Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.

Accident Analysis and Prevention provides comprehensive coverage of various aspects related to accidental injuries and damages, including the pre-injury and immediate post-injury phases. The published papers address medical, legal, economic, educational, behavioral, theoretical, or empirical aspects of transportation accidents, as well as accidents occurring in other contexts. Selected topics within the scope of the journal may include studies on human, environmental, and vehicular factors influencing the occurrence, type, and severity of accidents and injuries; the design, implementation, and evaluation of countermeasures; the biomechanics of impact and human tolerance limits to injury; modeling and statistical analysis of accident data; and policy, planning, and decision-making in safety.

《Accident Analysis and Prevention》期刊广泛涵盖与意外伤害和损害相关的各个领域，包括伤害发生前和伤害发生后的初步阶段。发表的论文涉及交通事故以及其他场所事故的医学、法律、经济、教育、行为、理论或实证方面。期刊的选定主题可能包括：影响事故发生、类型和严重程度的人类、环境和车辆因素的研究；对对策的设计、实施和评估；冲击生物力学和人类对伤害的耐受极限；事故数据的建模和统计分析；安全领域的政策、规划和决策。

Accident Analysis and Prevention

This study aims to compare the accident injury severity of e-bikes with that of other types of two-wheelers based on accident data and to analyze the factors influencing them. Using 1015 police accident records from Zhangjiakou City in 2020 and 2021, the accident injury severity of e-bikes was firstly compared with that of other two-wheelers based on five levels of accident injury severity classified according to the records. Two ordered Probit regression models were secondly used to compare the factors influencing the accident injury severity of e-bikes with that of other two-wheelers and the magnitude of their effects. At the same time, the contributions of each influential factor to the degree of accident injury of two-wheelers were estimated with the assistance of classification trees. Results show that e-bikes are closer to bicycles than motorcycles in terms of injury severities and the factors influencing them, in which the factors "accident configuration," "division of responsibility for the accident," and "collision with a heavy vehicle or four-wheeled vehicle" are significant. Based on the findings, potential measures are suggested to reduce e-bike accident casualties, such as improving rider education, ensuring speed limit enforcement, promoting safety equipment wearing, and making road design friendly to non-motorized and elderly riders. The results of this study can provide an essential reference for traffic management and rider education measures on e-bikes.

Qian Qian

Jing Shi

Accident; analysis and prevention

Accid Anal Prev

Comparison of injury severity between E-bikes-related and other two-wheelers-related accidents: Based on an accident dataset.

电动自行车相关事故与其他两轮车相关事故伤害严重程度的比较：基于事故数据集。

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

Abdalziz Alruwaili

Kun Xie

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment.

在不同天气条件下，连接车辆对高速公路碰撞场景中驾驶行为和安全结果的影响建模：基于驾驶模拟器实验的多组结构方程建模分析。

Among the three major safety assessment methods (i.e., simulation, test track, and on-road test) for highly automated driving systems (ADS), test tracks provide high fidelity and a safe and controllable testing environment. However, due to the lack of realistic background traffic, scenarios that can be tested in test tracks are usually static and limited. To address this limitation, a new safety assessment framework is proposed in this paper, which integrates an augmented reality (AR) testing platform and a testing scenario library generation (TSLG) method. The AR testing platform generates simulated background traffic in test tracks, which interact with subject ADS under test, to create a realistic traffic environment. The TSLG method can systematically generate a set of critical scenarios under each operational design domain (ODD) and the critical scenarios generated from the TSLG method can be imported into the AR testing platform. The proposed framework has been implemented in the Mcity test track at the University of Michigan with a Level 4 ADS. Field test results show that the proposed framework can accurately and efficiently evaluate the safety performance of highly ADS in a cost-effective fashion. In the cut-in case study, the proposed framework is estimated to accelerate the assessment process by 9.87×104 times comparing to the on-road test approach.

Shuo Feng

Yiheng Feng

Xintao Yan

Shengyin Shen

Shaobing Xu

Henry X. Liu

Accident Analysis & Prevention

Safety assessment of highly automated driving systems in test tracks: A new framework

测试轨道上高度自动化驾驶系统的安全评估：一种新框架

Situational discomfort awareness plays an important role in decision making among drivers and has rarely been discussed in detail in previous research. An instrumented vehicle was used to collect car-following data from multiple drivers, thereby quantitatively examining situational discomfort grading patterns using a new discomfort grading method and the latent Dirichlet allocation model. In this process, the gas pedal data and speed difference data are particularly involved in the computation for providing broader meaning to discomfort and building more comprehensive situations. The results show that individual discomfort awareness varies between drivers. More importantly, the potential patterns of situational discomfort grading are extracted, which provides knowledge for characterizing drivers in the context of discomfort awareness. The knowledge achieved can be further applied to distinguish drivers and identify the typical comfort and discomfort zones. This study has great value for promoting investigations on traffic psychology and developing more effective and customized driver assistant systems.

Geqi Qi

Wei Guan

Quantitatively mining and distinguishing situational discomfort grading patterns of drivers from car-following data.

从跟车数据中定量挖掘并区分驾驶员情境不适等级模式。

Over the past 50 years, research in maritime accidents has undergone a series of fundamental changes. Understanding the evolution of these changes can help maritime communities to know what has been done in the past, how maritime safety can be improved in the future, and how to reduce or eliminate the risks to ships, the lives aboard them, the cargo they carry, and the marine environment. This study conducts a comprehensive literature review on research in maritime accidents, comprising 572 papers published in 125 journals over the 50 years from 1965 to 2014. The patterns of evolution of the researchers, the journals, the disciplines involved, the research methods, the major issues and causes, and the data sources are identified, and the changes explained. We find that the main focus of research in maritime accidents has shifted over the past 50 years from naval architecture to human error, and may continue to expand into socio-economic factors. In addition, future research in maritime accidents will be multi-disciplinary, use multiple data sources, and adopt advanced research methods, to account for complex interactions between the natural environment, the development of naval technology, human behavior, and shipping market conditions.

Meifeng Luo

Sung-Ho Shin

Half-century research developments in maritime accidents: Future directions.

海事事故半世纪研究进展：未来方向。

Time series analysis plays a vital role in modeling historical crash trends and predicting the possible changes in future crash trends. In existing safety literature, earlier studies employed multiple approaches to model long-term crash risk profiles, such as integer-valued autoregressive Poisson regression model, integer-valued generalized autoregressive conditional heteroscedastic model, and generalized linear autoregressive and moving average models. However, these modeling frameworks often fail to fully capture several key properties of crash count data, especially negative serial correlation, and nonlinear dependence structures across temporal crash counts. To address these methodological gaps in existing safety literature, this study proposes to use a Gaussian Copula-based model for the long-term crash trend analysis. Specifically, this study proposes to use a Gaussian Copula-based Time Series Count Model with an Autoregressive Moving Average Process for the analysis of long-term trends in fatal crashes. The proposed approach can accommodate several data properties, which include (1) non-negative discrete property of count data, (2) positive and negative serial correlations among time series data, and (3) nonlinear dependence among time-series observations. The performance of the Gaussian Copula-based time series count model is compared with the generalized linear autoregressive and moving average model. The proposed modeling approaches are demonstrated by using yearly fatal crash count data for the years 1986 through 2022 from Queensland, Australia. The major safety interventions implemented in Queensland over those years are also highlighted to assess the possible and plausible impacts of these safety interventions in reducing fatal crash risks. Further, elasticity effects and overall percentage changes in fatal crashes across different time points are computed to demonstrate the implications of the proposed model. The policy analysis exercise shows that the implemented road safety interventions are likely to have diminishing marginal returns, underscoring the need for new and effective road safety policies to achieve the goal of zero fatalities within the set timeframe.

Yanqi Lian

Shamsunnahar Yasmin

Md Mazharul Haque

Influence of road safety policies on the long-term trends in fatal Crashes: A Gaussian Copula-based time series count model with an autoregressive moving average process

道路安全政策对致命事故长期趋势的影响：基于高斯Copula的自回归移动平均时间序列计数模型

This research investigates the effectiveness of nudge presentation on Hazard Perception (HP) during a computerized Hazard Perception Test (HPT). Three types of nudges were examined: Reminder, Social Norm, and Negative Reinforcement. Their effects on drivers' reaction times, hazard misidentifications (errors), and hazard recognition failures (misses) were analyzed. Additionally, the study explored how demographic and personality factors relate to individual differences in nudge responses. Results indicated that nudge presentation, regardless of type, improved reaction times and reduced errors. Reduction in errors was uniquely associated with personal characteristics, showing a positive correlation with age. Specifically, female participants and individuals low in conscientiousness exhibited fewer errors following the Social Norm nudge, while males and highly conscientious individuals showed reduced errors after the Reminder nudge. However, misses were unaffected by nudge presentation. All tested dependent variables were influenced by the order of hazard presentation, reflecting both contextual and nudge presentation effects. To further investigate the order's impact, a follow-up study examined specific hazards sensitive to nudge presentation. Findings revealed that some hazards were more influenced by nudge/contextual factors, while others were unaffected, highlighting the need to consider complex contextual dynamics in HP research. Overall, the study supports the conclusion that nudge presentation can positively influence HP without distracting drivers, offering a promising strategy for improving road safety.

Shiran Zadka-Peer

Tova Rosenbloom

Nudges may improve hazard perception in a contextual manner

提示可能以情境方式提高危险感知能力

Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This research proposes an innovative deep reinforcement learning-based (DRL) cooperative control framework, including signal and speed modules, to dynamically adapt signal timing and CAV velocities for traffic safety and efficiency optimization. Among the DRL-based signal modules, a traffic state prediction model is merged with the current state to augment characteristics and the agent-learning process. A multi-objective reward function is designed to evaluate safety and efficiency using a traffic conflict prediction model and vehicle waiting time. The double deep Q network (DDQN) model is used to design the agent observing the traffic state, learning the optimal signal control policy, and then inputting the signal phase into the speed module. Based on the green duration analysis and constraints of mixed traffic flow of CAVs and human-driven vehicles, a speed planning model is constructed to optimize CAVs' speed and alter traffic state, which in turn affects the agent's next signal decisions. The proposed framework is tested at isolated intersections simulated by two real-world intersections in Changsha, China. The results reveal the superiority of the proposed method over DRL-based traffic signal control (DRL-TSC) in terms of coverage speed and computation time. Compared to actuated signal control, adaptive traffic signal control, and DRL-TSC, the proposed method significantly optimizes traffic safety and efficiency across diverse intersections, temporal spans, and traffic demands. Furthermore, the advantage of the proposed method substantially amplifies with the increased CAV penetration, regardless of the intersection types.

Gongquan Zhang

Fengze Li

Dian Ren

Helai Huang

Zilong Zhou

Fangrong Chang

Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections

自学习交通信号与网联自动驾驶车辆的协同控制以优化交叉口安全与效率

Driving is a multifaceted activity involving a complex interplay of cognitive, perceptual, and motor skills, demanding continuous attention on the road. In recent years, the increased integration of automation and digitalization technologies in vehicles has improved drivers' convenience and safety. However, the spare attentional capacity available during automation and the prevalence of various infotainment systems in vehicles enable drivers to perform some secondary tasks not related to driving, which may divert their attention away from the road, increasing the chances of accidents. The objective of the present study is to conduct a comprehensive systematic review of existing literature utilizing an eye tracker to analyze driver distraction due to automation and/or digitalization in motorized vehicles, with a focus on identifying the key factors leading to visual distraction. Through a literature search on five databases: Google Scholar, PubMed, ScienceDirect, Scopus, and Web of Science, a total of 4769 articles were initially identified. After a systematic screening, 65 research articles are considered for the review. The findings of the study indicate an increase in the research conducted on driver distraction due to automation and/or digitalization over recent years, with the highest contribution of studies from the United States and China. The lack of studies from other parts of the world like South America, Africa and the limited representation from larger parts of Asia, specifically India, highlights the need for future research in the area. Studies report a diversion in drivers' visual attention away from the roadway, in terms of long and frequent off-road glances, while engaging in secondary tasks during automation and/or digitalization. Studies also demonstrate changes in the pattern of drivers' visual attention with respect to different factors like HMI information, type of secondary task, type of input modality, in-vehicle display characteristics, and vehicle automation. Studies have also found success in using feedback to reduce visual distraction and to bring back drivers' attention on the roads. In light of the findings observed, the review provides a discussion on the effects of automation and/or digitalization technologies on drivers' visual attention. The study also highlights the areas that are not explored despite the wealth of research available on the topic.

Neelima C Vijay

Amit Agarwal

Kamini Gupta

Do automation and digitalization distract drivers? A systematic review

自动化和数字化会分散驾驶员注意力吗？一项系统性综述

Navigating intersections is a major challenge for autonomous vehicles (AVs) because of the complex interactions between different roadway user types, conflicting movements, and diverse operational and geometric features. This study investigated intersection-related AV-involved traffic conflicts by analyzing the Arogoverse-2 motion forecasting dataset to understand the driving behavior of AVs at intersections. The conflict scenarios were categorized into AV-involved and no AV conflict scenarios. Depending on whether AVs passed the conflict region first or second in AV-involved scenarios, AV-involved scenarios were further classified into AV-first and AV-second scenarios. An agglomerative hierarchical clustering with t-SNE dimension reduction technique was applied to categorize the driving styles, and a three-layer Bayesian hierarchical model was applied to analyze the effect of driving volatility measures and traffic characteristics on relative crash risks. The clustering result showed that about 29% of the conflict events in the AV-first scenario (human-driven vehicle (HDV) was the following vehicle in passing the conflict region) exhibited high-risk of conflicts. In contrast, all conflicts events in the AV-second category were either low-risk or medium-risk conflicts. Parameter estimates showed that AVs had safer interactions with the other roadway users (i.e., HDVs, pedestrians/cyclists) while maintaining higher speeds and uniform driving profiles. AV's interaction with vulnerable road users (i.e., pedestrians and cyclists) showed lower crash risk compared to HDVs, indicating AV's safer driving behavior. AVs also demonstrated safer conflict resolution behavior in performing unprotected left turns compared to HDVs. This study discovered some unique insights into the challenges of introducing AVs in diverse intersection types (i.e., signalized, unsignalized, stop-controlled), which can be used to identify AV technology's improvement need to better adapt to the mixed traffic driving environment.

Md Tanvir Ashraf

Kakan Dey

Conflict resolution behavior of autonomous vehicles at intersections under mixed traffic environment

混合交通环境下自动驾驶车辆在交叉口的冲突解决行为

Abbreviation	Accid. Anal. Prev.
Journal Impact	5.75
Quartiles(Global)	TRANSPORTATION(Q1)

ISSN	0001-4575, 1879-2057
h-index	188
Top Journals	Yes

Publication Information	Publisher: Elsevier Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1969，Proportion of original research papers: 95.20%，Self Citation Rate:17.50%， Gold OA Rate: 16.01%

Accident Analysis and Prevention

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