.1016/j.trf.2026.103527","url":null,"abstract":"<div><div>The development of electric vehicle (EV) still suffers from the lack of sufficient charging facilities. Therefore, the idea of private charging pile sharing (PCPS) of EVs is proposed. To encourage people to participate in such projects, this paper aims to identify the factors influencing people's willingness of sharing their private charging piles using a modified UTAUT modeling framework, where a key modification lies in re-conceptualizing Facilitating Conditions as a foundational construct that shapes other constructs. A case study was carried out in Shanghai, China, in which 361 valid observations were collected and both single-group and multiple-group analyses were conducted. The findings are summarized as follows. First, the hypotheses proposed in the modified UTAUT framework are generally supported. Second, among the considered constructs, Facilitation Conditions has the largest total effect on behavioral intention, although this effect is entirely indirect. The second largest effect comes from Social Influence, which is also the strongest direct determinant. Conversely, the effect of Effort Expectancy is insignificant. Third, there are heterogeneous effects across different socio-demographic groups. Specifically, females' intention is more strongly driven by Performance Expectancy, whereas males' intention is solely affected by Social Influence. Charging frequency also moderates the formation of Performance Expectancy. Based on these conclusions, tailored policy implications were proposed for different stakeholders and user groups.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103527"},"PeriodicalIF":4.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037683","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 : 2026-01-20DOI: 10.1016/j.trf.2026.103526
Chenglong Wu , Guojun Chen , Pengfei Gao , Shuyang Zhang , Ruoyun He , Haode Liu
Job burnout is a critical occupational hazard that compromises the safety of public transport systems. Prevailing classification methods, however, often fail to establish a reliable, monotonic relationship between burnout severity and safety performance. To address this, we developed a novel framework that incorporates a safety-performance constraint, which requires the proportion of violation-involved drivers to increase with burnout severity. We constructed an information gain-based optimization model to identify the optimal burnout severity classification under this constraint. The framework was validated on a dataset of 1461 bus drivers, demonstrating its effectiveness. The model stratified drivers into four distinct tiers based on MBI-GS scores: no burnout [0, 0.87], mild (0.87, 2.27], moderate (2.27, 4.53], and severe (4.53, 6.00]. A clear, monotonic risk gradient was observed, with the proportion of drivers committing safety violations increasing consistently from 38.97 % (no burnout) to 46.26 % (mild), 51.40 % (moderate), and 60.00 % (severe). Comparative analyses confirmed the superiority of the proposed framework over conventional methods (Weighting and Dimensional Criteria). The framework achieved stronger correlations of the classified burnout levels with underlying burnout scores (r = 0.947 vs. 0.909 and 0.899) and safety outcomes (r = 0.131 vs. 0.093 and 0.088), higher information gain (IG = 8.6 × 10−3 vs. 4.3 × 10−3 and 3.9 × 10−3), and superior cluster validity (DBI = 0.4884 vs. 0.5693 and 0.9344). This indicates that, beyond most faithfully representing the continuum of burnout severity captured by the raw scores, the framework also enables a more precise characterization of violation risk. By translating burnout severity into a four-tiered risk classification with empirically defined violation rates (38.97 % to 60.00 %), this work provides transit agencies with a precise tool for identifying at-risk drivers and implementing targeted interventions, ultimately enhancing road safety.
{"title":"Prioritizing at-risk bus drivers: a safety-constrained burnout severity classification model using information gain","authors":"Chenglong Wu , Guojun Chen , Pengfei Gao , Shuyang Zhang , Ruoyun He , Haode Liu","doi":"10.1016/j.trf.2026.103526","DOIUrl":"10.1016/j.trf.2026.103526","url":null,"abstract":"<div><div>Job burnout is a critical occupational hazard that compromises the safety of public transport systems. Prevailing classification methods, however, often fail to establish a reliable, monotonic relationship between burnout severity and safety performance. To address this, we developed a novel framework that incorporates a safety-performance constraint, which requires the proportion of violation-involved drivers to increase with burnout severity. We constructed an information gain-based optimization model to identify the optimal burnout severity classification under this constraint. The framework was validated on a dataset of 1461 bus drivers, demonstrating its effectiveness. The model stratified drivers into four distinct tiers based on MBI-GS scores: no burnout [0, 0.87], mild (0.87, 2.27], moderate (2.27, 4.53], and severe (4.53, 6.00]. A clear, monotonic risk gradient was observed, with the proportion of drivers committing safety violations increasing consistently from 38.97 % (no burnout) to 46.26 % (mild), 51.40 % (moderate), and 60.00 % (severe). Comparative analyses confirmed the superiority of the proposed framework over conventional methods (Weighting and Dimensional Criteria). The framework achieved stronger correlations of the classified burnout levels with underlying burnout scores (<em>r</em> = 0.947 vs. 0.909 and 0.899) and safety outcomes (<em>r</em> = 0.131 vs. 0.093 and 0.088), higher information gain (<em>IG</em> = 8.6 × 10<sup>−3</sup> vs. 4.3 × 10<sup>−3</sup> and 3.9 × 10<sup>−3</sup>), and superior cluster validity (<em>DBI</em> = 0.4884 vs. 0.5693 and 0.9344). This indicates that, beyond most faithfully representing the continuum of burnout severity captured by the raw scores, the framework also enables a more precise characterization of violation risk. By translating burnout severity into a four-tiered risk classification with empirically defined violation rates (38.97 % to 60.00 %), this work provides transit agencies with a precise tool for identifying at-risk drivers and implementing targeted interventions, ultimately enhancing road safety.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103526"},"PeriodicalIF":4.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038213","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}
Understanding individual heterogeneity in preferences for advanced driver assistance systems (ADAS) is critical for improving user acceptance and personalization. To explore this heterogeneity, this study employs a person-centered behavioral framework integrating latent class analysis, multinomial logistic regression, multivariate analysis of covariance (MANCOVA), and within-segment regression modeling. Based on survey data from 581 licensed drivers in China, we identify five distinct user segments—Safety-Oriented Conservative, Risk-Averse Manualist, Adaptive Tech Explorer, Balanced Functionality Seeker, and Broadly Accepting Customizer—characterized by differences in ADAS feature preferences, attitudes toward control modes, and receptiveness to personalization. Segment membership is significantly associated with demographic characteristics, driving styles, and personality traits. The MANCOVA indicates class-level differences in personalization needs and price sensitivity, while within-class regressions reveal the psychological factors that shape these attitudes. Notably, traits such as agreeableness and neuroticism—along with risky driving tendencies—emerge as key differentiators among the segments. This multi-stage behavioral modeling approach advances traffic psychology by linking latent segmentation with intra-group explanatory modeling, thereby offering practical insights for personalized ADAS design, driver-centric human–machine interaction, and evidence-based policy formulation for heterogeneous driving populations.
{"title":"Personalizing ADAS through driver segmentation: A latent class and multistage Behavioral Modeling approach in China","authors":"Ziyu Chen, Guohua Liang, Yue Liu, Baojie Wang, Yixin Chen, Yuting Zhang","doi":"10.1016/j.trf.2026.103523","DOIUrl":"10.1016/j.trf.2026.103523","url":null,"abstract":"<div><div>Understanding individual heterogeneity in preferences for advanced driver assistance systems (ADAS) is critical for improving user acceptance and personalization. To explore this heterogeneity, this study employs a person-centered behavioral framework integrating latent class analysis, multinomial logistic regression, multivariate analysis of covariance (MANCOVA), and within-segment regression modeling. Based on survey data from 581 licensed drivers in China, we identify five distinct user segments—Safety-Oriented Conservative, Risk-Averse Manualist, Adaptive Tech Explorer, Balanced Functionality Seeker, and Broadly Accepting Customizer—characterized by differences in ADAS feature preferences, attitudes toward control modes, and receptiveness to personalization. Segment membership is significantly associated with demographic characteristics, driving styles, and personality traits. The MANCOVA indicates class-level differences in personalization needs and price sensitivity, while within-class regressions reveal the psychological factors that shape these attitudes. Notably, traits such as agreeableness and neuroticism—along with risky driving tendencies—emerge as key differentiators among the segments. This multi-stage behavioral modeling approach advances traffic psychology by linking latent segmentation with intra-group explanatory modeling, thereby offering practical insights for personalized ADAS design, driver-centric human–machine interaction, and evidence-based policy formulation for heterogeneous driving populations.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103523"},"PeriodicalIF":4.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038217","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 : 2026-01-18DOI: 10.1016/j.trf.2026.103528
Xinpo Ma , Xueqin Huang , Fei Li , Guang Chen , Zirui Xia , Rui Wang
This study investigated the effects of the odor type and concentration adjustment modes of olfactory stimulation on fatigue in young drivers. A multidimensional analysis was conducted using driving simulator data, the Karolinska Sleepiness Scale (KSS), and physiological indicators such as heart rate (HR) and HR variability. Under dynamic incremental concentrations, peppermint odor significantly reduced KSS fatigue scores and mean HR while elevating the root mean square of successive differences, indicating increased parasympathetic activity. Specifically, compared with the mild odor (lavender) under constant concentration, peppermint under dynamic incremental concentration most effectively alleviated fatigue, leading to a significant reduction of 18.7% in KSS scores, a 5.3% decrease in mean HR, and a 30.1% increase in root mean square of successive differences. Conversely, lavender odor at a constant concentration showed a certain degree of fatigue relief. Incremental concentrations countered the sensory adaptation and sustained driver fatigue reduction more effectively. These findings provide insight into the design of olfactory stimulation parameters for intelligent cockpits. Future research should prioritize road validation, analyze the heterogeneity of users' olfactory perceptions, and optimize dynamic concentration adjustment mechanisms. This study promoted the application and development of olfactory stimulation to reduce driver fatigue.
{"title":"Effects of odor types and concentration adjustment modes of olfactory stimulation on fatigue in young drivers","authors":"Xinpo Ma , Xueqin Huang , Fei Li , Guang Chen , Zirui Xia , Rui Wang","doi":"10.1016/j.trf.2026.103528","DOIUrl":"10.1016/j.trf.2026.103528","url":null,"abstract":"<div><div>This study investigated the effects of the odor type and concentration adjustment modes of olfactory stimulation on fatigue in young drivers. A multidimensional analysis was conducted using driving simulator data, the Karolinska Sleepiness Scale (KSS), and physiological indicators such as heart rate (HR) and HR variability. Under dynamic incremental concentrations, peppermint odor significantly reduced KSS fatigue scores and mean HR while elevating the root mean square of successive differences, indicating increased parasympathetic activity. Specifically, compared with the mild odor (lavender) under constant concentration, peppermint under dynamic incremental concentration most effectively alleviated fatigue, leading to a significant reduction of 18.7% in KSS scores, a 5.3% decrease in mean HR, and a 30.1% increase in root mean square of successive differences. Conversely, lavender odor at a constant concentration showed a certain degree of fatigue relief. Incremental concentrations countered the sensory adaptation and sustained driver fatigue reduction more effectively. These findings provide insight into the design of olfactory stimulation parameters for intelligent cockpits. Future research should prioritize road validation, analyze the heterogeneity of users' olfactory perceptions, and optimize dynamic concentration adjustment mechanisms. This study promoted the application and development of olfactory stimulation to reduce driver fatigue.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103528"},"PeriodicalIF":4.4,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038215","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 : 2026-01-16DOI: 10.1016/j.trf.2026.103522
Arkady Zgonnikov, Merijn van Niekerk, Yke Bauke Eisma, Joost de Winter
Merging onto a highway is a safety-critical task resulting in a large number of traffic accidents; fundamental research into merging behavior of human drivers can help reduce this toll. Two cognitive processes critical to merging, attention allocation and decision making, have been extensively studied in real-world and simulated driving scenarios. However, how these processes interact during highway merging remains poorly understood. While the relationship between attention and decision making has been widely examined in cognitive science, this work has largely relied on simple decision-making paradigms involving choices between static items on a computer screen, which limits the understanding of more dynamic and naturalistic decisions such as in driving. To address this gap, we investigated the relationship between attention and decision making in a simplified highway merging task. In a video-based experiment, participants (N=24) repeatedly made merging gap acceptance decisions based on the dynamic information about the distance and time-to-arrival to the end of the merging lane and the gap to the target-lane vehicle (available in the front view and the side mirror, respectively). Participants’ decisions, response times, and eye movements were recorded. We found that decisions to accept a gap were considerably faster than decisions to reject a gap. Decision outcomes and timing depended on the distance to and time-to-arrival of the target-lane vehicle, but also on the time pressure due to approaching the end of the merging lane. Most importantly, under high time pressure, a greater proportion of time spent looking at the side mirror was associated with a lower probability of accepting the gap. This finding indicates that differences in visual information sampling can be closely linked to decision outcomes when time budgets are constrained. Our results provide initial empirical insights relevant for future cognitive modeling of the interplay between decision making and attention during highway merging. This work can inform early-stage exploration of driver monitoring and support systems for partially automated driving.
{"title":"Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions","authors":"Arkady Zgonnikov, Merijn van Niekerk, Yke Bauke Eisma, Joost de Winter","doi":"10.1016/j.trf.2026.103522","DOIUrl":"10.1016/j.trf.2026.103522","url":null,"abstract":"<div><div>Merging onto a highway is a safety-critical task resulting in a large number of traffic accidents; fundamental research into merging behavior of human drivers can help reduce this toll. Two cognitive processes critical to merging, attention allocation and decision making, have been extensively studied in real-world and simulated driving scenarios. However, how these processes interact during highway merging remains poorly understood. While the relationship between attention and decision making has been widely examined in cognitive science, this work has largely relied on simple decision-making paradigms involving choices between static items on a computer screen, which limits the understanding of more dynamic and naturalistic decisions such as in driving. To address this gap, we investigated the relationship between attention and decision making in a simplified highway merging task. In a video-based experiment, participants (N=24) repeatedly made merging gap acceptance decisions based on the dynamic information about the distance and time-to-arrival to the end of the merging lane and the gap to the target-lane vehicle (available in the front view and the side mirror, respectively). Participants’ decisions, response times, and eye movements were recorded. We found that decisions to accept a gap were considerably faster than decisions to reject a gap. Decision outcomes and timing depended on the distance to and time-to-arrival of the target-lane vehicle, but also on the time pressure due to approaching the end of the merging lane. Most importantly, under high time pressure, a greater proportion of time spent looking at the side mirror was associated with a lower probability of accepting the gap. This finding indicates that differences in visual information sampling can be closely linked to decision outcomes when time budgets are constrained. Our results provide initial empirical insights relevant for future cognitive modeling of the interplay between decision making and attention during highway merging. This work can inform early-stage exploration of driver monitoring and support systems for partially automated driving.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103522"},"PeriodicalIF":4.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978299","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 : 2026-01-14DOI: 10.1016/j.trf.2026.103506
Daniel T. Bishop , Nathan Harpham , Rosa Shirm , Ilaria Marino , Lynne Smith , George Beard
Introduction
Road traffic collisions and the number of people killed or seriously injured (KSIs) are shaped by multiple factors. However, the role of cycle training in influencing KSIs has received little attention, despite Bikeability – a UK government-funded cycle training programme – having been delivered to millions of schoolchildren since 2007. This study aimed to examine whether higher levels of Bikeability training are associated with reductions in cyclist-involved KSIs across English local authorities.
Methods
Poisson and Negative Binomial models were applied to publicly available local authority-level data, controlling for population size, cycling prevalence, and geographic variation. Overall KSI rates and cyclist-involved KSI rates were analysed over a ten-year period across 112 local authorities in England.
Findings
The exploratory analysis identified a weak but statistically significant negative association between Bikeability Level 2 training delivery and cyclist-involved KSI rates. Specifically, higher levels of Bikeability Level 2 training were associated with lower KSI rates. In contrast, greater traffic volumes and higher deprivation levels were linked to increased KSI rates.
Conclusions
Bikeability Level 2 training may represent one of several factors that contribute to improved cyclist safety on roads. Nonetheless, further research is needed to strengthen this evidence base, ideally through studies that can establish causal relationships.
{"title":"Modelling the impact of Bikeability cycle training on the number of people killed or seriously injured on UK roads","authors":"Daniel T. Bishop , Nathan Harpham , Rosa Shirm , Ilaria Marino , Lynne Smith , George Beard","doi":"10.1016/j.trf.2026.103506","DOIUrl":"10.1016/j.trf.2026.103506","url":null,"abstract":"<div><h3>Introduction</h3><div>Road traffic collisions and the number of people killed or seriously injured (KSIs) are shaped by multiple factors. However, the role of cycle training in influencing KSIs has received little attention, despite Bikeability – a UK government-funded cycle training programme – having been delivered to millions of schoolchildren since 2007. This study aimed to examine whether higher levels of Bikeability training are associated with reductions in cyclist-involved KSIs across English local authorities.</div></div><div><h3>Methods</h3><div>Poisson and Negative Binomial models were applied to publicly available local authority-level data, controlling for population size, cycling prevalence, and geographic variation. Overall KSI rates and cyclist-involved KSI rates were analysed over a ten-year period across 112 local authorities in England.</div></div><div><h3>Findings</h3><div>The exploratory analysis identified a weak but statistically significant negative association between Bikeability Level 2 training delivery and cyclist-involved KSI rates. Specifically, higher levels of Bikeability Level 2 training were associated with lower KSI rates. In contrast, greater traffic volumes and higher deprivation levels were linked to increased KSI rates.</div></div><div><h3>Conclusions</h3><div>Bikeability Level 2 training may represent one of several factors that contribute to improved cyclist safety on roads. Nonetheless, further research is needed to strengthen this evidence base, ideally through studies that can establish causal relationships.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103506"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978288","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 : 2026-01-14DOI: 10.1016/j.trf.2026.103521
Hoang Phuong Nhi Do , Minh Phuc Nguyen , Cam Anh Thi Pham
Promoting public transport use among students is crucial for fostering sustainable urban mobility, yet public transport remains underutilized in many cities. This research investigates how message framing influences students' intentions to use public transport through the activation of consumption goals by emotional reactions. Furthermore, by focusing on Hanoi – a Global South megacity undergoing a public transport transformation - it enriches the current literature, which predominantly centers on developed countries. A survey of 611 students explored general school commuting behaviors, followed by a 2 × 2 factorial experiment with 245 participants to examine the independent and combined effects of gain/loss and individual/social frames on public transport usage intention. Results showed that gain frames were slightly better than loss frames, and social references outperformed individual remarks. The effects were congruent, making gain-framed, social-referencing messages the most effective in enhancing students' intention. Additionally, emotional reactions to these frames activated consumption goals, increasing students' behavioral intention. The activation was contextual, as social frames led to higher baseline levels of consumption goals yet less reliance on affective responses. These findings highlight the importance of public communication strategies that emphasize collective benefits and evoke emotional engagement to promote sustainable commuting choices.
{"title":"From messages to movements: how emotional reactions to message frames activate consumption goals to shape students' public transport usage intention","authors":"Hoang Phuong Nhi Do , Minh Phuc Nguyen , Cam Anh Thi Pham","doi":"10.1016/j.trf.2026.103521","DOIUrl":"10.1016/j.trf.2026.103521","url":null,"abstract":"<div><div>Promoting public transport use among students is crucial for fostering sustainable urban mobility, yet public transport remains underutilized in many cities. This research investigates how message framing influences students' intentions to use public transport through the activation of consumption goals by emotional reactions. Furthermore, by focusing on Hanoi – a Global South megacity undergoing a public transport transformation - it enriches the current literature, which predominantly centers on developed countries. A survey of 611 students explored general school commuting behaviors, followed by a 2 × 2 factorial experiment with 245 participants to examine the independent and combined effects of gain/loss and individual/social frames on public transport usage intention. Results showed that gain frames were slightly better than loss frames, and social references outperformed individual remarks. The effects were congruent, making gain-framed, social-referencing messages the most effective in enhancing students' intention. Additionally, emotional reactions to these frames activated consumption goals, increasing students' behavioral intention. The activation was contextual, as social frames led to higher baseline levels of consumption goals yet less reliance on affective responses. These findings highlight the importance of public communication strategies that emphasize collective benefits and evoke emotional engagement to promote sustainable commuting choices.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"118 ","pages":"Article 103521"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978287","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}
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