Accident; analysis and prevention最新文献

{"title":"How do drivers perceive collision risk? A quantitative exploration in generalized two-dimensional scenarios","authors":"Jinghua Wang ,&nbsp;Guangquan Lu ,&nbsp;Wenmin Long ,&nbsp;Zhao Zhang ,&nbsp;Miaomiao Liu ,&nbsp;Yong Xia","doi":"10.1016/j.aap.2024.107879","DOIUrl":"10.1016/j.aap.2024.107879","url":null,"abstract":"<div><div>Driving behavior is crucial in shaping traffic dynamics and serves as the foundation for safe and efficient autonomous driving. Despite the widespread interest in driving behavior modeling, existing models often focus on specific behaviors and cannot describe all types of vehicle movements, while vehicle status and driving scenarios are dynamic and infinite. That means comprehending and modeling generalized driving behavior mechanisms is essential. Risk Homeostasis Theory (RHT) emerges as a compelling conceptual framework to explain human risk behaviors comprehensively. The critical problem in modeling behavior using RHT is quantifying the subject risk precepted by humans. RHT has been applied in car-following behavior modeling based on the one-dimensional risk indicator Safety Margin (SM), simplifying the specific behavior along its direction. While the generalized perceived risk indicator on the two-dimensional surface still lacks. Considering the collision avoidance capacity from the driver’s perspective, this paper proposes the two-dimensional safety margin (TSM) to describe the driver’s risk perception in generalized driving scenarios with two-dimensional movements. Results demonstrate that TSM could accurately describe car-following behavior compared to existing risk indicators, with a 9.1 % correlation improvement and the reasonably calibrated response time (1.07 s). And TSM could effectively capture the discrepant risk perceptions of different drivers involved in the same conflict, underscoring the alignment of TSM with drivers’ subjective risk perceptions. Besides, TSM reflects the risk homeostasis of driving behaviors, as both typical scenarios have the normally distributed and concentrated target levels. Further, TSM also achieves a generalized, scenario-independent risk quantification with a mean target level of 0.85. As a good representation of driver’s risk perception in two-dimensional scenarios, TSM serves as a crucial basis in areas such as driving behavior modeling, and decision-making and testing of autonomous driving.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107879"},"PeriodicalIF":5.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the effectiveness of an online cycling training for adults to master complex traffic situations","authors":"Michael A.B. van Eggermond ,&nbsp;Dorothea Schaffner ,&nbsp;Nora Studer ,&nbsp;Leah Knecht ,&nbsp;Lucy Johnson","doi":"10.1016/j.aap.2024.107856","DOIUrl":"10.1016/j.aap.2024.107856","url":null,"abstract":"<div><h3>Background:</h3><div>Acknowledging the significance of both subjective and objective safety in promoting cycling, there is a need for effective measures aimed at improving cycling skills among a broader population. Hence, the aim of the current study is to evaluate and investigate the impact of online cycling training targeted at adults.</div></div><div><h3>Methods:</h3><div>An online cycling training consisting of three modules was developed to train safe behaviour in seven prototypical safety-relevant situations. 10,000 individuals were invited to participate, with 700 individuals completing the training. The effectiveness of the training was evaluated using a mixed-methods approach combining self-report measures with behavioural measures. Self-report measures were collected using four items of the Cycling Skills Inventory and knowledge-based questions. On a behavioural level, effectiveness was investigated using a virtual reality cycling simulator.</div></div><div><h3>Results:</h3><div>Participants’ self-reported cycling skills were evaluated before and after participation in the online training. Three out of four self-reported skills (i.e. predicting traffic situations, showing consideration, knowing how to act) improved on average, across participants. Moreover, participants who cycle less frequently benefited more from the training as they indicated their ability to recognise hazards, to predict traffic situations and to know how to appropriately after completion of the online training. Finally, all participants indicated that they felt more comfortable while cycling after completing the training.</div><div>In the training evaluation, it was found that the treatment group navigated through traffic more safely on a behavioural level, and/or possessed the required knowledge-based skills in three out of five evaluated situations.</div></div><div><h3>Conclusion:</h3><div>These promising findings indicate that online cycling training is one potential avenue to develop cycling skills within a target audience of adult cyclists: not only on a knowledge level, but also on a behavioural level. Notwithstanding limitations, we conclude that an online cycling training can contribute to safer cycling and the promotion of cycling in general.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107856"},"PeriodicalIF":5.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pedestrians’ perceptions, fixations, and decisions towards automated vehicles with varied appearances","authors":"Wei Lyu ,&nbsp;Yaqin Cao ,&nbsp;Yi Ding ,&nbsp;Jingyu Li ,&nbsp;Kai Tian ,&nbsp;Hui Zhang","doi":"10.1016/j.aap.2024.107889","DOIUrl":"10.1016/j.aap.2024.107889","url":null,"abstract":"<div><div>Future automated vehicles (AVs) are anticipated to feature innovative exteriors, such as textual identity indications, external radars, and external human–machine interfaces (eHMIs), as evidenced by current and forthcoming on-road testing prototypes. However, given the vulnerability of pedestrians in road traffic, it remains unclear how these novel AV appearances will impact pedestrians’ crossing behaviour, especially in relation to their multimodal performance, including subjective perceptions, gaze patterns, and road-crossing decisions. To address this gap, this study pioneers an investigation into the influence of AVs’ exterior design, in conjunction with their kinematics, on pedestrians’ road-crossing perception and decision-making. A video-based eye-tracking experimental study was conducted with 61 participants who were exposed to video stimuli depicting a manipulated vehicle approaching a predefined road-crossing location on an unsignalized, two-way road. The vehicle’s kinematic pattern was manipulated into yielding and non-yielding, and its external appearances were varied across five conditions: with a human driver (as a conventional vehicle), with no driver (as an AV), with text-based identity indications, with roof radar sensors, with dynamic eHMIs adjusted to vehicle kinematics. Participants’ perceived clarity, crossing initiation time (CIT), crossing initiation distance (CID), and gaze behaviour during interactions were recorded and reported. The results revealed that AVs’ yielding patterns play a dominant role in pedestrians’ road-crossing decisions, supported by their subjective evaluations and CID. Furthermore, it was found that both textual identity indications and roof radar sensors had no significant effect on pedestrians’ CIT and CID but did negatively impact their visual attention, as evidenced by heightened fixation counts and prolonged fixation durations. In contrast, the deployment of eHMIs helped mitigate the visual load and perceptual confusion associated with AV’s identity features, expedite road-crossing decisions in terms of both time and space, and thus improve overall communication efficiency. The practical and safety implications of these findings for future external interaction design of AVs are discussed from the perspective of vulnerable road users.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107889"},"PeriodicalIF":5.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142805951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does road environment aesthetics influence risky driving behavior of autonomous vehicles? An evaluation on road readiness using explainable machine learning and random parameters multinomial logit with heterogeneity","authors":"Sizhe Yao ,&nbsp;Bo Yu ,&nbsp;Yuren Chen ,&nbsp;Kun Gao ,&nbsp;Shan Bao ,&nbsp;Qiangqiang Shangguan","doi":"10.1016/j.aap.2024.107877","DOIUrl":"10.1016/j.aap.2024.107877","url":null,"abstract":"<div><div>Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environment design also has an impact on autonomous vehicles (AVs), resulting in that current evaluation models on road readiness for AVs (RRAV) do not consider road environment aesthetics. Therefore, this study aims to explore the relationship between road environment aesthetics and risky driving behavior of AVs (RDBAV) and propose an RRAV evaluation model from the new perspective of road environment aesthetics. Using real autonomous driving data, 1,491 longitudinal RDBAV events and 225 lateral RDBAV events are acquired together with corresponding road environment images. A novel quantitative model of road environment aesthetics is developed and 38 relevant feature variables are extracted from four aspects, including Naturalness, Vividness, Variety, and Unity. Then, an explainable machine learning that combines XGBoost (eXtreme Gradient Boosting) with SHAP (SHapley Additive exPlanation) is employed to establish an evaluation model of RRAV, by treating the occurrence of RDBAV as the dependent variable and feature variables of road environment aesthetics as independent variables. The results show that this XGBoost-based RRAV evaluation model performs better than other commonly-used methods, with accuracies of 96.9% and 91.8% for longitudinal and lateral RDBAV prediction, respectively. Due to the advantages of SHAP, the influence degrees of aesthetic features of road environments on RDBAV are calculated and explained based on global and individual feature contributions. In addition, a random parameters multinomial logit model with heterogeneity in means and variances reveals that the indicator of left visual curve length in the “middle scene” and the indicator of dominant color have significant heterogeneity for the analyses of longitudinal RDBAV. The findings of this study might contribute to the accurate evaluation of RRAV from the new viewpoint of aesthetics, the development of human-like visual perception systems of AVs, and the optimization of aesthetics-based road environment design.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107877"},"PeriodicalIF":5.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142805949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the factors influencing Repeatedly Crash-Involved Drivers (RCIDs): A Random Parameter Hazard-Based Duration approach","authors":"Hala A. Eljailany ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Helai Huang ,&nbsp;Hanchu Zhou ,&nbsp;Ali. M.A. Ibrahim","doi":"10.1016/j.aap.2024.107876","DOIUrl":"10.1016/j.aap.2024.107876","url":null,"abstract":"<div><div>Repeatedly Crash-Involved Drivers (RCIDs) pose significant challenges to traffic safety, contributing disproportionately to crash occurrences and their severe consequences. While existing research has explored factors influencing crash involvement, the literature often neglects the influence of a driver's crash history and inter-crash intervals on their evolving crash risk. Additionally, many traditional models fail to address unobserved heterogeneity, limiting their ability to capture the complex interplay of factors contributing to repeated crash involvement. This study investigates the factors influencing RCIDs using a hybrid methodology that integrates machine learning with a Random Parameter Hazard-Based Duration Model (HBDM). Machine learning techniques are employed to identify the most critical factors affecting RCID involvement, which are then incorporated into the HBDM framework. By leveraging machine learning's capacity to analyze complex relationships within high-dimensional data and the HBDM's ability to address unobserved heterogeneity, this approach provides a comprehensive understanding of RCID behavior. Key findings reveal that male drivers, individuals with histories of distracted or alcohol-impaired driving, and those with prior traffic violations exhibit heightened crash risks. Roadway conditions, vehicle age, and regional variations also emerge as significant contributors. Drivers with extensive crash histories demonstrate dynamic risk profiles, with cumulative hazard estimates indicating increased crash likelihood over time for those with multiple prior incidents. Additionally, unobserved heterogeneity (Theta) emphasized latent, driver-specific risk factors, especially in higher-tier drivers, highlighting the complex nature of crash repeating. These findings offer a more nuanced understanding of RCIDs and underscore the need for targeted interventions that account for both observable risks and more profound, unmeasured influences on driver behavior.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107876"},"PeriodicalIF":5.7,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in injury severities between elderly and non-elderly taxi driver at-fault crashes: Temporal instability and out-of-sample prediction","authors":"Reuben Tamakloe,&nbsp;Mahdi Khorasani,&nbsp;Inhi Kim","doi":"10.1016/j.aap.2024.107865","DOIUrl":"10.1016/j.aap.2024.107865","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The population of elderly individuals (over 64 years) in Seoul, South Korea, grew from 1.4 million to 1.7 million between 2018 and 2023. During the same period, the number of elderly taxi drivers rose from 27,739 to 35,166. Additionally, the number of fatal and severe injury (FSI) crashes caused by at-fault elderly taxi drivers has steadily increased, surpassing those caused by non-elderly taxi drivers since the onset of the COVID-19 pandemic. This shift has raised safety concerns among transportation authorities and the public. Previous studies have explored the factors influencing taxi driver crash injury severity outcomes; however, there has been little focus on investigating the stability of these factors over time and across taxi driver age groups. This study examines the stability of factors influencing taxi driver at-fault crash injury severity outcomes and the differences between elderly and non-elderly taxi driver at-fault crash severities using data from Seoul, South Korea (2017–2023). Risk factor stability across taxi driver at-fault age groups and time periods was assessed using log-likelihood ratio tests, which revealed that these factors were not stable, highlighting the need for estimating separate models. Separate statistical models were developed using the random parameters binary logit framework to examine the associations between risk factors and FSI outcomes. This approach allowed us to account for potential heterogeneity in the means of the random parameters for both elderly and non-elderly taxi driver at-fault crashes across different periods: pre-, during, and post-COVID-19. Factors such as midnight to early morning hours, dry roads, signal violations, elderly not-at-fault parties, and posted speed limits of 80 km/h increased the likelihood of FSI outcomes in most models. The results showed that the indicator for elderly not-at-fault drivers increased the probability of FSI outcomes the most when involved in a crash with elderly at-fault taxi drivers. Additionally, the probability of FSI outcomes was highest for elderly at-fault taxi drivers who violated traffic signals. Heterogeneity analysis revealed that intersection-related taxi driver at-fault crashes were likely to be more FSI on weekdays. Out-of-sample simulations demonstrated a clear difference in injury severities between elderly and non-elderly taxi drivers, with non-elderly taxi drivers predicting fewer FSI outcomes in recent years. Key measures to improve taxi safety for drivers over 64 include introducing free and mandatory assessments to ensure that taxi drivers are fit for the profession. Additionally, taxi management companies could implement fatigue and distracted driving detection systems to monitor driving behavior, especially during midnight and early morning hours. Collected data could be used to incentivize elderly taxi drivers to maintain safe driving practices. Further, introducing more flexible or reduced hours, part-time shifts, and retir","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107865"},"PeriodicalIF":5.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Riding safety Evaluation of food delivery motor scooters based on Associating Sensor-based riding behavior and road traffic characteristics","authors":"Yeseo Gu ,&nbsp;Eunsol Cho ,&nbsp;Cheol Oh ,&nbsp;Gunwoo Lee","doi":"10.1016/j.aap.2024.107871","DOIUrl":"10.1016/j.aap.2024.107871","url":null,"abstract":"<div><div>The safety of motor scooters used to deliver food has come under scrutiny due to the growing popularity of food delivery services in Republic of Korea. Policymakers have been tasked with investigating and identifying the factors associated with scooter safety to prevent accidents and develop mitigating strategies. A comprehensive analysis of the components of road traffic influencing the safety of motor scooters has received little attention to date. This study aims to identify the road- and traffic-related factors that affect the safety of such vehicles through GIS-based geographically weighted regression (GWR) analysis. First, it assesses safety by analyzing the riding characteristics of delivery scooters using naturalistic study data, including speed, acceleration, and direction. Second, it evaluates safety through the hazardous riding behavior rate, offering a proactive measure for preventing accidents. Third, it uses GWR analysis to examine safety factors at the scale of the individual road segments (referred to as ’links’), identifying hazardous road segments and proposing customized measures. The results show that number of lanes, signal density, speed limit, and average speed on road segments are key factors influencing motor scooter safety. A thorough interpretation of the geographical regression coefficients for the two most hazardous links suggests useful policy implications. Notably, the effects of speed limits and riding speeds on safety vary by link. We propose effective speed-management strategies by analyzing the relationship between speed limit and the average speed of delivery motor scooters. Our research provides valuable insights on how to improve the safety of delivery motor scooters.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107871"},"PeriodicalIF":5.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Activation strategies and effectiveness of Intelligent safety systems for reducing pedestrian injuries in autonomous vehicles","authors":"Quan Li ,&nbsp;Yiran Luo ,&nbsp;Siyuan Liu ,&nbsp;Tianle Lu ,&nbsp;Liangliang Shi ,&nbsp;Wei Ji ,&nbsp;Yong Han ,&nbsp;Hong Wang ,&nbsp;Bingbing Nie","doi":"10.1016/j.aap.2024.107870","DOIUrl":"10.1016/j.aap.2024.107870","url":null,"abstract":"<div><div>Intelligent safety systems (ISS) for autonomous vehicles, integrating advanced perception capabilities and passive protection devices, are expected to reshape traditional pedestrian safety systems and play a key role in reducing the risk of pedestrian injuries in traffic accidents. However, traditional active control and passive protection modules remain disconnected due to insufficient evidence supporting the effectiveness of collaborative strategies in integrated systems, particularly concerning activation criteria and timing. This study aims to address this gap by developing a comprehensive ISS that incorporates advanced perception systems, a vehicle dynamic control module, and controllable passive safety devices. Furthermore, the study evaluates the efficacy of trigger strategies in minimizing injury risks in various safety systems including Automatic Emergency Braking (AEB), Automatic Emergency Steering (AES), and ISS. To achieve this, we reconstructed the dynamics of pedestrian-vehicle interactions before collisions by examining 23 detailed collision cases. These cases were selected from real-world accident databases and included clear video recordings and detailed injury reports. Additionally, we analyzed the boundary conditions for collision avoidance by constructing vehicle steering and braking avoidance models. Our findings indicate that, in real-world accidents, the average Time-to-Collision (TTC) required for drivers to avoid collisions is −3.15 ± 1.00 s. In contrast, the AEB system requires −1.06 ± 0.23 s, and the AES system requires −0.44 ± 0.14 s. Building on this, we developed injury risk models for the system activation, predicting collision risks at various TTCs and pedestrian injury risks. The pedestrian injury risk prediction model effectively forecasts the risk of AIS3 + head injuries resulting from collisions between pedestrians aged 20 to 70 years and the vehicle hood. The threshold for a severe AIS3 + head injury risk is set at 10 %, with a trigger TTC of the ISS at −0.60 ± 0.20 s. When the system is activated at a TTC of −0.5 s, it can reduce the probability of severe head injury to pedestrians by 59 %. The design of the ISS shows significant potential for enhancing pedestrian safety. The findings of this research can offer guidance for the activation strategies of passive safety devices based on input signals from advanced perception systems in AVs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107870"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A discrete choice latent class method for capturing unobserved heterogeneity in cyclist crossing behaviour at crosswalks","authors":"Rulla Al-Haideri,&nbsp;Adam Weiss,&nbsp;Karim Ismail","doi":"10.1016/j.aap.2024.107850","DOIUrl":"10.1016/j.aap.2024.107850","url":null,"abstract":"<div><div>Conflicts between cyclists and motorized vehicles at crosswalks often lead to severe collisions. The varied behaviour of cyclists at these crossings introduces unobserved heterogeneity. Despite this, there is a notable research gap in studying the cyclist behaviour at roundabout crosswalks. To address this gap, we propose a discrete choice latent class method to capture the multi-level latent heterogeneity in cyclists’ crossing behaviour at roundabout crosswalks. Latent heterogeneity can be captured at multiple levels: site-level, interaction-level, choice-attribute level, and individual-level. This method, rooted in behavioural theory, aims to provide a deeper understanding of cyclists’ crossing decisions, enhancing safety measures at these intersections. We present an application of the proposed method to two publicly available drone datasets of naturalistic road user trajectories at roundabouts, including 8 roundabout sites that exhibit some level of similarity to minimize site heterogeneity. We capture the latent heterogeneity in the cyclists’ membership to a distinct behavioural class at two levels using these datasets: the individual level, represented by the speed of the cyclist as they enter the crosswalk, and the interaction level, defined by the presence of vehicles approaching the cyclist. Our findings align with previous studies that emphasize the significance of the initial speed variable in influencing cyclists’ subsequent behaviour and decisions. We identified two distinct classes of cyclists. We hypothesize that Class 1 cyclists, whom we refer to as passers, tend to bypass or overtake other road users at the crosswalk, especially in the absence of vehicles, prioritizing speed and efficiency. We also hypothesize that Class 2 cyclists, referred to as followers, exhibit more cautious behaviour, preferring to maintain a steady pace and avoid overtaking, particularly when vehicles are present. The proposed latent class model effectively captures this behavioural distinction, offering a more granular view of cyclists’ decision-making processes at roundabout crosswalks. A key finding is that the discrete choice model with a latent class structure outperforms the basic model without it, despite having more degrees of freedom, as it achieves a lower BIC and AIC but improved model fit statistic. This demonstrates that latent heterogeneity can be effectively captured, leading to improved predictions and outperforming the basic non-latent class model. Classifying cyclists into distinct behavioural classes not only enhances cyclist safety at crosswalks but also provides valuable insights for the development of autonomous vehicle-cyclist interactions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107850"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal shifts in safety states through the COVID-19 pandemic: Insights from hidden semi-Markov models","authors":"Xiaomeng Dong ,&nbsp;Kun Xie","doi":"10.1016/j.aap.2024.107875","DOIUrl":"10.1016/j.aap.2024.107875","url":null,"abstract":"<div><div>The COVID-19 pandemic significantly impacted transportation safety, with an increase in risky driving behaviors observed during the initial lockdown period, leading to a higher likelihood of severe crashes. However, there is limited research on the post-pandemic effects on driving behaviors and safety. This study addresses this gap by analyzing open data from the state of Virginia to examine shifts in safety states from 2016 to 2024, covering the pre-, during-, and post-pandemic periods. Structural equation modeling (SEM) was utilized to measure latent variables representing aggressive and inattentive driving behaviors and to model their impacts on crash severity. Additionally, hidden semi-Markov models (HSMMs) were applied to infer shifts in safety states associated with these risky driving behaviors and the proportion of severe crashes. The strength of HSMM models lies in the ability to distinguish meaningful pattern changes from random noise. Compared with hidden Markov models (HMMs), HSMMs provide greater flexibility by accommodating arbitrary state duration distributions, contributing to better model performance and more reliable inferences. The HSMMs with four hidden states were utilized to reveal shifts in safety states over the eight-year analysis period in Virginia. Results suggested that safety states related to risky driving behaviors and the proportion of severe crashes were at lower-risk levels pre-pandemic from 2016 to 2019, then escalated to the highest-risk levels during the pandemic in 2020 and remained at higher-risk levels in 2021, 2022 and 2023. By 2024, safety states have returned to lower-risk levels similar to those inferred in the pre-pandemic period. A seasonal pattern was also identified in safety states, with lower-or-lowest-risk levels occurring in winter near the holiday season.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107875"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}