Accident; analysis and prevention最新文献_第3页

Transferring crash modification factors to automated vehicle environments using surrogate endpoints: Theoretical considerations 使用代理端点将碰撞修改因素转移到自动车辆环境：理论考虑

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-06-13 DOI: 10.1016/j.aap.2025.108112

Gary A. Davis, Jingru Gao

{"title":"Transferring crash modification factors to automated vehicle environments using surrogate endpoints: Theoretical considerations","authors":"Gary A. Davis, Jingru Gao","doi":"10.1016/j.aap.2025.108112","DOIUrl":"10.1016/j.aap.2025.108112","url":null,"abstract":"<div><div>Although the <em>Highway Safety Manual</em> was developed primarily from statistical summaries of conditions prevailing on North American roads, engineers in other nations have expressed interest in applying, or “transferring,” its predictive methods to places other than those providing the source data. More recently, an emerging issue concerns the application of crash modification factors (CMF) estimated for recent conditions to possibly different conditions in the future, which could change significantly if and when automated vehicles increase their market share. This leads to the question of how the past investment in safety research might be leveraged with a limited experience of newer conditions in order to support reasonable decision-making. The main claim of this paper is that when background knowledge regarding a type of road crash can be reliably represented by a directed acyclic graph, the graph’s connectivity structure can be used to identify a set of surrogate endpoints that will support transfer of a CMF estimated in one situation to a different situation. We present two analytic results that explicate this claim and then use simulation to illustrate the potential applicability of these results. We end with suggestions for further research to help make this approach practical.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108112"},"PeriodicalIF":5.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271748","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}

引用次数: 0

Safety evaluation of protected bike Lane treatments at Intersections: A causal framework 交叉口保护自行车道处理的安全性评价：一个因果框架

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-06-12 DOI: 10.1016/j.aap.2025.108132

Bingyou Dai , Xuesong Wang , Qiming Guo , Lu Yang , Yu Bai

{"title":"Safety evaluation of protected bike Lane treatments at Intersections: A causal framework","authors":"Bingyou Dai , Xuesong Wang , Qiming Guo , Lu Yang , Yu Bai","doi":"10.1016/j.aap.2025.108132","DOIUrl":"10.1016/j.aap.2025.108132","url":null,"abstract":"<div><div>Intersections are a critical focus in bicycle safety research, as approximately one-thirds of bicycle-related crashes occur at these locations. Although protected bike lanes (PBL) at intersections, such as Lateral Shift and Bend-out treatments have been implemented, there is limited crash-based research on their safety performance. Furthermore, the prevailing use of before-after study designs for safety evaluation makes this approach susceptible to selection bias. To address this issue, this study proposes a causal inference framework that combines the advanced generalized causal random forest (GRF) and multimodal large language model (LLM). The LLM is used to extract contextual features from street view images, improving control over unobserved confounding bias. The GRF model is used for effectiveness evaluation by addressing selection bias through residual-based orthogonalization of treatment and outcome. The framework was applied to evaluate the safety impacts of Bend-out and Lateral Shift treatments at intersections. The results indicate that the proposed method outperforms both the baseline and comparative models across all metrics. The average treatment effect (ATE) of Lateral Shift treatments is 1.35 for total crashes and 1.21 for bicycle crashes, suggesting that these treatments tend to increase crashes. For Bend-out treatments, the ATE is −1.61 for total crashes and −0.55 for bicycle crashes, corresponding to a 32.2% reduction in total crashes and a 22.4% reduction in bicycle crashes. Analysis of road user behavior reveals that for Lateral Shift treatments, the low rate of drivers yielding to cyclists is a major issue, with only 30.7% of drivers yielding. To effectively implement Lateral Shift treatments, strengthening enforcement measures should be considered. Furthermore, riding in the wrong direction is a potential risk for both Lateral Shift and Bend-out treatments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108132"},"PeriodicalIF":5.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263797","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}

引用次数: 0

Modeling decision-making during unprotected left turns using interpretable deep learning and uncertainty quantification 利用可解释深度学习和不确定性量化建模无保护左转弯时的决策

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-06-12 DOI: 10.1016/j.aap.2025.108136

Yubin Chen , Yajie Zou , Jun Liu , Yuanchang Xie , Jinjun Tang

{"title":"Modeling decision-making during unprotected left turns using interpretable deep learning and uncertainty quantification","authors":"Yubin Chen , Yajie Zou , Jun Liu , Yuanchang Xie , Jinjun Tang","doi":"10.1016/j.aap.2025.108136","DOIUrl":"10.1016/j.aap.2025.108136","url":null,"abstract":"<div><div>Unprotected left turns present challenges to drivers, as they must manage potential conflicts at intersections, which requires a decision-making process different from that in other driving scenarios. While many studies have modeled human decision-making in unprotected left-turn situations at a behavioral level, most overlook the variability of key information that influences driving behavior and rarely explore the intrinsic mechanisms of decision-making. This study analyzes the decision-making process of drivers in unprotected left-turn scenarios from the perspective of decision uncertainty and explores the relationship between uncertainty and safety. First, a conflict area calculation method is introduced to identify unprotected left-turn interaction events. Next, a transformer model combined with Shapley Additive Explanations is used to identify the key variables driving left-turn decision-making. Finally, Jensen-Shannon divergence are employed to quantify decision-making uncertainty. We explore two types of unprotected left-turn scenarios: left-turn yielding and left-turn proceeding. The experimental results reveal that: (1) left-turning vehicles prioritize static variables, such as waiting time and vehicle type as key variables, while oncoming vehicles focus more on dynamic variables like time to the stop line and speed difference; (2) increased time pressure leads drivers to emphasize on lateral speed and yaw angles during critical decision phases; and (3) higher uncertainty levels are often accompanied by longer negotiation processes and shorter post-encroachment times, which can increase the likelihood of unsafe maneuvers, such as emergency braking. These insights are instrumental in informing decision-making frameworks for autonomous vehicles navigating unprotected left-turn scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108136"},"PeriodicalIF":5.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271747","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}

引用次数: 0

Composite safety potential field for highway driving risk assessment 公路行车风险评价的复合安全势场

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-06-08 DOI: 10.1016/j.aap.2025.108080

Dachuan Zuo, Zilin Bian, Fan Zuo, Kaan Ozbay

{"title":"Composite safety potential field for highway driving risk assessment","authors":"Dachuan Zuo, Zilin Bian, Fan Zuo, Kaan Ozbay","doi":"10.1016/j.aap.2025.108080","DOIUrl":"10.1016/j.aap.2025.108080","url":null,"abstract":"<div><div>In the era of rapid advancements in vehicle safety technologies, driving risk assessment has become a focal point of attention. Technologies such as collision warning systems, advanced driver assistance systems (ADAS), and autonomous driving require driving risks to be evaluated proactively and in real time. To be effective, driving risk assessment metrics must not only accurately identify potential collisions but also exhibit human-like reasoning to enable safe and seamless interactions between vehicles. Existing safety potential field models assess driving risks by considering both objective and subjective safety factors. However, their practical applicability in real-world risk assessment tasks is limited. These models are often challenging to calibrate due to the arbitrary nature of their structures, and calibration can be inefficient because of the scarcity of accident statistics. Additionally, they struggle to generalize across both longitudinal and lateral risks. To address these challenges, we propose a composite safety potential field framework, namely C-SPF, involving a subjective field to capture drivers’ risk perception about spatial proximity and an objective field to quantify the imminent collision probability, to comprehensively evaluate driving risks. Different from existing models, the C-SPF is calibrated using abundant two-dimensional spacing data from trajectory datasets, enabling it to effectively capture drivers’ proximity risk perception and provide a more realistic explanation of driving behaviors. Analysis of a naturalistic driving dataset demonstrates that the C-SPF can capture both longitudinal and lateral risks that trigger drivers’ safety maneuvers. Further case studies highlight the C-SPF’s ability to explain lateral driver behaviors, such as abandoning lane changes or adjusting lateral position relative to adjacent vehicles, which are capabilities that existing models fail to achieve.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108080"},"PeriodicalIF":5.7,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241780","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}

引用次数: 0

Understanding driver cognition and decision-making behaviors in high-risk scenarios: A drift diffusion perspective 高风险情景下驾驶员认知和决策行为的理解：漂移扩散视角

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-06-08 DOI: 10.1016/j.aap.2025.108123

Heye Huang , Zheng Li , Hao Cheng , Haoran Wang , Junkai Jiang , Xiaopeng Li , Arkady Zgonnikov

{"title":"Understanding driver cognition and decision-making behaviors in high-risk scenarios: A drift diffusion perspective","authors":"Heye Huang , Zheng Li , Hao Cheng , Haoran Wang , Junkai Jiang , Xiaopeng Li , Arkady Zgonnikov","doi":"10.1016/j.aap.2025.108123","DOIUrl":"10.1016/j.aap.2025.108123","url":null,"abstract":"<div><div>Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. This paper presents a cognition-decision framework that integrates individual variability and commonalities in driver behavior to quantify risk cognition and model dynamic decision-making. First, a risk sensitivity model based on a multivariate Gaussian distribution is developed to characterize individual differences in risk cognition. Then, a cognitive decision-making model based on the drift diffusion model (DDM) is introduced to capture common decision-making mechanisms in high-risk environments. The DDM dynamically adjusts decision thresholds by integrating initial bias, drift rate, and boundary parameters, adapting to variations in speed, relative distance, and risk sensitivity to reflect diverse driving styles and risk preferences. By simulating high-risk scenarios with lateral, longitudinal, and multidimensional risk sources in a driving simulator, the proposed model accurately predicts cognitive responses and decision behaviors during emergency maneuvers. Specifically, by incorporating driver-specific risk sensitivity, the model enables dynamic adjustments of key DDM parameters, allowing for personalized decision-making representations in diverse scenarios. Comparative analysis with IDM, Gipps, and MOBIL demonstrates that DDM more precisely captures human cognitive processes and adaptive decision-making in high-risk scenarios. These findings provide a theoretical basis for modeling human driving behavior and offer critical insights for enhancing AV-human interaction in real-world traffic environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108123"},"PeriodicalIF":5.7,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241779","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}

引用次数: 0

When language and vision meet road safety: Leveraging multimodal large language models for video-based traffic accident analysis 当语言和视觉满足道路安全：利用多模式大语言模型进行基于视频的交通事故分析

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-06-04 DOI: 10.1016/j.aap.2025.108077

Ruixuan Zhang , Beichen Wang , Juexiao Zhang , Zilin Bian , Chen Feng , Kaan Ozbay

{"title":"When language and vision meet road safety: Leveraging multimodal large language models for video-based traffic accident analysis","authors":"Ruixuan Zhang , Beichen Wang , Juexiao Zhang , Zilin Bian , Chen Feng , Kaan Ozbay","doi":"10.1016/j.aap.2025.108077","DOIUrl":"10.1016/j.aap.2025.108077","url":null,"abstract":"<div><div>The increasing availability of traffic videos functioning on a 24/7/365 time scale has the great potential of increasing the spatio-temporal coverage of traffic accidents, which will help improve traffic safety. However, analyzing footage from hundreds, if not thousands, of traffic cameras in a 24/7/365 working protocol still remains an extremely challenging task, as current vision-based approaches primarily focus on extracting raw information, such as vehicle trajectories or individual object detection, but require laborious post-processing to derive actionable insights. We propose SeeUnsafe, a new framework that integrates Multimodal Large Language Model (MLLM) agents to transform video-based traffic accident analysis from a traditional extraction-then-explanation workflow to a more interactive, conversational approach. This shist significantly enhances processing throughput by automating complex tasks like video classification and visual grounding, while improving adaptability by enabling seamless adjustments to diverse traffic scenarios and user-defined queries. Our framework employs a severity-based aggregation strategy to handle videos of various lengths and a novel multimodal prompt to generate structured responses for review and evaluation to enable fine-grained visual grounding. We introduce IMS (Information Matching Score), a new MLLM-based metric for aligning structured responses with ground truth. We conduct extensive experiments on the Toyota Woven Traffic Safety dataset, demonstrating that SeeUnsafe effectively performs accident-aware video classification and enables visual grounding by building upon off-the-shelf MLLMs. Our code will be made publicly available upon acceptance.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108077"},"PeriodicalIF":5.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205023","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}

引用次数: 0

Segment length optimization for crash frequency modelling: Evaluating power spectral segment length in safety performance assessment 碰撞频率建模中的段长度优化：安全性能评估中的功率谱段长度评估

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-05-27 DOI: 10.1016/j.aap.2025.108122

Parveen Kumar , Geetam Tiwari , Sourabh Bikas Paul

{"title":"Segment length optimization for crash frequency modelling: Evaluating power spectral segment length in safety performance assessment","authors":"Parveen Kumar , Geetam Tiwari , Sourabh Bikas Paul","doi":"10.1016/j.aap.2025.108122","DOIUrl":"10.1016/j.aap.2025.108122","url":null,"abstract":"<div><div>Selecting an appropriate segment length is essential for road safety analysis, as it directly influences crash analysis accuracy, hazardous location identification, and safety performance evaluation. The traditional segmentation approaches rely on individual expertise or engineering judgment and often lack standardized metrics for evaluating segmentation performance. Therefore, this study expands the utilization of Spatial Frequency Domain Analysis (SFDA) based Power Spectral Segment Length (PSSL) in crash frequency modeling for predicting fatal crash occurrence. The power spectral analysis reveals that crash frequencies predominantly concentrate in low-frequency bands, which helps in determining the Power Spectral Percentage (PSP), a critical measure for evaluating segmentation performance. The Random Parameters Negative Binomial (RPNB) models are developed for six rural two-lane highways in order to evaluate the effectiveness of PSSL, accounting for unobserved heterogeneity in crash data. The study results indicate that PSSL-based segmentation consistently outperforms traditional segmentation methods, as demonstrated by Cumulative Residual (CURE) plots and Goodness-of-Fit statistics. Additionally, the study results show that roadside service areas, population density, minor access points, and heterogeneous traffic characteristics are the most significant predictors of fatal crashes across all highway types. Hence, this study provides an optimized, data-driven, and theoretically justified framework for segment length selection, which improves accuracy, reliability, and scalability in crash modeling and road assessment.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108122"},"PeriodicalIF":5.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138521","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}

引用次数: 0

Exploring the relationship between cyclists’ perceived unsafety, crash risk, and exposure in Dutch cities 探索荷兰城市中骑自行车者感知不安全、碰撞风险和暴露之间的关系

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-05-24 DOI: 10.1016/j.aap.2025.108113

Teun Uijtdewilligen , Mehmet Baran Ulak , Gert Jan Wijlhuizen , Karst T. Geurs

{"title":"Exploring the relationship between cyclists’ perceived unsafety, crash risk, and exposure in Dutch cities","authors":"Teun Uijtdewilligen , Mehmet Baran Ulak , Gert Jan Wijlhuizen , Karst T. Geurs","doi":"10.1016/j.aap.2025.108113","DOIUrl":"10.1016/j.aap.2025.108113","url":null,"abstract":"<div><div>Road safety of cyclists can be investigated in terms of objective measures based on crashes and conflicts and in subjective measures based on perceptions of cyclists. The number of studies investigating these two safety measures simultaneously is limited, in particular studies that also include exposure metrics. Therefore, the present study aims to find out to what extent perceived unsafety and crash risk of cyclists correlate and spatially align and how this relates to exposure to cyclists and motorised vehicles in the four largest Dutch cities. For this purpose, data and models estimated in earlier work on objective road safety and perceived safety of cyclists are combined. Perceived unsafety is expressed as the probability that a cyclist indicates a road section as unsafe while crash risk is expressed as the probability of a bicycle crash occurring. Results show a significant positive correlation between perceived unsafety and crash risk. It is also shown that perceived unsafety increases stronger than crash risk, which is particularly related to an increase in exposure to cyclists, followed by exposure to motorised vehicles. Conversely, crash risk remains relatively low with an increase in exposure to cyclists, which might indicate a safety-in-numbers effect. However, from a certain point in the exposure to cyclists, crash risk increases more strongly. Presumably, this hints at a situation beyond the safety-in-numbers effect where increasing cycling volumes affect cycling safety more negatively. It can be concluded that the probability of perceiving a road section as unsafe significantly follows the same direction as the probability of a bicycle crash occurring, but with a increasing exposure to cyclists perceived unsafety increases stronger than crash risk. With higher exposure to motorised vehicles, on the other hand, the increase in perceived unsafety and crash risk is more gradual.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108113"},"PeriodicalIF":5.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131157","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}

引用次数: 0

Influencing factors of risky behavior in truck safety: A random parameter model incorporating trip-wise heterogeneity 卡车安全危险行为的影响因素：一个包含行程异质性的随机参数模型

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-05-22 DOI: 10.1016/j.aap.2025.108089

Xiao Hu , Yunxuan Li , Ke Zhang , Meng Li

{"title":"Influencing factors of risky behavior in truck safety: A random parameter model incorporating trip-wise heterogeneity","authors":"Xiao Hu , Yunxuan Li , Ke Zhang , Meng Li","doi":"10.1016/j.aap.2025.108089","DOIUrl":"10.1016/j.aap.2025.108089","url":null,"abstract":"<div><div>Truck-related crashes cause significant economic losses and casualties, making perception and control of truck driving risk a critical task for the logistics industry. However, heterogeneity in each truck trip is ignored in current studies, hindering precise prediction of truck driving risk. In this research, we defined trip-wise driving behavior as the driving characteristics extracted from real-time trajectory during a single trip, and investigated its impact on truck driving risk considering heterogeneity. Multi-source data were collected and aggregated, including on-board device data recording long-term trajectory and conflict events from 4,672 trucks in China, accompanied by high-resolution traffic environment data collected at the same time. We extracted trajectories on the same route for trucks within the selected fleet, and illustrated the existence of heterogeneity in trip-wise driving behavior using the Kruskal–Wallis test. A random parameter logit model was employed to study the influencing factors on truck driving risk, considering trip-wise heterogeneity. Results indicated that the heterogeneity of each truck trip was mainly reflected in standard deviation of trip-wise speed and environmental conditions (e.g., traffic speed, time of day). The effect of higher standard deviation of trip-wise speed varies significantly across trips, decreasing risk in 73.7% trips and increasing risk in 26.3% trips; this variability was shown through the normal distribution of the estimated parameter. Furthermore, heterogeneity shows the complex factors influencing truck driving risk and reveals overlooked patterns in long-term and trip-wise driving behavior, highlighting the importance of combining long-term behavior pattern with trip-wise behaviors for better risk prediction.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108089"},"PeriodicalIF":5.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106479","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}

引用次数: 0

Crash outcomes of yellow school buses: Random parameter and correlated random parameter logit models with heterogeneity in means 黄色校车碰撞结果：随机参数及相关随机参数logit模型

IF 5.7 1区工程技术

Accident; analysis and prevention Pub Date : 2025-05-21 DOI: 10.1016/j.aap.2025.108109

Monire Jafari , Subasish Das , Swastika Barua , Mahmuda Sultana Mimi , Michael Starewich

{"title":"Crash outcomes of yellow school buses: Random parameter and correlated random parameter logit models with heterogeneity in means","authors":"Monire Jafari , Subasish Das , Swastika Barua , Mahmuda Sultana Mimi , Michael Starewich","doi":"10.1016/j.aap.2025.108109","DOIUrl":"10.1016/j.aap.2025.108109","url":null,"abstract":"<div><div>Despite rigorous safety standards, school buses in the United States still experience around 26,000 crashes annually, resulting in approximately 10 fatalities, with a fatality rate persistently static despite advances in vehicle safety. Such crashes often have significant implications, particularly for the young passengers involved. This study utilized a novel approach by analyzing Texas Crash Records Information System (CRIS) data from 2017 to 2022 through a Random Parameter Logit Model with Heterogeneity in Means (RPLHM) and Correlated RPLHM (CRPLHM). This method allows for a detailed examination of unobserved heterogeneity and specific variances within the data, enhancing the understanding of the complex dynamics influencing crash severity. The analysis revealed that crashes on state highways typically presented a lower likelihood of fatal and severe crash outcomes. Additionally, demographic attributes such as age significantly impacted crash outcomes, with middle-aged drivers (25–54) often experiencing less severe injuries. Additionally, driver inattention was associated with an increased occurrence of no-injury crash outcomes. While daylight is associated with less moderate and possible injury crashes, clear weather was associated with higher no-injury crashes. The transferability tests revealed temporal instability in yellow school bus crash severity patterns across 2017–2022. Key variables such as intersections, daylight, and driver characteristics demonstrated varying effects over time. While morning and afternoon crashes increasingly reduced the likelihood of fatal and severe injuries in later years, factors like divided roadways and clear weather saw greater variability in their impact on no-injury and moderate injury outcomes. These findings highlight the importance of year-specific modeling and support data-driven policymaking to improve school bus safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108109"},"PeriodicalIF":5.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106585","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}

引用次数: 0