Accid Anal Prev [JOURNAL]
Sun
200 papers
Disparities in pedestrian crossing and driver yielding behaviors: evidence from a large-scale observational study at urban intersections.
Beliveau AE, Haddad AJ, Podnar EA … +2 more , Sharma D, Bhat CR
Accid Anal Prev · 2026 Jun · PMID 41722203 · Publisher ↗
Pedestrian safety remains a critical challenge in urban environments, marked by rising fatalities and persistent disparities across sociodemographic groups. Uncovering the drivers of these disparities requires a deeper u...
Pedestrian safety remains a critical challenge in urban environments, marked by rising fatalities and persistent disparities across sociodemographic groups. Uncovering the drivers of these disparities requires a deeper understanding of both pedestrian and driver behaviors. This study examines how individual attributes, social context, and time-of-day/weather conditions shape pedestrian crossing and driver yielding decisions. We analyzed over 1,000 hours of video footage from two intersections in Austin, Texas, documenting over 20,995 pedestrian crossings and 3,124 pedestrian-vehicle interactions. Manual annotation of this footage enabled the estimation of two binary logit models: one predicting non-compliant pedestrian crossings (NCPC) and the other predicting driver unyielding (DUY) (that is, driver failure to yield to pedestrians). The results indicate that male pedestrians, Black pedestrians, those displaying visible signs of housing insecurity (VHI), and individuals crossing solo are significantly more likely to cross non‑compliantly and to encounter lower driver‑yielding rates. Runners also exhibit higher NCPC rates than walkers, with peak non‑compliance occurring during late night and dawn periods. On the driver side, pedestrian NCPC behavior is the strongest predictor of failure to yield. DUY behavior is also more likely during morning periods and among drivers of personal (non-commercial) vehicles, and when the pedestrian in question is older, Black or Brown, and exhibits VHI. These findings highlight the importance of addressing social and behavioral factors in pedestrian safety interventions. By revealing how marginalization and context interact to shape risk, this research contributes to the transportation equity literature and supports interventions that go beyond infrastructure, such as education campaigns, bias reduction, and community-led safety initiatives.
Pedestrian-AV interactions at unmarked midblock: Effects of eHMI onset timing and vehicle kinematics on young adult pedestrian behavior and subjective safety perception.
Gao Y, Yao L, Jin H … +1 more , Feng Z
Accid Anal Prev · 2026 Jun · PMID 41719632 · Publisher ↗
Pedestrians are one of the road users who bear the highest risk in unmarked midblock areas. The emergence of autonomous vehicles (AVs) has introduced new challenges in the interaction between pedestrians and AVs, which r...
Pedestrians are one of the road users who bear the highest risk in unmarked midblock areas. The emergence of autonomous vehicles (AVs) has introduced new challenges in the interaction between pedestrians and AVs, which require efficient communication tools to coordinate traffic movement and maintain road safety. Previous studies have been conducted on vehicle kinematics (implicit communication) or external human-machine interface (eHMI) performance (explicit communication) of AVs. However, it remains unclear whether the eHMI onset timing and its interaction with the vehicle kinematics positively influence pedestrians' behavior and subjective experiences when crossing unmarked midblock. This study employed a within-subject repeated measures design and recruited 43 valid young adult pedestrians to participate in a virtual reality (VR) experiment. It examined the effects of vehicle kinematic (early yield, late yield), eHMI onset timing (early eHMI onset, late eHMI onset, and no eHMI), and their interactions on pedestrians' crossing performance, fixation allocation, and subjective safety perceptions. The results revealed that the early eHMI onset significantly reduced pedestrian crossing initiation time and entry time, and improved subjective safety perception. Moreover, under the condition of late vehicle yielding and early eHMI onset, pedestrians performed better in terms of crossing behavior and fixation allocation, demonstrating optimal crossing efficiency while maintaining safety. These findings provide strong theoretical support for safe pedestrian-AV and offer valuable guidance for future AV design and pedestrian safety system optimization.
Characterizing vehicle-pedestrian interaction behavior in near misses: Insights from three different cities.
Lanzaro G, Sayed T, Osama A … +1 more , Telima M
Accid Anal Prev · 2026 Jun · PMID 41719631 · Publisher ↗
Improving the safety of vulnerable road users such as pedestrians requires a good understanding of their interaction behavior and their collision avoidance mechanisms in interactions with other road users. Refining this...
Improving the safety of vulnerable road users such as pedestrians requires a good understanding of their interaction behavior and their collision avoidance mechanisms in interactions with other road users. Refining this understanding will become even more important in an automated driving environment, where properly representing road users' evasive actions is required to develop effective collision avoidance systems, especially in mixed and less organized traffic conditions. This study models vehicle-pedestrian interactions using a multi-agent Markov game modeling framework to measure the degree of cooperation as road users interact with each other (e.g., collectively try to avoid a crash). Data from three cities with different traffic environments were used, including Boston (US), Cairo (Egypt), and Singapore. The model adopts an Inverse Reinforcement Learning framework that captures road users' utilities from their trajectories while accounting for the equilibrium in their actions. Results demonstrate substantial variations in behavior across different cities. For example, Cairo was shown to be the most cooperative environment, whereas Singapore presented the lowest levels of cooperation. Moreover, the level of cooperation is negatively associated with speed variables, which shows that road users were expected to cooperate more when they reduced their speeds. This paper provides valuable insights into road users' cooperation levels in different environments. This is useful for accurately modeling road users' actions and incorporating their behaviors in advanced automated driving systems, which should properly reflect local traffic environment conditions.
Probabilistic vehicle speed prediction and reliability-based design optimization of mountainous freeway renovation using Transformer and active learning surrogates.
Yu S, Chen Y, Wang S … +1 more , Li Y
Accid Anal Prev · 2026 Jun · PMID 41702043 · Publisher ↗
Accurate characterization of vehicle speed distributions is essential for evaluating driving safety and supporting reliability-based geometric design of mountainous freeways. Conventional deterministic approaches based o...
Accurate characterization of vehicle speed distributions is essential for evaluating driving safety and supporting reliability-based geometric design of mountainous freeways. Conventional deterministic approaches based on design speed fail to capture the variability of driver behavior under complex road and terrain conditions, leading to insufficient safety margins. This study proposes a reliability-based design optimization (RBDO) framework that integrates probabilistic vehicle speed prediction with safety-oriented geometric design when freeway plans to be renovated. A Transformer-based architecture is developed to establish the mapping between freeway alignment and affiliated facilities and vehicle speed distributions, enabling accurate probabilistic characterization of driving behavior. Driving safety reliability is quantified through three indicators-lateral stability, speed harmonization, and stopping sight distance sufficiency-formulated as stochastic limit state functions. To balance safety and renovation cost, the RBDO problem is solved using an active learning Kriging surrogate model, which enhances computational efficiency while maintaining accuracy. Numerical experiments on a typical mountainous freeway demonstrate that the proposed approach outperforms conventional regression and recurrent models in capturing multimodal speed distributions, and that modest adjustments to longitudinal slopes and curve radii can substantially improve driving safety reliability with limited renovation costs. The findings highlight the potential of combining deep learning and surrogate-based RBDO to support data-driven, safety-oriented design optimization of transportation infrastructure.
Decision-Making of automated vehicles under diverse risky pedestrian crossing behaviors.
Chen X, Wang H
Accid Anal Prev · 2026 Jun · PMID 41702042 · Publisher ↗
Uncontrolled midblocks are frequently associated with elevated traffic conflict rates but often lack effective mitigation measures. Risky pedestrian crossing behaviors, such as jaywalking, distracted walking, and dart-ou...
Uncontrolled midblocks are frequently associated with elevated traffic conflict rates but often lack effective mitigation measures. Risky pedestrian crossing behaviors, such as jaywalking, distracted walking, and dart-outs from occluded areas, combined with heterogeneous driving styles, further complicate automated vehicle (AV) decision-making. However, most existing studies focus on simplified scenarios and rarely consider complex settings. This gap limits the realism and applicability of current AV decision-making research in urban environments. To address these challenges, a high-fidelity multi-agent simulation platform replicates the dynamic interactions among AVs, human-driven vehicles, and pedestrians with diverse risky crossing behaviors. A general visibility modeling method using polar-sector analysis simulates perceptual limitations caused by occlusions for multiple agents. On this basis, a deep reinforcement learning (DRL)-based decision-making framework is developed to integrate risk assessment with safety filtering. The framework dynamically incorporates environmental risk into the behavior policy of AVs and, during execution, employs a safety filter to correct or replace unsafe actions. Experimental results show that the proposed approach substantially improves safety margins and control smoothness in complex scenarios with occluded or distracted pedestrians. Compared to rule-based and risk-unaware DRL baselines, the learned policy exhibits stronger anticipatory behavior and achieves a better balance between safety and traffic efficiency. These findings highlight the promise of risk-aware DRL for managing highly uncertain and interactive urban driving environments. The approach provides new insights for the safe deployment of AVs in real-world traffic.
Enhancing legal driving for autonomous vehicles through law-compliance potential fields.
Zhao C, Xiong Z, Song L … +6 more , Ma X, Chen Z, Wu S, Li J, Yu W, Wang H
Accid Anal Prev · 2026 Jun · PMID 41691776 · Publisher ↗
With the advent of the mixed traffic era, ensuring autonomous vehicles' compliance with traffic laws alongside human drivers has become increasingly critical. Existing decision-making methods predominantly emphasize safe...
With the advent of the mixed traffic era, ensuring autonomous vehicles' compliance with traffic laws alongside human drivers has become increasingly critical. Existing decision-making methods predominantly emphasize safety, inadequately addressing systematic compliance with traffic laws, leading to potential legal violations in complex driving scenarios. To bridge this gap, this paper proposes a comprehensive Law-Compliance Potential Fields-based method. Traffic law constraints are systematically categorized into four potential fields, which explicitly encode vehicle states, static and dynamic elements, and compliance thresholds. A novel fusion strategy is further designed to effectively resolve field-overlap distortions. Finally, the constructed law-compliance potential fields are integrated into a model predictive control-based decision-making framework, and five representative scenarios are designed for experimental validation, including a critical scenario of safety-compliance conflict. The evaluated results of scenario tests demonstrate that the proposed method markedly enhances autonomous vehicles' compliance capabilities, effectively balancing safety considerations even under challenging driving conditions.
Railway drivers' physiological responses to typical hazardous scenarios: differences between professional drivers and student drivers.
Guo Z, Tang J, Li M … +3 more , Zhang J, Zhang Y, Li G
Accid Anal Prev · 2026 Jun · PMID 41691775 · Publisher ↗
Unexpected object intrusions on railways present significant safety hazards that can lead to accidents, injuries, and operational disruptions. Railway drivers serve as the critical final line of defense in accident preve...
Unexpected object intrusions on railways present significant safety hazards that can lead to accidents, injuries, and operational disruptions. Railway drivers serve as the critical final line of defense in accident prevention, it is very necessary to study railway drivers' physiological responses to unexpected object intrusions. Existing studies primarily addresses behavioral responses, with few considering drivers' physiological cognition responses. This study recruited both professional railway drivers and student participants to investigate drivers' physiological responses to different hazardous scenarios. Electroencephalogram (EEG) data were collected during driving tasks across four typical hazardous scenarios for time-domain and frequency-domain analyses. Results revealed that professional drivers exhibited greater efficiency in neural resource allocation, cognitive resource integration, executive control and decision-making, as well as visual processing compared to student. Subjective hazard ratings were higher for drivers than for students, indicating greater perceived hazard. Professional drivers displayed distinct response patterns across different hazard scenarios: large-volume hazardous obstacles trigger sustained high cognitive load and executive control activation during the middle and later stages of hazard encounters, with moderate increases observed in the later stage, whereas small-volume hazardous obstacles elicit elevated cognition that remains stable upon hazard detection. Dynamic hazard scenarios elicited stronger visuospatial activation in drivers. Additionally, higher speeds imposed greater cognitive demands on drivers, with enhanced activation of brain regions associated with executive function, control, and decision-making observed during the early stage of hazard encounters. This study advances understanding of expertise-driven neurophysiological responses and provides evidences for developing targeted training programs and neurocognitive frameworks for railway safety enhancement.
Study on a multi-factor lane-changing risk resilience assessment model based on genetic algorithm and fault tree analysis.
Luo Q, Wang H, Yang J … +3 more , Zang X, Chen X, Postolache O
Accid Anal Prev · 2026 Jun · PMID 41689994 · Publisher ↗
Current lane-change risk assessment models often lack dynamic adaptation to adverse weather and validation against real-world outcomes. To bridge this gap, this study re-frames the problem through a resilience engineerin...
Current lane-change risk assessment models often lack dynamic adaptation to adverse weather and validation against real-world outcomes. To bridge this gap, this study re-frames the problem through a resilience engineering lens, defining risk resilience as the lane-changing system's capacity to absorb weather disturbances and maintain safety through adaptation. To operationalize this concept, we introduce two complementary metrics: the Risk Exposure Level (REL) and the Risk Severity Level (RSL). We propose a weather-aware, resilience-oriented assessment framework that integrates a Genetic Algorithm (GA)-calibrated Stopping Sight Distance (SSD) model with Fault Tree Analysis (FTA). Using the CitySim naturalistic driving dataset, a dual-threshold identification algorithm was applied to extract 310 lane-change events (218 sunny, 92 rainy). Key influencing factors, including weather, surrounding vehicle distribution, lane-change direction, and location, were identified through statistical testing. The GA was employed to optimize critical braking parameters (deceleration, reaction time) in the SSD/SDI model, enabling self-adaptive risk thresholds under different weather conditions. REL and RSL quantify the probability (exposure) and severity (consequence) of conflicts from multiple vehicle groups, which are systematically integrated via FTA to assess overall system robustness. Model calibration and testing using trajectory data showed a 42.38% improvement in fitness over the baseline model. A PyQt5-based visualization platform was developed to support practical application. The results confirm that the model effectively captures real-time lane-changing risk, providing a reliable tool for proactive safety management and resilience-oriented decision support in intelligent transportation systems.
GeoShapley-based interpretation of older adult pedestrian fatal vs injury crash frequency in dense urban environments.
Putra IGB, Kuo PF, Susanta FF … +2 more , Tedjo BH, Lord D
Accid Anal Prev · 2026 Jun · PMID 41679261 · Publisher ↗
As the world's population ages, ensuring the safety of older adult pedestrians has become an urgent priority in transportation planning. However, most existing studies rely on global models that overlook spatial heteroge...
As the world's population ages, ensuring the safety of older adult pedestrians has become an urgent priority in transportation planning. However, most existing studies rely on global models that overlook spatial heterogeneity and fail to capture nonlinear, location-specific interactions between the environmental factors and crash outcomes. Moreover, subjective perceptions (e.g., how safe or walkable an area feels) may influence pedestrian behavior and crash exposure but are underexplored in traffic safety research. This study addresses these gaps by integrating subjective perception indicators extracted from Street View Images (SVI) with machine learning models to examine the severity of older adult pedestrian crashes at intersections in Taipei City. Three modeling frameworks are evaluated and compared: global Negative Binomial Regression (NBR), Geographically Weighted Negative Binomial Regression (GWNBR), and GeoShapley, a spatially interpretable extension of the SHAP framework for XGBoost. A total of 36 environmental and perceptual variables are evaluated in relation to injury and fatal crash frequencies. Among these models, GeoShapley achieved the best performance and revealed that spatial location (GEO) and its interactions with environmental factors and subjective perceptions were among the most influential predictors. In some areas, higher walkability was associated with reduced injury crash frequencies, especially in older and urban districts. In addition, the effect of convenience stores and nursing homes on the frequency of fatal crashes varied significantly across locations, reflecting the spatial clustering of pedestrian activity and older adults. Overall, the findings demonstrate the value of spatially explicit machine learning tools and subjective perceptions in understanding localized crash dynamics in aging urban populations.
Modeling crash frequencies at highway-railroad grade crossings in Kentucky in the United States.
Banerjee A, Haleem K
Accid Anal Prev · 2026 Jun · PMID 41679260 · Publisher ↗
Previous safety studies at highway-railroad grade crossings (HRGCs) have typically examined crash severities. However, there remains a notable gap in studies that developed safety performance functions (SPFs) (or crash f...
Previous safety studies at highway-railroad grade crossings (HRGCs) have typically examined crash severities. However, there remains a notable gap in studies that developed safety performance functions (SPFs) (or crash frequency models) at these critical locations. This study develops SPFs for both total and fatal-and-injury (FI) crashes at HRGCs in the state of Kentucky in the U.S. using ten years (2014-2023) of crashes. The study merged extensive crash records and roadway attributes from the Kentucky Transportation Cabinet (KYTC) and Federal Railway Administration (FRA). Additional effort was made to manually collect geometric and infrastructure features nearby HRGCs (e.g., skewness, presence of exclusive turns, presence of channelizing island, and different sign types). The negative binomial "NB", heterogeneous NB "HTNB", Conway-Maxwell-Poisson "CMP", and heterogeneous CMP "HTCMP" models were fitted to handle data over-dispersion. The HTCMP model (with varying dispersion parameter) demonstrated the best performance. The model results showed that urban locations with poor illumination, fewer number of warning bells (≤2), the absence of track signals, and skewed geometry were associated with increased crash frequencies. Angle and rear-end HRGC-related crashes were predominant at the high-crash-prone HRGC locations, often involving left-turn movements and driver distractions. Key risk factors varied between total and FI crashes, with features like the presence of stop lines, the presence of parking structures, skewness, and the number of through lanes differently influencing each model. Based on the model findings and high-crash location analysis, several countermeasures were recommended near HRGCs, e.g., installing high-intensity LED lighting to improve nighttime visibility, and installing static signs (skewed-intersection warning and distracted driving).
CoDEA: A framework for extraction and augmentation of cooperative lane-changing scenarios from naturalistic driving data.
Li Y, Li W, Zhou R … +3 more , Xing L, Dong C, Wu D
Accid Anal Prev · 2026 Jun · PMID 41671736 · Publisher ↗
As modern transportation systems face increasing complexity, with challenges such as increased vehicle volumes, limited road resources, and rising safety concerns, there is an urgent need for innovative solutions. Cooper...
As modern transportation systems face increasing complexity, with challenges such as increased vehicle volumes, limited road resources, and rising safety concerns, there is an urgent need for innovative solutions. Cooperative driving, which enables vehicles to share information and collaborate through communication technologies, presents a promising solution to enhance safety, reduce congestion, and improve mobility. However, the validation of cooperative driving systems is hindered by a critical scarcity of real-world data. To address this challenge, we introduce CoDEA (Cooperative Driving Extraction and Augmentation), a comprehensive three-stage pipeline designed to generate robust and realistic cooperative driving datasets. First, a systematic method is developed to extract cooperative lane-changing behaviors from large-scale Naturalistic Driving Data (NDD), ensuring that the extracted data captures the key kinematic and cooperative features of real-world scenarios. Next, to effectively generate realistic cooperative lane-changing scenarios, we enhance the DiffTraj framework by introducing our Interaction-Aware Context Encoding (IA-CE) module. This module allows the diffusion model to condition its generation process on the nuanced interactions between vehicles, leading to the creation of more realistic and diverse cooperative trajectories. Finally, the effectiveness of the generated trajectories is evaluated using computational metrics such as RMSE and MAE, and by comparing key feature distributions between real and generated trajectories. The results show a strong similarity between the generated data and real-world cooperative lane-changing patterns, while also introducing greater diversity in certain features. Ultimately, the proposed CoDEA approach lays a solid foundation for advancing cooperative lane change control algorithms by providing a robust dataset for both training and evaluation, effectively bridging the gap between real-world complexity and algorithm testing environments.
The myth of quick conflict-based road safety analysis: Limits of short-term conflict data in collision risk prediction.
Aminghafouri R, Fu L
Accid Anal Prev · 2026 May · PMID 41666690 · Publisher ↗
Traditional road safety analysis is reactive and often hindered by scarce collision data. Traffic conflicts, or near-misses, offer a proactive surrogate for safety assessment, using Extreme Value Theory to extrapolate co...
Traditional road safety analysis is reactive and often hindered by scarce collision data. Traffic conflicts, or near-misses, offer a proactive surrogate for safety assessment, using Extreme Value Theory to extrapolate collision risk from these more frequent events. However, a critical methodological issue is the lack of guidance on prediction reliability. Many studies use short observation periods, yielding predictions with unacceptably wide credible intervals and fostering a misleading impression that such studies are quick. This research demonstrates that because severe conflicts remain rare, hundreds of days of continuous data collection are required for reliable results. This paper systematically assesses the reliability of collision predictions using conflict data with EVT models. The study utilizes a large dataset of traffic conflicts that was collected continuously via LiDAR sensors at four unsignalized intersections in Kitchener, Canada, for periods of up to one year. Using a Peak Over Threshold approach in a Bayesian framework, the analysis evaluates how collision estimates, and their 95% credible intervals converge as data collection increases from two to 365 days. The results demonstrate that while mean collision predictions can stabilize with limited data, the associated credible intervals for short collection periods are so wide that they practically offer no meaningful information. This research concludes that the common practice of using a few days of data is insufficient for reliable safety analysis. It provides an evidence-based methodology for determining the necessary data collection duration, enabling practitioners to balance resource efficiency with the need for robust and reliable proactive safety assessments.
Evaluating the impact of vehicle automation on the safety of highway design: A 3D risk assessment approach using reliability theory.
Liu Y, Gargoum S, Sayed T
Accid Anal Prev · 2026 May · PMID 41666689 · Publisher ↗
Empirical quantification of how automation affects road safety, particularly whether current highway designs can accommodate autonomous vehicles (AV), remains under-researched. This study addresses the gap using a 3D ris...
Empirical quantification of how automation affects road safety, particularly whether current highway designs can accommodate autonomous vehicles (AV), remains under-researched. This study addresses the gap using a 3D risk assessment framework that integrates reliability theory with mobile Light Detection and Ranging (LiDAR) data to examine the interaction between sight distance and vehicle automation. Using 308 curves along a rural highway in Canada, a three-phase analysis was conducted. First, available sight distance (ASD) was estimated using 2D analytical formulas and a voxel-based 3D LiDAR approach. Second, a reliability-based risk assessment compared ASD with the stopping sight distance (SSD) requirements of three vehicle types representing increasing automation levels-human-driven vehicles (HDVs), transition-stage AVs, and fully developed AVs-using probability of non-compliance (P) as a risk index. Finally, sensitivity analyses assessed the effects of operational parameters and sensor configurations on risk levels. Results show that the 3D method provided a more realistic and context-sensitive evaluation of ASD and associated obstructions than the 2D method, and was therefore used in the risk assessment. Overall risk decreased with increasing automation, although some scenarios showed elevated risk for fully developed AVs. Segment- and curve-level analysis attribute these increases to higher speeds and gentler deceleration rates assumed on sharper curves. Sensitivity analyses show that higher deceleration rates substantially reduce AV risk, while increased sensor height offers limited benefits. Overall, this study demonstrates the value of LiDAR-based assessment and P as a quantitative risk index, enabling identification of critical locations to guide highway infrastructure improvements and AV algorithm refinement.
Driver behavior analysis at alternative intersection corridors through driving simulator.
Yang G, Chase RT, Liu Y … +3 more , Pyo K, Cunningham CM, Kaber DB
Accid Anal Prev · 2026 May · PMID 41666688 · Publisher ↗
Alternative intersection (AI) designs, such as the Median U-Turn (MUT), Reduced Conflict Intersection (RCI), Continuous Flow Intersection (CFI), and Quadrant Roadway Intersection (QRI), introduce innovative geometric and...
Alternative intersection (AI) designs, such as the Median U-Turn (MUT), Reduced Conflict Intersection (RCI), Continuous Flow Intersection (CFI), and Quadrant Roadway Intersection (QRI), introduce innovative geometric and control features compared to a conventional intersection (CI), which offer the potential for substantial safety and operational improvements. Nevertheless, most AI designs present unconventional ways of maneuvering traffic through an intersection, such as restriction of movements, crossover of traffic to the opposite side of the road, separating left turning movements, etc. As corridor construction or improvement projects continue to utilize AI designs, understanding their impacts on driver behavior, especially when implemented successively along a corridor, is essential for effective deployment. This research developed a comprehensive driving simulator experiment to evaluate driver performance when navigating AI corridors, focusing on four key metrics: number of failure movements (FMs), approach speed (AS), hard-braking events (HBEs), and approach lane changes (ALCs). Three background corridor treatments were investigated: a CI corridor, an RCI corridor, and a corridor with varied AI designs. A total of 12 intersection pairs were created to represent typical and practical combinations of AIs, with each pair consisting of a test intersection and a preceding intersection. Based on data collected from 48 participants, this research found that gender, age, and background corridor treatment did not significantly influence driver behavior. In contrast, trial number, preceding intersection configuration, test intersection movement, and intersection pair were all found to have significant effects. Among the test movements, the MUT side street left-turn presented the highest risk of FMs. Approach speed was lowest at the MUT side street left-turn and highest at the RCI side street through movement, with speeds generally increasing over successive trials. HBEs occurred most frequently at QRI, MUT, and RCI configurations. ALCs were more common when the test intersection was preceded by a CI, with the highest ALCs observed during CI main street left-turns, followed by MUT and CFI configurations. Post-experiment interviews highlighted the importance of clear and reasonably placed traffic signs and pavement markings to inform drivers of unconventional traffic patterns at AIs.
Speeding across Texas: identifying high-risk locations using probe data.
Li K, Kockelman KM
Accid Anal Prev · 2026 May · PMID 41666687 · Publisher ↗
Speeding contributes to one-third of all motor vehicle fatalities in the U.S., making it crucial to understand speeding behaviors for traffic safety. This work compares a random sample of 39.1 million vehicle speeds in N...
Speeding contributes to one-third of all motor vehicle fatalities in the U.S., making it crucial to understand speeding behaviors for traffic safety. This work compares a random sample of 39.1 million vehicle speeds in November 2024 to posted speed limits (PSLs) at 1.04 million roadway points across the Texas network, and reviews non-infrastructure strategies to ensure greater PSL compliance. Weighted least squares (WLS) and spatially weighted binomial logistic regression models control for design variables, congestion, and land use to examine how average speeds and shares of vehicles exceeding the PSL vary by time of day, day of week, and a host of other variables. Results indicate that speeding is most common during late-night hours and on weekends, especially on roads with lower PSLs (30 and 40 mph), where 43% of drivers exceeded the limit. Almost half the probe vehicles exceeded the PSL and 20% exceeded it by more than 15% (e.g., by more than 9 mph in a 60 mph zone) between 3 a.m. and 5 a.m. each day. PSL and rural settings were the top predictors of increased average traffic speed, with a 1 mph increase in PSL associated with a 0.72 mph rise in average speed - everything else constant. Access control and weekend variables were the factors most strongly associated with increased speeding behaviors, with weekends showing 7.7% higher speeding and access-controlled roads exhibiting 5.7% to 9.8% higher speeding, depending on the level of access control. This paper also reviews the effectiveness of non-infrastructure speed management strategies (like automated speed enforcement), and connects speeding behaviors to appropriate non-infrastructure strategies, highlighting enforcement for high-speed hotspots and community or operational measures for lower-PSL local roads. This work provides insights to help transportation agencies identify targeted enforcement locations and implement more effective speed management policies.
Modeling interactive car-following behaviors of automated and human-driven vehicles in safety-critical events: a multi-agent state-space attention-enhanced framework.
Pu Q, Xie K, Guo H
Accid Anal Prev · 2026 May · PMID 41655467 · Publisher ↗
As automated vehicles (AVs) become increasingly prevalent in mixed-traffic environments, it is essential to understand how they interact with human-driven vehicles (HDVs), especially in safety-critical situations. Existi...
As automated vehicles (AVs) become increasingly prevalent in mixed-traffic environments, it is essential to understand how they interact with human-driven vehicles (HDVs), especially in safety-critical situations. Existing research has primarily focused on AVs' collision avoidance strategies, often neglecting how AV maneuvers simultaneously influence the decision-making behaviors of HDVs. This study develops the multi-agent state-space attention-enhanced deep deterministic policy gradient (MA-ASS-DDPG) framework, leveraging the Third Generation Simulation (TGSIM) dataset for the first time to learn interactive car-following behaviors of an AV and the following human-driven vehicles (FHDV) in safety-critical scenarios. By integrating the attention mechanism to dynamically prioritize critical motion features and the state-space model to effectively capture temporal dependencies, the proposed framework models AVs executing collision avoidance strategies while simultaneously prompting HDVs to adapt their behaviors to mitigate potential risks. Results showed that MA-ASS-DDPG demonstrated superior performance in learning maneuvers of both the AV and the FHDV, outperforming counterpart models. Further, the MA-ASS-DDPG was used to reconstruct evasive trajectories of AVs and HDVs in safety-critical scenarios, and the reconstructed data successfully replicated reaction times comparable to real-world observations, further validating the model's effectiveness. Analysis showed that AVs following HDVs reacted 0.3473 s faster than HDV-HDV pairs, while HDVs following AVs reacted 0.2143 s faster, demonstrating more cautious and adaptive driving in response to AV maneuvers. Counterfactual analysis revealed that HDVs following AVs adopt more conservative speeds and larger acceleration variability. In addition, incorporating a safety term into the reward function of the learning framework leads to substantial improvements in safety performance, including reduced conflict occurrences, fewer high-risk deceleration events, and enhanced car-following stability. These outcomes of this study can support safety-aware traffic simulation, scenario-based safety testing, and enhanced AV control strategies in mixed-traffic environments.
HSPG: An open-loop testing framework for autonomous driving based on proactive generation of hazardous scenario.
Wang C, Liu Q, Fang W … +1 more , Xiong C
Accid Anal Prev · 2026 May · PMID 41653536 · Publisher ↗
Autonomous driving algorithms struggle to achieve sufficient coverage of long-tail scenarios in complex traffic environments, primarily due to the scarcity of high-risk samples in real-world data. Existing scenario gener...
Autonomous driving algorithms struggle to achieve sufficient coverage of long-tail scenarios in complex traffic environments, primarily due to the scarcity of high-risk samples in real-world data. Existing scenario generation methods also have limitations, as they mostly rely on trajectory perturbation without realistic perception support. To address this issue, we propose the HSPG (Hazardous Scenario Proactive Generation) framework, a proactive hazardous scenario generation approach based on naturalistic driving data. HSPG systematically amplifies potential risks through structural perturbations of original traffic scenarios. A sliding-window-based risk index is introduced to automatically identify interaction-intensive periods and extract candidate scenarios. A high-risk vehicle detection mechanism then selects critical surrounding vehicles as interaction agents. By integrating a Linear Quadratic Regulator (LQR) with Recurrent Posterior Policy Optimization (RPPO) and adversarial strategies, high-risk trajectories are generated. These trajectories are further transformed into realistic street scenarios via an image synthesis module coupled with real-world map data, forming a comprehensive safety-critical test dataset. Experimental results demonstrate that HSPG effectively identifies latent risks, enhances collision likelihood by at least an order of magnitude under autonomous driving test models, and generalizes across diverse scenarios. A dataset comprising 150 scenarios, 6019 samples, and six multi-perspective camera views has been constructed, providing a valuable benchmark for safety evaluation in autonomous driving. Our dataset can be found at https://huggingface.co/datasets/gitchee/nuScenes-Atk.
P-AEB performance and limiting factors for superior-rated P-AEB systems based on simulations of real-world pedestrian crashes: A simulation study on the VIPA database.
Perez-Rapela D, Riexinger LE, Kidd DG … +2 more , Mueller BC, Jermakian JS
Accid Anal Prev · 2026 May · PMID 41653535 · Publisher ↗
Pedestrian automatic emergency braking systems (P-AEB) have recently been introduced in the vehicle fleet to reduce vehicle-to-pedestrian collisions. However, studies on the real-world efficacy of these systems have yiel...
Pedestrian automatic emergency braking systems (P-AEB) have recently been introduced in the vehicle fleet to reduce vehicle-to-pedestrian collisions. However, studies on the real-world efficacy of these systems have yielded mixed results. To better understand the factors that influence P-AEB performance, previous simulation and counterfactual studies have evaluated the effects of different P-AEB characteristics on collision avoidance. Previous studies have focused on using a hypothetical P-AEB response model to either estimate the potential benefit of P-AEB or evaluate system configuration performance to optimize P-AEB design. This study aimed to understand the shortcomings of current production P-AEB systems for consumer testing organizations to use for encouraging the continuous improvement of those systems. The present study re-simulated 64 vehicle-to-pedestrian collision cases included in the in-depth Vulnerable Road Users Injury Prevention Alliance database to evaluate the stochastic response of rating-specific P-AEB systems and identify the most challenging pedestrian scenarios and the factors limiting P-AEB performance. Our P-AEB models represented the test responses of systems rated as superior, advanced, or basic by the Insurance Institute for Highway Safety (IIHS). We explored the effects of detection range, detection angle, and the lateral distance threshold for system activation. Results indicated a clear correlation between collision avoidance and the IIHS P-AEB rating. The study also identified three challenging scenarios: (1) highly obstructed cases, (2) high-speed vehicle cases, and (3) cases with high pedestrian crossing speed. None of the explored system designs were able to eliminate collisions in highly obstructed cases due to the late appearance of the pedestrian. In high-speed vehicle cases and in those with high pedestrian crossing speeds, P-AEB performance was limited by the detection range and the lateral distance threshold, respectively. Consumer testing organizations can use these findings to revise existing test programs, improve program relevance for vehicle-to-pedestrian crashes, and incentivize improvements to P-AEB systems.
Spotting Danger: How child and adult pedestrians assess distracted drivers in hazard perception.
Liu M, Chen Y, Zhuang X … +1 more , Ma G
Accid Anal Prev · 2026 May · PMID 41650692 · Publisher ↗
Child pedestrian casualties in traffic accidents remains high, particularly when they cross the street alone. One contributing factor is their limited ability to identify potential risks. While vehicle motion cues and en...
Child pedestrian casualties in traffic accidents remains high, particularly when they cross the street alone. One contributing factor is their limited ability to identify potential risks. While vehicle motion cues and environmental factors are known to influence hazard perception, a driver's distracted state may also signal risk. However, it remains unclear whether pedestrians, especially children, can assess danger based on a driver's distraction. This study aims to investigate the effects of driver distraction on hazard perception of child (6-10 years old) and adult pedestrians. Participants assessed the safety of crossing at a crosswalk based on videos of approaching vehicles with drivers in various states of distraction (undistracted, texting, chatting, etc.). Results from Experiment 1 show that although both children and adults perceived greater danger when drivers were distracted, children were not as sensitive to different driver states as adults. However, when participants were guided to focus more on driver cues by enlarging driver images (Experiment 2), the influence of driver's distraction on safety assessments increased significantly, particularly for children. This study reveals that even children can perceive potential hazards from driver, which highlights the significant role of driver distraction in pedestrians' safety judgments and provide valuable insights for designing training programs to enhance children's hazard perception skills.
A spatially structured empirical Bayes framework for the evaluation of network-wide safety countermeasures.
Wu M, Labbe A, Schmidt AM … +1 more , Miranda-Moreno L
Accid Anal Prev · 2026 May · PMID 41650691 · Publisher ↗
This study proposes a two-step, spatially structured Empirical Bayes (EB) framework for evaluating the safety effectiveness of network-wide countermeasures, leveraging the Network Process Convolution (NPC) model. A centr...
This study proposes a two-step, spatially structured Empirical Bayes (EB) framework for evaluating the safety effectiveness of network-wide countermeasures, leveraging the Network Process Convolution (NPC) model. A central challenge in road safety evaluation is not only estimating treatment effects but also accurately quantifying uncertainty, particularly when interventions generate local and spillover effects. The NPC uses a network-based Gaussian Process with reweighted kernel convolution to capture spatial correlations of collisions along road networks, enabling robust estimation of both site-specific and network-wide effects. The two-step procedure ensures an unbiased prior structure for generating counterfactual outcomes. We conducted a simulation study under varying spatial correlation scenarios and applied the method to the City of Edmonton's Driver Feedback Sign (DFS) program using 10 years of collision data across 1,366 road segments. Performance was benchmarked against the traditional EB Poisson-Gamma (EB-PG) method. Simulations show that while both methods accurately recover counterfactual collisions and reduction ratios, EB-NPC provides more reliable and well-calibrated uncertainty quantification, particularly under moderate to strong spatial correlation. In the Edmonton case study, EB-NPC mostly produced slightly higher estimated reductions and more informative predictive uncertainty, whereas EB-PG remained more robust in areas with weak spatial structure. Beyond numerical estimation, EB-NPC generates continuous spatial risk surfaces, allowing practitioners to visualize network-wide safety patterns and prioritize high-risk segments. Overall, the proposed approach improves recovery of counterfactual outcomes and delivers accurate, interpretable uncertainty characterization, offering a powerful tool for data-driven transportation safety management.
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