Accident; analysis and prevention最新文献

{"title":"A comprehensive multi-objective framework for the estimation of crash frequency models.","authors":"Zeke Ahern, Paul Corry, Mohammadali Shirazi, Alexander Paz","doi":"10.1016/j.aap.2024.107844","DOIUrl":"10.1016/j.aap.2024.107844","url":null,"abstract":"<p><p>A common and challenging data and modeling aspect in crash analysis is unobserved heterogeneity, which is often handled using random parameters and special distributions such as Lindley. Random parameters can be estimated with respect to each observation for the entire dataset, and grouped across segments of the dataset, with variable means, or variable variances. The selection of the best approach to handle unobserved heterogeneity depends on the data characteristics and requires the corresponding hypothesis testing. In addition to dealing with unobserved heterogeneity, crash frequency modeling often requires explicit consideration of functional forms, transformations, and identification of likely contributing factors. During model estimation, it is important to consider multiple objectives such as in- and out-of-sample goodness-of-fit to generate reliable and transferable insights. Taking all of these aspects and objectives into account simultaneously represents a very large number of modeling decisions and hypothesis testing. Limited testing and model development may lead to bias and missing relevant specifications with important insights. To address these challenges, this paper proposes a comprehensive optimization framework, underpinned by a mathematical programming formulation, for systematic hypothesis testing considering simultaneously multiple objectives, unobserved heterogeneity, grouped random parameters, functional forms, transformations, heterogeneity in means, and the identification of likely contributing factors. The proposed framework employs a variety of metaheuristic solution algorithms to address the complexity and non-convexity of the estimation and optimization problem. Several metaheuristics were tested including Simulated Annealing, Differential Evolution and Harmony Search. Harmony Search provided convergence with low sensitivity to the choice of hyperparameters. The effectiveness of the framework was evaluated using three real-world data sets, generating sound and consistent results compared to the corresponding published models. These results demonstrate the ability of the proposed framework to efficiently estimate sound and parsimonious crash data count models while reducing costs associated with time and required knowledge, bias, and sub-optimal solutions due to limited testing. To support experimental testing for analysts and modelers, the Python package \"MetaCountRegressor,\" which includes algorithms and software, is available on PyPi.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"107844"},"PeriodicalIF":5.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765362","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":"Conflict resolution behavior of autonomous vehicles at intersections under mixed traffic environment.","authors":"Md Tanvir Ashraf, Kakan Dey","doi":"10.1016/j.aap.2024.107897","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107897","url":null,"abstract":"<p><p>Navigating intersections is a major challenge for autonomous vehicles (AVs) because of the complex interactions between different roadway user types, conflicting movements, and diverse operational and geometric features. This study investigated intersection-related AV-involved traffic conflicts by analyzing the Arogoverse-2 motion forecasting dataset to understand the driving behavior of AVs at intersections. The conflict scenarios were categorized into AV-involved and no AV conflict scenarios. Depending on whether AVs passed the conflict region first or second in AV-involved scenarios, AV-involved scenarios were further classified into AV-first and AV-second scenarios. An agglomerative hierarchical clustering with t-SNE dimension reduction technique was applied to categorize the driving styles, and a three-layer Bayesian hierarchical model was applied to analyze the effect of driving volatility measures and traffic characteristics on relative crash risks. The clustering result showed that about 29% of the conflict events in the AV-first scenario (human-driven vehicle (HDV) was the following vehicle in passing the conflict region) exhibited high-risk of conflicts. In contrast, all conflicts events in the AV-second category were either low-risk or medium-risk conflicts. Parameter estimates showed that AVs had safer interactions with the other roadway users (i.e., HDVs, pedestrians/cyclists) while maintaining higher speeds and uniform driving profiles. AV's interaction with vulnerable road users (i.e., pedestrians and cyclists) showed lower crash risk compared to HDVs, indicating AV's safer driving behavior. AVs also demonstrated safer conflict resolution behavior in performing unprotected left turns compared to HDVs. This study discovered some unique insights into the challenges of introducing AVs in diverse intersection types (i.e., signalized, unsignalized, stop-controlled), which can be used to identify AV technology's improvement need to better adapt to the mixed traffic driving environment.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"107897"},"PeriodicalIF":5.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862749","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 contributing factors to autonomous Vehicle-Road user Conflicts: A Data-Driven approach.","authors":"Mahdi Gabaire, Haniyeh Ghomi, Mohamed Hussein","doi":"10.1016/j.aap.2024.107898","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107898","url":null,"abstract":"<p><p>With the imminent widespread integration of Autonomous Vehicles (AVs) into our traffic ecosystem, understanding the factors that impact their safety is a vital research area. To that end, this study assessed the impact of a wide range of factors on the frequency of AV-road user conflicts. The study utilized the Woven prediction and validation dataset, which contains over 1000 h of data collected from the onboard sensors of 20 AVs in California. Two Copula-based models were developed to investigate the contributing factors to total and severe AV conflicts in road segments (model M1) and intersections (model M2). For road segments, results indicated that road characteristics (direction, number of lanes, road length, speed limit, the presence of a dividing median) and road infrastructure (presence of bus stops, presence of cycle lanes, and presence of on-street parking) have a significant impact on the hourly conflict rates. Regarding the rate of severe conflicts, road user volume, road characteristics (direction, road type, access point density, the presence of a dividing median), and the presence of cycle lanes were identified as the most influential factors. For intersections, the road user volume and the presence of a physical median were found to be positively associated with the hourly conflict rates, while road user volume, intersection characteristics (posted speed limit, lack of traffic control signals, presence of pedestrian crossing, presence of cycle lane, presence of a dividing median, and truck percentage), and the dominant land use at the intersection area were the most impactful variables on the frequency of severe conflicts.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"107898"},"PeriodicalIF":5.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862776","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":"Analysis of factors affecting pedestrian safety for the elderly and identification of vulnerable areas in Seoul.","authors":"Soyoon Kim, Sangwon Choi, Brian H S Kim","doi":"10.1016/j.aap.2024.107878","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107878","url":null,"abstract":"<p><p>Walking is the primary means of mobility and a daily activity for the elderly. Despite the need to ensure pedestrian safety given their physical limitations, elderly pedestrian traffic accidents in South Korea occur at a rate 7.7 times higher than in OECD member countries. In preparation for an aging society, there is a growing need to create a safe walking environment for the elderly. This study focuses on Seoul, analyzing the factors that compromise pedestrian safety for the elderly and identifying the characteristics of vulnerable areas. By using elderly pedestrian traffic accident data provided by the Road Traffic Authority and applying factors influencing accident occurrence to the MaxEnt model, the study identified priority elements for ensuring pedestrian safety. Additionally, the study predicted the regional vulnerability of elderly pedestrian accidents with the increasing elderly population in the future and reviewed possible measures to mitigate the risks. The study indicates that areas where elderly pedestrian safety is vulnerable tend to have lower budget allocations for road management, suggesting a need for future policy support. The prediction of elderly pedestrian accident occurrences through this study is expected to be useful in identifying areas with vulnerable pedestrian safety in Seoul, which can be utilized in prioritizing road improvement projects.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"107878"},"PeriodicalIF":5.7,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827153","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":"How does distraction affect cyclists' severe crashes? A hybrid CatBoost-SHAP and random parameters binary logit approach.","authors":"Ali Agheli, Kayvan Aghabayk","doi":"10.1016/j.aap.2024.107896","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107896","url":null,"abstract":"<p><p>Cyclists are among the most vulnerable road users, increasingly subject to various sources of distraction, including the use of mobile phones and engagement in other tasks while navigating urban environments. Understanding and mitigating the impact of these distractions on cyclist safety is crucial. Despite the importance of this issue, the effect of distraction on injury severity in cycling crashes has not been extensively studied. This research analyzes four years of U.S. crash data (2019-2022) from the Crash Report Sampling System (CRSS) database, employing a hybrid framework that integrates CatBoost-based SHAP algorithm and the random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV). The proposed approach confirms the significant role of cyclist distraction in crash injury severity. Subsequently, the analysis identifies several factors influencing the likelihood of severe injuries in distracted cyclist crashes. Crashes involving the front of motor vehicles, occurring in rural areas, on two-way roads, at higher speed limits, and during weekends were associated with a higher probability of severe injuries. Conversely, crashes at T-intersections, involving the side or rear of motor vehicles, where cyclists wore helmets, or during rush hour were linked to a reduced likelihood of severe injuries. Notably, interaction effects reveal nuanced patterns. For instance, while crossing roadway actions and rush hour periods individually decrease the likelihood of severe crashes, their combination increases the probability of such outcomes. The findings suggest targeted safety measures and policy interventions aimed at enhancing cyclist safety and promoting safer cycling environments by mitigating distraction-related risks.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"107896"},"PeriodicalIF":5.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823940","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, Dorothea Schaffner, Nora Studer, Leah Knecht, Lucy Johnson","doi":"10.1016/j.aap.2024.107856","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107856","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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. 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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How do drivers perceive collision risk? A quantitative exploration in generalized two-dimensional scenarios.","authors":"Jinghua Wang, Guangquan Lu, Wenmin Long, Zhao Zhang, Miaomiao Liu, Yong Xia","doi":"10.1016/j.aap.2024.107879","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107879","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"107879"},"PeriodicalIF":5.7,"publicationDate":"2024-12-11","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":"Pedestrians' perceptions, fixations, and decisions towards automated vehicles with varied appearances.","authors":"Wei Lyu, Yaqin Cao, Yi Ding, Jingyu Li, Kai Tian, Hui Zhang","doi":"10.1016/j.aap.2024.107889","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107889","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"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, Bo Yu, Yuren Chen, Kun Gao, Shan Bao, Qiangqiang Shangguan","doi":"10.1016/j.aap.2024.107877","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107877","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"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, Jaeyoung Jay Lee, Helai Huang, Hanchu Zhou, Ali M A Ibrahim","doi":"10.1016/j.aap.2024.107876","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107876","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"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}