Accident; analysis and prevention最新文献

{"title":"Activation strategies and effectiveness of Intelligent safety systems for reducing pedestrian injuries in autonomous vehicles","authors":"Quan Li ,&nbsp;Yiran Luo ,&nbsp;Siyuan Liu ,&nbsp;Tianle Lu ,&nbsp;Liangliang Shi ,&nbsp;Wei Ji ,&nbsp;Yong Han ,&nbsp;Hong Wang ,&nbsp;Bingbing Nie","doi":"10.1016/j.aap.2024.107870","DOIUrl":"10.1016/j.aap.2024.107870","url":null,"abstract":"<div><div>Intelligent safety systems (ISS) for autonomous vehicles, integrating advanced perception capabilities and passive protection devices, are expected to reshape traditional pedestrian safety systems and play a key role in reducing the risk of pedestrian injuries in traffic accidents. However, traditional active control and passive protection modules remain disconnected due to insufficient evidence supporting the effectiveness of collaborative strategies in integrated systems, particularly concerning activation criteria and timing. This study aims to address this gap by developing a comprehensive ISS that incorporates advanced perception systems, a vehicle dynamic control module, and controllable passive safety devices. Furthermore, the study evaluates the efficacy of trigger strategies in minimizing injury risks in various safety systems including Automatic Emergency Braking (AEB), Automatic Emergency Steering (AES), and ISS. To achieve this, we reconstructed the dynamics of pedestrian-vehicle interactions before collisions by examining 23 detailed collision cases. These cases were selected from real-world accident databases and included clear video recordings and detailed injury reports. Additionally, we analyzed the boundary conditions for collision avoidance by constructing vehicle steering and braking avoidance models. Our findings indicate that, in real-world accidents, the average Time-to-Collision (TTC) required for drivers to avoid collisions is −3.15 ± 1.00 s. In contrast, the AEB system requires −1.06 ± 0.23 s, and the AES system requires −0.44 ± 0.14 s. Building on this, we developed injury risk models for the system activation, predicting collision risks at various TTCs and pedestrian injury risks. The pedestrian injury risk prediction model effectively forecasts the risk of AIS3 + head injuries resulting from collisions between pedestrians aged 20 to 70 years and the vehicle hood. The threshold for a severe AIS3 + head injury risk is set at 10 %, with a trigger TTC of the ISS at −0.60 ± 0.20 s. When the system is activated at a TTC of −0.5 s, it can reduce the probability of severe head injury to pedestrians by 59 %. The design of the ISS shows significant potential for enhancing pedestrian safety. The findings of this research can offer guidance for the activation strategies of passive safety devices based on input signals from advanced perception systems in AVs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107870"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790872","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 discrete choice latent class method for capturing unobserved heterogeneity in cyclist crossing behaviour at crosswalks","authors":"Rulla Al-Haideri,&nbsp;Adam Weiss,&nbsp;Karim Ismail","doi":"10.1016/j.aap.2024.107850","DOIUrl":"10.1016/j.aap.2024.107850","url":null,"abstract":"<div><div>Conflicts between cyclists and motorized vehicles at crosswalks often lead to severe collisions. The varied behaviour of cyclists at these crossings introduces unobserved heterogeneity. Despite this, there is a notable research gap in studying the cyclist behaviour at roundabout crosswalks. To address this gap, we propose a discrete choice latent class method to capture the multi-level latent heterogeneity in cyclists’ crossing behaviour at roundabout crosswalks. Latent heterogeneity can be captured at multiple levels: site-level, interaction-level, choice-attribute level, and individual-level. This method, rooted in behavioural theory, aims to provide a deeper understanding of cyclists’ crossing decisions, enhancing safety measures at these intersections. We present an application of the proposed method to two publicly available drone datasets of naturalistic road user trajectories at roundabouts, including 8 roundabout sites that exhibit some level of similarity to minimize site heterogeneity. We capture the latent heterogeneity in the cyclists’ membership to a distinct behavioural class at two levels using these datasets: the individual level, represented by the speed of the cyclist as they enter the crosswalk, and the interaction level, defined by the presence of vehicles approaching the cyclist. Our findings align with previous studies that emphasize the significance of the initial speed variable in influencing cyclists’ subsequent behaviour and decisions. We identified two distinct classes of cyclists. We hypothesize that Class 1 cyclists, whom we refer to as passers, tend to bypass or overtake other road users at the crosswalk, especially in the absence of vehicles, prioritizing speed and efficiency. We also hypothesize that Class 2 cyclists, referred to as followers, exhibit more cautious behaviour, preferring to maintain a steady pace and avoid overtaking, particularly when vehicles are present. The proposed latent class model effectively captures this behavioural distinction, offering a more granular view of cyclists’ decision-making processes at roundabout crosswalks. A key finding is that the discrete choice model with a latent class structure outperforms the basic model without it, despite having more degrees of freedom, as it achieves a lower BIC and AIC but improved model fit statistic. This demonstrates that latent heterogeneity can be effectively captured, leading to improved predictions and outperforming the basic non-latent class model. Classifying cyclists into distinct behavioural classes not only enhances cyclist safety at crosswalks but also provides valuable insights for the development of autonomous vehicle-cyclist interactions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107850"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783492","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":"Temporal shifts in safety states through the COVID-19 pandemic: Insights from hidden semi-Markov models","authors":"Xiaomeng Dong ,&nbsp;Kun Xie","doi":"10.1016/j.aap.2024.107875","DOIUrl":"10.1016/j.aap.2024.107875","url":null,"abstract":"<div><div>The COVID-19 pandemic significantly impacted transportation safety, with an increase in risky driving behaviors observed during the initial lockdown period, leading to a higher likelihood of severe crashes. However, there is limited research on the post-pandemic effects on driving behaviors and safety. This study addresses this gap by analyzing open data from the state of Virginia to examine shifts in safety states from 2016 to 2024, covering the pre-, during-, and post-pandemic periods. Structural equation modeling (SEM) was utilized to measure latent variables representing aggressive and inattentive driving behaviors and to model their impacts on crash severity. Additionally, hidden semi-Markov models (HSMMs) were applied to infer shifts in safety states associated with these risky driving behaviors and the proportion of severe crashes. The strength of HSMM models lies in the ability to distinguish meaningful pattern changes from random noise. Compared with hidden Markov models (HMMs), HSMMs provide greater flexibility by accommodating arbitrary state duration distributions, contributing to better model performance and more reliable inferences. The HSMMs with four hidden states were utilized to reveal shifts in safety states over the eight-year analysis period in Virginia. Results suggested that safety states related to risky driving behaviors and the proportion of severe crashes were at lower-risk levels pre-pandemic from 2016 to 2019, then escalated to the highest-risk levels during the pandemic in 2020 and remained at higher-risk levels in 2021, 2022 and 2023. By 2024, safety states have returned to lower-risk levels similar to those inferred in the pre-pandemic period. A seasonal pattern was also identified in safety states, with lower-or-lowest-risk levels occurring in winter near the holiday season.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107875"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789444","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":"Assessing e-scooter rider safety perceptions in shared spaces: Evidence from a video experiment in Sweden","authors":"Khashayar Kazemzadeh","doi":"10.1016/j.aap.2024.107874","DOIUrl":"10.1016/j.aap.2024.107874","url":null,"abstract":"<div><div>Shared spaces prioritise the role of micromobility in urban environments by separating vulnerable road users from motorised vehicles, aiming to enhance both actual and perceived safety. However, the presence of various transport modes, such as pedestrians, cyclists and e-scooters, with differing navigation behaviours, increases the heterogeneity of these spaces and impacts the perception of safety. Despite the increasing use of e-scooters, the safety perceptions of e-scooter riders remain largely underexplored in the literature. In response, I conducted an online video experiment and polled 920 e-scooter users in Sweden to assess their safety perceptions when interacting exclusively with cyclists. I collected data on socio-demographics, travel habits, crash history, and responses to hypothetical video scenarios depicting interactions in shared spaces, where e-scooter riders overtake or meet cyclists. I then employed a random effect latent class ordered logit model to quantify the determinants of e-scooter riders’ safety perceptions. The findings indicate that women feel less safe in shared spaces compared to men. Additionally, the direction of encounters significantly affected young adults, who perceived meeting other users as more unsafe than overtaking them. These findings highlight the importance of accounting for unobserved heterogeneity in safety perceptions, emphasise the significant role of demographic variables in understanding users’ safety perceptions, and reinforce the need for inclusive design of shared spaces for all road users.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107874"},"PeriodicalIF":5.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783498","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":"Examining macro-level traffic crashes considering nonlinear and spatiotemporal spillover effects","authors":"Wei Zhou ,&nbsp;Pengpeng Xu ,&nbsp;Jiabin Wu ,&nbsp;Junda Huang","doi":"10.1016/j.aap.2024.107852","DOIUrl":"10.1016/j.aap.2024.107852","url":null,"abstract":"<div><div>Understanding the impacts of traffic crashes is essential for safety management and proactive safety protection. Current studies often hold the assumption of linearity and spatial dependence, which may lead to underestimated results. To address these gaps, this study considers both nonlinear and spatiotemporal spillover effects to explore the intricate relationships between vehicular crashes and their influencing factors at a macro level. Spatiotemporal spillover effects are captured by creating exogenous variables from neighboring zones and their historical status through a geographically and temporally weighted method. Then, the extracted spillover factors are combined with factors from internal zones to construct independent variables. Their nonlinear characteristics are modeled by the gradient boosting decision trees model and interpreted through accumulated local effect plots. A case study was conducted in New York City spanning four years from 2016 to 2019, considering six categories of influencing factors: street view imagery, exposure, land use, points of interest, traffic network, and socioeconomic attributes. The experimental results demonstrate that model performance is improved by incorporating nonlinear and spatiotemporal spillover effects. Additionally, the proposed model highlights the significant nonlinear effects of factors including mixed land uses, sidewalks, and junction density, and emphasizes the presence of spatiotemporal spillover effects, such as building density, bike parking density, and education attainment. These findings offer insightful implications for transportation practitioners and policymakers to devise safety countermeasures and policies, emphasizing the importance of collaboration across neighboring urban regions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107852"},"PeriodicalIF":5.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783505","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 game-theoretic driver steering model with individual risk perception field generation","authors":"Wenfeng Guo ,&nbsp;Jun Li ,&nbsp;Xiaolin Song ,&nbsp;Weiwei Zhang","doi":"10.1016/j.aap.2024.107869","DOIUrl":"10.1016/j.aap.2024.107869","url":null,"abstract":"<div><div>Driver-automation shared steering control (SSC) has emerged as a promising technology for enhancing vehicle safety, but desire to achieve seamless collaboration between the driver and automation requires an in-depth understanding of driver steering behavior in interaction with automation. In this paper, we introduce a game-theoretic driver steering model with individual risk perception field generation. Firstly, a driver risk perception field is developed based on a novel concept of potential injury risk (PIR) to provide a quantitative estimation of the driver’s perceived driving risk. This approach offers an explicit and physically meaningful structure for simulating the driver’s risk perception process and elucidating the reasons for discrepancies in risk perception. Then, this driver risk perception field is integrated into the framework of non-cooperative Nash game to model the steering interaction between the driver and automation, and the analytical expression of Nash equilibrium is derived in detail. The resulting combined driver model effectively captures the driver adaptation at both the control and planning levels. Next, the key parameters of the combined driver model and its comparators are identified using measured driver steering behavior data from thirty subjects in a series of driving simulator experiments. Finally, the effectiveness and superiority of the combined driver model is validated through a comprehensive comparative analysis. The results demonstrate that the combined driver model achieves the lowest prediction errors compared to its comparators and effectively captures the individual differences in risk perception and steering behavior.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107869"},"PeriodicalIF":5.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778913","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":"Young Motorcyclists’ Behavior Analysis in Pakistan based on Modified Motorcycle Rider Behavior Questionnaire (MRBQ)","authors":"Amjad Pervez ,&nbsp;Suyi Mao ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Muhammad Hussain","doi":"10.1016/j.aap.2024.107873","DOIUrl":"10.1016/j.aap.2024.107873","url":null,"abstract":"<div><div>In many low- and middle-income countries, including Pakistan, young motorcyclists are overrepresented in crashes, primarily due to risky behaviors. To examine these behaviors, the Motorcycle Rider Behavior Questionnaire (MRBQ) has been modified for young motorcyclists in Pakistan to better capture the unique and culturally relevant behaviors affecting their safety. In addition, the study seeks to identify the factor structure of the MRBQ tailored for young motorcyclists in Pakistan, explore the determinants of self-reported incidents (i.e., crashes, near crashes, and violations), and provide effective policy recommendations to enhance road safety. For this purpose, data are collected from 721 young motorcyclists across Pakistan. In addition, exploratory factor analysis was conducted to determine the underlying factor structure of the adapted MRBQ, while a multivariate binary probit model was employed to assess the determinants of self-reported incidents. The findings reveal a five-factor solution comprising safety violations, speeding violations, traffic errors, stunts, and control errors, which differ from previous studies. Notably, “safety violations” emerge as the most significant factor, highlighting the prevalence of risky behaviors among young motorcyclists in Pakistan. In addition, the study indicates that young motorcyclists with lower levels of education, lack a valid riding license, or ride motorcycles with higher engine capacities and for longer durations are more likely to experience crashes, near crashes, and violations. The MRBQ factors, particularly safety violations, speeding violations, and traffic errors, substantially increase the risk of these incidents among young motorcyclists in Pakistan. These findings highlight the importance of addressing both sociodemographic and MRBQ factors in reducing risky riding behaviors among this vulnerable group. The study also offers several policy recommendations to promote safe behaviors and reduce the risk of crashes and injuries among young motorcyclists in Pakistan and other regions with similar contexts.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"211 ","pages":"Article 107873"},"PeriodicalIF":5.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778923","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":"Drivers’ reactions to real-world forward collision warnings at both macroscopic and microscopic longitudinal levels: A functional approach","authors":"Di Yang ,&nbsp;Fan Zuo ,&nbsp;Kaan Ozbay ,&nbsp;Jingqin Gao","doi":"10.1016/j.aap.2024.107853","DOIUrl":"10.1016/j.aap.2024.107853","url":null,"abstract":"<div><div>Understanding drivers’ reactions to in-vehicle forward collision warnings (FCWs) is vital for advancing FCW design and improving road safety. However, past studies often used aggregated safety measures to analyze the drivers’ reactions to FCWs, thereby at the microscopic level, limiting our ability to understand drivers’ reactions to FCWs at particular timestamps immediately after FCWs are issued. Additionally, there has been a notable absence of studies at the macroscopic perspective focusing on analyzing how drivers’ reactions to FCWs evolve over an extended period of time. To overcome these two limitations, this study proposes a new research framework using Functional data analysis (FDA) approach to model driver behavior profile in response to FCWs at both microscopic and macroscopic longitudinal levels. Real-world FCW data collected from the New York City Connected Vehicle Pilot Deployment project is used for the case study. At the microscopic level, a sparse functional design is adopted to model driver behavior profiles, accounting for irregularly spaced functional measurements. Nonparametric functional linear regression is then used to estimate the drivers’ reactions to FCWs at a particular timestamp immediately after FCWs are issued. At the macroscopic level, the functional two-sample test and a functional distance metric are used to examine changes in drivers’ reactions to FCWs over the study period and quantify the magnitude of these changes. Time to collision (TTC) and modified time to collision (MTTC) measures are used to represent driver behavior profiles, and both TTC and MTTC after FCWs are issued are modeled as functions with respect to time based on the proposed FDA approach. Compared to using aggregated safety measures including minimum TTC and MTTC as well as mean TTC and MTTC, new patterns of drivers’ reactions to FCWs are unveiled at both microscopic and macroscopic longitudinal levels. Study outputs reveal several key insights, including driver compensation behavior that escalates safety risk after an initial safety improvement and the diminishing safety benefits of FCWs from the beginning to the end of the after period. The proposed research framework can be generalized to analyze various types of in-vehicle driver warnings at both microscopic and macroscopic longitudinal levels. The findings of this study can support the calibration of detailed driver response behavior to in-vehicle warnings and facilitate the design of driver warning applications and further investigation of their safety benefits.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107853"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757025","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":"Burning gig, rewarding risk: Effects of dual exposure to incentive structure and heat condition on risky driving among on-demand food-delivery motorcyclists in Kaohsiung, Taiwan","authors":"Cheng-Kai Hsu","doi":"10.1016/j.aap.2024.107841","DOIUrl":"10.1016/j.aap.2024.107841","url":null,"abstract":"<div><div>The gig economy, characterized by short-term, task-based work facilitated via digital platforms, has raised various occupational safety concerns, including road safety risks and heat exposure faced by on-demand food delivery (ODFD) workers. Often using open modes of transportation, such as motorcycles and bicycles, these workers have minimal physical protection and direct environmental exposure while working long hours on the road, interacting with larger vehicles. Prior research has suggested that their road risks result from prevalent risky driving incentivized by platform-established business models, but quantitative evidence is lacking. Furthermore, while prolonged heat exposure may contribute to increased risky driving, our understanding of this relationship remains limited. This study investigates the impact of dual exposures to incentive structure and heat condition on risky driving among ODFD motorcyclists in Kaohsiung, Taiwan. A wearable sensing scheme was implemented, tracking a cohort of 40 ODFD workers during their work shifts in real time, collecting data on their speed, acceleration/deceleration patterns, incentive issuances, and heat exposure. Through a case-crossover approach, generalized linear cross-level mixed-effects models were employed to demonstrate the impact of incentive issuance on increasing risky driving among ODFD workers, including faster driving speeds, higher risks of speeding, harsher acceleration and braking, and more erratic acceleration patterns. Additionally, this study reveals that heat exposure, characterized by higher temperatures and humidity levels, exacerbates speed-related risky driving. These findings advance our understanding of causal mechanisms in two key areas of literature: firstly, the road safety risks faced by ODFD gig workers, and secondly, the broader relationship between heat exposure and risky driving. This research offers insights for policymakers to mitigate risky driving among ODFD workers, which is crucial in the context of climate change, where such urban economic dynamics may amplify climate-related inequities and place disproportionate safety burdens on vulnerable workers within the rapidly evolving gig economy.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107841"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757026","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":"Impact of speed on injury severity in single-vehicle run-off-road crashes: Insights from partially temporal constrained modeling approach","authors":"Zhe Wang ,&nbsp;Chenzhu Wang ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Lei Han ,&nbsp;Helai Huang ,&nbsp;Jinjun Tang","doi":"10.1016/j.aap.2024.107848","DOIUrl":"10.1016/j.aap.2024.107848","url":null,"abstract":"<div><div>Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019–2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"210 ","pages":"Article 107848"},"PeriodicalIF":5.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746091","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}