Accident; analysis and prevention最新文献

{"title":"The validity of self-assessment predicts on-road driving performance beyond the effects of age and sex in older drivers with and without MCI","authors":"Daniel A. Schlueter,&nbsp;Kim L. Austerschmidt,&nbsp;Jessica Koenig,&nbsp;Maximilian Flieger,&nbsp;Julia Bergerhausen,&nbsp;Thomas Beblo,&nbsp;Martin Driessen,&nbsp;Max Toepper","doi":"10.1016/j.aap.2025.108172","DOIUrl":"10.1016/j.aap.2025.108172","url":null,"abstract":"<div><h3>Objectives</h3><div>Higher age is often seen as a key factor in the decline of driving skills. Moreover, there is some evidence that overestimation is related to both higher age and poorer on-road performance in older drivers. However, it is unknown how the extent of overestimation or underestimation affects on-road driving performance beyond age.</div></div><div><h3>Methods</h3><div>112 older drivers with and without mild cognitive impairment participated in this prospective on-road study. All participants underwent a standardized on-road driving assessment, neuropsychological testing, collection of driving-related data and different self-assessments. Statistical analyses included a hierarchical regression analysis to predict on-road driving performance by adding age and sex in the first step and the validity of self-assessment (VSA) in the second step. Correlation analyses focused on the association between VSA and cognitive and driving-related behavioral factors.</div></div><div><h3>Results</h3><div>Results revealed that the combination of age and sex significantly predicted on-road driving skills (<em>R²_adjusted</em> = 0.320). The inclusion of VSA led to a significant increase of explained variance in the criterion (<em>R²_adjusted</em> = 0.639). Moreover, the degree of overestimation correlated with higher age, lower cognitive performance and more risky driving behavior.</div></div><div><h3>Discussion</h3><div>Our results highlight the importance of the VSA for on-road driving performance beyond the effects of age and sex. Moreover, the VSA appears to decrease towards overestimation with increasing age and decreasing cognition and should thus be a focus of safety research.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108172"},"PeriodicalIF":6.2,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739513","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":"Safe streets for cyclists? Quantifying the causal impact of cycling infrastructure interventions on safety","authors":"Anupriya ,&nbsp;Xiaowei Zhu ,&nbsp;Emma McCoy ,&nbsp;Daniel J. Graham","doi":"10.1016/j.aap.2025.108168","DOIUrl":"10.1016/j.aap.2025.108168","url":null,"abstract":"<div><div>London’s Cycle Superhighways (CS) form a network of cycle routes connecting central London to outer boroughs, introduced in 2010 to promote cycling and improve safety. This paper examines their causal impact on cycling volume and safety using detailed road traffic and road safety data from the UK’s Department for Transport. To estimate these effects, we employ propensity score-matched difference-in-differences and panel outcome regression models, comparing two distinct infrastructure types: segregated and non-segregated CS. A key contribution of this study is the development of a novel safety indicator — the normalised collision rate — that accounts for changes in cyclist volume (exposure) while incorporating expected non-linearities in the relationship between collisions and exposure. Our findings indicate that non-segregated CS did not increase cycling volume but led to a substantially higher collision rate. This increase appears to be driven by a post-intervention surge in the proportion of new, inexperienced cyclists along these routes. In contrast, segregated CS effectively increased cycling volume without increasing collision rates. Further, an evaluation of a major segregation upgrade along an existing non-segregated CS route revealed a notable reduction in collision rates. These results highlight the crucial role of segregated infrastructure in not only encouraging cycling but also ensuring it remains a safe and viable urban transport option.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108168"},"PeriodicalIF":6.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725094","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":"Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data: A spatial machine learning approach","authors":"Lei Han,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.aap.2025.108180","DOIUrl":"10.1016/j.aap.2025.108180","url":null,"abstract":"<div><div>Existing intersection safety analysis studies have primarily focused on macro-level static infrastructure and highly aggregated traffic features. The emergence of Connected Vehicle (CV) has enabled researchers to extract micro-level driving behavior attributes from CVs. Although longitudinal driving behaviors (e.g., hard braking) have been studied recently, critical lateral left and right turn behaviors, which are common and pose potential conflict risk at intersections, have been largely overlooked. Meanwhile, dealing with both spatial heterogeneity and nonlinear effects between crash frequency and multitudinous driving features is another critical challenge for intersection safety analysis. To address such gaps, this study extracted driving behavior features for both longitudinal movements and lateral left and right turns to comprehensively capture driving dynamics at intersections. A novel spatial ML framework was proposed to integrate nonlinear ML models (e.g., LightGBM) with geographically weighted regression: Besides a global ML model training on all samples to fit average estimations, distinct local ML models are trained for each spatial sample with its neighbors to capture localized spatial heterogeneity. Empirical experiments using CV data at a Florida county show that the inclusion of lateral turning behavior (e.g., hard left/right turns) leads to improved accuracy of intersection crash frequency prediction. Compared to traditional Rrandom Forest, XGBoost, LightGBM, and Multilayer Perceptron models, the spatial ML integrating LightGBM demonstrates significant improvements of 5.8%, 6.3%, and 5.6% in RMSE, MAE, and R², respectively. The results reveal the nonlinear impact of driving features and their spatial heterogeneity: In downtown, hard braking events primarily influence the risk of rear-end (RE) crashes. Drivers’ acceleration also is more likely to lead to RE crashes in urban areas. While hard left turns show greater influence of sideswipe and left turn crashes at suburban intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108180"},"PeriodicalIF":6.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739512","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":"Safety performance analysis of toll plaza diverging area based on an improved simulation platform for weak-constraint driving behaviors","authors":"Yi Fei ,&nbsp;Kejun Long ,&nbsp;Lu Xing ,&nbsp;Xin Pei ,&nbsp;Xi Li ,&nbsp;Lan Yao","doi":"10.1016/j.aap.2025.108177","DOIUrl":"10.1016/j.aap.2025.108177","url":null,"abstract":"<div><div>Toll plaza diverging area is a typical non-lane-based high-risk area characterized by frequent weaving and complex vehicle interactions. While observation-based approaches are effective for analyzing current safety conditions, they lack the flexibility in evaluating the safety impacts of infrastructure designs and traffic control strategies under future scenarios. To address this limitation, this study proposes a microsimulation-based approach to analyze the safety performance of toll plaza diverging areas by simulating the realistic conflict distributions under various traffic conditions. Based on the perception-decision-action (PDA) framework, the proposed approach improves the conflict simulation accuracy by more accurately modeling the weak-constraint driving behavior, including non-lane-based perception, dynamic toll lane selection, and car-following under weak-constraint conditions. Validated on real-world trajectory data from two distinct toll plaza diverging areas, the simulated conflict distributions by the PDA approach closely align with the observed data, while SUMO significantly underestimates the safety risks in diverging areas. Furthermore, a simulation platform is developed based on the PDA approach to quantitatively analyze the safety performance of toll plaza diverging areas under different diverging lengths and traffic volumes. Results indicate that insufficient diverging lengths increase severe conflicts, whereas excessively long diverging areas lead to inefficiencies without substantial safety benefits. This study provides novel insights into safety performance analysis in non-lane-based areas, offering a reliable simulation tool for optimizing management strategies in complex weaving scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108177"},"PeriodicalIF":6.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722339","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":"Dynamic decision-making in subway stations: A new model for stair/escalator choice and its impact on accident prevention","authors":"Huayan Shang ,&nbsp;Yan Yang ,&nbsp;Hai-Jun Huang ,&nbsp;Junzhu Mao","doi":"10.1016/j.aap.2025.108170","DOIUrl":"10.1016/j.aap.2025.108170","url":null,"abstract":"<div><div>Frequent escalator-related incidents in subway stations have prompted the authorities to promote the use of public stairs. In daily life, however, pedestrians instinctively prefer to take escalators rather than use stairs. How to better induce pedestrians to choose stairs? It is necessary to reveal the internal mechanism of pedestrian choice. In this paper, we propose a dynamic model of pedestrian flow assignment with hierarchical choices of various facilities in the station. The route choices of facilities are at the tactical level while the path choices of walking directions are at the operational level. A case study in Beijing is conducted to verify the model. It is shown that the lengths of escalators and stairs, escalator speed, and the pedestrian distribution have significant influences on pedestrians’ choice behavior. Long and fast escalators attract more pedestrians and lead to severe crowding at the entrance. Thus, escalator segmentation is suggested for safety. This study can help prevent accidents and manage crowds in busy subway stations.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108170"},"PeriodicalIF":5.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713471","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":"Systematically modeling take-over performance: Considering the indirect effect of meteorological visibility mediated by drivers’ attention","authors":"Qingkun Li ,&nbsp;Zhenyuan Wang ,&nbsp;Wenjun Wang ,&nbsp;Guofa Li ,&nbsp;Jibo He ,&nbsp;Liang Ma ,&nbsp;Bo Cheng","doi":"10.1016/j.aap.2025.108174","DOIUrl":"10.1016/j.aap.2025.108174","url":null,"abstract":"<div><div>Drivers’ take-over performance in conditionally automated driving is simultaneously affected by multiple factors, making the involved causal relationships complex. Although existing studies have explored the mechanism, there is still a lack of models for comprehensively analyzing drivers’ take-over performance under diverse meteorological visibility and take-over time budget (TB) conditions. This study established a structural equation model to systematically investigate the complicated causal relationships among TB, meteorological visibility, drivers’ attention, and take-over performance. Based on a driving simulation experiment, we developed a measurement model of drivers’ attention and take-over performance via confirmatory factor analysis. We deconstructed take-over performance into three aspects: reaction time, control instability, and safety margin. Subsequently, we revealed the causal relationships among the above factors by using path analysis. Our results demonstrated the significant total effects of meteorological visibility on reaction time and safety margin, where the indirect effects are mediated by drivers’ attention. However, we found that meteorological visibility barely impacts the control instability aspect of take-over performance. Moreover, the direct effects of TB and drivers’ attention on take-over performance were substantial. This study reveals the complex mechanism of take-over performance under diverse conditions and provides a theoretical basis for enhancing the safety and user experience of conditionally automated vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108174"},"PeriodicalIF":5.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713463","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":"Heterogeneous and differential treatment effect analysis of safety improvements on freeways using causal inference","authors":"Syed Zaier Zaidi ,&nbsp;Xuesong Wang ,&nbsp;Yesihati Azati ,&nbsp;Jiaqi Li ,&nbsp;Tianxiang Fan ,&nbsp;Mohammed Quddus","doi":"10.1016/j.aap.2025.108173","DOIUrl":"10.1016/j.aap.2025.108173","url":null,"abstract":"<div><div>Evaluating safety effectiveness of freeway design improvements is crucial for enhancing overall safety and confirming the efficacy of specific measures implemented. Limited research has addressed treatment heterogeneities that influence crash outcomes, and previous studies have often been susceptible to confounding biases, which may distort causal inference results. To mitigate confounding biases and establish reliable causal relationships between crashes and treatment interventions, this study employed a causal forest (CF) model to assess the safety efficacy of freeway exit improvements – including lane control, traffic signs, speed-limit signs, and crash attenuators – on freeways in Suzhou, China. We compared naïve and empirical Bayes before-after methods against the Average Treatment Effect (ATE) estimated by the CF approach. Geometric design and traffic operation characteristics were then considered in measuring the Heterogeneous Treatment Effects (HTE) of these improvements, with the aim of identifying road features where treatment benefits were most pronounced. Additionally, a Differential Treatment Effects (DTE) analysis within a causal framework was employed to estimate treatment effects on the residuals, uncovering more intricate and complex causal relationships. The study demonstrated that CF method provides more stable ATE estimates. An analysis of the distribution of the treatment effects revealed a diverse range of impacts, indicating both positive and negative outcomes. Significant variability in treatment effects was evident from heterogeneous testing results. Noteworthy outcomes from treating freeway exits were observed in areas with an Average Annual Daily Traffic (AADT) ranging from 12,000 to 28,000 vehicles per day, average speeds of 95 km/h and above, two or four lanes on each side, and an exit-only ramp configuration. These findings contribute to valuable technical insights for selecting and evaluating safety enhancement strategies on freeways.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108173"},"PeriodicalIF":5.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704434","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 secondary risks induced by defensive braking in autonomous vehicles: a study based on stochastic distribution of drivers","authors":"Tingyu Liu ,&nbsp;Zhenyu Zhao ,&nbsp;Miaomiao Yang ,&nbsp;Tianyuan Han","doi":"10.1016/j.aap.2025.108176","DOIUrl":"10.1016/j.aap.2025.108176","url":null,"abstract":"<div><div>Defensive braking measures in autonomous vehicles effectively enhance driving safety but also raise concerns about the secondary risks they may pose, particularly the potential for rear-end collisions caused by following vehicles. Indeed, being rear-ended by human driven vehicles is already the most common type of accident involving autonomous vehicles. However, the uncertainty in driver following behavior makes it challenging to assess this risk directly. In response, this paper characterizes the stochastic distribution of drivers to simulate and evaluate the impact of defensive braking behavior on the likelihood of rear-end collisions. First, based on Risk Homeostasis Theory and the central limit theorem, we propose the hypothesis that the risk tolerance levels (RTL) of driver populations follow a normal distribution. This hypothesis is validated using the Waymo dataset, leading to the development of a Stochastic Following Model (SFM) that effectively represents the stochastic distribution of drivers. Subsequently, a comparison with the Intelligent Driver Model (IDM) reveals that the SFM not only accurately reflects the stochastic distribution of drivers in mixed traffic flow but also demonstrates its effectiveness in capturing the diversity of driving behaviors. Finally, through the design of simulation experiments across various scenarios using Monte Carlo methods, the results indicate that while brief defensive braking by autonomous vehicles does not significantly affect the collision probability of following vehicles compared to manually driven vehicles, continuous defensive braking behavior substantially increases the likelihood of being rear-ended. The proposed SFM captures the extensive diversity of drivers and the stochasticity of the following process, illustrating the uncertainties inherent in mixed traffic flow. This model may serve as a valuable reference for future studies on the safety characteristics of mixed traffic flows.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108176"},"PeriodicalIF":5.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704435","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":"Interpretable multi-variable transformer network for regional-level short-term bicycle crash risk prediction","authors":"Ling Deng,&nbsp;Chengcheng Xu,&nbsp;Pan Liu,&nbsp;Yuxuan Wang,&nbsp;Kequan Chen","doi":"10.1016/j.aap.2025.108169","DOIUrl":"10.1016/j.aap.2025.108169","url":null,"abstract":"<div><div>Effective short-term prediction of bicycle crashes at the urban regional level is critical for proactive infrastructure safety interventions and data-driven traffic management. However, three key challenges persist: (1) inadequate modeling of complex spatiotemporal dependencies in multi-source heterogeneous data; (2) poor handling of extreme class imbalance and lack of interpretability in deep learning-based short-term predictions; and (3) limited exploration of bicycle infrastructure’s role in regional crash risk assessment. In response to these challenges, we propose an Interpretable Multi-variable Transformer Network (IMTN) that employs four specialized Transformer encoder blocks to extract spatial and temporal dependencies from heterogeneous inputs. To mitigate the severe class imbalance, our approach uses a single, shared model to predict crash risk for one region at a time, rather than all regions simultaneously. This reformulation avoids data sparsity while retaining multi-region inputs, and a spatial weighting mechanism is used to preserve inter-regional dependencies. Meanwhile, an improved Layer-wise Relevance Propagation (LRP) framework is employed to enhance the interpretability of IMTN. We conduct our experiments on a four-year dataset from London, which includes crash records, public bicycle trips, time, weather, road networks, land use, and a rich set of 48 bicycle infrastructure features. The model comparison demonstrates that IMTN consistently outperforms competitive baselines, reducing false positive rate (FPR) by up to 9.08%, improving the area under the curve (AUC) by up to 3.49%, and increasing the G-mean by up to 5.39%. Our model achieves the best performance at the finest temporal resolution (1-hour aggregation), contrary to common expectations. This suggests that the proposed class imbalance handling method may enhance model performance in high-resolution settings. In addition, interpretability analysis identifies segregated cycle lanes, Sheffield stands, and colored path markings as high-impact infrastructure variables, providing data-driven insights that can help inform urban safety planning.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108169"},"PeriodicalIF":5.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703825","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 autonomous vehicle buyer’s decisions: Balancing ethics with Innovation in the trolley dilemma","authors":"Youngjae Yoo ,&nbsp;Hun Kim ,&nbsp;Jiseob Park","doi":"10.1016/j.aap.2025.108175","DOIUrl":"10.1016/j.aap.2025.108175","url":null,"abstract":"<div><div>This study explores complex decision-making processes in autonomous driving, focusing on the ethical challenges presented by the trolley dilemma and the regulatory focus theory. When faced with significant choices in autonomous driving scenarios, such as the trolley dilemma, passengers prefer to make their own decisions rather than rely on a system’s automated choices. This preference of having a choice significantly increases their trust in the technology and their willingness to purchase autonomous vehicles. No notable difference was found in the moral judgment between decisions made by participants and those made by the autonomous system. Moreover, this research highlights the influence of the regulatory focus theory, demonstrating that participants placed greater trust in the system and made safer decisions when presented with prevention-focused messages, emphasizing avoiding adverse outcomes rather than promotion-focused messages highlighting positive outcomes and aspirations. The findings suggest that the presentation of the information and decisions by autonomous vehicles can profoundly influence passengers’ ethical choices, affecting their trust in and acceptance of autonomous technology. This study contributes to the understanding of consumer behavior and ethical decision-making in the rapidly advancing domain of autonomous vehicle technology, offering valuable insight into policy-making and the future design of these systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108175"},"PeriodicalIF":5.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703827","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}