Accident; analysis and prevention最新文献

{"title":"Reliable imputation of incomplete crash data for predicting driver injury severity","authors":"Xiaowei Gao ,&nbsp;Xinke Jiang ,&nbsp;Dingyi Zhuang ,&nbsp;James Haworth ,&nbsp;Shenhao Wang ,&nbsp;Ilya Ilyankou ,&nbsp;Huanfa Chen","doi":"10.1016/j.aap.2025.108020","DOIUrl":"10.1016/j.aap.2025.108020","url":null,"abstract":"<div><div>Traffic crash analyses are frequently challenged by incomplete documentation, particularly in standardised multi-party crash full records. Traditional imputation methods like MICE and KNN, while effective for single-category analyses, fail to address the complex interdependencies inherent in standardised crash records where different types of road user are present. This study introduces a novel graph-based imputation framework that integrates an Inexact Match Bipartite-Graph with Contrastive Learning in a Transformer-GNN architecture, providing a unified solution to handle missing data of various crash types in a complete crash record database. Testing on UK traffic crash records (2018–2022) demonstrates the robust performance of the imputation model, achieving imputation accuracy between 99.24% and 94.74% across missing data rates from 10% to 70%. In the downstream task of classifying the severity of the injury, our imputed data set proved to be highly reliable, achieving a Gmean score of 62.19% to identify levels of imbalanced severity, even under severe missing with a missing rate of 70%. Furthermore, explainable SHAP values demonstrated that data imputation preserved the most important contributing factors. These results validate our framework’s effectiveness in maintaining both data integrity and essential relationship structures in standardised crash records, advancing the field of traffic safety analysis through improved imputation methodology.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"216 ","pages":"Article 108020"},"PeriodicalIF":5.7,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783838","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":"Exploring the effect of driver drowsiness on takeover performance during automated driving: An updated literature review","authors":"Hengyan Pan ,&nbsp;David B. Logan ,&nbsp;Amanda N. Stephens ,&nbsp;William Payre ,&nbsp;Yonggang Wang ,&nbsp;Zhipeng Peng ,&nbsp;Yang Qin ,&nbsp;Sjaan Koppel","doi":"10.1016/j.aap.2025.108023","DOIUrl":"10.1016/j.aap.2025.108023","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Introduction&lt;/h3&gt;&lt;div&gt;Vehicle automation technology has considerable potential for reducing road crashes associated with human error, including issues related to driver drowsiness. However, before full automation becomes available on public roads, it will be essential for drivers to take back control from automated driving systems when requested. This poses a challenge for drivers, particularly as automation may further exacerbate drowsiness. This paper aims to update a systematic review published in 2022 (Merlhiot &amp; Bueno, Accident Analysis and Prevention, 170, 106536), to discuss factors affecting driving drowsiness and takeover performance with a particular focus on those not identified in previous review.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Method&lt;/h3&gt;&lt;div&gt;Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, three databases: &lt;em&gt;Web of Science&lt;/em&gt;, &lt;em&gt;PubMed&lt;/em&gt; and &lt;em&gt;Scopus&lt;/em&gt; were searched for studies published between March 2021 and October 2024. The following eligibility criteria were applied for study inclusion: 1) participants must have interacted with a simulated or real-world vehicle featured with driving automation Level 2 or above; 2) with at least one measurement indicator of driver drowsiness; 3) with at least one measurement indicator of takeover performance; 4) be conducted within a controlled experimental design. From an initial selection of 182 articles from databases, a total of twelve published articles were obtained after removing duplicates, title, abstracts and full texts checking. Additionally, 17 articles from the previous review were included, resulting in a total of 29 articles for this review study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Driver drowsiness (e.g, increased Karolinska Sleepiness Scale levels, blink frequency) tended to increase with both the duration of automated driving and automation levels. Engaging in non-driving related tasks (NDRTs) alleviates drowsiness (e.g, lower heart rate and percentage of eye closure), but reduces takeover performance (e.g., longer braking reaction times, stronger longitudinal acceleration, shorter minimal time to collision). Compared to older drivers, younger drivers were more susceptible to drowsiness, while older drivers had worse takeover performance (e.g., delayed steering reaction time, higher collision rates). Sleep inertia and circadian rhythms were also identified as factors influencing takeover performance. The road monitoring task helps prevent excessive participation in NDRTs and improves takeover performance (e.g, reduced brake reaction times and maximum steering velocity, increased the minimum time to collision). Digital voice assistants and scheduled manual driving help maintain alertness (e.g, decreased blink duration) and enhance takeover performance (e.g, shorter reaction time to resume steering).&lt;/div&gt;&lt;div&gt;There were several limitations of the methodologies applied in the existing studies, among which were: 1) a la","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"216 ","pages":"Article 108023"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739227","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":"An integrated potential safety hazard assessment framework in connected car-following scenario","authors":"Guilong Xu ,&nbsp;Zhen Yang ,&nbsp;Jinfeng Ying ,&nbsp;Shikun Xie ,&nbsp;Shumin Bai ,&nbsp;Yani Qi","doi":"10.1016/j.aap.2025.108010","DOIUrl":"10.1016/j.aap.2025.108010","url":null,"abstract":"<div><div>In continuous car-following scenarios, a minor conflict could be amplified along the platoon over time due to system instability, resulting in high-risk rear-end situations. Conventional surrogate safety measures (SSM) only adopt the motion information of ego vehicle and preceding one, failing to capture potential safety hazard beyond the leading vehicle in the platoon. Identifying potential safety hazard beyond the 1st preceding one is substantially important for connected and automated vehicles (CAVs) which could take proactive actions to prevent high-risk scenarios. Toward this end, drawing on reliability theory, the current paper developed an integrated potential safety hazard assessment framework considering the motion information of preceding multiple vehicles. An innovative surrogate safety measures (SSM), named the potential safety hazard index (PSHI), is developed to capture potential safety hazard from preceding multiple vehicles in the car-following environment. We tested the validity of proposed PSHI in a real-world rear-end crash scenario. To real-timely apply PSHI to CAVs, we developed an orthogonal transformation first order reliability method (OTFORM) to accelerate computational time, keeping computation burden within 0.01 s. Another worthwhile finding is potential safety hazard mainly comes from preceding three vehicles in risky car-following cases. The new potential safety hazard evaluation framework provides a creative perspective for safety assessment and also shows a good prospect for longitudinal safety control of CAVs strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"216 ","pages":"Article 108010"},"PeriodicalIF":5.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739226","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":"Joint analysis of crash injury severities for autonomous and conventional vehicles in mixed traffic environments: Application of random parameter bivariate probit model","authors":"Jian Xiang ,&nbsp;Zhengwu Wang ,&nbsp;Yibo Chen ,&nbsp;Ziran Meng ,&nbsp;Jie Wang","doi":"10.1016/j.aap.2025.108017","DOIUrl":"10.1016/j.aap.2025.108017","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) are expected to significantly enhance road safety in the future. However, until fully autonomous driving systems are widely adopted, mixed traffic with AVs and conventional vehicles (CVs) will remain a typical feature of roadways. Consequently, it is crucial to understand how roadway and built environment factors impact traffic safety in mixed traffic settings. This study proposes a joint model to analyze crash injury severity for both autonomous and conventional vehicles within a unified framework. A random parameter bivariate probit model (RBP) is used as the methodological approach, as it accounts for the correlation between injury outcomes for AVs and CVs, while also capturing unobserved heterogeneity among the factors influencing safety. The model is developed using a dataset of 699 paired crashes, involving both AVs and CVs, occurring in proximity to each other in mixed traffic conditions in California. For comparison, both a random parameters univariate probit model (RUP) and a bivariate probit model (BP) are also developed. Model comparison results demonstrate that the proposed RBP model outperforms both the RUP and BP model in terms of explanatory power and goodness-of-fit. The parameter estimates reveal divergent effects of crash type and cause, natural environmental conditions, roadway features, and built environment factors on injury severity for autonomous and conventional vehicle crashes. The key results include: (1) A primary cause of AV crashes is the failure of CV drivers to respond appropriately or in a timely manner to unexpected changes in AV behaviors. (2) Adverse natural conditions, such as dark, pose a greater safety risk for AVs compared to CVs. (3) Road features with complex traffic conditions—such as Y-shaped intersections, traffic signals, and areas where lanes merge or diverge—are associated with a higher likelihood of injury in AV crashes, whereas these factors do not significantly affect injury severity in CV crashes. (4) Built environment factors related to vulnerable road users and public transportation infrastructure, such as crosswalks, schools, bus stops, and metro stops, exhibit notably heterogeneous effects on injury severity in AV crashes. The findings of this study have important implications for developing targeted strategies to enhance safety in mixed traffic environments. These strategies include establishing effective communication systems between autonomous and conventional vehicles, improving obstacle detection and performance in low-visibility conditions, and ensuring well-equipped road infrastructure for vulnerable road users.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108017"},"PeriodicalIF":5.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734565","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":"Bivariate Bayesian hierarchical extreme value modeling using multi-type traffic conflict for crash estimation on freeway horizontal curves","authors":"Zhenlin Hu,&nbsp;Pengru Wei,&nbsp;Lin Sheng,&nbsp;Guorui Wang,&nbsp;Xianghai Meng","doi":"10.1016/j.aap.2025.108019","DOIUrl":"10.1016/j.aap.2025.108019","url":null,"abstract":"<div><div>Freeway horizontal curves pose great challenges to vehicle driving safety due to suboptimal road alignment, poor visual conditions, and higher demands for driving maneuvers. The interaction between multiple conflicting vehicles may generate multi-type crash risks with correlations. Modeling individual types of crash risks separately will result in biased crash estimation. In this study, a bivariate Bayesian hierarchical extreme value modeling approach, which consists of a bivariate extreme value model and a Bayesian hierarchical structure, is developed. The former integrates two different conflict indicators while also accounting for their correlation. The latter combines traffic conflicts across different sites, incorporating block-level and site-level covariates and unobserved heterogeneity. Using rear-end and lane-changing conflicts collected from 14 directional curved segments of the Yinkun freeway, several univariate Bayesian hierarchical extreme value models (UBHMS) and bivariate Bayesian hierarchical extreme value models (BBHMS) were constructed to estimate expected rear-end crashes and side crashes. The crash estimation results show that the bivariate model considering correlation between multi-type conflicts has smaller standard deviations of the model parameters and outperforms the univariate models in both accuracy and precision of crash estimation. The covariate analysis suggests that a larger proportion of large vehicles and standard deviation of speed will lead to an increase in both rear-end and side crash risks; the number of car-following vehicles and the number of lane-changing vehicles have positive influences on rear-end and side crash risks, respectively, whereas the higher the overspeed and the lane space occupancy instead reduce rear-end crash risk. Finally, when vertical curves overlap with horizontal curves, the rear-end and side crash risks on sag vertical curves exceed those on crest vertical curves.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108019"},"PeriodicalIF":5.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734564","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 time interval and request modality on driver takeover responses: Identifying the optimal time interval for two-stage warning system","authors":"Jie Zhang ,&nbsp;Zhi Zhang ,&nbsp;Tingru Zhang ,&nbsp;Yijing Zhang ,&nbsp;Shanguang Chen","doi":"10.1016/j.aap.2025.108008","DOIUrl":"10.1016/j.aap.2025.108008","url":null,"abstract":"<div><div>Two-stage warning system plays a critical role in guiding drivers to prepare for takeovers in conditional automated driving. However, the optimal time interval for this system, especially under different takeover request (TOR) modalities, remains unclear. A driving simulator experiment with 36 participants was conducted to investigate the effects of time interval and TOR modality of two-stage warning system on drivers’ takeover responses from a multidimensional perspective. Each participant completed takeovers with four time intervals (3 s, 5 s, 7 s, and 9 s) and three TOR modalities (visual-only, auditory-only, and auditory-visual). Drivers’ takeover performance, mental workload, situation awareness (SA), user experience, and eye movements during the takeover process were recorded. The results indicated that drivers showed faster and higher-quality takeovers as the time interval increased from 3 s to 9 s. Their ratings of satisfaction, usefulness, effectiveness, and safeness of the warning system showed the inverted U-shaped trends, with the 7 s as a turning point. The 7 s interval was also favored for drivers to regain sufficient SA while maintaining an appropriate mental workload, as evidenced by both subjective measures and eye-tracking metrics. This allowed drivers to adopt more focused visual strategies for the takeover after receiving TOR warning, thereby improving takeover performance. Additionally, the auditory-visual TOR was found to be the most effective across all measures, followed by the auditory-only TOR, and finally the visual-only TOR. No significant interaction effects between time interval and TOR modality were observed. In conclusion, regardless of TOR modality, the 7 s time interval was generally favored for young drivers with relatively limited driving experience for swift takeover responses, high takeover quality, sufficient SA, appropriate mental workload, and good satisfaction ratings. When the interval was extended to 9 s, drivers’ takeover performance improved, but with the cost of reduced satisfaction and potential shift in visual attention from driving task to non-driving-related task. These findings had implications for the design and application of appropriate time interval of two-stage warning system for Level 3 automatic vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108008"},"PeriodicalIF":5.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725735","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":"Understanding e-scooter rider crash severity using a built environment typology: A two-stage clustering and random parameter model analysis","authors":"Amirhossein Abdi ,&nbsp;Steve O’Hern","doi":"10.1016/j.aap.2025.108018","DOIUrl":"10.1016/j.aap.2025.108018","url":null,"abstract":"<div><div>E-scooters are an emerging transport mode that is transforming urban mobility; however, their proliferation has raised concerns about safety. This study combines UK e-scooter crash data with built environment characteristics from the crash locations. A two-stage framework was followed: first, a typology of built environments was developed using K-means++; second, crash severity within each cluster was analysed using a random parameter binary logit model. Four built environment clusters were identified: (1) car-centric and mixed-use zones, (2) commercial and industrial zones, (3) intersection-dense areas, and (4) residential and central areas. Collisions with motor vehicles, younger e-scooter riders, and higher speed limits were the most common risk factors across the clusters, with the first two clusters showing a higher impact of these factors on the likelihood of severe crashes. In the first and second clusters, riding on the carriageway significantly increased injury severity. In the second cluster, three collision types were significant, more than in other clusters where only side-impact collisions were significant. This indicates high e-scooter–motor vehicle friction in the second cluster. Among all collision types, head-on collisions increased the likelihood of severe outcomes more than others. In the third and fourth clusters, peak hours were associated with a lower likelihood of severe crashes, while this variable showed the opposite impact in the first cluster. The results highlight that consideration of the surrounding built environment is paramount when analysing e-scooter crash severity, as unique contributing factors were identified specific to each built environment type, along with varying magnitudes or directions of marginal effects.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108018"},"PeriodicalIF":5.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725734","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 systematic review on test performance of the driver takeover process in automated driving","authors":"Haolin Chen ,&nbsp;Xiaohua Zhao ,&nbsp;Chen Chen ,&nbsp;Zhenlong Li ,&nbsp;Haijian Li ,&nbsp;Qiuhong Wang","doi":"10.1016/j.aap.2025.108012","DOIUrl":"10.1016/j.aap.2025.108012","url":null,"abstract":"<div><div>Much research has been conducted on takeover behavior in automated driving, and integrating these studies into a knowledge system can help to gain a deeper understanding of the current research status and guide critical future research. The takeover focused in this study refers to the takeover related to human intervention (i.e. the transfer of control between the human driver and the auto drive system), rather than the context of overtaking another vehicle (e.g., lane changes and acceleration). The takeover behavior is a multi-stage process consisting of situation awareness, decision &amp; reaction, and takeover performance stages. An in-depth review of takeover behavior characteristics from the three takeover stages is helpful to describe the takeover process and analyze takeover behavior characteristics systematically. Therefore, this paper aims to review driver’s takeover performance from the three levels of driver, automated vehicle, and road environment based on the takeover behavior mode. First, we identified 1329 articles through a systematic literature search. 122 articles were included in this review. Second, we use the knowledge graph method for bibliometric analysis. Third, we systematically review the characteristics of takeover behavior in three stages (situation awareness, decision &amp; reaction, takeover performance) from three dimensions: driver, vehicle, and road environment. At the same time, this study develops scoring rules that quantify each factor’s contribution to takeover behavior. Fourth, based on the reviewed literature and scores, 18 suggestions were proposed to improve takeover behavior from three levels: drivers, vehicles, road environment. Finally, we have outlined the future fundamental research of takeover behavior. This review summarizes the research content of takeover behavior testing and forms a knowledge system, which provides researchers with a window to understand the research status and development context. This review can guide future research on takeover behavior.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108012"},"PeriodicalIF":5.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680343","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":"Incorporating multi-path risk assessment in transformer-based pedestrian crossing action prediction","authors":"Bowen Liu ,&nbsp;Meng Li ,&nbsp;Ruyi Feng ,&nbsp;Wei Zhou ,&nbsp;Zhibin Li","doi":"10.1016/j.aap.2025.108002","DOIUrl":"10.1016/j.aap.2025.108002","url":null,"abstract":"<div><div>This paper proposes a Transformer-based framework for predicting pedestrian crossing actions that uses visualized pedestrian-vehicle collision risks, which are assessed from multiple potential paths. Our framework contains two sequential steps: (1) multi-path risks of a pedestrian-vehicle interaction (PVIs) at each time point are estimated and encoded into an RGB image, which captures a high-density array of safety information. (2) a multi-modal fusion architecture that incorporates both risk images and historical sequential data (e.g., pedestrian action and vehicle velocity) is developed based on the Cross-Attention Transformer. The model outputs are also risk-informed, categorized as yielding, risky crossing, and safe crossing. Experiments are conducted on real-world data from the Euro-PVI dataset. Through two-dimensional mapping tests, risk images are validated to have significant spatiotemporal feature differences and transition associations under different PVIs. The Transformer architecture proves to be an effective method for processing multi-path risk images. Prediction accuracy reaches 87.34% for short-term forecasts (0.5 s ahead), maintains stability as the prediction time horizon progressively extends to 2 s, and improves the prediction of abrupt action switches. For further exploration and validation, the risk image data and imaging code are available at <span><span>www.github.com/Sivan0227/PVI-Risk-Image</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108002"},"PeriodicalIF":5.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680346","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":"Estimating safety benefit of in-vehicle work zone safety technology alerts: A counterfactual Monte-Carlo simulation approach","authors":"Qishen Ye ,&nbsp;Yihai Fang ,&nbsp;Nan Zheng","doi":"10.1016/j.aap.2025.108014","DOIUrl":"10.1016/j.aap.2025.108014","url":null,"abstract":"<div><div>Work Zone Safety Technologies (WSTs) have exhibited great potential to improve road work zone safety by detecting safety risks and providing warnings to drivers and workers involved. Yet, it remains extremely challenging to quantify the actual safety benefits of such technologies in reducing work zone intrusion accidents, mainly due to a lack of empirical data and robust evaluation methods. This paper aims to explore the patterns of drivers’ behavioural responses when approaching work zones and estimate the safety benefits of in-vehicle WSTs. First, a VR-based driving simulation experiment was conducted to collect human behavioural data on drivers’ responses when approaching work zones in critical scenarios. Second, a Linear Mixed Effect (LME) model was developed to capture the impact of in-vehicle WST alerts and scenario criticality, i.e., speed and Time-to-Collision (TTC), on drivers’ behavioural responses. Finally, the safety benefits of in-vehicle WST alerts were estimated through counterfactual Monte-Carlo simulations of vehicle trajectories. The findings highlight the mechanism by which in-vehicle WST alerts improve driver response in various critical driving scenarios involving work zones and provide crucial evidence for future decision-making regarding the evaluation of WSTs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 108014"},"PeriodicalIF":5.7,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680345","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}