Traffic Inj Prev [JOURNAL]
Sun
200 papers
An investigation into road safety performance in Chinese provincial-level administrative regions: Insights from the input-output analysis.
Kang L
Traffic Inj Prev · 2025 Oct · PMID 41105017 · Publisher ↗
OBJECTIVES: Road safety is a vital public concern aimed at preventing road-related injuries and fatalities. Assessing road safety status can be effectively conducted using performance measurement tools, which help identi...
OBJECTIVES: Road safety is a vital public concern aimed at preventing road-related injuries and fatalities. Assessing road safety status can be effectively conducted using performance measurement tools, which help identify areas for improvement and guide the development of targeted safety strategies. METHODS: This study evaluates the road safety performance of Chinese provinces from 2018 to 2020 using an undesirable data envelopment analysis model and the meta-frontier approach to measure input-output efficiency. RESULTS: During the three-year period, two, three, and two provinces, respectively, achieved efficiency scores considered as benchmarks. Clustering analysis grouped the performance into three tiers, with Beijing and Shanghai consistently in the highest-performing tier. Provinces in eastern China demonstrated relatively stable and high performance across all years. Meanwhile, Guangxi, Qinghai, and Guizhou showed potential for reducing undesirable output by over 80% annually. Changes in scores for 20, 19, and 20 provinces were driven by differences in production frontier technologies over time. A comparative evaluation incorporating desirable output offers an additional perspective on performance measurement. CONCLUSIONS: This research presents a framework for assessing road safety performance using both undesirable and desirable outputs and offers insights for policymakers to understand regional safety variations under diverse economic contexts.
Vehicle trajectory-based prediction of traffic conflicts on sharp horizontal curves.
Li H, Zhang X
Traffic Inj Prev · 2025 Oct · PMID 41105005 · Publisher ↗
OBJECTIVE: The traffic conflict situations at sharp curve sections are evaluated by analyzing vehicle trajectory data during navigation through these hazardous road segments. METHODS: This study develops a methodology fo...
OBJECTIVE: The traffic conflict situations at sharp curve sections are evaluated by analyzing vehicle trajectory data during navigation through these hazardous road segments. METHODS: This study develops a methodology for quantifying traffic conflict probabilities in curve scenarios based on multi-source trajectory data acquisition. Vehicle movement trajectories through curves are captured integrated UAV aerial photography systems and onboard vehicle recorders. High-precision spatiotemporal coordinates with dynamic parameters (instantaneous velocity and acceleration) are extracted using the professional trajectory analysis software. To address noise interference in raw trajectory data, a Kalman filtering algorithm is implemented for optimal motion state estimation and data smoothing. At the model architecture level, we propose a CNN-LSTM hybrid predictive model that synergistic-ally combines the spatial-temporal feature extraction capabilities of convolutional neural networks with the temporal dependency modeling advantages of long short-term memory networks, enabling end-to-end learning for quantitative trajectory conflict prediction. To validate model generalizability, this study concurrently constructed multiple benchmark models-including Support Vector Machine (SVM), Gradient Boosted Trees (XGBoost), GNN-LSTM, Vanilla LSTM, and Bi-LSTM-for comparative experiments. The evaluation framework employed a rigorous multi-dimensional validation protocol from machine learning, assessing all models not only by fundamental classification accuracy but also through fine-grained efficacy metrics (Precision, Recall, F1-score). Results demonstrated the superior performance of the hybrid CNN-LSTM model in predicting traffic conflicts at curves. Ultimately, curve-specific conflict probabilities were derived by applying the CNN-LSTM model to experimental data analysis. The generalization performance under class-imbalanced conditions was quantified using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), while prediction accuracy was validated through metrics including classification accuracy. This establishes a comprehensive multi-capability evaluation framework covering model stability, sensitivity, and generalization capability. RESULTS: Empirical results confirm the CNN-LSTM model's superior performance in sharp-curve conflict prediction, achieving a mean accuracy exceeding 85%, precision above 82.7%, recall over 89.9%, and F1-score surpassing 86.1%, complemented by a 93.5% or higher average AUC-ROC that demonstrates robust generalization in class-imbalanced scenarios. These metrics collectively substantiate its exceptional spatiotemporal feature extraction capability and precise risk evolution pattern fitting, enabling enhanced representation of interactive vehicle conflicts in complex environments. CONCLUSIONS: The research outcomes provide intelligent decision support for geometric optimization design of sharp curve sections and establish a reliable theoretical foundation for developing real-time dynamic risk warning systems. This work holds significant practical value for advancing the transformation of intelligent transportation management systems toward data-driven paradigms.
Driving risk variation in mountainous highway tunnels of different lengths: Field evidence from Chongqing, China.
Meng Y, Hu J, Shen J … +4 more , Li B, Zheng W, Qing G, Chen F
Traffic Inj Prev · 2025 Oct · PMID 41091838 · Publisher ↗
OBJECTIVE: This study aims to reveal the spatial distribution characteristics of driving risks in two-lane mountainous highway tunnels, with a particular focus on the influence of different tunnel lengths on risk levels,...
OBJECTIVE: This study aims to reveal the spatial distribution characteristics of driving risks in two-lane mountainous highway tunnels, with a particular focus on the influence of different tunnel lengths on risk levels, thereby contributing to improved tunnel operational safety. METHODS: Field driving tests were conducted in 21 short, medium, and long tunnels located on two-lane highways in Chongqing, China. Multisource data were collected from 27 drivers, including heart rate growth rate, speed, illuminance change rate, and alignment complexity indices. The entropy-weighted method was used to determine the weights of various risk evaluation indicators, which were then integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to compute the comprehensive risk value for each tunnel. Risk levels were classified into low, relatively high, and high using the K-means clustering algorithm to analyze spatial distribution patterns. RESULTS: The study showed that short tunnels exhibited the highest overall risk level, while long tunnels had the lowest. All three tunnel types displayed a consistent pattern, which is that entrance zones exhibited significantly higher risk than exit zones, with the lowest risk occurring in the middle segments. Specifically: (1) For short tunnels, the peak risk appeared 21 m after the entrance, with high-risk zones extending up to 144 m; (2) For medium tunnels, high-risk spans were concentrated within 50-75 m before and after the entrance, with the exit zone presenting the second-highest risk; (3) For long tunnels, the peak risk was found 2 m after the entrance, and both entrance and exit zones had significantly elevated risk. The average risk value in entrance segments was approximately 1.5 times that of the middle segments. CONCLUSIONS: Driving risks in two-lane highway tunnels exhibit distinct spatial distribution characteristics, with tunnel entrances and exits being the most risk-prone zones. Short tunnels, due to the frequent transition effect, present more pronounced risks. The findings provide theoretical support for tunnel structural design optimization, speed limit, and lighting system.
Enhancing traffic safety by forecasting the severity of road accident injuries using pyramidal dilation attention convolutional networks designed by the reptile search algorithm.
Boddu N, K VR, Cheripelli R
Traffic Inj Prev · 2026 · PMID 41091806 · Publisher ↗
OBJECTIVE: This work aims to give a method that is both efficient and comprehensible for forecasting the extent of injuries sustained in traffic accidents. This addresses the limitations of existing GNN-based frameworks,...
OBJECTIVE: This work aims to give a method that is both efficient and comprehensible for forecasting the extent of injuries sustained in traffic accidents. This addresses the limitations of existing GNN-based frameworks, which often struggle with complexity, limited interpretability, scalability issues, and the need for extensive data pre-processing and advanced graph representation learning. METHODS: In this manuscript, Predicting Road Crash Injury Severity utilizing Pyramidal Dilation Attention Convolutional Network optimized with Reptile Search Algorithm (PRCIS-PDACN-RSA) is proposed. Firstly, the input data is gathered from the UK road accident dataset. The data is then sent to pre-processing, where the Robust Maximum Correntropy Kalman Filter (RMCKF) is applied to eliminate null, noisy, or incomplete entries. The pre-processed data is fed into Adaptive SV-Borderline SMOTE (ASV-SMOTE) to balance the imbalanced dataset. Then the balanced dataset is given to the Pyramidal Dilation Attention Convolutional Network (PDACN) to predict road crash injury severity and classify it as either severe or non-severe. The Reptile Search Algorithm (RSA) is used to optimize the PDACN parameters, enhancing its predictive performance. RESULTS: The proposed PRCIS-PDACN-RSA technique is implemented in Python and evaluated using performance metrics, including accuracy, F1-score, recall, precision, Receiver Operating Characteristic (ROC), and Matthews's correlation coefficient (MCC), to assess its efficiency. The proposed PRCIS-PDACN-RSA approach attains 97.2% accuracy, 0.90% MCC, and 98.11% recall compared with existing methods, including Road Crash Injury Severity Prediction Utilizing a Grey Wolf Optimization-driven Artificial Neural Network for Predicting Road Crash Severity (GWO-ANN-PRCS), Graph Neural Network Framework (RCI-SP-GNN), and Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction (MGCN-TARP). CONCLUSIONS: The results demonstrate that the proposed PRCIS-PDACN-RSA framework outperforms existing methods in predicting road crash injury severity. Its high accuracy, robustness, and efficient handling of pre-processing and optimization highlight its suitability for real-world intelligent traffic safety systems.
Helmet feature preferences and willingness to pay among iranian motorcyclists: A discrete choice experiment.
Najafi M, Hallajian H, Eshik Aghasi M … +4 more , Golshekan M, Rahbar-Taramsari M, Davoudi Kiakalayeh A, Homaie Rad E
Traffic Inj Prev · 2026 · PMID 41091804 · Publisher ↗
OBJECTIVES: Helmets are vital for motorcyclists. They prevent death and head injuries. Researchers have found many barriers to motorcyclists' helmet use, one of which is related to helmet features. Studying motorcyclists...
OBJECTIVES: Helmets are vital for motorcyclists. They prevent death and head injuries. Researchers have found many barriers to motorcyclists' helmet use, one of which is related to helmet features. Studying motorcyclists' choice of helmet features can reduce these barriers. METHODS: In this choice experiment, 250 motorcyclists in Rasht, Iran, were surveyed using convenience sampling in 2023. Motorcyclists were presented with 14 choice sets with two scenarios about helmet features. They were asked to choose between the two helmets which is more aligned with their preferences. Attributes of scenarios were selected in a qualitative study and literature reviews. These attributes included: price, rigidity of the outer helmet, and being full face, flip front, or open face. Also, the internals must be washable, and the helmet's weight. Data were analyzed using conditional logistic regression. RESULTS: Participants did not prefer a higher price (ß = -0.270 ± 0.025), an open face (ß = -0.463 ± 0.082), a weighted design (ß = -1.970 ± 0.060), non-washable internal parts (ß = -0.183 ± 0.060), and poor rigidity of external parts (ß = -1.977 ± 0.086) of helmets. The preferences were different among wealth, education, and age subgroups. CONCLUSIONS: To boost helmet use among motorcyclists, policymakers must use market fragmentation techniques and subsidize helmets in different subgroups. This intervention can work with health campaigns and fines for not wearing proper helmets.
HR-YOLO: Segmentation and detection of emergency escape ramp scenes using an integrated HR-net and improved YOLOv12 model.
Li G, Hu Z, Zhang H
Traffic Inj Prev · 2026 · PMID 41091745 · Publisher ↗
OBJECTIVE: With the development of intelligent transportation systems, the demand for automatic recognition and monitoring of critical road safety infrastructure has been increasing. Particularly in high-risk road sectio...
OBJECTIVE: With the development of intelligent transportation systems, the demand for automatic recognition and monitoring of critical road safety infrastructure has been increasing. Particularly in high-risk road sections such as mountainous areas and steep downhill stretches, Emergency Escape Ramps (EERs) play a crucial role in preventing severe accidents caused by out-of-control vehicles. Accurate detection of these ramps is essential for enhancing road traffic safety. METHODS: To address the limitations of existing methods, such as inadequate segmentation accuracy and poor robustness in object detection, this paper proposes a new model (HR-YOLO) that integrates the HR-Net model with an improved YOLOv12 model. To support the segmentation and detection of emergency escape ramp scenarios, we constructed a custom dataset consisting of 411 annotated images across 8 categories. This dataset reflects typical road environments and object types encountered in emergency ramp areas, providing a reliable foundation for model training and evaluation. RESULT: The HR-Net achieves a mIoU and mPA of 85.50% and 91.23%, respectively. The YOLOv12 network attains a mAP and F1 score of 78.7% and 75.9%, respectively, for unsegmented images. The YOLOv12 model enhanced with Coordinate Attention (CAYOLOv12) is combined with the HR-Net model. Compared to the standalone YOLOv12 model, the HR-YOLO model improves mAP and F1 scores by 10% and 9.6%, respectively. CONCLUSIONS: The proposed approach provides an efficient and reliable technological solution for the intelligent recognition of key infrastructure in traffic safety monitoring systems.
Causal analysis of injury severity of two-vehicle collision crashes: Insight from a correlated random parameters approach.
Liu H, Ma Y, Li T
Traffic Inj Prev · 2025 Oct · PMID 41091724 · Publisher ↗
OBJECTIVE: Two-vehicle collision crashes are always tough challenges for traffic management departments due to its severe consequences. This study aims to investigate risk mechanism of three types of two-vehicle collisio...
OBJECTIVE: Two-vehicle collision crashes are always tough challenges for traffic management departments due to its severe consequences. This study aims to investigate risk mechanism of three types of two-vehicle collisions (i.e., head on, sideswipe, and rear end) in the same city from a comprehensive perspective. METHODS: A random parameters binary logit framework was employed to capture the unobserved heterogeneity across individual observations and reveal potential correlations between risk indicators of injury severity. RESULTS: The results indicate that the correlated random parameters model performs best, and the impacts of risk indicators involving unsafe driving behavior and driver, vehicle, roadway, environment, and temporal characteristics on two-vehicle collisions are quite different. Based on the determinants of each two-vehicle collision, some recommendations have been proposed to improve the level of road traffic safety. CONCLUSIONS: Fatal collisions are more likely to happen when driving on the roads with higher road function classification and involving the presence of heavy trucks. Road type, weather condition, and unsafe driving behavior are primary contributors to two-vehicle collisions. Besides, fatal head-on collisions are prone to occurring when exceeding speed limits and driving on rainy days. Illegal overtaking or lane changing significantly increases the risk of fatal injuries in both sideswipe and rear-end collisions. Moreover, significant correlations between the on-ramp of highways and violating traffic lights or signs, and between cloudy days and visibility range within 50-100 m, are identified in the injury severity models of sideswipe and rear-end collisions, respectively. Current findings suggest that more attention should be devoted to intervening unsafe driving behaviors for sideswipe collisions, as well as enhancing the provision of visibility range information to mitigate rear-end collisions in adverse weather conditions.
Simulation-based assessment of driving confidence in hazardous situations under different warning levels.
Lou E, Li H, Zhao G … +2 more , Qin L, Zhao X
Traffic Inj Prev · 2025 Oct · PMID 41081596 · Publisher ↗
OBJECTIVE: Although warning systems in connected environments have become increasingly common, their psychological impact on driving confidence remains underexplored. This study aims to analyze driving confidence under h...
OBJECTIVE: Although warning systems in connected environments have become increasingly common, their psychological impact on driving confidence remains underexplored. This study aims to analyze driving confidence under hazardous road events-such as emergency braking of front vehicles (EB-FV), work zones (WZ), and tunnels (Tun)-in response to warning systems, using a connected simulation platform. METHODS: By integrating traffic psychology using hazardous event warnings with connected-vehicle technology, a unique perspective that has not been covered in previous studies is provided. Driving confidence was quantified using driving simulation technology in two dimensions: speed performance and driving operations. RESULTS: The results show that predictive warning systems significantly improve driver confidence and control. Specifically, in the Tun, compared to the no-warning condition, the average driving speed decreased by 16.38%, and speed variability StdV decreased by 27.75%. Additionally, steering control was more stable, with a 18.40% decrease in steering wheel angle variability (SDSA) in the EB-FV scenario, and 7.31% in the WZ scenario. CONCLUSIONS: Additionally, the study highlights a significant improvement in driver confidence when warning information is provided. The conclusions are particularly applicable to structured road environments with reliable V2X communication and assume that drivers have some degree of familiarity with connected systems. This study provides theoretical and practical insights into the design of adaptive warning strategies for future intelligent transportation systems.
Factors associated with child safety seat use in Bangkok Metropolitan Region, Thailand.
Chinaphan A, Sinitkul R, Ratanatharathorn C … +1 more , Taschanchai N
Traffic Inj Prev · 2025 Oct · PMID 41081581 · Publisher ↗
OBJECTIVES: To understand factors associated with proper child safety seat (CSS) use in the Bangkok Metropolitan Region (BMR), Thailand, after the child restraint legislation and to provide evidence to inform policy for...
OBJECTIVES: To understand factors associated with proper child safety seat (CSS) use in the Bangkok Metropolitan Region (BMR), Thailand, after the child restraint legislation and to provide evidence to inform policy for increasing proper CSS use. METHODS: A cross-sectional study was conducted. Primary caregivers of at least one child aged 0-6 years or height 135 cm or less, who own a car, reside in BMR, and achieve literacy in the Thai language were included. The recruitment was done by distributing posters with QR codes to access information sheets and online self-administered questionnaires both online and offline (Pediatrics outpatient and postpartum units at Ramathibodi Hospital; 273 public early childhood centers; and 102 kindergartens in Bangkok) between April and December 2024. The questionnaire comprised questions regarding demographic data, knowledge, attitude, practice, and other information about CSS. Data were compared between the proper and improper CSS users Stata version 17. RESULTS: 330 respondents with a median (Q1, Q3) age of 38 (34, 41) years were included; most were female (83.2%) and residents of Bangkok (68.7%). Two hundred ninety-six respondents (89.7%) reported CSS usage, with 170 respondents reporting regular use (51.5%). Among respondents, 135 (40.9%) were categorized as proper users (regularly use an age-appropriate type of CSS and locate it on the back seat). Logistic regression showed factors associated with proper CSS used were higher household income (OR 9.97, 95%CI: [4.06-24.52], < 0.001) and barriers to using CSS including child-related barriers (OR 0.10, 95%CI: [0.05-0.22], < 0.001), caregivers-related barriers (OR 0.19, 95%CI: [0.04-0.99], = 0.049), and car-related barrier (OR 0.38, 95%CI: [0.15-0.92], = 0.031). The leading reported barriers to CSS use were children's refusal (42%) and the high cost of CSS (23%). The trusted sources of information regarding CSS were social media/internet (43%) and healthcare providers (34%). Respondents preferred educational intervention for improving knowledge (22%) and tax deduction policy (17%) to help increase CSS use. CONCLUSIONS: After the child restraint legislation, the proper usage of CSS to ensure child safety was still low. Factors associated with proper CSS usage included higher household income and barriers to using CSS. Multilevel interventions and policies were suggested to address the issues. Further research could be done to evaluate the effectiveness of those measures and their impacts on increasing proper CSS usage in Thailand.
Expressway conflict risk mechanism considering the interactions between vehicle-group and road-segment.
Wang L, He J, Zhang Y … +3 more , Xing Y, Park J, Ma W
Traffic Inj Prev · 2026 · PMID 41081579 · Publisher ↗
OBJECTIVES: There have been numerous studies on conflict risk for expressways, with the majority of previous studies focusing on road-segments' conflict risk while neglecting the impact of moving vehicles. In recent year...
OBJECTIVES: There have been numerous studies on conflict risk for expressways, with the majority of previous studies focusing on road-segments' conflict risk while neglecting the impact of moving vehicles. In recent years, some studies have begun to work on vehicle-group without the consideration of the traffic on the road-segment. However, as the vehicle-group travels along the road-segment, the conflict risk of road-segment and vehicle-group will interact with each other. The aim of this study is to analyze the interactive mechanism of conflict risk between vehicle-groups and road-segments on expressways. METHODS: This study utilized high-resolution vehicle trajectory data to separately build conflict risk prediction models for vehicle-groups and road-segments. The best performing models were selected and explainability algorithms were applied. The analysis then focused on two aspects: (1) the influence of downstream high-risk vehicle-groups on upstream road-segment conflict risk and (2) the impact of geometric and traffic parameters of downstream road-segments on the conflict risk of vehicle-groups. RESULTS: The results show that vehicle-group characteristics significantly affect road-segment conflict risk. When high-risk vehicle-groups appear downstream, the conflict risk of the road-segment increases by about 6%, and the impact is stronger when the propagation distance is shorter. In turn, when the conflict risk of a downstream road-segment increases, this risk propagates upstream through the traffic flow, affecting the behavior of vehicle-groups and raising their conflict risk. A difference of 29% in vehicle-group conflict risk was observed depending on the median downstream road-segment conflict risk. CONCLUSIONS: This study demonstrates that vehicle-groups and road-segments interact in the propagation of expressway conflict risk. Integrating these two dimensions enables more accurate conflict risk prediction and analysis. This research provides a novel perspective by integrating vehicle-group and road-segment interactions for more accurate conflict risk prediction and analysis.
Incidence and outcomes of road traffic crashes among commercial motor tricycle drivers in Kumasi, Ghana: A population-based survey.
Gyedu A, Abdullah M, Sulaiman S … +2 more , Donkor P, Mock C
Traffic Inj Prev · 2026 · PMID 41081574 · Publisher ↗
OBJECTIVES: The injury burden of motorized tricycles in African countries is not well-known despite their increasing use for commercial activities on the continent. To address this gap, we sought to understand the injury...
OBJECTIVES: The injury burden of motorized tricycles in African countries is not well-known despite their increasing use for commercial activities on the continent. To address this gap, we sought to understand the injury burden and crash risk factors for commercial motor tricycles (CMT) in Ghana. METHODS: We conducted a survey of all CMT drivers at 11 groupings within Kumasi, Ghana. The survey utilized a structured questionnaire based on previous injury questionnaires used extensively in Ghana. The questionnaire sought information about characteristics and modifiable risk factors for road traffic crashes (RTCs) as well as safety-related road signs. The primary outcome was respondents experiencing at least one RTC in the past 1 year. Chi-square tests were used to determine differences between the primary outcome and various covariates. RESULTS: There were 84 RTCs reported by the 710 respondents over the past 1-year with an incidence of 11.8%. Half (48%) of crashes caused injuries. Drivers reported overloading of vehicles (32%), not having a valid license (26%), never wearing a helmet (92%), long work hours (median 10 [range: 3-18] hours/day), and lack of scheduled maintenance (52%). Drivers had low knowledge of road signs (e.g. only 41% could identify a "give way" sign). Consumption of the stimulant "ataya" was higher among drivers with crashes in the past year compared to those without (34% vs 16%, < 0.001). CONCLUSIONS: There is a significant injury burden from CMTs in Ghana. Several risk factors should be addressed: vehicle overloading, low vehicle maintenance, prolonged work hours, low helmet use, low knowledge of safety-related signage, and use of the stimulant "ataya."
Injury risk curves for motorcycles in the United States.
Terranova P, Guo F, Doerzaph Z … +1 more , Perez MA
Traffic Inj Prev · 2026 · PMID 41065502 · Publisher ↗
OBJECTIVE: This study introduces a comprehensive injury risk model for motorcycle collisions tailored to the United States (U.S.). The proposed innovative approach enables the prediction of injury risk across the full ra...
OBJECTIVE: This study introduces a comprehensive injury risk model for motorcycle collisions tailored to the United States (U.S.). The proposed innovative approach enables the prediction of injury risk across the full range of crash speed and vehicle orientations, making it an essential tool for evaluating emerging safety measures for motorcycles. METHODS: Data from the Motorcycle Crash Causation Study (MCCS) is used to train two multivariate regression models for predicting motorcycle injury risk. The data is weighted to align with national crash statistics and effectively represents the U.S. crash population. The models adopt motorcycle impact speed and vehicle-relative speed as key predictors while incorporating rider age, helmet use, and opponent vehicle type as covariates. The motorcycle's principal direction of force (PDoF) is also employed as a surrogate metric for the vehicle orientation, enabling the models to account for the full range of two-vehicle crash configurations. RESULTS: The analysis clearly indicates that the motorcycle front-end crash-i.e., the motorcycle's front collides with any side of the opposing vehicle-is the most dangerous crash configuration. Additionally, the results demonstrate significant variability in injury likelihood based on the PDoF, emphasizing the influence of vehicle alignment and orientation on riders' injury outcomes. CONCLUSIONS: This study provides the first comprehensive injury risk models for motorcycle-involved two-vehicle collisions in the U.S., offering critical insights into the role of speed and vehicle orientation in injury outcomes. While further validation with larger datasets is required, the findings serve as a foundation for the evaluation of the safety benefits of emerging traffic safety systems.
Modeling geospatial determinants of pedestrian fatalities on high-speed rural roads using satellite imagery: A statewide analysis from Haryana, India.
Aman P, Tiwari G, Rao KR
Traffic Inj Prev · 2026 · PMID 41052409 · Publisher ↗
OBJECTIVE: Pedestrian fatalities on high-speed rural roads in low- and middle-income countries remain an underexplored but critical road safety issue. This study investigates pedestrian safety on high-speed rural roads i...
OBJECTIVE: Pedestrian fatalities on high-speed rural roads in low- and middle-income countries remain an underexplored but critical road safety issue. This study investigates pedestrian safety on high-speed rural roads in Haryana, India, spanning 5,069 km. METHODS: A geospatial methodology was developed to assess changes in land use and population distribution within a 500 m buffer from 2017 to 2022 using satellite imagery. Sentinel-2 imagery and population density data helped estimate growth in built-up areas and demographic shifts. Fatality rates per 100 km and 10,000 population were calculated to identify high-risk corridors. RESULTS: Analysis of 4,020 pedestrian fatalities showed peak occurrences in the evenings (7-9 PM), with the highest share on Sundays and in winter. Generalized Poisson and Negative Binomial (NB) regression models examined the impact of road, landuse, and demographic variables, with the NB model showing a better fit. Results show the significant impact of highly-exposed population, road length, village density, built-up areas, road category, and multilane roads on pedestrian fatalities, with a positive association. CONCLUSIONS: The study proposes a novel methodology to identify high-risk roads and significant risk factors for pedestrians on high-speed rural roads. It highlights the utility of satellite-derived data for large-scale analysis and the need for pedestrian-centric road designs in rural areas.
The influence of Anthropomorphism, Interactivity, Perceived Green Value, and Social Influence on pedestrian acceptance of fully autonomous vehicles: The mediating effect of Perceived Risk and Perceived Safety.
Zhang H, Wei Y, Chen Y … +1 more , Cai Y
Traffic Inj Prev · 2025 Oct · PMID 41052407 · Publisher ↗
OBJECTIVE: Although Fully Autonomous Vehicles (FAVs) will bring significant benefits to road traffic, the replacement of human drivers by automated systems weakens pedestrians' ability to socially interact with road traf...
OBJECTIVE: Although Fully Autonomous Vehicles (FAVs) will bring significant benefits to road traffic, the replacement of human drivers by automated systems weakens pedestrians' ability to socially interact with road traffic and reduces their acceptance of FAVs. This study aims to explore factors influencing pedestrians' acceptance of FAVs. METHODS: This study proposes a novel FAVs Acceptance Model from the pedestrian perspective, integrating the Stimulus-Organism-Response (S-O-R) framework and Perceived Risk Theory. We examine four key antecedent variables Anthropomorphism, Interactivity, Perceived Green Value, and Social Influence and explore their influence on pedestrian acceptance through the mediating roles of Perceived Risk and Perceived Safety. Data were collected from 301 Chinese participants through an online survey, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to empirically analyze the model. RESULTS: The results reveal that Interactivity significantly enhances acceptance by reducing Perceived Risk and increasing Perceived Safety. Anthropomorphism influences acceptance primarily through reducing Perceived Risk, while its effect Perceived Safety is not significant. Perceived Green Value improves Perceived Safety but has no significant impact on reducing Perceived Risk. Notably, Social Influence increases Perceived Risk and reduces Perceived Safety, leading to a negative indirect effect on Acceptance. CONCLUSIONS: This study makes several contributions: (a) it expands the FAVs acceptance literature by shifting the focus to pedestrians, an underexplored stakeholder group; (b) it provides an integrated model combining psychological and design-related variables; (c) it offers actionable insights for policymakers and designers to improve human-FAVs interactions. These study findings offer a robust theoretical foundation and practical guidance for advancing safe and inclusive autonomous mobility.
Driving style modulates earthquake alert responses: EEG and eye-tracking evidence from simulated emergencies.
Wang J, Li J, Xiao Y
Traffic Inj Prev · 2026 · PMID 41052402 · Publisher ↗
OBJECTIVE: Sudden earthquakes can severely disrupt urban transportation networks, leading to traffic interruptions and secondary accidents. Earthquake early warning (EEW) systems offer drivers a short window to respond....
OBJECTIVE: Sudden earthquakes can severely disrupt urban transportation networks, leading to traffic interruptions and secondary accidents. Earthquake early warning (EEW) systems offer drivers a short window to respond. However, reactions vary by driving style, which influences attention and decision-making under stress. This study aims to examine how individual driving styles influence cognitive and behavioral responses to EEW alerts, with the goal of informing behavior-aware safety strategies during seismic emergencies. METHODS: A total of 92 licensed drivers were classified into three driving style categories: cautious, aggressive, and defensive. Participants were exposed to simulated earthquake scenarios while driving, during which their eye movements and electroencephalogram (EEG) signals were continuously recorded. Behavioral responses were analyzed to identify common and style-specific patterns in attention allocation, cognitive load, and vehicle control. RESULTS: All drivers followed a three-stage response pattern consisting of perception, fluctuation, and execution after EEW alerts. However, the duration and transition path of these stages varied by driving style. Aggressive drivers reacted quickly with frequent eye shifts and abrupt changes. Cautious drivers showed prolonged scanning and higher cognitive load, leading to delayed responses. Defensive drivers maintained steady attention, low tension, and smooth vehicle control. CONCLUSION: Driving style significantly influences both the timing and quality of responses to EEW alerts during earthquakes. The integration of behavioral profiling and neurocognitive metrics highlights the need for adaptive EEW interfaces tailored to individual drivers. These findings provide a foundation for enhancing transport resilience through personalized alert systems and targeted safety interventions.
Analysis of conflict between right-turning vehicles and pedestrians at urban intersections using random parameter Logit model.
Peng T, Liu J
Traffic Inj Prev · 2026 · PMID 41052401 · Publisher ↗
OBJECTIVE: Pedestrians are prone to more dangerous conflicts with right-turning vehicles when crossing the street because of sharing phases with right-turning vehicles or their own violations. This paper aims to alleviat...
OBJECTIVE: Pedestrians are prone to more dangerous conflicts with right-turning vehicles when crossing the street because of sharing phases with right-turning vehicles or their own violations. This paper aims to alleviate the frequent conflicts between right-turning vehicles and pedestrians and explores the significant influencing factors behind these conflicts. METHOD: This study conducted field investigations at two representative signalized intersections in Guangzhou. During the research process, unmanned aerial vehicles (UAVs) were employed for oblique photography to collect data, which were then preprocessed using T-Analyst software. Based on this, intersection characteristics, characteristics of conflict participants, and unsafe crossing behaviors were extracted in detail, and this information was correlated with the behavioral trajectories of both parties involved in the traffic conflicts. In terms of conflict identification, the study utilized the time difference to collision (TDTC) method to identify 701 valid conflicts. In the data analysis phase, ordered Logit models, generalized ordered Logit models, and random parameter ordered Logit models were employed for modeling and analysis. Among them, the random coefficients ordered Logit model exhibited the best fit. RESULT: The model results indicate that pedestrian pairing and the sequence in which conflict participants traverse the conflict point. Specifically, pedestrians crossing in pairs face a 13.9% higher risk of severe conflicts compared to those crossing alone, while the probability of minor conflicts decreases by 14.5%. Although vehicles passing first (VPF) through the conflict point reduce the occurrence of general and minor conflicts to some extent, they may trigger more severe conflicts. Furthermore, pedestrian and right-turning vehicle violations such as running red lights and failing to pay attention to oncoming traffic significantly increase the risk of severe conflicts. CONCLUSION: To mitigate conflicts between right-turning vehicles and pedestrians, it is recommended to appropriately adjust the length and width of pedestrian crossings and implement dynamic signal phase control strategies at intersections where significant temporal variations in pedestrian and vehicular traffic are observed, ensuring orderly traffic movement across all time periods. Additionally, surveillance equipment should be installed at intersections to conduct real-time monitoring and management of pedestrian and vehicular violations, thereby curbing the occurrence of unsafe behaviors.
Real-world pedestrian crash reconstructions: Vehicle model validation and biomechanical injury analysis.
Poveda L, Miller LE, Armstrong W … +6 more , Check K, Hsu FC, Gayzik FS, Weaver AA, Stitzel JD, Devane K
Traffic Inj Prev · 2025 Oct · PMID 41032680 · Publisher ↗
OBJECTIVES: The overarching objective of this study was to reconstruct five real-world pedestrian crashes using data from the Vulnerable Road User In-Depth Crash Investigation Study (VICIS) database, the Global Human Bod...
OBJECTIVES: The overarching objective of this study was to reconstruct five real-world pedestrian crashes using data from the Vulnerable Road User In-Depth Crash Investigation Study (VICIS) database, the Global Human Body Models Consortium (GHBMC) simplified pedestrian models, and morphed generic vehicle (GV) models reflecting U.S. vehicle front-end geometry to investigate pedestrian injury risks, compare simulated injury outcomes and contact kinematics with real-world observations, and evaluate the suitability of these simplified models for crash reconstruction. METHODS: Five real-world pedestrian crashes from VICIS were reconstructed based on injury distribution and test data availability. Cases included four males (ages 14, 48, 56, and 64) and one female (age 57). Vehicles included three sport utility vehicles (SUVs) and two sedans, impacting at an average speed of 47 kph (range: 16-65 kph). Sedan and SUV GVs were morphed using computer-aided design (CAD) models to match front-end geometry. The windshield was modeled as a three-layer structure with fracture-enabled outer glass layers. Morphed models were validated against Euro New Car Assessment Program (NCAP) headform, upper legform, and lower legform tests using correlation and analysis (CORA) ratings. The models were used to reconstruct crashes by applying initial velocity and scaling GHBMC pedestrian models to match the case pedestrian height and weight. The contact points from simulations were compared with real-world crash evidence. AIS2+ injuries from the cases were compared to reconstructed results using injury metrics and risk functions. RESULTS: The average ± CORA score for all pedestrian NCAP validation tests was 0.72 ± 0.1, indicating a good rating. Contact points from reconstructions closely matched real-world crashes. Brain injury criterion (BrIC) and cumulative strain damage measure (CSDM) injury risks (>90%) predicted cerebral injuries, while the Head Injury Criterion (HIC) injury risks remained low in two cases (<5%), underpredicting skull fractures. Chest deflection predicted thorax injury (injury risk >73%), whereas thoracic trauma index (TTI) risks were low (<50%). Tibia fractures from the cases were confirmed by injury risk estimations (>90%) using the revised tibia index (RTI). CONCLUSIONS: The GV-based pedestrian crash reconstruction framework demonstrated strong potential for real-world crash studies. CAD-based morphing enabled close matching of case vehicle front geometry, and material/structural tuning enhanced model responses aligned with physical vehicle data. The results of the reconstruction matched well with the actual crash data.
Examining traffic violations in severe casualty truck crashes: A text mining and reliable network analysis of narrative reports.
Zhao Y, Kang K, Jia W … +3 more , Guo Z, Zhang J, Zhu T
Traffic Inj Prev · 2026 · PMID 40982712 · Publisher ↗
OBJECTIVE: Trucks are more likely to be involved in severe casualty crashes compared with other vehicle types. The elimination of traffic violations is crucial to preventing severe casualty truck crashes. However, there...
OBJECTIVE: Trucks are more likely to be involved in severe casualty crashes compared with other vehicle types. The elimination of traffic violations is crucial to preventing severe casualty truck crashes. However, there is a lack of comprehensive analyses of truck violations and their conditions related to severe casualty crashes. This study aims to identify thematic communities of truck driver violations through a modeling framework integrating text mining and reliable network analysis. METHODS: This study collected 432 textual reports of severe truck casualty crashes in China from 2013 to 2020, which were divided into crash narratives and metadata for separate preprocessing. For the narrative part, the ELECTRA model was used for Chinese word segmentation and part-of-speech tagging, and keywords were extracted by combining with TF-IDF. The metadata was processed through named entity recognition, geocoding, etc., and then merged with the narrative keywords. Association rules were mined by the Apriori algorithm to construct a network with keywords as nodes and lift values as edge weights, which was visualized by the ForceAtlas2 algorithm. The Leiden algorithm was adopted to detect thematic communities, whose significance was validated by QStest. RESULTS: Text mining results reveal 77 most relevant keywords extracted from 432 police narratives. Overloading and speeding emerge as predominant traffic violations, correlating with 43% and 30% of severe casualty truck crashes, respectively. A total of four overloading and five speeding statistically significant thematic communities are identified. Notably, the circumstances associated with truck overloading and speeding manifest distinct characteristics. For overloading, conditions contributing to severe casualty crashes encompass rural highways with curves or slopes, provincial or national highways in the afternoon, expressways during nighttime, and locations proximate to signalized intersections. In contrast, five circumstances are linked to speeding: curved or sloped road segments during the afternoon, rural highways in autumn, straight road sections during the night, work zone areas on four-lane roadways, and un-signalized intersections on weekdays. Moreover, we also extracted vehicle and driver features across diverse environments, facilitating the identification of key elements for preventing severe casualty truck crashes. For instance, light trucks exhibit a higher susceptibility to severe casualty crashes attributed to overloading on rural highways. CONCLUSIONS: This study demonstrates the advantages of textual data and reliable network analysis. Text data analysis proves to be more convenient, yielding a richer array of comprehensive information while demanding less subjective judgment. The findings of this paper inform consequent enforcement and engineering measures for mitigating severe casualty truck crashes.
Unsafe driving behaviours in northwest Iran: A cross-sectional study using observational methods.
Bakhtari Aghdam F, Schwebel DC, Jafari-Khounigh A … +6 more , Shokrvash B, Harzand-Jadidi S, Sadeghi-Bazargani H, Jahangiry L, Papi S, Shahsavari Nia K
Traffic Inj Prev · 2026 · PMID 40982707 · Publisher ↗
OBJECTIVE: This cross-sectional study using observational methods study was conducted in 2022 to investigate risky driving behaviors among 3005 drivers in various areas of Tabriz, the largest city in northwest Iran. Obse...
OBJECTIVE: This cross-sectional study using observational methods study was conducted in 2022 to investigate risky driving behaviors among 3005 drivers in various areas of Tabriz, the largest city in northwest Iran. Observations were made when drivers stopped at intersections or before entering their government workplace. METHODS: Observational sites represented low, middle, and high income areas, and locations serving local areas, commuting areas, and workplaces. Observations occurred at various times of day and were conducted by recording drivers' behavior using a checklist based on the Martinez-Sanchez method. Chi-square and binary logistic regression analyses examined relations between demographic variables and drivers' behavior. RESULTS: Among the observed drivers, 67.39% failed to use seat belts, 29.72% used mobile phones while driving, and 74.24% stopped beyond the stop line. Women used seat belts 1.64 times more often than men [OR = 1.64; 95% CI: 1.36-1.97]. Drivers estimated to be under 25 years and aged 25-40 years used mobile phones significantly more often than drivers estimated to be over age 50 [OR = 2.65; 95% CI: 1.96-3.60], [OR = 1.75; 95% CI: 1.34-2.30]. Drivers were significantly more likely to use mobile phones on weekends than during the week [OR =1.49; 95% CI: 1.15-1.93] and at noon compared to the morning [OR = 1.25; 95% CI: 1.03-1.53]. Drivers observed in middle socioeconomic status (SES) locations failed to fasten seat belts 1.23 times more frequently than drivers in high SES areas [OR = 1.23; 95% CI: 1.01-1.51]. Drivers at workplaces and in local areas failed to fasten their seat belts 2.07 and 1.78 times more than drivers in commuting areas, respectively [OR = 2.07; 95% CI: 1.71-2.49; OR = 1.78; 95% CI: 1.45-2.17]. CONCLUSION: In summary, we observed considerable risk-taking behavior among drivers in Tabriz, Iran, with the highest risk occurring among male and younger drivers. Multifaceted intervention programs and policymaking, building off successful programs in other countries, should be implemented to increase safe driving behaviors.
Impact of Music Tempo on Driving Behavior and Vigilance in Speed-Limited Areas on Urban Roads.
Huo Y, Peng Z, Pan H … +2 more , Li D, Wang Y
Traffic Inj Prev · 2026 · PMID 40982706 · Publisher ↗
OBJECTIVE: The study aims to investigate the effects of varying music tempos on driving behavior and vigilance across three distinct urban speed-limited environments: generic roads, school zones, and work zones, utilizin...
OBJECTIVE: The study aims to investigate the effects of varying music tempos on driving behavior and vigilance across three distinct urban speed-limited environments: generic roads, school zones, and work zones, utilizing a driving simulation experiment. METHODS: The study constructed three urban road scenarios with distinct speed limit challenges: generic road section with 60 km/h limit, school zone with 30 km/h limit, and work zone with 30 km/h limit and a lane closure. Participants were recruited to conduct driving simulation experiments, executing speed-limited tasks under conditions of no music, slow tempo music, and fast tempo music. Throughout the process, their changes in driving behaviors and vigilance were recorded. Using non-parametric tests, the study examined differences in speed fluctuation, mean deceleration and skin conductance level (SCL) across varying music tempos, as well as the differential effects of music tempo on drivers' handling of distinct speed-limited tasks. RESULTS: The results revealed that playing music in the easier speed-limited task scenario (generic road section) reduced speed fluctuations and enhanced vigilance. In the school zone with a 30 km/h limit, fast tempo music notably reduced both speed fluctuation and mean deceleration while experiencing lower SCL. In the work zone scenario, where the speed-limited task was more challenging, listening to slow tempo music appeared to distract drivers, leading to increased speed fluctuations and deceleration. In contrast, the absence of music was associated with heightened vigilance during this task. Ultimately, while the introduction of music can alleviate physiological stress induced by challenging driving tasks, it may also impair driving behavior. CONCLUSIONS: The findings provide important insights for intelligent driving systems to select appropriate music types based on different driving tasks to enhance driving experience and safety.
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