Traffic Injury Prevention最新文献

{"title":"Fuzzy-based driver monitoring system to assess dangerous driving on roads.","authors":"SeyedAman Zargari, Alireza Jarrah, Fahimeh Baghbani","doi":"10.1080/15389588.2025.2539923","DOIUrl":"https://doi.org/10.1080/15389588.2025.2539923","url":null,"abstract":"<p><strong>Objectives: </strong>Road accidents result from various contributing factors, including driver fatigue, inappropriate vehicle speed, adverse weather, and temporal factors. The research in this paper aims to design and evaluate a Fuzzy Driver Monitoring System (FDMS) that automatically identifies dangerous driving behavior by considering critical driving parameters to enhance road safety.</p><p><strong>Methods: </strong>In this work, a fuzzy logic driver alert system is designed that considers five key driving parameters: vehicle speed, driver drowsiness, weather, day of the week, and time of day. To detect the driver's drowsiness, a BlazeFace-based detection stage is first utilized to accurately identify and crop the driver's face from the video feed to ensure the model focuses on pertinent facial cues. The drowsiness level is then estimated using an improved deep-learning model (LSTM, CNN) with a longer temporal window for facial expression recognition. The FDMS evaluates driving risks from <i>very low</i> to <i>very high</i> according to its five inputs.</p><p><strong>Results: </strong>The system proposed here accurately evaluated driving risk levels under various simulated conditions. Scenarios involving high drowsiness of the driver, higher vehicle speeds, and poor weather conditions all yielded stable high-risk levels. Specifically, the improved drowsiness detection algorithm reached an accuracy rate of 70.46%, enhancing the reliability of risk assessment by including a broader range of risk factors than earlier studies. Furthermore, the model demonstrated a robust classification performance with an F1-score of 71.64% and an Area Under the Curve (AUC) of 0.75, confirming its effectiveness in distinguishing between drowsy and alert states. Additionally, a Graphical User Interface (GUI) was developed to display real-time data and the driving risk level based on simulated or collected data from the Global Positioning System (GPS) sensor, weather Application Programming Interface (API), and camera. The proposed FDMS was evaluated under various driving conditions and achieved an accuracy of 77.5% in true alerts provided to the driver. Finally, the proposed FDMS is experimentally assessed using an experimental hardware setup consisting of a laptop, webcam, GPS, and General Packet Radio Service (GPRS) module to demonstrate its real-world applicability.</p><p><strong>Conclusions: </strong>The proposed FDMS is shown to detect high-risk driving conditions precisely with timely and precise risk estimation. The addition of various significant risk factors significantly enhanced prediction accuracy, indicating its potential for preventing many accidents through timely warnings to the driver.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978973","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":"Knowledge graph-based safety risk evaluation method for hazardous behaviors of road transport vehicles.","authors":"Yadi Hao, Gen Li, Jiwei Lu, Wanrong Cheng, Quan Yuan, Zhihong Yao","doi":"10.1080/15389588.2025.2540554","DOIUrl":"https://doi.org/10.1080/15389588.2025.2540554","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop a knowledge graph (KG)-based framework to quantify and analyze the impact of hazardous driving behaviors on road transport safety.</p><p><strong>Method: </strong>A top-down approach was adopted to construct a multilayered KG incorporating seven categories of hazardous behavior factors (C1-C7). Multisource accident datasets were integrated to map the relationships among hazardous behavior factors, accident types, and accident causes. The Criteria Importance Through Intercriteria Correlation (CRITIC) method was applied to calculate the safety risk levels of various hazardous behaviors. Cosine similarity analysis was used to quantify correlations between hazardous behavioral factors and calculated risk metrics. Furthermore, KG-based path reasoning was used to trace causal chains linking hazardous behaviors to accidents.</p><p><strong>Results: </strong>Dangerous driving (C5) and driver technical competency (C1) emerged as the two most influential risk factor categories, with correlation coefficients of 0.995 and 0.987, respectively. Rear-end collisions were identified as the most probable accident type caused by C5, with a conditional probability of 0.5. Fatigue and speeding were identified as the most common behavioral triggers. KG pathway analysis effectively traced risk propagation paths, highlighting key links in accident causation.</p><p><strong>Conclusions: </strong>This study integrates the multidimensional correlation analysis of knowledge graphs with the weighting advantages of the CRITIC method, explicitly expressing the causal chain of \"hazardous behavior-accident type-accident cause\" through graph structures to comprehensively analyze the behavioral mechanisms of traffic accidents.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979129","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":"Potential safety benefits associated with speed limit compliance in San Francisco and phoenix.","authors":"Eamon T Campolettano, Kristofer D Kusano, Trent Victor","doi":"10.1080/15389588.2025.2538726","DOIUrl":"10.1080/15389588.2025.2538726","url":null,"abstract":"<p><strong>Objective: </strong>Safer Speeds represents one part of the Vision Zero and Safe System Approach to eliminate traffic fatalities and serious injuries. The objective of this paper was to estimate the potential safety benefits if all drivers in two U.S. cities complied with roadway speed limits on surface streets.</p><p><strong>Methods: </strong>Sensor data from a fleet of automated driving system (ADS)-equipped vehicles operating a ride-hailing service were used to determine aggregate traffic speeds during free-flow conditions in Phoenix and San Francisco from over 1 million unique vehicle-road segment traversals. The current human driving speed distribution was estimated using opposite direction traffic speed observations to limit the influence of the ADS-equipped vehicle on surrounding vehicles' travel speeds. The speed-limit-compliant driving fleet consisted of speed observations involving the ADS-equipped vehicles. To estimate the potential safety benefits from reduced travel speeds associated with speed limit compliance, an exponential model relating the effect of speed reduction on fatal and injury crashes was applied, stratified by roadway speed limit. Recent fatality data from these cities was then used to quantify an estimate for lives saved simply through speed limit compliance.</p><p><strong>Results: </strong>Across the roadway-location combinations considered, 33-49% of human drivers were observed to be speeding, with 85th percentile speeds 3.6-7.2 mph over the speed limit. Serious injury and fatality reductions associated with altering the current human-driven vehicle fleet speed distribution toward one that is speed limit compliant were observed to vary by roadway from 18-30% and 27-43%, respectively. When considering these fatality reduction rates in conjunction with available fatality data from FARS, an estimated 82 lives could be saved annually simply through speed limit compliance on surface streets, with 75 lives saved in the Phoenix metro area and 7 lives saved in San Francisco.</p><p><strong>Conclusion: </strong>Using novel data from an ADS-equipped vehicle fleet to estimate the travel speed distribution of both the current human driven and a speed compliant fleet, in conjunction with the Elvik speed framework, this study estimated a 30% reduction in fatalities on surface streets in two U.S. cities, highlighting the impact of speed limit compliance on fatality prevention for all road users and building on the existing body of traffic safety literature capturing the deleterious effects of speeding.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884313","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":"Pre-crash injury risk prediction with guaranteed confidence level: a conformal and interpretable framework.","authors":"Junhao Wei, Yusuke Miyazaki, Fusako Sato","doi":"10.1080/15389588.2025.2538725","DOIUrl":"10.1080/15389588.2025.2538725","url":null,"abstract":"<p><strong>Objective: </strong>Pre-crash injury risk prediction is crucial for proactive safety measures, while traditional models, which output single-point predictions without explaining the decision reasons, often lack interpretability and reliable uncertainty estimation to reflect potential risk distributions. These drawbacks limit their practical effectiveness in mitigating injury severity. To overcome these limitations, this study develops a novel framework that outputs potential risk distributions and their corresponding probabilities using only pre-crash data, thereby delivering probabilistic outputs with a statistically guaranteed 90% confidence level. By introducing such a framework, we aim to provide a more convincing and interpretable analysis of the injury distribution and its underlying causes in traffic accidents, ultimately offering data-driven guidance for injury mitigation strategies.</p><p><strong>Methods: </strong>Data from the National Automotive Sampling System-Crashworthiness Data System and the Crash Investigation Sampling System were used, incorporating 28 pre-crash risk factors. Several machine learning models, including ensemble methods and the deep learning model TabNet, were evaluated. To address the significant class imbalance, particularly the limited number of serious injury cases, various resampling strategies were applied. The core contribution lies in integrating conformal prediction methods, both naive and class-conditional, to generate prediction sets at a 90% confidence level. Model performance was assessed <i>via</i> global evaluation metrics (i.e., f1-score) and serious injury recall, and interpretability was enhanced using explainable machine learning and statistical analysis.</p><p><strong>Results: </strong>Comparative experiments indicate a nearly 90% prediction coverage and a 70.3% recall rate for serious injuries by proposed framework, which is significantly higher than those reported in related studies. Further model interpretation highlights key risk factors such as intersection relevance, crash type, and speed limits and how they effect injury severity prediction.</p><p><strong>Conclusions: </strong>Proposed framework demonstrates significant potential in pre-crash injury risk prediction by introducing conformal prediction techniques to machine learning models. In addition to enhancing predictive performance to nearly 90% prediction coverage and a 70.3% recall rate for serious injuries, this framework also provides enhanced interpretability by quantifying prediction uncertainty and identifying key risk factors. Unlike traditional methods, the framework remains valid under distribution shifts and combines uncertainty estimation with model interpretability. These advantages collectively lay a foundation for developing proactive traffic safety applications and formulating data-driven road safety policies.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-11"},"PeriodicalIF":1.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884314","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":"Exploring the impact of multiple factors on crash risk among Indian motorized two-wheeler riders: Insights from a Rider Behavior Questionnaire.","authors":"Madurai Krishna Kishore, Darshana Othayoth","doi":"10.1080/15389588.2025.2537838","DOIUrl":"10.1080/15389588.2025.2537838","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to validate a modified Motorized Two-Wheeler Rider Behavior Questionnaire (MRBQ) tailored to Indian riders by integrating ordinary violations and safety-related behaviors and to further analyze the crash likelihood by establishing causal relationships among demographics, MRBQ factors, and crash history.</p><p><strong>Methods: </strong>The MRBQ is employed to evaluate rider behaviors, but it requires adaptation to fit local, cultural, legal, and psychological contexts. A sample of 603 responses was collected through an online questionnaire survey. Exploratory Factor Analysis (EFA) was performed to identify a suitable factor structure for the modified MRBQ. Followed by, Confirmatory Factor Analysis (CFA) was used to validate the identified factor structure. Finally, the Structural Equation Modeling (SEM) approach was employed to analyze the causal relationships among multiple factors and crash likelihood.</p><p><strong>Results: </strong>Exploratory factor analysis revealed that the 34-item MRBQ with five factors - error, speed violation, stunt, safety violation, and ordinary violation - fits the Indian context. Confirmatory factor analysis confirms the construct and discriminant validity of this factor structure. Based on the findings of structural equation modeling, the odds of being involved in crashes are considerably increased for young male riders with less than a year of experience, inadequate training, and a history of committing frequent errors, speeding violations, safety violations, and ordinary violations.</p><p><strong>Conclusions: </strong>Overall, the study findings underscore the need to identify the key factors that impact the crash risk probability. To enhance MTW rider safety in developing nations, researchers, policymakers, and authorities should prioritize enforcing strict regulations targeting young, inexperienced riders lacking formal training; revise licensing procedures to include cognitive assessments for novice riders; develop tailored training programs for managing complex riding scenarios; and implement targeted educational campaigns and workshops.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884311","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":"Impact of Canada's Cannabis Act on drug- and alcohol-related collisions in Québec: an interrupted time-series analysis of five major cities.","authors":"José Ignacio Nazif-Munoz, Marie Claude Ouimet","doi":"10.1080/15389588.2025.2537145","DOIUrl":"10.1080/15389588.2025.2537145","url":null,"abstract":"<p><strong>Objective: </strong>This study examines the impact of non-medical cannabis laws (NMCLs) on road safety outcomes, specifically focusing on drug- and alcohol-related traffic crashes. Using cannabis sales data as a proxy for consumption trends, the study aims to assess how changes in cannabis availability may influence road safety outcomes, particularly exploring the potential for drugs and alcohol to have distinct yet related influences on impaired driving.</p><p><strong>Methods: </strong>An interrupted time-series design was used to assess the impact of NMCLs on daily drug- and alcohol-related traffic crashes, including fatalities and severe injuries (KSI). The analysis covered five cities in the province of Québec-Montréal, Québec, Laval, Longueuil, and Sherbrooke-using data from January 1, 2015; to December 31, 2022. The dependent variables included KSI, drug-related crashes, and alcohol-related crashes, while the independent variables were daily cannabis legal sales (kg) and total legal and estimated illegal cannabis sales. Control variables accounted for temperature, time trends, and the COVID-19 non-pharmaceutical interventions' index for the province of Québec (QCnPI-Index). To estimate effects, we applied Generalized Linear Models using Negative binomial regression, followed by a random-effects meta-analysis to assess overall effects across cities.</p><p><strong>Results: </strong>Higher total cannabis sales were significantly associated with a 12% increase in drug-related crashes (IRR: 1.12; 95% CI: 1.01-1.27) and a 12% rise in alcohol-related crashes (IRR: 1.12; 95% CI: 1.06-1.18) across all cities combined. In Montréal, cannabis sales were linked to an 87% increase in drug-related crashes (IRR: 1.87; 95% CI: 1.54-2.28) and a 93% increase in alcohol-related crashes (IRR: 1.93; 95% CI: 1.58-2.36). In Longueuil, drug-related crashes rose by 76% (IRR: 1.76; 95% CI: 1.02-3.02) and alcohol-related crashes by 43% (IRR: 1.43; 95% CI: 1.08-1.92). Québec City only showed a 44% increase in alcohol-related crashes (IRR: 1.44; 95% CI: 1.28-1.64). No significant associations were found in Laval or Sherbrooke.</p><p><strong>Conclusion: </strong>The findings suggest that increased cannabis availability, as measured by cannabis sales, is associated with higher rates of both drug- and alcohol-related crashes, particularly in Montréal and Longueuil. These results support the hypothesis that changes in cannabis availability may influence two distinct impaired driving patterns, highlighting the need for region-specific road safety interventions.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884312","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":"THOR-50M thoracic deflections: Preliminary dynamic assessment in frontal barrier crash tests.","authors":"Hans W Hauschild, Peter G Martin","doi":"10.1080/15389588.2025.2522933","DOIUrl":"https://doi.org/10.1080/15389588.2025.2522933","url":null,"abstract":"<p><strong>Objective: </strong>The research objective is to assess the potential for injury reduction by implementing the THOR-50M as the primary device for vehicle restraint system design.</p><p><strong>Methods: </strong>Data for this analysis was gathered from NHTSA crash testing. Data were collected from two series of frontal rigid barrier tests using a THOR-50M in the driver position. Twenty tests with the THOR-50M in the driver position were conducted with 16 different make/model/generation vehicles. Matched-pair data from the same make and model/platform vehicle were collected from the US NCAP testing which used HIII-50M ATDs in the driver position, for model year vehicles between 2013 to 2023. Thoracic deflection data were analyzed for matched-pair tests and trends over model year. Lap and shoulder belt loads, pelvis excursion, and femur forces were examined to determine how they influenced thoracic deflections in each ATD. Additionally, US NCAP and EuroNCAP test data were examined for ATD thoracic deflection trends.</p><p><strong>Results: </strong>Data from frontal crash testing indicated minor, if any, deviations in HIII-50M thoracic deflections on average for vehicle model years between 2013 and 2023. HIII-50M deflections ranged from 18 to 26 mm, and averaged 22 mm. In contrast, the THOR-50M deflections were more scattered among vehicles tested. The THOR-50M resultant maximum thoracic deflections ranged from 38 to 66 mm, and averaged 46 mm.</p><p><strong>Conclusion: </strong>Vehicle design related to thoracic injury mitigation has leveled off possibly due to the HIII-50M's limited thoracic deflection measurement instrumentation and vehicle design optimization for the HIII ATDs. Injury reduction would involve designing improved restraint systems beyond the HIII-50M capabilities and require the THOR-50M capabilities to advance safety systems. Designing and tuning restraint systems with the THOR-50M, which has thoracic measurement improvements over the HIII-50M may result in thoracic deflection reductions and an associated potential thoracic injury reduction.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859925","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":"Exploring the endogeneity between the autonomous vehicle takeover and crash severity: comparative analysis of structural equation modeling and generalized linear logit model.","authors":"Yiyong Pan, Saisai Yang, Congwei Wang","doi":"10.1080/15389588.2025.2492821","DOIUrl":"https://doi.org/10.1080/15389588.2025.2492821","url":null,"abstract":"<p><strong>Objectives: </strong>Understanding the factors influencing crash severity of autonomous vehicles is important for increasing road safety. This study focuses on a multi-source accident dataset of vehicles equipped with autonomous driving systems to explore the endogenous relationship between manual takeover of autonomous vehicles and the severity of crash, as well as the influencing factors.</p><p><strong>Methods: </strong>By screening and summarizing data on autonomous vehicle accidents. We choose self-driving car takeover and crash severity as potential variables to build a structural equation model to explore the influences of crash severity through continuous variable updating and path improvement. We select autonomous vehicle takeover and crash severity as potential variables and designed a structural equation model to explore the factors affecting crash severity through continuous variable updating and path improvement. Meanwhile, we establish a generalized linear logit model to analyze the factors affecting manual takeover. Finally, the intrinsic link between crash severity and manual takeover is discussed through path analysis and comparison of model results.</p><p><strong>Results: </strong>Cloudy and rainy weather, left rear of vehicle contact area, and daylight lighting significantly impact manual takeover and crash severity. Specifically, wet road surface, rainy weather, and daylight have relatively more significant effects on takeover in the structural equation model. And takeover, roadway type including non-freeway and intersection can significantly impact crash severity. Additionally, the study demonstrates the endogeneity between crash severity and takeover at the time of autonomous vehicle crash.</p><p><strong>Conclusions: </strong>This study analyzes the potential relationships and influencing factors between takeover events of autonomous vehicles and crash severity. It is found that the frequency of takeover events significantly increases when driving in rainy weather and at night. It is suggested that a real-time monitoring module for adverse weather or lighting conditions should be added to the autonomous driving system to provide early warnings and reduce the occurrence of takeover events, thereby enhancing the safety and reliability of autonomous vehicles.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.9,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857043","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":"Using simulation to develop protocols for bicycle crash-avoidance testing.","authors":"Luke E Riexinger, David G Kidd, Jessica S Jermakian","doi":"10.1080/15389588.2025.2520477","DOIUrl":"https://doi.org/10.1080/15389588.2025.2520477","url":null,"abstract":"<p><strong>Objective: </strong>In the U.S., bicyclist fatalities have risen 47.5% over the last decade. On some of their latest vehicles, automakers have introduced bicycle-detecting automatic emergency braking (AEB) systems that automatically apply the brakes to avoid or mitigate collisions with bicyclists. These systems are not evaluated in the U.S. market, although similar tests are conducted elsewhere. The purpose of this study was to use simulation to understand the AEB system characteristics that might perform well in potential testing protocols.</p><p><strong>Methods: </strong>Using openPASS, a bicycle and passenger vehicle were simulated traversing through a four-way intersection of two- lane roadways. Both a straight crossing path and a parallel path scenario were simulated with the subject vehicle traveling between 20 and 80 km/h and the bicycle traveling between 5 and 20 km/h. The subject vehicle's sensor field of view (30, 60, 90, 120, 150, 180 degrees) and range (10, 20, 30, 40, 50, 60 m) were varied, and the AEB response was designed to match the braking characteristics observed in pedestrian crash-avoidance testing. In total, 30 hypothetical AEB systems were tested in 20 unique straight crossing path scenarios and 18 hypothetical AEB systems were tested in 24 unique parallel path scenarios.</p><p><strong>Results: </strong>In the straight crossing path scenario, when evaluating based on avoidance, the simulations where the subject vehicle and bicycle were moving at similar speeds differentiated systems by the sensor field of view. In both straight crossing path and parallel path scenarios, collision avoidance at higher relative speeds was differentiated by the sensor range.</p><p><strong>Conclusions: </strong>A straight crossing path protocol with the subject vehicle and bicycle moving at similar, low speeds could lead to bicycle-detecting AEB implementations with a wider field of view. The test speed in both scenarios primarily influenced the sensor range. This research provides testing agencies with information about how testing protocol decisions could influence AEB system design. In addition, this study demonstrates the feasibility of using simulation tools to develop relevant crash avoidance testing protocols. Future simulations could predict the performance in real-world bicycle crashes of systems that would also perform well in the potential testing protocols.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-6"},"PeriodicalIF":1.9,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859926","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 ride-hailing services on road traffic accidents in China: Focused on the moderating effect of ride-hailing regulatory policy.","authors":"Zhuolun Wang","doi":"10.1080/15389588.2025.2534995","DOIUrl":"https://doi.org/10.1080/15389588.2025.2534995","url":null,"abstract":"<p><strong>Objective: </strong>As ride-hailing services become more widespread, traffic safety has raised attention among the public. However, existing research has not fully revealed the impact of ride-hailing services on road traffic accidents in Chinese cities. Although ride-hailing regulatory policy sets industry entry thresholds, it remains to verify whether the policy can change the relationship between ride-hailing services and road traffic accidents. The aims of the study are to assess the impact of the entry of ride-hailing services on road traffic accidents, exploring the moderating effect of ride-hailing regulatory policy in this relationship.</p><p><strong>Methods: </strong>Based on panel data from 79 Chinese cities between 2007 and 2017, this article uses a multi-period difference-in-differences model to analyze the impact of the entry of ride-hailing services on accident casualties. To explore ride-hailing regulatory policy validity, this article uses a moderating effect model to assess whether the regulatory policy influences the relationship between ride-hailing services and accident casualties.</p><p><strong>Results: </strong>The entry of ride-hailing services has no significant impact on accident injuries but has increased accident fatalities. Although the implementation of ride-hailing regulatory policy has not significantly moderated the irrelevant relationship between ride-hailing services and accident injuries, it significantly alleviates the positive impact of ride-hailing services on accident fatalities. The varying policy intensities were ineffective in moderating the relationship between ride-hailing services and accident injuries. Under the positive effect of ride-hailing services on accident fatalities, loose policy strengthens this effect, medium-intensity policy has no significant moderating effect, while strict policy weakens the effect.</p><p><strong>Conclusions: </strong>In China, ride-hailing services are closely linked to an increase in accident fatalities. By raising industry entry thresholds, the implementation of strict regulatory policy can improve road traffic safety. The findings provide management insights for the government in regulating the ride-hailing market.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.9,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805325","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}