Traffic Injury Prevention最新文献

{"title":"Convenience or safety system? Crash rates of vehicles equipped with partial driving automation.","authors":"Jessica B Cicchino","doi":"10.1080/15389588.2024.2448511","DOIUrl":"https://doi.org/10.1080/15389588.2024.2448511","url":null,"abstract":"<p><strong>Objective: </strong>Although partial driving automation systems are usually discussed as convenience features, consumers sometimes consider them to be safety features. The goal of this study was to assess if partial driving automation reduces rear-end and lane departure crashes beyond safety systems like automatic emergency braking (AEB) and lane departure prevention (LDP).</p><p><strong>Methods: </strong>Analyses examined crash rates of model year 2017-2019 Nissan Rogues and model year 2013-2017 BMW vehicles. Negative binomial regression was used to assess the association of Nissan's partial driving automation system, ProPILOT Assist, and BMW's system, Driving Assistant Plus, with police-reported rear-end and lane departure crash rates on the limited-access roads where they are designed to be used per vehicle mile traveled. Crash rates were also examined on roads with speed limits of ≤ 35 mph, where the systems were expected to have limited functionality and not be used much.</p><p><strong>Results: </strong>Equipment with BMW's Driving Assistant Plus was not associated with significantly lower crash rates than equipment with LDP alone. Rear-end crash rates were 26% lower on limited-access roads and 43% lower on roads with speed limits ≤ 35 mph for Nissan Rogues with ProPILOT Assist than for those with AEB alone. Similarly, lane departure crash rates were 25% lower for Nissan Rogues with ProPILOT Assist compared with those with LDP alone on limited-access roads, but were 31% lower on roads with speed limits ≤ 35 mph and 43% lower on limited-access roads in the dark. This brings into question if the lower crash rates associated with ProPILOT Assist can be attributed to use of the system, given that it would be activated infrequently on residential roads and that vehicles with it generally had better headlights than those unequipped.</p><p><strong>Discussion: </strong>There is no convincing evidence that partial driving automation is a safety system that is preventing crashes in the real world. Research incorporating system use will be key to understanding safety effects. Considering that drivers have been documented misusing these systems, designing partial driving automation with robust safeguards to deter misuse will be crucial to minimizing the possibility that the systems will inadvertently increase crash risk.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-11"},"PeriodicalIF":1.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crash risk prediction and analysis from the perspective of alignment and environment features: A study on an expressway in a hilly area.","authors":"Pengcheng Qin, Jie He, Changjian Zhang, Xintong Yan, Chenwei Wang, Yuntao Ye, Zhiming Fang","doi":"10.1080/15389588.2025.2459297","DOIUrl":"https://doi.org/10.1080/15389588.2025.2459297","url":null,"abstract":"<p><strong>Objective: </strong>Expressways in hilly areas feature complex alignment and environments constrained by terrain conditions, significantly threatening life and property safety. This study aims to investigate crash risk prediction of expressways in hilly areas through alignment and environment features and identify determinants of the high risk for safety improvement.</p><p><strong>Methods: </strong>Based on 5 years of crash data on casualties and property damage of an expressway in southwestern China, the order technique and five clustering algorithms were employed to determine and classify risk levels. Environment features were extracted by semantic segmentation with a DeepLabv3 model. The study established four ensemble learning models to predict crash risks, and the interpretable model approach was adopted to understand contributing features.</p><p><strong>Results: </strong>XGBoost achieved the best overall performance, with the accuracy and F1 score reaching 0.9259 and 0.8886. The proportion and variation rate of trucks and cars, and the proportions of constructions and the road positively correlated with high risks, while the proportions of the vegetation and road had negative correlations. The horizontal and vertical alignments, including long steep slopes, smaller curve radii, shorter transition curves, and smaller convex and concave curves radii, were linked to high risks.</p><p><strong>Conclusions: </strong>This study proposes an approach to predict crash risks on road sections without historical crash data. Combining the XGBoost model with the SHAP approach, enables accurate identification of risks on expressways in hilly areas using alignment and environment features and enhances the understanding of how these factors contribute to high risks.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-11"},"PeriodicalIF":1.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drunk driving has a speeding problem.","authors":"Mark B Johnson","doi":"10.1080/15389588.2025.2456942","DOIUrl":"https://doi.org/10.1080/15389588.2025.2456942","url":null,"abstract":"<p><strong>Objectives: </strong>Alcohol and excessive speeding are both linked to elevated crash risk. Alcohol-related and speeding-related crashes are recorded and treated as distinct, with separate etiologies. Yet, speeding and alcohol use are interrelated. We speculate that speeding might account for some of the crash risk associated with drunk driving.</p><p><strong>Methods: </strong>Data from the Crash Investigation Sampling System were analyzed. Vehicle speeds, measured moments before crashes, were estimated from driver blood alcohol concentrations (BACs) for different levels of injury severity. We first applied a previously published formula to estimate the relative crash risk associated with speeds that occur at different BACs. Then, from the literature we obtained relative crash risk odds ratios associated with different BACs. Finally, for BACs of 0.08 g/dl and 0.16 g/dl, separately for serious injury and fatality crashes, we created ratios to estimate what portion of the alcohol-crash risk might be attributed to higher travel speeds.</p><p><strong>Results: </strong>A statistically significant BAC × Injury Severity interaction indicated that crash drivers with higher BACs drove faster than their sober counterparts, and that this was exacerbated for more serious injuries. Among drivers with fatal injuries, those with BACs of 0.16 g/dl were traveling over 10 mph faster than their sober counterparts. Finally, using this information, for drivers at different BACs, we compared the crash risk attributable to speed with the crash risk as a function of alcohol levels. Accordingly, we estimate that at 0.08 g/dl, higher speeds accounted for nearly 50% of the fatality crash risk attributed to alcohol, and 25% of the fatality crash risk at 0.16 g/dl. For serious injuries, estimates were 39% and 16%, respectively.</p><p><strong>Conclusions: </strong>The literature on alcohol-related crashes widely attributes the increased crash risk to impaired driving skills, such as attention, coordination and reaction time. Our analysis suggests that speeding alone might account for some of this elevated risk. This has implications for understanding the etiology of alcohol-related crashes. We also suggest that speed control may be a viable means of reducing the harm from alcohol-related crashes.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-5"},"PeriodicalIF":1.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of curve radius variations on vehicle driving states on 2-lane roads.","authors":"Qing-Qing Li, Hong-Bin Zhang, Guang-Hui Sun, Yu Han, Hao-Jie Liu","doi":"10.1080/15389588.2024.2447088","DOIUrl":"https://doi.org/10.1080/15389588.2024.2447088","url":null,"abstract":"<p><strong>Objective: </strong>The complex alignment of 2-lane roads results in a higher risk of traffic crashes. This study aims to identify key factors influencing vehicle driving states on curves.</p><p><strong>Methods: </strong>Five curve radii were selected to create simulated driving scenarios. Vehicle operation data were collected from 36 drivers using a driving simulator. Vehicle speed and lateral lane position were chosen as indicators of driving states. A 3-factor analysis of variance (ANOVA) was conducted to assess the effects of curve radius, curve direction, and curve position on driving speed and lateral lane position. Polynomial fitting models were also developed to examine the relationship between curve radius, mean speed, and mean lateral lane position magnitude.</p><p><strong>Results: </strong>Curve radius had the most significant effect on driving speed (partial η² = 0.536), and curve position had the greatest influence on lateral lane position (partial η² = 0.283). The polynomial fitting models for right turns (adjusted <i>R</i>² = 0.768) and left turns (adjusted <i>R</i>² = 0.754) both exhibited adjusted <i>R</i>² values greater than 0.75, indicating that the models adequately explain the relationships among the 3 variables.</p><p><strong>Conclusions: </strong>Smaller curve radii result in lower average driving speeds. In the circular curve sections of the road, lateral lane position tends to be larger. As curve radius decreases, the influence of driving speed on lateral lane position magnitude becomes more pronounced. Higher average speeds on curves are associated with larger lateral lane position magnitudes. Under the same speed conditions, vehicles making left turns exhibit larger lateral lane position magnitudes compared to those making right turns. These findings highlight the significant impact of curve geometry on vehicle driving behavior, providing insights for curve alignment design.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System failures and extreme behavior in fatal and injury crashes in South Australia.","authors":"Lisa Wundersitz, Simon Raftery","doi":"10.1080/15389588.2025.2454945","DOIUrl":"https://doi.org/10.1080/15389588.2025.2454945","url":null,"abstract":"<p><strong>Objectives: </strong>Within the road system, there are compliant road users who may make an error that leads to a crash, indicating a \"system failure.\" There are also road users who deliberately take risks and engage in dangerous or \"extreme\" behavior that leads to a crash. This study aims to assess the relative contribution of system failures and extreme behavior to guide the development of future strategies and interventions needed to create a safe system and prevent road trauma.</p><p><strong>Methods: </strong>This study used the same methodology as Wundersitz et al. to provide an update on the relative contribution of system failures and extreme behaviors in more recent crashes. Two samples were used for the study: 157 fatal crashes from Coroner's investigation files and 235 injury crashes from in-depth crash investigations conducted by the Center for Automotive Safety Research.</p><p><strong>Results: </strong>Consistent with previous findings, the results indicated that the majority of fatal (70%) and injury crashes (93%) in South Australia were attributable to failures within the road transport system. In almost half of the fatal crashes and 72% of injury crashes, road users were fully compliant (i.e., no illegal behaviors). A comparison of the relative contributions over time revealed that the proportion of extreme behaviors in fatal crashes has decreased, which may be, at least partly, attributable to a reduction in alcohol-related crashes within South Australia.</p><p><strong>Conclusions: </strong>Overall, the findings suggest that strategies continuing to focus on system wide improvements to the road transport system such as providing safe road infrastructure (e.g., side and center barriers) and the accelerated uptake of safe vehicle technologies (e.g., lane keeping technology, autonomous emergency braking) can be expected to be effective in reducing the incidence and severity of a large proportion of fatal and serious injury crashes. For more extreme behaviors, greater control of road user behavior may be required through the increased use of vehicle technologies and more holistic social health initiatives.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Budget cuts at NHTSA: programs to cease and areas to cut.","authors":"David C Viano","doi":"10.1080/15389588.2025.2461431","DOIUrl":"https://doi.org/10.1080/15389588.2025.2461431","url":null,"abstract":"","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-4"},"PeriodicalIF":1.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perception and performance: Evaluating driver behavior on horizontal curves at day and night based on optical flow model analysis.","authors":"Jie Wang, Jiangtong Li","doi":"10.1080/15389588.2024.2444471","DOIUrl":"https://doi.org/10.1080/15389588.2024.2444471","url":null,"abstract":"<p><strong>Objective: </strong>This study investigates the interaction between human perception and driver behavior on horizontal curves, focusing on how road geometry and visibility affect driving performance.</p><p><strong>Methods: </strong>A driving simulator replicated 3 curve types by radius-200 m (sharp), 400 m (moderate), and 600 m (loose)-under day and night conditions. The focus of expansion (FOE) is the source point of optical flow, and an FOE model was established to linked the driver's visual perception with vehicle dynamics. Data on eye movement and vehicle dynamics were collected from 24 drivers (mean age: 27 years, mean driving experience: 3.8 years).</p><p><strong>Results: </strong>The results indicate that driving at night on sharp curves significantly impairs the ability of drivers to align their perception with vehicle motion, leading to delayed steering adjustments and increased lateral errors. The most dangerous areas, identified as the back half of the test curves and corresponding to the minimum FOE radius, were where the misalignment between perception and motion was most significant. On loose curves, decreased driver vigilance was observed, potentially due to a perceived reduction in steering demands, underscoring the role of psychological and contextual factors during curve negotiation.</p><p><strong>Conclusion: </strong>This study underscores the importance of optimizing curve radii and enhancing the alignment between drivers' visual perceptions and vehicle dynamics to reduce accident risks. In real traffic, placing traffic guidance mid-curve may better prompt drivers to slow down, particularly at night. Integrating FOE-based feedback into advanced driver assistance systems (ADAS) could further enhance performance by offering real-time cues tailored to curve geometry in low visibility.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating safety field theory into interactive trajectory prediction between VRU and vehicle: An integrated spatial-temporal and risk-aware model.","authors":"Dianchen Zhu, Zheyan Fan, Wei Ma, Xuxin Zhang, Ho-Yin Chan, Mingming Zhao","doi":"10.1080/15389588.2024.2443008","DOIUrl":"https://doi.org/10.1080/15389588.2024.2443008","url":null,"abstract":"<p><strong>Objectives: </strong>Intersections represent critical points where conflicts between vulnerable road users (VRUs) and vehicles often occur, posing significant safety challenges globally. Despite efforts to mitigate heterogeneous traffic individual conflict, the accurate trajectory prediction of VRU-vehicle interactions remains elusive due to asymmetric information, unequal risks, and uncertainties in decision-making behaviors. Most existing trajectory prediction models predominantly focus on either VRUs or vehicles, neglecting the complex mechanisms of interactions between heterogeneous traffic agents. This article proposes a trajectory prediction model that incorporates spatial-temporal characteristics and security risk awareness as an alternative approach to these challenges.</p><p><strong>Methods: </strong>The proposed spatial-temporal risk network (STRN) combines the awareness of time, space, and quantified safety risk to improve the performance of this model. First, the safety potential field theory is used to quantify and label the risks in the VRU-vehicle interaction scenario, and the effect of conflict risk on the agents' motion trajectory is considered. Second, the spatial constraint features of agent movement are extracted from the spatial dimension. Third, the change characteristics of the agent's motion trajectory over time are extracted from time dimension.</p><p><strong>Results: </strong>The experimental results show that the model can effectively identify the motion trajectories under the complex interaction between VRUs and vehicles. The ablation experiments confirm that the integration of spatial-temporal risk dimensions positively impacts the accuracy of interaction prediction. The model shows great robustness in different scenario transferability tests.</p><p><strong>Conclusions: </strong>This study revisits the challenge of predicting interaction trajectories of heterogeneous objects in unsignalized intersection scenarios, a topic that has not been extensively explored. The proposed STRN model, with its performance and robustness, provides a new scheme for improving the level of traffic safety and promoting intelligent autonomous vehicle decision system.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mesopic and glare driving performance in a driving simulator.","authors":"Thomas Kerwin, Cameron Wrabel, Deyue Yu, Zhong-Lin Lu, Jingzhen Yang","doi":"10.1080/15389588.2024.2446398","DOIUrl":"https://doi.org/10.1080/15389588.2024.2446398","url":null,"abstract":"<p><strong>Objective: </strong>Difficulties in night driving due to deficits in visual function are a common problem among older drivers. Signage, hazards, and road markings can be more difficult to identify for those with presbyopia, especially in nighttime conditions that include glare from headlights. This study evaluated the visual function and driving abilities of participants in various lighting conditions to find efficient and effective testing procedures for predicting how older individuals will perform in night driving conditions.</p><p><strong>Methods: </strong>A driving simulator study was conducted to investigate the relationship between driving performance measures and visual function measures in different lighting conditions. Participants were examined in a laboratory setting to measure visual acuity and contrast sensitivity in bright light, dim light, and dim light with glare conditions. The participants were asked to drive in a driving simulator in simulated day, night, and night with glare conditions. Analysis using a linear mixed model was performed across the lighting conditions.</p><p><strong>Results: </strong>There are statistically significant relationships between the experimental variables, with age being an important factor. Age was found to have a statistically significant effect on the standard deviation of lateral position, which is a measure of vehicle control. Age was also found to have a statistically significant effect on reaction time and accuracy of a secondary identification task. Visual acuity and contrast sensitivity were found to have a statistically significant effect on mean velocity.</p><p><strong>Conclusions: </strong>Results suggest that visual acuity alone is insufficient to fully describe visual function for predicting performance in a driving task, but the ideal variables for prediction of performance in a night driving situation are not clear.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-7"},"PeriodicalIF":1.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the impacts of road pavement roughness and rutting on traffic safety: A macrolevel analysis.","authors":"Viet N Huynh, Long T Truong, Chris De Gruyter","doi":"10.1080/15389588.2024.2448838","DOIUrl":"https://doi.org/10.1080/15389588.2024.2448838","url":null,"abstract":"<p><strong>Objectives: </strong>Many studies have examined the safety effects of pavement conditions at microscopic and mesoscopic levels, focusing on road segments, intersections, horizontal curves, and tunnels. These studies have often reported mixed safety effects. However, a macrolevel analysis would provide a more comprehensive understanding by considering the safety effects across the entire road network. Despite its potential advantages, there has been limited macrolevel research to examine the effects of pavement conditions on traffic safety. This research explores the impacts of pavement roughness and rutting on traffic safety, using traffic analysis zones (TAZs) and statistical area level 2 (SA2) as spatial units.</p><p><strong>Methods: </strong>Random parameter negative binomial regression models are estimated using several data sets encompassing traffic crashes, census, traffic, and pavement conditions in Victoria, Australia.</p><p><strong>Results: </strong>Though the effects vary across zones, those with a greater share of arterials and freeways with very poor rutting or roughness generally tend to have more traffic crashes. Additionally, evidence at the SA2 level suggests that a greater share of arterials and freeways with very poor roughness is generally associated with more fatal crashes.</p><p><strong>Conclusions: </strong>Overall, the findings suggest that improving road conditions by reducing pavement roughness and rutting could enhance safety, from a network perspective. Road authorities should prioritize maintenance and rehabilitation efforts in areas where roughness and rutting are most severe to maximize safety outcomes. In addition, integrating macrolevel models to predict the safety effects of pavement conditions in strategic planning can enable road agencies to plan infrastructure investments more effectively and proactively.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-7"},"PeriodicalIF":1.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}