Accident; analysis and prevention最新文献_第4页

Classification of driving simulators validation: A case study using an immersive driving simulator

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-12 DOI: 10.1016/j.aap.2025.107944

César Andriola , Gustavo Rubén Di Rado , Daniel Sergio Presta García , Christine Tessele Nodari

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.

{"title":"Classification of driving simulators validation: A case study using an immersive driving simulator","authors":"César Andriola , Gustavo Rubén Di Rado , Daniel Sergio Presta García , 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}

引用次数: 0

Probabilistic modelling of optimal placement strategies of hazardous materials railcars in freight trains

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-10 DOI: 10.1016/j.aap.2025.107957

Chen-Yu Lin , Xinhao Liu , Christopher P.L. Barkan

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.

{"title":"Probabilistic modelling of optimal placement strategies of hazardous materials railcars in freight trains","authors":"Chen-Yu Lin , Xinhao Liu , 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}

引用次数: 0

Bayesian survival analysis of interactions between truck platoons and surrounding vehicles through a two-dimensional surrogate safety measure

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-09 DOI: 10.1016/j.aap.2025.107945

Ma Xiaoxiang , Xiang Mingxin , Jiang Xinguo , Shao Xiaojun

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.

{"title":"Bayesian survival analysis of interactions between truck platoons and surrounding vehicles through a two-dimensional surrogate safety measure","authors":"Ma Xiaoxiang , Xiang Mingxin , Jiang Xinguo , 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}

引用次数: 0

Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-08 DOI: 10.1016/j.aap.2025.107934

Tian Li , Shuqi Liu , Guoqing Fan , Hanlin Zhao , Mengmeng Zhang , Jieyu Fan , Changxing Li

The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and contradictory findings. While some studies have investigated spatial heterogeneity using geographically weighted regression models (GWR), these approaches frequently neglect critical aspects including the road network distance between built environment features and the non-linear decay of influence relationships. To address these methodological limitations, this study develops a geographically weighted atrous convolutional neural network regression model (GACNNWR) to more accurately capture the spatial heterogeneity in the impact of built environment factors on traffic safety. Based on empirical data of traffic accidents and built environment from Jinan City, our results demonstrate that the GACNNWR model outperforms traditional analytical methods such as GWR model. Intersection density and bus stop density are identified as having a more substantial impact on traffic accidents compared to population density, land use mix, and destination accessibility. Additionally, population density is shown to exert a bidirectional influence on traffic accidents, while the spatial variability in the effects of land use mix is relatively pronounced. These findings provide important implications for the design of context-sensitive built environments and the formulation of localized traffic safety management strategies aimed at mitigating crash risks.

{"title":"Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network","authors":"Tian Li , Shuqi Liu , Guoqing Fan , Hanlin Zhao , Mengmeng Zhang , Jieyu Fan , Changxing Li","doi":"10.1016/j.aap.2025.107934","DOIUrl":"10.1016/j.aap.2025.107934","url":null,"abstract":"<div><div>The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and contradictory findings. While some studies have investigated spatial heterogeneity using geographically weighted regression models (GWR), these approaches frequently neglect critical aspects including the road network distance between built environment features and the non-linear decay of influence relationships. To address these methodological limitations, this study develops a geographically weighted atrous convolutional neural network regression model (GACNNWR) to more accurately capture the spatial heterogeneity in the impact of built environment factors on traffic safety. Based on empirical data of traffic accidents and built environment from Jinan City, our results demonstrate that the GACNNWR model outperforms traditional analytical methods such as GWR model. Intersection density and bus stop density are identified as having a more substantial impact on traffic accidents compared to population density, land use mix, and destination accessibility. Additionally, population density is shown to exert a bidirectional influence on traffic accidents, while the spatial variability in the effects of land use mix is relatively pronounced. These findings provide important implications for the design of context-sensitive built environments and the formulation of localized traffic safety management strategies aimed at mitigating crash risks.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107934"},"PeriodicalIF":5.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348680","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

Exploration of the tram-involved crashes’ characteristics and contributing factors to fatality in tram crashes in Japan

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-08 DOI: 10.1016/j.aap.2025.107919

Yefan Yang , Suyi Mao , Marco Bassani , Emanuele Sacchi , Jaeyoung Jay Lee

Modern trams have garnered worldwide attention as an alternative urban rail transit system since they offer advantages such as higher accessibility, convenience, lower construction and maintenance costs compared with subways. Nevertheless, due to the system’s characteristics, particularly regarding right-of-way issues, crashes involving trams often lead to severe consequences. This study utilizes traffic crash data from across Japan, including 1,121,299 crashes that occurred from 2019 to 2022, of which 304 were tram involved. The study has two major analyses using random-parameter model with heterogeneity in means: (1) tram-involved crashes’ characteristics (vs. non-tram-involved) and (2) factors affecting the probability of fatality in tram crashes. The first analysis’ findings suggest that fatalities and crashes at rail crossings are more likely to be associated with tram crashes, while non-tram crashes are more likely to occur during daytime, at intersections, or on narrower lanes. Furthermore, the presence of a median between opposing directions increases the likelihood of a non-tram crash. The second analysis to identify the factors influencing fatality reveals how factors such as the season, lighting conditions, road conditions, crash locations, and the age and category of victims affect the characteristics of tram crashes and fatalities. Specifically, it explains the heterogeneous effects of daytime and intersections on tram crashes, as well as the heterogeneous impacts of rail crossings and densely populated areas on tram fatalities. The findings are expected to assist authorities in formulating strategies to minimize the incidence of tram crashes and related fatalities, potentially saving lives and bringing about economic benefits.

{"title":"Exploration of the tram-involved crashes’ characteristics and contributing factors to fatality in tram crashes in Japan","authors":"Yefan Yang , Suyi Mao , Marco Bassani , Emanuele Sacchi , Jaeyoung Jay Lee","doi":"10.1016/j.aap.2025.107919","DOIUrl":"10.1016/j.aap.2025.107919","url":null,"abstract":"<div><div>Modern trams have garnered worldwide attention as an alternative urban rail transit system since they offer advantages such as higher accessibility, convenience, lower construction and maintenance costs compared with subways. Nevertheless, due to the system’s characteristics, particularly regarding right-of-way issues, crashes involving trams often lead to severe consequences. This study utilizes traffic crash data from across Japan, including 1,121,299 crashes that occurred from 2019 to 2022, of which 304 were tram involved. The study has two major analyses using random-parameter model with heterogeneity in means: (1) tram-involved crashes’ characteristics (vs. non-tram-involved) and (2) factors affecting the probability of fatality in tram crashes. The first analysis’ findings suggest that fatalities and crashes at rail crossings are more likely to be associated with tram crashes, while non-tram crashes are more likely to occur during daytime, at intersections, or on narrower lanes. Furthermore, the presence of a median between opposing directions increases the likelihood of a non-tram crash. The second analysis to identify the factors influencing fatality reveals how factors such as the season, lighting conditions, road conditions, crash locations, and the age and category of victims affect the characteristics of tram crashes and fatalities. Specifically, it explains the heterogeneous effects of daytime and intersections on tram crashes, as well as the heterogeneous impacts of rail crossings and densely populated areas on tram fatalities. The findings are expected to assist authorities in formulating strategies to minimize the incidence of tram crashes and related fatalities, potentially saving lives and bringing about economic benefits.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107919"},"PeriodicalIF":5.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350286","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

MineSim: A scenario-based simulation test system and benchmark for autonomous trucks in open-pit mines

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-08 DOI: 10.1016/j.aap.2025.107938

Zhifa Chen , Guizhen Yu , Peng Chen , Guoxi Cao , Zheng Li , Yifang Zhang , Haoyuan Ni , Bin Zhou , Jian Sun , Huanyu Ban

Simulation environments are essential for validating algorithms, evaluating system performance, and ensuring safety in autonomous driving systems before real-world deployment. Existing autonomous driving simulators are designed for urban scenarios but lack coverage of unstructured road environments in open-pit mining. This paper introduces MineSim, an open-source, scenario-based simulation test system specifically developed for planning tasks in autonomous trucks operating in open-pit mines. MineSim includes several components: automated scenario parsing, state update models for the ego vehicle, state update policies for other agents, metric evaluation, and scenario visualization tools. It incorporates numerous real-world traffic scenarios from two open-pit mines that capture the unique challenges of unstructured road environments, including irregular intersections, roads without clear lane markings, and the response lags of heavy autonomous mining trucks. Furthermore, MineSim provides scenario libraries and benchmarks for static and dynamic obstacle avoidance problems, facilitating research into planning algorithms in these complex settings. By offering reproducible testing methods and scenario data, MineSim serves as a critical resource for advancing autonomous driving technologies in non-urban and unstructured road environments (see https://buaa-trans-mine-group.github.io/minesim).

{"title":"MineSim: A scenario-based simulation test system and benchmark for autonomous trucks in open-pit mines","authors":"Zhifa Chen , Guizhen Yu , Peng Chen , Guoxi Cao , Zheng Li , Yifang Zhang , Haoyuan Ni , Bin Zhou , Jian Sun , Huanyu Ban","doi":"10.1016/j.aap.2025.107938","DOIUrl":"10.1016/j.aap.2025.107938","url":null,"abstract":"<div><div>Simulation environments are essential for validating algorithms, evaluating system performance, and ensuring safety in autonomous driving systems before real-world deployment. Existing autonomous driving simulators are designed for urban scenarios but lack coverage of unstructured road environments in open-pit mining. This paper introduces MineSim, an open-source, scenario-based simulation test system specifically developed for planning tasks in autonomous trucks operating in open-pit mines. MineSim includes several components: automated scenario parsing, state update models for the ego vehicle, state update policies for other agents, metric evaluation, and scenario visualization tools. It incorporates numerous real-world traffic scenarios from two open-pit mines that capture the unique challenges of unstructured road environments, including irregular intersections, roads without clear lane markings, and the response lags of heavy autonomous mining trucks. Furthermore, MineSim provides scenario libraries and benchmarks for static and dynamic obstacle avoidance problems, facilitating research into planning algorithms in these complex settings. By offering reproducible testing methods and scenario data, MineSim serves as a critical resource for advancing autonomous driving technologies in non-urban and unstructured road environments (see <span><span>https://buaa-trans-mine-group.github.io/minesim</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107938"},"PeriodicalIF":5.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350285","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

Validation of human benchmark models for automated driving system approval: How competent and careful are they really?

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-05 DOI: 10.1016/j.aap.2025.107922

Pierluigi Olleja , Gustav Markkula , Jonas Bärgman

Over the last few decades, new technological solutions have enabled the fast development of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). These systems are expected to improve comfort, productivity and, most importantly, safety for all road users. To ensure that the systems are safe, rules and regulations describing the systems’ approval and validation procedures are in effect in Europe. The UNECE Regulation 157 (R157) is one of those. Annex 3 of R157 describes two driver models, representing the performance of a “competent and careful” driver, which can be used as benchmarks to determine whether, in certain situations, a crash would be preventable by a human driver. However, these models have not been validated against human behavior in real safety–critical events. Therefore, this study uses counterfactual simulation to assess the performance of the two models when applied to 38 safety–critical cut-in near-crashes from the SHRP2 naturalistic driving study. The results show that the two computational models performed rather differently from the human drivers: one model showed a generally delayed braking reaction compared to the human drivers, causing crashes in three of the original near-crashes. The other model demonstrated, in general, brake onsets substantially earlier than the human drivers, possibly being overly sensitive to lateral perturbations. That is, the first model does not seem to behave as the competent and careful driver it is supposed to represent, while the second seems to be overly careful. Overall, our results show that, if models are to be included in regulations, they need to be substantially improved. We argue that achieving this will require better validation across the scenario types that the models are intended to cover (e.g., cut-in conflicts), a process which should include applying the models counterfactually to near-crashes and validating them against several different safety related metrics. Possible improvements to the models include adding components that better reflect the level of urgency of the traffic situation, something which is lacking in the current models.

{"title":"Validation of human benchmark models for automated driving system approval: How competent and careful are they really?","authors":"Pierluigi Olleja , Gustav Markkula , Jonas Bärgman","doi":"10.1016/j.aap.2025.107922","DOIUrl":"10.1016/j.aap.2025.107922","url":null,"abstract":"<div><div>Over the last few decades, new technological solutions have enabled the fast development of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). These systems are expected to improve comfort, productivity and, most importantly, safety for all road users. To ensure that the systems are safe, rules and regulations describing the systems’ approval and validation procedures are in effect in Europe. The UNECE Regulation 157 (R157) is one of those. Annex 3 of R157 describes two driver models, representing the performance of a “competent and careful” driver, which can be used as benchmarks to determine whether, in certain situations, a crash would be preventable by a human driver. However, these models have not been validated against human behavior in real safety–critical events. Therefore, this study uses counterfactual simulation to assess the performance of the two models when applied to 38 safety–critical cut-in near-crashes from the SHRP2 naturalistic driving study. The results show that the two computational models performed rather differently from the human drivers: one model showed a generally delayed braking reaction compared to the human drivers, causing crashes in three of the original near-crashes. The other model demonstrated, in general, brake onsets substantially earlier than the human drivers, possibly being overly sensitive to lateral perturbations. That is, the first model does not seem to behave as the competent and careful driver it is supposed to represent, while the second seems to be overly careful. Overall, our results show that, if models are to be included in regulations, they need to be substantially improved. We argue that achieving this will require better validation across the scenario types that the models are intended to cover (e.g., cut-in conflicts), a process which should include applying the models counterfactually to near-crashes and validating them against several different safety related metrics. Possible improvements to the models include adding components that better reflect the level of urgency of the traffic situation, something which is lacking in the current models.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107922"},"PeriodicalIF":5.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177477","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}

引用次数: 0

Assessing conflict likelihood and its severity at interconnected intersections: Insights from drone trajectory data

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-04 DOI: 10.1016/j.aap.2025.107943

Qianqian Jin, Mohamed Abdel-Aty, Chenzhu Wang, Siyuan Tang

Unsignalized intersections are complex and hazardous due to their numerous conflicts. However, most studies analyzing crash factors at unsignalized intersections focus solely on the isolated intersection itself. This study investigates how proximity to signalized intersections affects traffic conflicts at unsignalized intersections (divided into three segments). Traditional segment-level traffic flow data often fail to capture the nuanced short-term traffic conditions that contribute to conflicts; thus, we utilized microscopic high-resolution trajectory data extracted from the CitySim drone dataset. To represent real dangerous events, conflict probability and severity were introduced and assessed using two surrogate safety measures: time-to-collision (TTC) and the predicted change in velocity post-collision (Delta-V). A Structural Equation Model (SEM) is applied to explore the interactive relationship within the interconnected intersections. Then, a hierarchical Joint Generalized Linear Mixed Model (JGLMM) was employed to identify factors contributing to conflict risks and severity across the three segments. SEM findings reveal that upstream traffic volume can significantly mitigate conflict risks downstream at interconnected intersections. Estimation results show that angled conflicts are prominent in weaving sections, with increased conflict probability and severity as the angle increases. Meanwhile, conflict potential decreases as vehicle queue length increases in right-turn lanes, but it increases with longer queues in left-turn lanes. Suggested countermeasures include clearly marking the left-turn lane at the intersection and installing clear left-turn signs in advance of the intersection. This study highlights the value of high-resolution trajectory data for in-depth variable analysis, facilitating hierarchical safety assessments and pinpointing influential interactions at interconnected intersections.

{"title":"Assessing conflict likelihood and its severity at interconnected intersections: Insights from drone trajectory data","authors":"Qianqian Jin, Mohamed Abdel-Aty, Chenzhu Wang, Siyuan Tang","doi":"10.1016/j.aap.2025.107943","DOIUrl":"10.1016/j.aap.2025.107943","url":null,"abstract":"<div><div>Unsignalized intersections are complex and hazardous due to their numerous conflicts. However, most studies analyzing crash factors at unsignalized intersections focus solely on the isolated intersection itself. This study investigates how proximity to signalized intersections affects traffic conflicts at unsignalized intersections (divided into three segments). Traditional segment-level traffic flow data often fail to capture the nuanced short-term traffic conditions that contribute to conflicts; thus, we utilized microscopic high-resolution trajectory data extracted from the CitySim drone dataset. To represent real dangerous events, conflict probability and severity were introduced and assessed using two surrogate safety measures: time-to-collision (TTC) and the predicted change in velocity post-collision (Delta-V). A Structural Equation Model (SEM) is applied to explore the interactive relationship within the interconnected intersections. Then, a hierarchical Joint Generalized Linear Mixed Model (JGLMM) was employed to identify factors contributing to conflict risks and severity across the three segments. SEM findings reveal that upstream traffic volume can significantly mitigate conflict risks downstream at interconnected intersections. Estimation results show that angled conflicts are prominent in weaving sections, with increased conflict probability and severity as the angle increases. Meanwhile, conflict potential decreases as vehicle queue length increases in right-turn lanes, but it increases with longer queues in left-turn lanes. Suggested countermeasures include clearly marking the left-turn lane at the intersection and installing clear left-turn signs in advance of the intersection. This study highlights the value of high-resolution trajectory data for in-depth variable analysis, facilitating hierarchical safety assessments and pinpointing influential interactions at interconnected intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107943"},"PeriodicalIF":5.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177478","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

Tunnel crash severity and congestion duration joint evaluation based on cross-stitch networks

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-04 DOI: 10.1016/j.aap.2025.107942

Chenzhu Wang, Mohamed Abdel-Aty, Lei Han

Tunnels, with limited space and restricted widths/heights, increase the likelihood of crashes and traffic congestion, where the severity and duration of one often exacerbate the other. However, existing studies mainly conducted separate models, which cannot simultaneously analyze the joint impacts of contributing factors on both crash severity and duration. To address such gap, a joint modeling approach was proposed to explore critical features affecting both crash severity and duration and their joint relationships. A total of 2,454 tunnel crashes in Shanxi, China were collected. Five types of characteristics were extracted as inputs: crash, vehicle, road, environment, and temporal features. Then, a joint cross-stitch network model was proposed with two sub-multilayer perceptron (MLP) networks to establish the relationships between input features with crash severity and duration, respectively. Cross-stitch units were introduced between the two sub-networks to share each model parameters with specific weights, enforcing the sub-networks to simultaneously estimate the coupling relationships between variables and two targets (i.e., crash severity and duration). Compared to existing separate models, the joint cross-stitch network models achieved best performance on estimation of both crash severity (7.0%, 10.2% increase in sensitivity and F1 score, respectively) and congestion duration (3.7% reduction in mean squared error). Though the parameter sharing mechanism, it could simultaneously learn the coupling relationships between contributing factors on both crash severity and duration to offer more reasonable interpretations than separate models. The results indicate that congested traffic conditions significantly increase injury severity, while truck-only, two-vehicle, and multi-vehicle crashes notably prolong congestion duration. Moreover, the joint models exhibited some features presenting inverse effects on injury severity in the separate models. The results enhance our understanding of crashes and congestion in tunnels and inform several recommendations for tunnel management to reduce both crash severity and congestion duration.

{"title":"Tunnel crash severity and congestion duration joint evaluation based on cross-stitch networks","authors":"Chenzhu Wang, Mohamed Abdel-Aty, Lei Han","doi":"10.1016/j.aap.2025.107942","DOIUrl":"10.1016/j.aap.2025.107942","url":null,"abstract":"<div><div>Tunnels, with limited space and restricted widths/heights, increase the likelihood of crashes and traffic congestion, where the severity and duration of one often exacerbate the other. However, existing studies mainly conducted separate models, which cannot simultaneously analyze the joint impacts of contributing factors on both crash severity and duration. To address such gap, a joint modeling approach was proposed to explore critical features affecting both crash severity and duration and their joint relationships. A total of 2,454 tunnel crashes in Shanxi, China were collected. Five types of characteristics were extracted as inputs: crash, vehicle, road, environment, and temporal features. Then, a joint cross-stitch network model was proposed with two sub-multilayer perceptron (MLP) networks to establish the relationships between input features with crash severity and duration, respectively. Cross-stitch units were introduced between the two sub-networks to share each model parameters with specific weights, enforcing the sub-networks to simultaneously estimate the coupling relationships between variables and two targets (i.e., crash severity and duration). Compared to existing separate models, the joint cross-stitch network models achieved best performance on estimation of both crash severity (7.0%, 10.2% increase in sensitivity and F1 score, respectively) and congestion duration (3.7% reduction in mean squared error). Though the parameter sharing mechanism, it could simultaneously learn the coupling relationships between contributing factors on both crash severity and duration to offer more reasonable interpretations than separate models. The results indicate that congested traffic conditions significantly increase injury severity, while truck-only, two-vehicle, and multi-vehicle crashes notably prolong congestion duration. Moreover, the joint models exhibited some features presenting inverse effects on injury severity in the separate models. The results enhance our understanding of crashes and congestion in tunnels and inform several recommendations for tunnel management to reduce both crash severity and congestion duration.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"213 ","pages":"Article 107942"},"PeriodicalIF":5.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177479","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

Face to planning risk: A hierarchical risk-aware prediction module for the safe planning system

IF 5.7 1区工程技术 Q1 ERGONOMICS

Accident; analysis and prevention

Pub Date : 2025-02-03 DOI: 10.1016/j.aap.2024.107906

Jiahui Xu , Wenbo Shao , Bingbing Nie , Weida Wang , Chao Yang , Hong Wang

引用次数: 0