Accident; analysis and prevention最新文献

{"title":"Changes in road safety following regional administrative reforms in Norway","authors":"Rune Elvik ,&nbsp;Tor-Olav Nævestad ,&nbsp;Jenny Blom ,&nbsp;Lars Even Egner ,&nbsp;Vibeke Milch ,&nbsp;Markus Bugge ,&nbsp;Håkon Endresen Normann ,&nbsp;Erland Skogli","doi":"10.1016/j.aap.2025.107969","DOIUrl":"10.1016/j.aap.2025.107969","url":null,"abstract":"<div><div>A reform of regional government in Norway was implemented on January 1, 2020. The management of county roads was transferred from the National Public Roads Administration to the counties. The number of counties was reduced from 19 to 11. In 2022 it was decided to split some of the counties that were merged in 2020, and the number of counties increased to 15 from January 1, 2024. This paper studies whether these reforms were associated with changes in the number of injured road users on county roads. Four counties and the city of Oslo were included in the study. The city of Oslo was not affected by the reforms in 2020 and 2024. The other counties were affected either by: (1) Transfer of the management of county roads only (one county); (2) transfer of the management of county roads and merger with another county (one county); or (3) transfer of the management of county roads and merger with another county, followed by splitting up the merged counties (two counties). A before-and-after study with comparison group using county roads as treated group and other public roads as comparison group, and 2010–2019 as before-period and 2020–2023 as after-period found very small changes in the number of injured road users. Trends established during 2010–2019 continued almost unchanged after 2020. It is concluded that the regional administrative reforms were not associated with any detectable changes in road safety in the counties included in the study.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107969"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429549","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 deep transfer learning approach for Real-Time traffic conflict prediction with trajectory data","authors":"Qinzhong Hou,&nbsp;Yonghao Yang,&nbsp;Jiatong Liang,&nbsp;Xiaoyan Huo,&nbsp;Junqiang Leng","doi":"10.1016/j.aap.2025.107966","DOIUrl":"10.1016/j.aap.2025.107966","url":null,"abstract":"<div><div>Recently, real-time traffic conflict prediction has drawn increasing attention due to its significant potential in proactive traffic safety systems. While various statistical and machine learning models have been developed for conflict prediction, transferability remains a fundamental issue across these models. Specifically, the predictive performance of a real-time conflict prediction model developed for a specific location can significantly decline when directly applied to a new location without any modifications, primarily due to substantial differences in traffic environments between these areas. To address this gap, this study proposed a novel deep transfer learning approach aimed at enhancing the transferability of real-time conflict prediction models. Initially, a real-time conflict prediction framework was designed utilizing trajectory data for merging areas with consideration of temporal variations in traffic flow characteristics. Subsequently, the Gated-Transformer, Fully Convolutional Networks (FCN), Long Short-Term Memory Fully Convolutional Networks (LSTM-FCN), and Multivariate Long Short-Term Memory Fully Convolutional Networks (MLSTM-FCN) were employed as backbone feature extraction networks to capture the hidden correlations between time-varying traffic flow characteristics and traffic conflicts. After that, an independent transfer learning architecture was established to assess the similarity of the distribution of traffic flow characteristics at different locations, based on the maximum mean discrepancy criteria. For empirical evaluation, merging areas from the exiD dataset were differentiated into source and target domains. The results demonstrated that the Gated-Transformer model outperforms other baseline models (FCN, LSTM–FCN and MLSTM–FCN) in both feature extraction and predictive performance, achieving an F1 score of 0.864 and an area under the curve (AUC) of 0.980. Furthermore, the transfer learning architecture can substantially enhance the predictive performance of a model trained in the source domain when applied to the target domain. In particular, the F1 score and AUC for the Gated-Transformer model improved by 11.9% and 10.2%, respectively, after incorporating the transfer learning architecture. Finally, the optimal values of key model parameters, including the sliding time window (6 s) and the prewarning time (5 s), were recommended for practical applications through sensitivity analysis. This study illustrates the potential of the deep transfer learning approach as a reliable and effective alternative to improve the transferability of real-time conflict prediction models. Additionally, results from this study can offer valuable insights for practical applications in traffic safety warning systems, particularly in vehicle-to-infrastructure traffic environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107966"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421093","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":"Multi-scenario driving style research based on driving behavior pattern extraction","authors":"Yi He ,&nbsp;Yingrui Hu ,&nbsp;Jipu Li ,&nbsp;Ke Sun ,&nbsp;Jianhua Yin","doi":"10.1016/j.aap.2025.107963","DOIUrl":"10.1016/j.aap.2025.107963","url":null,"abstract":"<div><div>Accurately analyzing drivers’ driving styles is crucial for road safety and enhancing intelligent driving systems. However, existing studies have not fully explored the hidden information in driving sequences or considered the influence of driving environments on driving styles. Based on natural driving data from electric vehicles in Wuhan, a framework for driving style analysis based on driving behavior pattern extraction was proposed. Driving sequences were extracted under free-driving and car-following scenarios, where the convergence of driving features was verified using kernel density estimation and relative entropy. A driving propensity indicator based on a dynamic threshold was constructed, and combined with the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM) and K-means clustering algorithm, 4 and 5 types of driving behavior pattern were extracted under free-driving and car-following scenarios, respectively. Energy consumption distribution was introduced to verify the validity of driving pattern extraction. Jensen–Shannon (JS) divergence was utilized to calculate the difference in the distribution of the driving propensity indicator among different drivers. By quantifying behavioral differences, drivers were categorized into aggressive, moderate, and conservative types. The results show that the statistical characteristics of driving patterns are consistent with the distribution of energy consumption, with the highest energy consumption occurs in aggressive acceleration and high-speed steady-state patterns, and the highest braking energy recovery occurs in aggressive deceleration pattern. Furthermore, the driving environment influences driving styles to certain degree while exhibiting consistent or diverse driving styles in different driving scenarios and patterns.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107963"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421094","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":"Crash localization and traffic impact assessment via spatio-temporal analysis of connected vehicle data","authors":"L. Querfurth ,&nbsp;H. Rehborn ,&nbsp;B. Bernhardt ,&nbsp;Y. Guan ,&nbsp;W. Huang ,&nbsp;S. Hoffmann","doi":"10.1016/j.aap.2025.107956","DOIUrl":"10.1016/j.aap.2025.107956","url":null,"abstract":"<div><div>Crashes pose high risk to traffic participants. To enhance rapid response capabilities and improve traffic management, accurate and immediate detection of crashes is essential. This paper investigates a probe vehicle data-based crash detection approach for freeways that solely relies on transmitted position data by a connected vehicle fleet. The algorithm is capable of reconstructing time and location of a crash, even if the vehicles directly involved in the crash are not connected. The method can detect the emergence as well as the resolution of the crash faster than currently used methods. It surpasses traffic information services by detecting crashes 07:40<!--> <!-->min earlier and the resolution of the congestion 05:59<!--> <!-->min faster. The method also improves spatial uncertainty by detecting precise crash locations instead of incident ranges. The algorithm can classify the detected crashes based on their characteristic traffic patterns, which may facilitate different reaction strategies. Relying solely on global positioning system (GPS) data, it offers a low-cost, real-time solution applicable on a large scale with existing vehicle hardware. A dataset from a connected vehicle fleet, along with ground truth crash data, verifies the proposed method’s results. The algorithm achieves an F1-score of 82.45<!--> <!-->% on a dataset containing 1601<!--> <!-->congestion patterns with 50<!--> <!-->crashes. The paper demonstrates its effectiveness across different regions with varying fleet penetration rates, using empirical examples from freeways in the USA, Germany and the UK.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107956"},"PeriodicalIF":5.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A safe self-evolution algorithm for autonomous driving based on data-driven risk quantification model","authors":"Shuo Yang ,&nbsp;Shizhen Li ,&nbsp;Yanjun Huang ,&nbsp;Hong Chen","doi":"10.1016/j.aap.2025.107941","DOIUrl":"10.1016/j.aap.2025.107941","url":null,"abstract":"<div><div>Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios. However, as a result of the safety-performance trade-off mechanism of evolutionary algorithms, it is difficult to ensure safe exploration without sacrificing the improvement ability. This problem is especially prominent in dynamic traffic scenarios. Therefore, this paper proposes a safe self-evolution algorithm for autonomous driving based on data-driven risk quantification model. Specifically, a risk quantification model based on the attention mechanism is proposed by modeling the way humans perceive risks during driving, with the idea of achieving safety situation estimation of the surrounding environment through a data-driven approach. To prevent the impact of over-conservative safety guarding policies on the self-evolution capability of the algorithm, a safety-evolutionary decision-control integration algorithm with adjustable safety limits is proposed, and the proposed risk quantization model is integrated into it. Simulation and real-vehicle experiments results illustrate the effectiveness of the proposed method. The results show that the proposed algorithm can generate safe and reasonable actions in a variety of complex scenarios and guarantee safety without losing the evolutionary potential of learning-based autonomous driving systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107941"},"PeriodicalIF":5.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421091","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":"Modelling low temporal, large spatial data of fatal crashes: An application of negative binomial GSARIMAX time series","authors":"Sara Ghalehnovi ,&nbsp;Abolfazl Mohammadzadeh Moghaddam ,&nbsp;Seyed Iman Mohammadpour","doi":"10.1016/j.aap.2025.107958","DOIUrl":"10.1016/j.aap.2025.107958","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Road traffic injuries represent a critical public health concern, particularly in developing nations such as Iran, where the incidence of fatal crashes is escalating. Addressing this issue effectively requires sophisticated analytical methodologies to elucidate and mitigate the multifaceted factors contributing to traffic fatalities. This study introduces the Negative Binomial Generalized Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (GSARIMAX) model as an innovative approach for analyzing low temporal (daily) and large spatial count data of fatal crashes over a ten-year period (March 2014 to March 2022) in Iran. Unlike traditional models that predominantly focus on aggregated monthly or high-resolution data, the proposed negative binomial GSARIMAX model leverages daily count data, accommodating over-dispersion inherent in crash counts and providing a more granular and accurate analysis across extensive spatial regions. The model integrates significant exogenous variables, including traffic volume, maximum and minimum temperatures, wind speed, and wind direction, alongside harmonic seasonal components to capture both annual and semi-annual periodic fluctuations in crash occurrences. Model performance was rigorously evaluated using Deviance Information Criterion (DIC) and Mean Absolute Relative Error (MARE) metrics, alongside out-of-sample predictive accuracy assessments. The negative binomial GSARIMAX (0,1,2)-SOH model demonstrated superior performance compared to the Gaussian GSARIMAX counterpart, evidenced by lower MARE and DIC values. Notably, traffic volume and maximum temperature emerged as significant predictors of fatal crashes, while seasonal harmonic terms further enhanced model accuracy by effectively capturing temporal dynamics. The Bayesian estimation framework employed facilitates robust inference and the analysis of posterior predictive distributions, affirming the Negative Binomial GSARIMAX model’s superior fit and forecasting capabilities. These findings underscore the model’s potential advantages over conventional Gaussian statistical methods, particularly in handling low temporal resolution and large spatial datasets. Moreover, dynamic models incorporating exogenous variables demonstrated enhanced predictive performance, highlighting the importance of integrating diverse factors in crash analysis. This study not only advances the methodological landscape for traffic crash analysis but also provides actionable insights for policymakers and safety authorities. By identifying key determinants of fatal crashes and accounting for seasonal variations, the Negative Binomial GSARIMAX model serves as a valuable tool for informing targeted interventions aimed at reducing traffic fatalities. Future research should extend this approach by incorporating additional environmental and behavioral variables and conducting comparative analyses across multiple provinces to capture a broader spectrum of influen","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107958"},"PeriodicalIF":5.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421090","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 on the Line: Examining the impacts of crosswalk design on Child’s perceived Safety, cautious Behavior, and visual attention with VR Technology","authors":"Chaeseung Lee,&nbsp;Hyunseong Yun,&nbsp;Junseung Lee,&nbsp;Seung-Nam Kim","doi":"10.1016/j.aap.2025.107959","DOIUrl":"10.1016/j.aap.2025.107959","url":null,"abstract":"<div><div>Although children are particularly vulnerable to accidents while crossing a street, knowledge about their specific perceptual and behavioral responses to crosswalk design and conditions is limited. By employing simulated virtual reality (VR) experiments, this study investigated how the environment of an unsignalized T-junction in a school zone influenced the perceived safety, crossing behaviors, and visual attention of 178 participants, consisting of younger (aged 8–10) and older children (aged 11–12) and their parents. Key findings from cross-classified multilevel, trajectory, and counterfactual analyses using Reproduced Virtual Experiment Data (CARVED) techniques for viewed scene comparisons are as follows. First, despite their physical and cognitive limitations, children—particularly younger ones—exhibited fewer attentive behaviors on sidewalks and roadways, along with a higher informal crossing ratio than parents. Second, responses to environmental conditions varied by developmental stage. While older children and parents generally responded to hazardous conditions with increased caution, younger children exhibited minimal behavioral adjustments. For example, parents perceived environments with parked cars as less safe and demonstrated more attentive and preventive behaviors on roadways. In contrast, younger children, despite having their line of sight obstructed, failed to recognize these hazards and did not adopt compensatory strategies, reinforcing their vulnerability. Similarly, only older children and parents perceived decorative crosswalk patterns as less safe than traditional ones. Third, while traditional crosswalks facilitated orthogonal crossings and provided clearer guidance for safer movements, decorative patterns often encouraged diagonal and dispersed crossings, resulting in a higher jaywalking ratio. Lastly, speed limits showed no significant influence on perceived safety or crossing behaviors. These findings highlight the critical influence of developmental stages on safety perceptions and behaviors, emphasizing the need to reassess crosswalk designs and address visibility challenges caused by illegal on-street parking to enhance child pedestrian safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107959"},"PeriodicalIF":5.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395349","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":"Classification of driving simulators validation: A case study using an immersive driving simulator","authors":"César Andriola ,&nbsp;Gustavo Rubén Di Rado ,&nbsp;Daniel Sergio Presta García ,&nbsp;Christine Tessele Nodari","doi":"10.1016/j.aap.2025.107944","DOIUrl":"10.1016/j.aap.2025.107944","url":null,"abstract":"<div><div>Driving simulators present themselves as tools of high potential for the study of human behavior during the driving task, given their importance in the occurrence of traffic accidents. The use of driving simulators is still on the rise, with the increased number of low-cost desktop-based driving simulations, primarily motivated by research in the intersection of vehicle automation and human factors. However, it is necessary to ensure that the simulators correctly represent the driving task, which is done through the validation process and has been particularly rare in recent years. In this sense, the objective of the present work is to support the validation efforts of low-cost driving simulators in two ways: (i) by proposing a study configuration categorization to guide validation studies and (ii) by presenting the validation of an immersive driving simulation based on the proposed classification. Considering the former, an extensive literature review of existing validation studies was conducted to support this classification’s development. Regarding the latter, data from the same scenario in the real world and in the virtual environment were collected and analyzed. The study also evaluated the occurrence of simulator sickness and the perception of realism in the simulated scenario, which are important elements in the context of low-cost driving simulators. The results show relative validation for the particular simulator analyzed, as well as the relevance of virtual reality for constructing an immersive environment, despite a slight increase in some symptoms of simulator sickness. Although such validation studies apply to a specific context, they broaden the overall reliability of research on driving simulators.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107944"},"PeriodicalIF":5.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386901","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":"Probabilistic modelling of optimal placement strategies of hazardous materials railcars in freight trains","authors":"Chen-Yu Lin ,&nbsp;Xinhao Liu ,&nbsp;Christopher P.L. Barkan","doi":"10.1016/j.aap.2025.107957","DOIUrl":"10.1016/j.aap.2025.107957","url":null,"abstract":"<div><div>Hazardous materials (hazmat) cars are subject to differing probabilities of being involved in a derailment depending on their position in trains. For decades there has been discussion and debate about whether operating practices and regulations should account for this to reduce the chance of railcars carrying hazmat being involved if a train derails. This paper presents a new, position-dependent, railcar-based method to systematically analyze derailment probability of hazmat cars and identify optimal placement strategies that minimize the expected number of hazmat cars derailed. This new method iteratively accounts for train makeup, derailment speed, train length, and the fraction of hazmat cars in the train. A case study based on realistic train configurations and operational conditions with a sensitivity analysis is presented. The results indicate that there is no single placement strategy that minimizes hazmat car derailment probability under the variety of operational characteristics typical of North American freight train operation. This has implications for rail hazmat transportation safety, operations, efficiency, and regulatory policy. This research advances our understanding of the effect of hazmat car placement on operating safety and risk and enables development of holistic quantitative models to address the trade-off between hazmat train operating safety and efficiency that accounts for both mainline derailment severity and yard activities related to train make-up.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107957"},"PeriodicalIF":5.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377853","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":"Bayesian survival analysis of interactions between truck platoons and surrounding vehicles through a two-dimensional surrogate safety measure","authors":"Ma Xiaoxiang ,&nbsp;Xiang Mingxin ,&nbsp;Jiang Xinguo ,&nbsp;Shao Xiaojun","doi":"10.1016/j.aap.2025.107945","DOIUrl":"10.1016/j.aap.2025.107945","url":null,"abstract":"<div><div>The road freight transport sector is one of the largest contributors to carbon emissions. To address this issue and reduce both carbon emissions and fuel consumption, the road transportation system is undergoing a significant transformation with the development of autonomous truck platoons (ATPs). Despite the promising potential for large-scale deployment of ATPs and the substantial number of human-driven heavy-duty trucks currently in operation, research on the lateral interactions between truck platoons—whether human-driven or automated—and surrounding passenger cars remains limited. Given the absence of commercially deployed ATPs, this study proposes extracting truck platoons from real-world trajectory datasets to investigate the interactions between truck platoons and surrounding vehicles. A two-dimensional surrogate safety measure (SSM) known as Anticipated Collision Time (ACT) was employed to characterize these interactions. Bayesian Survival Analysis was developed to examine the interactions between truck platoons and adjacent passenger cars to provide some insights into how truck platoon might impact surrounding traffic. The results reveal that the position of the adjacent or right-leading truck in the platoon greatly influence the hazard of a minimum collision time. The increase of average time headway between trucks in platoon is found to shorten human drivers’ responsiveness time to truck platoons. Moreover, the presence of a leading vehicle causes human drivers to reach the minimum collision time with truck platoons earlier, and this impact strengthens as the passenger car overtakes the truck platoon. These findings help us better understand the lateral interactions between truck platoon and adjacent passenger car, offering a theoretical foundation for traffic simulation involving heavy-duty truck platoons and recommendations for safety management of traffic flow involving truck platoons for future highways.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107945"},"PeriodicalIF":5.7,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372229","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}