Accident; analysis and prevention最新文献

{"title":"Investigating risk factors associated with injury severity in highway crashes: A hybrid approach integrating two-step cluster analysis and latent class ordered regression model with covariates","authors":"Siliang Luan ,&nbsp;Zhongtai Jiang ,&nbsp;Dayi qu ,&nbsp;Xiaoxia Yang ,&nbsp;Fanyun Meng","doi":"10.1016/j.aap.2024.107805","DOIUrl":"10.1016/j.aap.2024.107805","url":null,"abstract":"<div><div>Highway crashes are responsible for a significant number of severe and fatal injuries drawing considerable attention from transportation authorities and safety researchers. This paper aims to investigate the unobserved heterogeneous effects of various risk factors, such as pre-crash circumstances, environmental and road conditions, vehicle-involved information, and driver attributes on injury severities. Our methodology uses a hybrid approach that combines two-step cluster analysis and latent class ordered regression model with covariates. The proposed approach extends traditional latent class model by elucidating potential relationships among predictors, covariates, and outcomes. A cross-sectional crash data covering a period of over five years (2011–2016) was obtained via the Dutch crash registration database for modeling injury severity outcomes. The results reveal substantial and statistically significant differences in injury severity between two latent classes. Moreover, we identify road lighting, time of crash, road surface conditions, weather, and season as covariates influencing class membership prediction. Factors such as high speed, alcohol involvement, frontal collision points, and older driver demographics increase the probability of serious injury and facility across all cases analyzed. Additionally, we observe notable heterogeneity effects between the two classes regarding temporal characteristics, the number and type of vehicles involved, as well as driver gender. Our findings provide specific and valuable insights into injury severity outcomes, which can inform the formulation of targeted safety countermeasures and regulatory strategies for traffic policies and relevant agencies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107805"},"PeriodicalIF":5.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378965","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":"Predicting risky driving behaviours using the theory of planned behaviour: A meta-analysis","authors":"Klaire Somoray ,&nbsp;Katherine M. White ,&nbsp;Barry Watson ,&nbsp;Ioni Lewis","doi":"10.1016/j.aap.2024.107797","DOIUrl":"10.1016/j.aap.2024.107797","url":null,"abstract":"<div><div>The current <em>meta</em>-analysis explored the efficacy of the theory of planned behaviour (TPB) in predicting high-risk driving behaviours. Specifically, we examined speeding (in relation to exceeding the limit as well as speed compliance), driving under the influence, distracted driving, and seat belt use. We searched four electronic databases (i.e., PubMed, Web of Science, Scopus, and ProQuest) and included original studies that quantitatively measured the relationships between the TPB variables (attitude, subjective norm, perceived behavioural control [PBC], intention, and prospective/objective behaviour). The study identified 80 records with 94 independent samples. Studies were assessed for risk of bias using the JBI checklist for cross-sectional studies and compliance with the TPB guidelines. Together, attitude, subjective norm and PBC explained between 30 % and 51 % of variance found in intention, with attitude showing as the strongest predictor for intention across the different driving behaviours. The findings also showed that the model explained 36 %–48 % variance found in predicting the observed and/or prospective behaviours for distracted driving, speed compliance and speeding. Understanding the varying strengths and thus relative importance of TPB constructs in predicting different risky driving behaviours is crucial for developing targeted road safety interventions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107797"},"PeriodicalIF":5.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374953","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":"The effect of dual training on the hazard response and attention allocation of novice drivers when driving with advanced driver assistance system","authors":"Chunxi Huang ,&nbsp;Zuyuan Wang ,&nbsp;Dengbo He","doi":"10.1016/j.aap.2024.107802","DOIUrl":"10.1016/j.aap.2024.107802","url":null,"abstract":"<div><div>To ensure traffic safety when driving with an advanced driving assistance system (ADAS), drivers are still required to take over control of the vehicle in case of emergency. Drivers’ takeover performance jointly relies on their capability to anticipate the potential hazards in traffic scenarios and an appropriate understanding of ADAS capabilities. However, previous research mostly focused on strengthening drivers’ understanding of ADAS capabilities but ignored drivers’ hazard perception capabilities when using ADAS – the latter is especially weak among novice drivers. This study proposed and evaluated three training methods for novice drivers, i.e., ADAS training only (AD training), hazard perception training only (HP training), and AD+HP training. Their effectiveness on drivers’ attention allocation strategies and responses to hazardous scenarios when handling hazardous scenarios with different levels of complexity were evaluated among 32 novice drivers in a driving simulator study. Results show that the proposed AD+HP training outperformed AD training and HP training in terms of attention allocation strategies (i.e., wider distribution of attention) and responses in hazardous scenarios (i.e., quicker and more attention to cues of importance and larger minimum time gap). However, the effectiveness of all kinds of training was weakened in more complex scenarios. Findings from this study provide insights into driver training in the context of driving automation.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107802"},"PeriodicalIF":5.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370699","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":"Uncertainty-aware probabilistic graph neural networks for road-level traffic crash prediction","authors":"Xiaowei Gao ,&nbsp;Xinke Jiang ,&nbsp;James Haworth ,&nbsp;Dingyi Zhuang ,&nbsp;Shenhao Wang ,&nbsp;Huanfa Chen ,&nbsp;Stephen Law","doi":"10.1016/j.aap.2024.107801","DOIUrl":"10.1016/j.aap.2024.107801","url":null,"abstract":"<div><div>Traffic crashes present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic crash prediction model is crucial to address growing public safety concerns and improve the safety of urban mobility systems. Traditional methods face limitations at fine spatiotemporal scales due to the sporadic nature of high-risk crashes and the predominance of non-crash characteristics. Furthermore, while most current models show promising occurrence prediction, they overlook the uncertainties arising from the inherent nature of crashes, and then fail to adequately map the hierarchical ranking of crash risk values for more precise insights. To address these issues, we introduce the <strong><u>S</u></strong>patio<strong><u>t</u></strong>emporal <strong><u>Z</u></strong>ero-<strong><u>I</u></strong>nflated <strong><u>T</u></strong>wee<strong><u>d</u></strong>ie <strong><u>G</u></strong>raph <strong><u>N</u></strong>eural <strong><u>N</u></strong>etworks (STZITD-GNN), the first uncertainty-aware probabilistic graph deep learning model in road-level daily-basis traffic crash prediction for multi-steps. Our model combines the interpretability of the statistical Tweedie family with the predictive power of graph neural networks, excelling in predicting a comprehensive range of crash risks. The decoder employs a compound Tweedie model, handling the non-Gaussian distribution inherent in crash data, with a zero-inflated component for accurately identifying non-crash cases and low-risk roads. The model accurately predicts and differentiates between high-risk, low-risk, and no-risk scenarios, providing a holistic view of road safety that accounts for the full spectrum of probability and severity of crashes. Empirical tests using real-world traffic data from London, UK, demonstrate that the STZITD-GNN surpasses other baseline models across multiple benchmarks, including a reduction in regression error of up to 34.60% in point estimation metrics and an improvement of above 47% in interval-based uncertainty metrics.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107801"},"PeriodicalIF":5.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370700","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":"Anticipated buffer time – An evasive surrogate safety indicator for risk assessment of unsignalized intersections under heterogeneous traffic and aggressive driving conditions","authors":"Manish Dutta ,&nbsp;Suprava Jena ,&nbsp;Bansil Korat ,&nbsp;Sarthak Bhandari ,&nbsp;George Kennedy Lyngdoh","doi":"10.1016/j.aap.2024.107796","DOIUrl":"10.1016/j.aap.2024.107796","url":null,"abstract":"<div><div>Risk assessment of unsignalized intersections is particularly challenging when confronted with a combination of factors such as heavy traffic, diverse vehicle types, lane indiscipline, aggressive driving, and evasive manoeuvres. Understanding how people drive in these situations is crucial for accurately assessing the risks at unsignalized intersections. This study introduces a novel surrogate safety indicator, i.e. Anticipated Buffer Time (ABT), designed to account for these various factors. Additionally, three new indicators derived from ABT are introduced, namely ABT Negation Ratio, ABT Extremity Ratio, and ABT Progression Ratio. A risk assessment measure, denoted as UnSigRisk Score, is formulated using these three indicators for unsignalized intersections. Three intersections in Ahmedabad, India, were selected for the study due to their manifestation of these challenging conditions. Spearman Rank Correlation Coefficient was estimated to find out how well can UnSigRisk Score measure is able to quantify evasive behaviour. The results indicate that this score proficiently measures evasive behaviour, exhibiting coefficients exceeding 0.6 in all cases—significantly outperforming the current evasive indicators, Yaw Rate Ratio and Jerk. The proposed risk assessment score could serve as a practical tool for transportation authorities, enabling them to identify the most vulnerable intersections and allocate resources for targeted safety interventions wisely. The study unequivocally demonstrates that the use of ABT paves the way for a thorough examination of safety at unsignalized intersections, regardless of driving behaviour and traffic conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107796"},"PeriodicalIF":5.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370698","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":"Effects of helmet usage on moped riders’ injury severity in moped-vehicle crashes: Insights from partially temporal constrained random parameters bivariate probit models","authors":"Chenzhu Wang ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Pengfei Cui ,&nbsp;Lei Han","doi":"10.1016/j.aap.2024.107800","DOIUrl":"10.1016/j.aap.2024.107800","url":null,"abstract":"<div><div>Mopeds are small and move unpredictably, making them difficult for other drivers to perceive. This lack of visibility, coupled with the minimal protection that mopeds provide, can lead to serious crashes, particularly when the rider is not wearing a helmet. This paper explores the association between helmet usage and injury severity among moped riders involved in collisions with other vehicles. A series of joint bivariate probit models are employed, with injury severity and helmet usage serving as dependent variables. Data on two-vehicle moped crashes in Florida from 2019 to 2021 are collected and categorized into three periods: before, during, and after the COVID-19 pandemic. Crash involvement ratios are calculated to examine the safety risk elements of moped riders in various categories, while significant temporal shifts are also explored. The correlated joint random parameters bivariate probit models with heterogeneity in means demonstrate their superiority in capturing interactive unobserved heterogeneity, revealing how various variables significantly affect injury outcomes and helmet usage. Temporal instability related to the COVID-19 pandemic is validated through likelihood ratio tests, out-of-sample predictions, and calculations of marginal effects. Additionally, several parameters are noted to remain temporally stable across multiple periods, prompting the development of a partially temporally constrained modeling approach to provide insights from a long-term perspective. Specifically, it is found that male moped riders are less likely to wear helmets and are negatively associated with injury/fatality rates. Moped riders on two-lane roads are also less likely to wear helmets. Furthermore, moped riders face a lower risk of injury or fatality during daylight conditions, while angle crashes consistently lead to a higher risk of injuries and fatalities across the three periods. These findings provide valuable insights into helmet usage and injury severity among moped riders and offer guidance for developing countermeasures to protect them.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107800"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142363846","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":"Endogeneity of pedestrian survival time and emergency medical service response time: Variations across disadvantaged and non-disadvantaged communities","authors":"A. Latif Patwary ,&nbsp;Asad J. Khattak","doi":"10.1016/j.aap.2024.107799","DOIUrl":"10.1016/j.aap.2024.107799","url":null,"abstract":"<div><div>The Vision Zero-Safe Systems Approach prioritizes fast access to Emergency Medical Services (EMS) to improve the survivability of road users in transportation crashes, especially concerning the recent increase in pedestrian-involved crashes. Pedestrian crashes resulting in immediate or early death are considerably more severe than those taking longer. The time gap between injury and fatality is known as survival time, and it heavily relies on EMS response time. The characteristics of the crash location may be associated with EMS response and survival time. A US Department of Transportation initiative identifies communities often facing challenges. Six disadvantaged community (DAC) indicators, including economy, environment, equity, health, resilience, and transportation access, enable an analysis of how survival and EMS response times vary across DACs and non-DACs. To this end, this study created a unique and comprehensive database by linking DACs data with 2017–2021 pedestrian-involved fatal crashes. This study utilizes two-stage residual inclusion models with segmentation for DACs and non-DACs accounting for the endogenous relationship between EMS response and pedestrian survival time. The results indicate that EMS response time is higher and pedestrian survival time is lower in DACs than in non-DACs. A delayed EMS response time is associated with a greater reduction in survival time in DACs compared to non-DACs. Factors, e.g., nighttime and interstate crashes, contribute to higher EMS response time, while pedestrian drugs, driver speeding, and hit-and-run behaviors are associated with a greater reduction in survival time in DACs than non-DACs. The implications of the findings are discussed in the paper.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107799"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142363847","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":"Driving risk identification of urban arterial and collector roads based on multi-scale data.","authors":"Xintong Yan, Jie He, Guanhe Wu, Shuang Sun, Chenwei Wang, Zhiming Fang, Changjian Zhang","doi":"10.1016/j.aap.2024.107712","DOIUrl":"10.1016/j.aap.2024.107712","url":null,"abstract":"<p><p>Urban arterial and collector roads, while interconnected within the urban transportation network, serve distinct purposes, leading to different driving risk profiles. Investigating these differences using advanced methods is of paramount significance. This study aims to achieve this by primarily collecting and processing relevant vehicle trajectory data alongside driver-vehicle-road-environment data. A comprehensive risk assessment matrix is constructed to assess driving risks, incorporating multiple conflict and traffic flow indicators with statistically temporal stability. The Entropy weight-TOPSIS method and the K-means algorithm are employed to determine the risk scores and levels of the target arterial and collector roads. Using risk levels as the outcome variables and multi-scale features as the explanatory variables, random parameters models with heterogeneity in means and variances are developed to identify the determinants of driving risks at different levels. Likelihood ratio tests and comparisons of out-of-sample and within-sample prediction are conducted. Results reveal significant statistical differences in the risk profiles between arterial and collector roads. The marginal effects of significant parameters are then calculated separately for arterial and collector roads, indicating that several factors have different impacts on the probability of risk levels for arterial and collector roads, such as the number of movable elements in road landscape pictures, the standard deviation of the vehicle's lateral acceleration, the average standard deviation of speed for all vehicles on the road segment, and the number of one-way lanes on the road segment. Some practical implications are provided based on the findings. Future research can be implemented by expanding the collected data to different regions and cities over longer periods.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"206 ","pages":"107712"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141603109","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 CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks","authors":"Danni Cao ,&nbsp;Yunchao Qu ,&nbsp;Jianhua Chen ,&nbsp;Jianjun Wu ,&nbsp;Tianyu Li","doi":"10.1016/j.aap.2024.107798","DOIUrl":"10.1016/j.aap.2024.107798","url":null,"abstract":"<div><div>Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107798"},"PeriodicalIF":5.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356985","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":"Personalized forward collision warning model with learning from human preferences","authors":"Ning Xie ,&nbsp;Rongjie Yu ,&nbsp;Weili Sun ,&nbsp;Shi Qiu ,&nbsp;Kailun Zhong ,&nbsp;Ming Xu ,&nbsp;Guobin Wu ,&nbsp;Yi Yang","doi":"10.1016/j.aap.2024.107791","DOIUrl":"10.1016/j.aap.2024.107791","url":null,"abstract":"<div><div>The Forward Collision Warning (FCW) system has been widely equipped on vehicles to reduce rear-end crashes, which are considered the most common type of crash. However, existing FCW systems have the problem of low response rates, which restrict their safety improvement effects. This study aims to address this issue by building personalized FCW models based on human risk preferences. First, a warning feedback index ranks the gaps between drivers’ risk perceptions and FCW models. Then, reward models are developed to characterize the risk perception preferences of each individual driver. After that, the reward models serve as guidelines to fine-tune the benchmark FCW model using the Proximal Policy Optimization (PPO) algorithm. In the empirical analyses, a total of 95,814 warning fragments collected from 74 drivers are used, and the proposed method generates pseudo warning results. By comparing the pseudo and historical warnings, it shows that the precision of pseudo warning results increases from 53.5% to 78.2%. Furthermore, the average differences between the moment of warning and the moment of braking behavior decrease from 2.4 s to 1.6 s. This demonstrates a higher synchronization level in the timing of risk perception between the personalized FCW models and individual drivers, which enhances the driver’s trust in the warning system.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107791"},"PeriodicalIF":5.7,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142339030","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}