Accident; analysis and prevention最新文献

{"title":"Estimating occupation-related crashes in light and medium size vehicles in Kentucky: A text mining and data linkage approach","authors":"Caitlin A. Northcutt ,&nbsp;Nikiforos Stamatiadis ,&nbsp;Michael A. Fields ,&nbsp;Reginald Souleyrette","doi":"10.1016/j.aap.2024.107749","DOIUrl":"10.1016/j.aap.2024.107749","url":null,"abstract":"<div><p>Occupational motor vehicle (OMV) crashes are a leading cause of occupation-related injury and fatality in the United States. Statewide crash databases provide a good source for identifying crashes involving large commercial vehicles but are less optimal for identifying OMV crashes involving light or medium vehicles. This has led to an underestimation of OMV crash counts across states and an incomplete picture of the magnitude of the problem. The goal of this study was to develop and pilot a systematic process for identifying OMV crashes in light and medium vehicles using both state crash and health-related surveillance databases. A two-fold process was developed that included: 1) a machine learning approach for mining crash narratives and 2) a deterministic data linkage effort with crash state data and workers compensation (WC) claims records and emergency medical service (EMS) data, independently. Overall, the combined process identified 5,302 OMV crashes in light and medium vehicles within one year’s worth of crash data. Findings suggest the inclusion of multi-method approaches and multiple data sources can be implemented and used to improve OMV crash surveillance in the United States.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107749"},"PeriodicalIF":5.7,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998331","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":"Network-level crash risk analysis using large-scale geometry features","authors":"Shi Qiu ,&nbsp;Hanzhang Ge ,&nbsp;Zheng Li ,&nbsp;Zhixiang Gao ,&nbsp;Chengbo Ai","doi":"10.1016/j.aap.2024.107746","DOIUrl":"10.1016/j.aap.2024.107746","url":null,"abstract":"<div><p>Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways’ horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model’s attributes with the Harris Hawks Optimization (HHO) algorithm—referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of “region-road-segment” for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107746"},"PeriodicalIF":5.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993683","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 vehicle-vehicle and vehicle–pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit","authors":"Chiang Fu,&nbsp;Hsin-Tung Tu","doi":"10.1016/j.aap.2024.107745","DOIUrl":"10.1016/j.aap.2024.107745","url":null,"abstract":"<div><p>Street intersection crashes often involve two parties: either two vehicles hitting each other (i.e., a vehicle-vehicle crash) or a vehicle colliding with a pedestrian (i.e., a vehicle–pedestrian crash). In such crashes, the severity of injuries can vary considerably between the parties involved. It is necessary to understand the injuries of both parties simultaneously to identify the causality of a vehicle–pedestrian or two-vehicle crash. While the latent class ordinal model has been used in crash severity studies to capture heterogeneity in crash propensity, most of these studies are univariate, which is inappropriate for crashes involving two parties. This study proposes a latent class parameterized correlation bivariate generalized ordered probit (LC<em>p</em>-BGOP) model to examine 32,308 vehicle-vehicle and vehicle–pedestrian crashes at intersections in Taipei City, Taiwan. The model parameterizes thresholds and within-crash correlations of crash severity involving two parties and classifies these crashes into two distinct risk groups: the “Ordinary Crash Severity” (OCS) group and the “High Crash Severity” (HCS) group. The OCS group is mainly two-vehicle crashes involving motorcycles. The HCS group comprises vulnerable road users such as pedestrians and cyclists, mainly in mixed traffic with heavy volumes. The results also show that the effects of party-specific factors contributing to injury severity are greater than those of generic factors. Our study provides invaluable insight into intersection crashes, helping to reduce the severity of injuries in vehicle-vehicle and vehicle–pedestrian crashes.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107745"},"PeriodicalIF":5.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993682","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":"Enhancing mutual understanding of e-scooter user’s perspective in overtaking maneuver through replaying own driving trajectory","authors":"Taeho Oh ,&nbsp;Jaehyuck Lim ,&nbsp;Reuben Tamakloe ,&nbsp;Zhibin Li ,&nbsp;Inhi Kim","doi":"10.1016/j.aap.2024.107750","DOIUrl":"10.1016/j.aap.2024.107750","url":null,"abstract":"<div><p>The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers’ viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107750"},"PeriodicalIF":5.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993681","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":"The efficacy of training to improve road safety in elderly pedestrians: A systematic review","authors":"Llewella N. Blanks ,&nbsp;Zoe T. Carrick ,&nbsp;Thomas B. McGuckian ,&nbsp;Joanne M. Bennett","doi":"10.1016/j.aap.2024.107735","DOIUrl":"10.1016/j.aap.2024.107735","url":null,"abstract":"<div><p>Elderly pedestrians are involved in disproportionately more vehicle–pedestrian crashes than younger age groups. Training programs have been found to be effective in training children in pedestrian behaviours that improve their safety, however there is no consensus on whether older adults benefit from training. This systematic review aimed to identify whether training is effective for older adult pedestrians through analysis of training type, modalities, and the lasting effects of training. A systematic search of Medline, PsycINFO, and Scopus was conducted in March 2022 and updated in September 2023. Eight studies met the criteria all of which were high quality. Four distinct training types were found: physical (e.g., training physical strength or balance), behavioural (e.g., training specific pedestrian safety behaviours), cognitive (e.g., training reaction time and executive functioning), and educational (training knowledge about pedestrian safety behaviours). Physical training types were found to be most effective, followed by behavioural, cognitive, and educational respectively. Twelve pedestrian behaviours were measured across the eight studies. Reaction time was the most effectively trained outcome, followed by missed crossing opportunities. Errors of stimuli, median accepted time gap, initiation time and crossing were not effectively trained. The effects of training were maintained at follow-up for missed crossing opportunities only. There was preliminary evidence of potential efficacy of training for specific pedestrian safety behaviours, however, the long-term efficacy of training was not promising. Theory-driven research is needed to better understand why some behaviours are more trainable than others. More research is also needed to determine the real-world generalisability if training is to be recommended for older adult pedestrians.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107735"},"PeriodicalIF":5.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S000145752400280X/pdfft?md5=69e7eef01af5fce0bc21c203e6afd39a&pid=1-s2.0-S000145752400280X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987171","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":"Real-time conflict risk at signalized intersection using drone video: A random parameters logit model with heterogeneity in means and variances","authors":"Shile Zhang,&nbsp;N.N. Sze","doi":"10.1016/j.aap.2024.107739","DOIUrl":"10.1016/j.aap.2024.107739","url":null,"abstract":"<div><p>Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107739"},"PeriodicalIF":5.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141990474","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":"Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework","authors":"Zhiyuan Sun ,&nbsp;Zhoumeng Ai ,&nbsp;Zehao Wang ,&nbsp;Jianyu Wang ,&nbsp;Xin Gu ,&nbsp;Duo Wang ,&nbsp;Huapu Lu ,&nbsp;Yanyan Chen","doi":"10.1016/j.aap.2024.107740","DOIUrl":"10.1016/j.aap.2024.107740","url":null,"abstract":"<div><p>The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into three levels (i.e., slight, ordinary, severe) based on point deduction, and explore the patterns of serious traffic violations (i.e., ordinary, severe) using multi-source data. This paper designed an interpretable machine learning framework, in which four popular machine learning models were enhanced and compared. Specifically, adaptive synthetic sampling method was applied to overcome the effects of imbalanced data and improve the prediction accuracy of minority classes (i.e., ordinary, severe); multi-objective feature selection based on NSGA-II was used to remove the redundant factors to increase the computational efficiency and make the patterns discovered by the explainer more effective; Bayesian hyperparameter optimization aimed to obtain more effective hyperparameters combination with fewer iterations and boost the model adaptability. Results show that the proposed interpretable machine learning framework can significantly improve and distinguish the performance of four popular machine learning models and two post-hoc interpretation methods. It is found that six of the top ten important factors belong to multi-scale built environment attributes. By comparing the results of feature contribution and interaction effects, some findings can be summarized: ordinary and severe traffic violations have some identical influencing factors and interactive effects; have the same influencing factors or the same combinations of influencing factors, but the values of the factors are different; have some unique influencing factors and unique combinations of influencing factors.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107740"},"PeriodicalIF":5.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979102","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 negative binomial Lindley approach considering spatiotemporal effects for modeling traffic crash frequency with excess zeros","authors":"Wencheng Wang ,&nbsp;Yang Yang ,&nbsp;Xiaobao Yang ,&nbsp;Vikash V. Gayah ,&nbsp;Yunpeng Wang ,&nbsp;Jinjun Tang ,&nbsp;Zhenzhou Yuan","doi":"10.1016/j.aap.2024.107741","DOIUrl":"10.1016/j.aap.2024.107741","url":null,"abstract":"<div><p>Statistical analysis of traffic crash frequency is significant for figuring out the distribution pattern of crashes, predicting the development trend of crashes, formulating traffic crash prevention measures, and improving traffic safety planning systems. In recent years, the theory and practice for traffic safety management have shown that road crash data have characteristics such as spatial correlation, temporal correlation, and excess zeros. If these characteristics are ignored in the modeling process, it may seriously affect the fitting performance and prediction accuracy of traffic crash frequency models and even lead to incorrect conclusions. In this research, traffic crash data from rural two-way two-lane from four counties in Pennsylvania, USA was modeled considering the spatiotemporal effects of crashes. First, a negative binomial Lindley spatiotemporal effect model of crash frequency was constructed at the micro level; Simultaneously, the characteristics and problems of excess zeros and potential heterogeneity of the crash data were resolved; Finally, the effects of road characteristics on crash frequency were analyzed. The results indicate a significant spatial correlation between the crash frequency of adjacent road sections. Compared with the negative binomial model, the negative binomial Lindley model can better handle the excess zeros characteristics in traffic crash data. The model that considers both spatial correlation and temporal conditional autoregressive effects has the best fit for the observed data. In addition, for road sections that allow passing and have a speed limitation of not less than 50 miles per hour, the crash frequency corresponding to these sections is lower due to their good visibility and road conditions. The increase in average turning angle and intersection density on the horizontal curve of the road section corresponds to an increase in crash frequency.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107741"},"PeriodicalIF":5.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141974755","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":"Identifying factors related to pedestrian and cyclist crashes in ACT, Australia with an extended crash dataset","authors":"Bo Du ,&nbsp;Cheng Zhang ,&nbsp;Arupa Sarkar ,&nbsp;Jun Shen ,&nbsp;Akbar Telikani ,&nbsp;Hao Hu","doi":"10.1016/j.aap.2024.107742","DOIUrl":"10.1016/j.aap.2024.107742","url":null,"abstract":"<div><p>As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels. At the macro-level, random parameters negative binomial (RPNB) model is applied to evaluate the effects of Suburbs and Localities Zones (SLZs) based variables on the frequency of pedestrian and cyclist crashes. At the micro-level, binary logit model is adopted to evaluate the effects of event-based variables on the severity of pedestrian and cyclist crashes. The research findings show that multiple factors are associated with high frequency of pedestrian total crashes and fatal/injury crashes, including high population density, high percentage of urban arterial road, low on-road cycleway density, high number of traffic signals and high number of schools. Meanwhile, many factors have positive relations with high frequency of cyclist total crashes and fatal/injury crashes, including high population density, high percentage of residents cycling to work, high median household income, high percentage of households with no motor vehicle, high percentage of urban arterial road and rural road, high number of bus stops and high number of schools. Additionally, it is found that more severe pedestrian crashes occur: (i) at non-signal intersections, (ii) in suburb areas, (iii) in early morning, and (iv) on weekdays. More severe cyclist crashes are observed when the crash type is overturned or struck object/pedestrian/animal; when more than one cyclist is involved; and when crash occurs at park/green space/nature reserve areas.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107742"},"PeriodicalIF":5.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0001457524002872/pdfft?md5=20a0cb6b015faf47ae346ad854f7a2e9&pid=1-s2.0-S0001457524002872-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951208","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":"A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model","authors":"Dan Wu,&nbsp;Jaeyoung Jay Lee,&nbsp;Ye Li,&nbsp;Jipu Li,&nbsp;Shan Tian,&nbsp;Zhanhao Yang","doi":"10.1016/j.aap.2024.107738","DOIUrl":"10.1016/j.aap.2024.107738","url":null,"abstract":"<div><p>For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models. An analytical framework was proposed, further illustrated through a detailed case study. In the case study, real vehicle trajectory data from HighD were processed and applied, which can be aggregated to extract both traffic state variables and corresponding conflict data during a specific time interval. As for the conflict detection, Time-to-Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) indicators were utilized to identify risky conditions. Based on the proposed approach, the selection for the optimal prediction model was conducted, and the variable importance in conflict prediction within the optimal models derived from the SM-OPMS was also investigated. Finally, a comparative analysis with the enumeration-based optimal prediction model selection (E-OPMS) approach was conducted to validate the superiority of the proposed approach. Results indicate that SM-OPMS outperforms E-OPMS in optimal model selection, notably enhancing computational efficiency by up to 94.03%, while maintaining prediction precision within a maximum reduction of only 7.91%. The significance of the SM-OPMS approach is revealed by its comprehensive selection of the optimal prediction models for specific traffic scenarios, taking into account both prediction efficiency and precision simultaneously. The proposed approach is expected to contribute to the development of real-time conflict prediction in the future.</p></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"207 ","pages":"Article 107738"},"PeriodicalIF":5.7,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141911321","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}