Accident; analysis and prevention最新文献

{"title":"Why they take the risk to perform a direct left turn at intersections: A data-driven framework for cyclist violation modeling.","authors":"Hui Bi, Xuejun Zhang, Weiwei Zhu, Hui Gao, Zhirui Ye","doi":"10.1016/j.aap.2024.107846","DOIUrl":"https://doi.org/10.1016/j.aap.2024.107846","url":null,"abstract":"<p><p>Bicycle crashes at intersection areas are posed a worrying traffic safety issue, and one of the main reasons for bicycle crashes is failing to avoid conflicts with motor vehicles and other bicycles. Clearly, cyclists are more exposed to risk if they perform a direct left turn (DLT) being mixed with left-turning vehicle under a left-turn phase. Owing to the lack of exposure data, the detection of DLT event and the mechanism behind the risky riding behavior have yet to be discovered. To bridge these gaps, this study proposes a DLT detection framework based on bike sharing trajectories. Moreover, this study seeks to understand the contributing factors to DLT behavior using the random parameters logit model with heterogeneity in means and variances (RPLHMV) to account for unobserved heterogeneity in the DLT cases dataset. Statistical analysis shows that DLT is most likely to occur on weekdays during peak periods under large commuting demand. As to what caused the DLT violations, law-obeying cyclists are more susceptible to external events, while risk-taking cyclists are subtly undermined by their habits. In addition, the model of RPLHMV reveals several significant contributing factors to the propensity of DLT violations, such as event time, available passing time for left-turning bicycles, and average cycling speed, whereas the indicator variables of actual waiting time, available passing space for left-turning bicycles, and preference for DLT violation become the emerging influential variables. This study is expected to help better understand DLT occurrence and propose countermeasures more efficiently for reducing cyclists' DLT rate.</p>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"107846"},"PeriodicalIF":5.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674737","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 the safety impacts of winter road maintenance operations using connected vehicle data","authors":"Minsoo Oh,&nbsp;Jing Dong-O’Brien","doi":"10.1016/j.aap.2024.107837","DOIUrl":"10.1016/j.aap.2024.107837","url":null,"abstract":"<div><div>This paper investigates the impacts of winter maintenance operations (WMO) on road safety under different weather conditions using connected vehicle data. In particular, the impacts of WMO on incident-induced delays (IID) and harsh braking events are highlighted, representing the influence on traffic flow and vehicle stability, respectively. Taking advantage of emerging connected vehicle data, the impacts of WMO on IIDs and vehicle harsh braking events are estimated. Data analysis revealed that WMO plays an important role in reducing the mean IID and the average number of harsh braking events, particularly when roads were covered with ice, frost, slush, or snow in snowy weather. The presence of WMO reduced the mean IID from 145.93 veh-h to 57.70 veh-h, representing a 60% decrease, and the number of harsh braking events from 3.58 cases per crash to 2.90 cases per crash, making a 19% reduction. Last, the multiple linear regression (MLR) model highlights that WMO effectively reduces IID by 23.36 veh-h. In addition, the MLR model indicates that IID is influenced by traffic volume, driving behaviors immediately before a crash, crash severity, road weather conditions, with more severe crashes and worse pavement conditions contributing to longer delays. These findings suggest that the WMO can improve road safety by reducing incident-induced delays and improving traffic stability in winter weather conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107837"},"PeriodicalIF":5.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646745","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":"Partially constrained latent class analysis of highway crash injury severities: Investigating discrete spatial heterogeneity from regional data sources","authors":"Jiabin Wu ,&nbsp;Yiming Bie ,&nbsp;Qihang Li ,&nbsp;Zuogan Tang","doi":"10.1016/j.aap.2024.107834","DOIUrl":"10.1016/j.aap.2024.107834","url":null,"abstract":"<div><div>A comprehensive investigation into the mechanisms and causes of traffic crashes holds significant implications for crash prevention and mitigating crash injury severity. Under the influence of unobservable factors, the impact of the same factor on crash injury severity might not only vary spatially but also exhibit temporal instability. Neglecting these characteristics could lead to biased model estimations and confounding effects, potentially resulting in ineffective or even counterproductive traffic safety strategies. Simultaneously considering the spatial heterogeneity and temporal instability of factors that influence crash injury severity, this paper first collects traffic crash data from the Austin metropolitan area in Texas, USA, spanning the years 2017 to 2019, where various independent variables are selected as candidate variables for analyzing crash injury severity, and a latent class logit model is constructed. Subsequently, annual traffic-related statistical exogenous data involving 11 counties are utilized to establish class probability functions within the latent class logit model, thereby accounting for the spatial heterogeneity of crash injury severity. Finally, this study conducts the partially constrained approach for modeling annual basis, simultaneously analyzing the temporal instability of safety factors’ impact on crash injury severity. Notably, this paper not only identifies numerous factors significantly influencing crash injury severity but also discovers that certain factors exhibit significant temporal instability effects on crash injury severity. Several explanatory variables showed temporally instability in terms of their effect on resulting injury severities. Such as, crash locations, lighting conditions, driver age, driver gender, vehicle types, vehicle model year. The findings of this study serve as a valuable reference for delving deeper into the causal mechanisms of crash injury severity as well as formulating effective safety measures.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107834"},"PeriodicalIF":5.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611896","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":"Study on optimization design of guide signs in dense interchange sections of eight-lane freeway","authors":"Qiqi Liu ,&nbsp;Jianling Huang ,&nbsp;Xiaohua Zhao ,&nbsp;Jia Li ,&nbsp;Yanan Chen ,&nbsp;Chengyu Wu","doi":"10.1016/j.aap.2024.107828","DOIUrl":"10.1016/j.aap.2024.107828","url":null,"abstract":"<div><div>The eight-lane freeway resulting from reconstruction and expansion typically exhibits short distances between interchanges and a wide road section. Nonetheless, the absence of specific guidelines for the placement of guide signs in dense interchange sections of the eight-lane freeway results in inadequate design, thereby impeding drivers’ ability to read and comprehend the signs. To tackle this issue, the study employs two interchanges 2.48 km apart on the Jinan-Qingdao Freeway as a case study. Four optimization schemes for guide signs are developed based on drivers’ information requirements and compared with the current guide sign design scheme. Thirty-nine drivers were recruited to gather detailed driving behavior indicators via a driving simulation experiment. The impact of the guide sign optimization scheme on driving behavior is analyzed, and the overall effects are evaluated using the non-integer rank RSR method. This study aims to identify an optimal approach to guide sign design for dense interchange sections.</div><div>The results indicate that the impact of guide signs in dense interchange sections on drivers is primarily concentrated between the two interchanges. Specifically, the addition of a 2.5 km exit advance sign enhances drivers’ speed regulation level, the inclusion of navigation voice improves operational stability, and the presence of pavement words at exit diversion locations enhances psychological comfort for drivers. By considering the comprehensive effectiveness of each optimization scheme, it is evident that schemes 5 and 2 exhibit superior optimization effects. This suggests that providing advanced notice of exit information in dense interchange sections of eight-lane freeways is an effective measure to enhance freeway service levels and ensure driving safety. It is recommended that under the conditions of insufficient interchange spacing, the information of interchange exits should be forewarned in advance. Additionally, auxiliary navigation voice and pavement words should be employed to enhance drivers’ information perception levels, thereby mitigating the risk of missing exits due to limited reaction time. This paper serves as a significant reference for informing the optimal configuration of guide signs, thereby contributing to the meticulous development of standardized specifications.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107828"},"PeriodicalIF":5.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611902","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":"Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals","authors":"Liu Yang ,&nbsp;Ruoling Zhou ,&nbsp;Guofa Li ,&nbsp;Ying Yang ,&nbsp;Qianxi Zhao","doi":"10.1016/j.aap.2024.107835","DOIUrl":"10.1016/j.aap.2024.107835","url":null,"abstract":"<div><div>Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account. In this study, an explainable driving stress recognition framework was presented to quantify stress based on electroencephalography (EEG) and behavior data. Based on the extraction of key EEG and behavior features and feature selection, low, medium, and high levels of driving stress were identified using seven machine learning algorithms. The recognition results when only using EEG or behavior features were compared with the result when fusing EEG together with behavior features. Then, the dependency effects between brain activity, driving behavior, and stress were analyzed using the SHapley Additive exPlanation (SHAP) method, and fuzzy rules were obtained by decision tree method. Results indicated that after feature selection, the accuracy of the combined EEG and behavior feature set improved by 8.56% and 26.51% compared to the single EEG and behavior feature sets respectively, and the accuracy rate of 84.93% was achieved. Furthermore, the variations in driver behavior and physiology under stress were identified by the visualization results of SHAP and the quantitative analysis method of decision tree. The changes of different brain regions in the same frequency band showed higher synchronicity under driving stress stimulation. The changes caused by increased stress can be explained by lower speed, smaller maximum lateral lane deviation, smaller accelerator pedal depth and larger brake depth, along with the power changes of the θ and β-band of the brain.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107835"},"PeriodicalIF":5.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611899","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":"Understanding factors influencing e-scooterist crash risk: A naturalistic study of rental e-scooters in an urban area","authors":"Rahul Rajendra Pai,&nbsp;Marco Dozza","doi":"10.1016/j.aap.2024.107839","DOIUrl":"10.1016/j.aap.2024.107839","url":null,"abstract":"<div><div>In recent years, micromobility has seen unprecedented growth, especially with the introduction of dockless e-scooters. However, the rapid emergence of e-scooters has led to an increase in crashes, resulting in injuries and fatalities, highlighting the need for in-depth analysis to understand the underlying mechanisms. While helpful in quantifying the problem, traditional crash database analysis cannot fully explain the causation mechanisms, e.g., human adaptation failures leading to safety–critical events. Naturalistic data have proven extremely valuable for understanding why crashes happen, but most studies have addressed cars and trucks.</div><div>This study is the first to systematically analyze factors contributing to crashes and near-crashes involving rental e-scooters in an urban environment, utilizing naturalistic data. The collected dataset included 6868 trips, covering 9930 km over 709 h with 4694 unique participants. We identified 61 safety–critical events, including 19 crashes and 42 near-crashes, and subsequently labeled variables associated with each event according to the codebook using video data.</div><div>Our odds ratio analysis identified that rider experience and behavior (e.g., phone usage, single-handed riding, and pack riding) significantly increase the crash risk. Given the accessibility of rental e-scooters to individuals regardless of their experience, our findings emphasize the need for rider training in addition to education. Influenced by their experience with bicycles, riders may anticipate a similar self-stabilizing mechanism in e-scooters. We found that single-handed riding, which compromises balance, poses a heightened risk, underscoring the crucial role of balance in safe e-scooter operation. Furthermore, the purpose (leisure or commute) and directness (point-to-point or detour) of the trip were also identified as factors influencing the risk, suggesting that user intent plays a role in safety–critical events. Interestingly, our analysis underscores the importance of adapting the crash and near-crash definitions when working with two-wheeled vehicles, especially those in the shared mobility system.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107839"},"PeriodicalIF":5.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611903","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":"Personality, functional performance, and travel patterns related to older drivers’ risky driving behavior: A naturalistic driving study","authors":"Yuanfang Zhu ,&nbsp;Meilan Jiang ,&nbsp;Toshiyuki Yamamoto","doi":"10.1016/j.aap.2024.107833","DOIUrl":"10.1016/j.aap.2024.107833","url":null,"abstract":"<div><div>Older drivers are among the most vulnerable demographics within the road traffic system. The rising number of elderly motorists has raised public concern regarding their driving safety. It is crucial to understand the factors influencing risky driving behaviors among older drivers to enhance their safety. This study aimed to analyze the personality, functional performance, and travel patterns related to older drivers’ risky driving behavior. The analysis utilized a sample of 58 older drivers, aged 65 years and above (mean age = 72.41 years; 40 males and 18 females) from the Nagoya metropolitan area. Risky driving behaviors and travel patterns were assessed using naturalistic driving data. Bivariate correlation analysis revealed that impulsivity and diminished contrast sensitivity were significantly correlated with more frequent risky driving behaviors. Additionally, both low driving exposure and high-risk driving routes (i.e., more frequent left and right turns, driving more on minor roads) were significantly correlated with an increased risk of harsh events. Moreover, a strong association was observed between driving exposure and driving route, indicating that the driving route of lower mileage drivers tend to be riskier. When the relationship between driving exposure and risky driving behaviors was adjusted for driving route, the strength of the correlation diminished from 0.35 to 0.16, rendering it insignificant. This partial correlation analysis suggests that the increased driving risk among low-mileage drivers can be partially attributed to their high-risk driving routes. The findings of this study provide further evidence regarding the role of personality in explaining older drivers’ risky driving behavior and the explanation of older drivers’ low-mileage bias.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107833"},"PeriodicalIF":5.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602983","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":"Predicting lane change maneuver and associated collision risks based on multi-task learning","authors":"Liu Yang ,&nbsp;Jike Zhang ,&nbsp;Nengchao Lyu ,&nbsp;Qianxi Zhao","doi":"10.1016/j.aap.2024.107830","DOIUrl":"10.1016/j.aap.2024.107830","url":null,"abstract":"<div><div>The lane-changing (LC) maneuver of vehicles significantly impacts highway traffic safety. Therefore, proactively predicting LC maneuver and associated collision risk is of paramount importance. However, most of the previous LC risk prediction research overlooks the prediction of LC maneuver, limiting its practical utility. Furthermore, the effectiveness of LC maneuver recognition tends to be moderate as the prediction horizon extends. To fill the gaps, this paper proposes a multi-task learning model that simultaneously predicts the probability of LC maneuver, LC risk level, and time-to-lane-change (TTLC), while further analyzing the intrinsic correlation between LC maneuver and LC risk. The model consists of a Convolutional Neural Network (CNN) and two Long Short-Term Memory networks (LSTM). The CNN is employed to extract and fuse shared features from the dynamic driving environment, while one LSTM is dedicated to estimating the probability of LC maneuver and TTLC, and the other LSTM focuses on estimating the LC risk level. Evaluation of the proposed method on the HighD dataset demonstrates its excellent performance. It can almost predict all LC maneuvers within 2 s before the vehicle crosses lane boundaries, with an 80% recall rate for high-risk LC levels. Even 3.6 s before crossing lane boundaries, the model can still predict approximately 95% of LC maneuvers. The use of the multi-task learning strategy enhances the model’s understanding of traffic scenarios and its prediction robustness. LC risk analysis based on the HighD dataset shows that the risk distribution and influencing factors for left and right lane changes differ. In right lane changes, collision risks primarily arise from the leading and following vehicles in the current lane, while in left lane changes, collision risks mainly stem from the leading vehicle in the current lane and the following vehicle in the target lane. The proposed approach can be applied to advanced driver assistance systems (ADAS) to reliably and early identify LC during highway driving, while correcting potentially dangerous LC maneuvers, ensuring driving safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107830"},"PeriodicalIF":5.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578332","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":"Pedestrians’ Interaction with eHMI-equipped Autonomous Vehicles: A Bibliometric Analysis and Systematic Review","authors":"Siu Shing Man ,&nbsp;Chuyu Huang ,&nbsp;Qing Ye ,&nbsp;Fangrong Chang ,&nbsp;Alan Hoi Shou Chan","doi":"10.1016/j.aap.2024.107826","DOIUrl":"10.1016/j.aap.2024.107826","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) should prioritise pedestrian safety in a traffic accident. External human–machine interfaces (eHMIs), which enhance communication through visual and auditory signals, become essential as AVs become prevalent. This study aimed to investigate the current state of research on eHMIs, with a specific focus on pedestrian interactions with eHMI-equipped AVs. A bibliometric analysis of 234 papers published between January 2014 and December 2023 was conducted using the Web of Science database. The analysis revealed a remarkable increase in eHMI research since 2018, with the principal research topics on crossing behaviour and eHMI evaluations of pedestrians. Subsequently, 38 articles were selected for a systematic review. The systematic review, conducted through a detailed examination of each selected article, showed that pedestrian crossing behaviour is usually measured using crossing initiation time, response time, walking speed and eye tracking data. The eHMI evaluations of pedestrians were made through questionnaires that measure clarity, preference and acceptance. Research findings showed that pedestrians’ crossing behaviour and eHMI evaluations are influenced by human factors (age and nationality), vehicle factors (eHMI type, eHMI colour and eHMI position) and environmental factors (signalisation and distractions). The results also revealed that current eHMI experiments often use virtual reality and video methodologies, which do not fully replicate the complexities of real-world environments. Additionally, the exploration regarding the impact of human factors, such as gender and familiarity with AVs, on pedestrian crossing behaviour is lacking. Furthermore, the investigation of multimodal eHMI systems is limited. This review highlighted the importance of standardising eHMI design, and the key gaps in the current eHMI research were revealed. These insights will guide future research towards effective eHMI solutions through informed theoretical studies and practical applications in autonomous driving.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107826"},"PeriodicalIF":5.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578420","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":"Collaborative effects of vehicle speed and illumination gradient at highway intersection exits on drivers’ stress response capacity","authors":"Hongtao Li ,&nbsp;Linhong Wang ,&nbsp;Menglin Yang ,&nbsp;Yiming Bie","doi":"10.1016/j.aap.2024.107829","DOIUrl":"10.1016/j.aap.2024.107829","url":null,"abstract":"<div><div>Inadequate visibility is a critical factor contributing to the heightened occurrence of nighttime accidents at highway intersections. The installation of smart streetlights which are equipped to detect vehicle positions and speed information, thereby dynamically adjusting illumination, offers a promising solution to significantly reduce nighttime accident rates while conserving lighting energy. Nevertheless, as vehicles travel through illuminated intersections in a relative high speed and enter unlighted highway segments, drivers often experience dynamic visual illusions during dark adaptation, consequently impairing their stress response capacity and generating driving safety concerns. Therefore, we investigate the collaborative impact of illumination gradient and vehicle speed at intersection exits on driver stress response, aiming to provide a theoretical foundation for gradual illumination designs dynamically aligning with various vehicle speeds. Specifically, with reaction time employed as a metric to quantify driver stress response, and intersection area illuminance and vehicle speed utilized as input parameters, a safety assessment method for illumination gradients at exit sections is developed using variance analysis and multiple comparison techniques. Subsequently, a high-fidelity nighttime driving simulation platform is established, integrating initial illuminance, vehicle speed, and illumination gradient distance within exit sections as influential factors. Through simulated driving experiments, the collaborative effects of illumination gradient schemes and vehicle speed on reaction time is systematically examined. Ultimately, we propose optimal illumination gradient schemes and the minimum required number of streetlights for exit sections corresponding to specific vehicle speeds. Results reveal that exit section illumination is unnecessary when the vehicle speed is below 40 km·h⁻¹. For vehicle speeds of 50, 60, and 70 km·h⁻¹, the minimum required exit section lengths are determined to be 35, 70, and 105 m, respectively. Moreover, it is established that a minimum of one streetlight is indispensable within the exit section at a speed limit of 50 km·h⁻¹, while at 60 km·h⁻¹, at least two streetlights are required. Lastly, under a speed limit of 70 km·h⁻¹, the exit section should accommodate no fewer than three streetlights to ensure optimal safety conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107829"},"PeriodicalIF":5.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567183","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}