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Objective: To examine changes in red-light running and distracted riding among motorcyclists and cyclists before and after the 2020 implementation of the One Helmet, One Seatbelt campaign in China., Methods: We obtained 192 hours of film before (2019) and after (2021) implementation of the campaign in eight road intersections in Changsha. We calculated percentages and ratios of red-light running and distracted riding. To assess the associations between these traffic behaviours and the campaign, we used multivariable logistic regression models to calculate adjusted odds ratios (aOR)., Findings: We filmed 5256 motorcyclists and cyclists in 2019 and 6269 in 2021. Red-light running decreased from 45.1% to 41.5% during this period (ratio: 0.92; 95% confidence interval, CI: 0.88-0.96), while distracted riding increased from 3.5% to 5.0% (ratio: 1.42; 95% CI: 1.19-1.69). After adjusting for covariates, male riders were more likely to run a red light compared to female riders (aOR: 1.28; 95% CI: 1.06-1.55). Red-light running was also more likely among electric bicycle riders (aOR: 1.46; 95% CI: 1.10-1.95) and motorcyclists (aOR: 1.47; 95% CI: 1.13-1.90) compared to traditional cyclists. All types of riders were less prone to run a red light during peak hours than off-peak hours (aOR: 0.85; 95% CI: 0.73-0.99). Distracted riding was more common on weekends compared to weekdays (aOR: 3.01; 95% CI: 2.02-4.49)., Conclusion: China's national road safety campaign, which focuses on helmet and seatbelt use, was associated with reduced red-light running. Strict enforcement and targeted modifications could improve the campaign's effectiveness., ((c) 2024 The authors; licensee World Health Organization.)

Yellow dilemma, at which a driver can neither stop nor go safely after the onset of yellow signals, is one of the major crash contributory factors at the signal junctions. Studies have visited the yellow dilemma problem using observation surveys. Factors including road environment, traffic conditions, and driver characteristics that affect the driver behaviours are revealed. However, it is rare that the joint effects of situational and attitudinal factors on the driver behaviours at the yellow dilemma zone are considered. In this study, drivers' propensity to stop after the onset of yellow signals is examined using the driving simulator approach. For instances, the association between driver propensity, socio-demographics, safety perception, traffic signals, and traffic and weather conditions are measured using a binary logit model. Additionally, variations in the effect of influencing factors on driver behaviours are accommodated by adding the interaction terms for driver characteristics, traffic flow characteristics, traffic signals, and weather conditions. Results indicate that weather conditions, traffic volume, position of yellow dilemma in the sequence, driver age and safety perception significantly affect the drivers' propensity to stop after the onset of yellow signals. Furthermore, there are remarkable interactions for the effects of driver gender and location of yellow dilemma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Electric vehicles (EVs) differ significantly from their internal combustion engine (ICE) counterparts, with reduced mechanical parts, Lithium-ion batteries and differences in pedal and transmission control. These differences in vehicle operation, coupled with the proliferation of EVs on our roads, warrant an in-depth investigation into the divergent risk profiles and driving behaviour of EVs, Hybrids (HYB) and ICEs. In this unique study, we analyze a novel telematics dataset of 14,642 vehicles in the Netherlands accompanied by accident claims data. We train a Logistic Regression model to predict the occurrence of driver at-fault claims, where an at-fault claim refers to First and Third Party damages where the driver was at fault. Our results reveal that EV drivers are more exposed to incurring at-fault claims than ICE drivers despite their lower average mileage. Additionally, we investigate the financial implications of these increased at-fault claims likelihoods and have found that EVs experience a 6.7% increase in significant first-party damage costs compared to ICE. When analyzing driver behaviour, we found that EVs and HYBs record fewer harsh acceleration, braking, cornering and speeding events than ICE. However, these reduced harsh events do not translate to reducing claims frequency for EVs. This research finds evidence of a higher frequency of accidents caused by Electric Vehicles. This burden should be considered explicitly by regulators, manufacturers, businesses and the general public when evaluating the cost of transitioning to alternative fuel vehicles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Engaging in phone conversations or other cognitively challenging tasks while driving detrimentally impacts cognitive functions and has been associated with increased risk of accidents. Existing EEG methods have been shown to differentiate between load and no load, but not between different levels of cognitive load. Furthermore, it has not been investigated whether EEG measurements of load can be used to predict safety outcomes in critical events. EEG microstates analysis, categorizing EEG signals into a concise set of prototypical functional states, has been used in other task contexts with good results, but has not been applied in the driving context. Here, this gap is addressed by means of a driving simulation experiment. Three phone use conditions (no phone use, hands-free, and handheld), combined with two task difficulty levels (single- or double-digit addition and subtraction), were tested before and during a rear-end collision conflict. Both conventional EEG spectral power and EEG microstates were analyzed. The results showed that different levels of cognitive load influenced EEG microstates differently, while EEG spectral power remained unaffected. A distinct EEG pattern emerged when drivers engaged in phone tasks while driving, characterized by a simultaneous increase and decrease in two of the EEG microstates, suggesting a heightened focus on auditory information, potentially at a cost to attention reorientation ability. The increase and decrease in these two microstates follow a monotonic sequence from baseline to hands-free simple, hands-free complex, handheld simple, and finally handheld complex, showing sensitivity to task difficulty. This pattern was found both before and after the lead vehicle braked. Furthermore, EEG microstates prior to the lead vehicle braking improved predictions of safety outcomes in terms of minimum time headway after the lead vehicle braked, clearly suggesting that these microstates measure brain states which are indicative of impaired driving. Additionally, EEG microstates are more predictive of safety outcomes than task difficulty, highlighting individual differences in task effects. These findings enhance our understanding of the neural dynamics involved in distracted driving and can be used in methods for evaluating the cognitive load induced by in-vehicle systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper evaluates the performance of two different types of long combination vehicles (A-double and DuoCAT) using naturalistic driving data across four scenarios: lane changes, manoeuvring through roundabouts, turning in intersections, and negotiating tight curves. Four different performance-based standards measures are used to assess the stability and tracking performance of the vehicles: rearward amplification, high-speed transient offtracking, low-speed swept path, and high-speed steady-state offtracking. Also, the steering reversal rate metric is employed to estimate the cognitive workload of the drivers in low-speed scenarios. In the majority of the identified cases of the four scenarios, both combination types have a good performance. The A-double shows slightly better stability in high-speed lane changes, while the DuoCAT has slightly better manoeuvrability at low-speed scenarios like roundabouts and intersections., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an innovative framework, AccNet, which significantly advances the prediction capabilities beyond the current state-of-the-art 2D-based methods by incorporating monocular depth cues for sophisticated 3D scene modeling. Addressing the prevalent challenge of skewed data distribution in traffic accident datasets, we propose the Binary Adaptive Loss for Early Anticipation (BA-LEA). This novel loss function, together with a multi-task learning strategy, shifts the focus of the predictive model towards the critical moments preceding an accident. We rigorously evaluate the performance of our framework on three benchmark datasets - Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), and DADA-2000 Dataset - demonstrating its superior predictive accuracy through key metrics such as Average Precision (AP) and mean Time-To-Accident (mTTA)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This submission is the result of the authors’ own research and analysis. All sources of funding for the research reported have been duly acknowledged within the manuscript., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Crashes are frequently disproportionally observed in disadvantaged areas. Despite the evident disparities in transportation safety, there has been limited exploration of quantitative approaches to incorporating equity considerations into road safety management. This study proposes a novel concept of equity-aware safety performance functions (SPFs), enabling a distinct treatment of equity-related variables such as race and income. Equity-aware SPFs introduce a fairness distance and integrate it into the log-likelihood function of the negative binomial regression as a form of partial lasso regularization. A parameter λ is used to control the importance of the regularization term. Equity-aware SPFs are developed for pedestrian-involved crashes at the census tract level in Virginia, USA, and then employed to compute the potential for safety improvement (PSI), a prevalent metric used in hotspot identification. Results show that equity-aware SPFs can diminish the effects of equity-related variables, including poverty ratio, black ratio, Asian ratio, and the ratio of households without vehicles, on the expected crash frequencies, generating higher PSIs for disadvantaged areas. Based on the results of Wilcoxon signed-rank tests, it is evident that there are significant differences in the rankings of PSIs when equity awareness is considered, especially for disadvantaged areas. This study adds to the literature a new quantitative approach to harmonize equity and effectiveness considerations, empowering more equitable decision-making in safety management, such as allocating resources for safety enhancement., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The rise of Express Lanes also known as High Occupancy Toll (HOT) Lanes and Managed Lanes, signifies a major leap in traffic management and transportation funding. Despite their increased deployment to ensure reliable travel times through dynamic tolling during peak traffic periods, a comprehensive evaluation of their safety impact is notably lacking. Presently, the Crash Modification Factors Clearinghouse, a vital resource, only lists two case studies related to Express Lanes, one of which is our own research. This lack of data highlights the critical need for more extensive studies to thoroughly assess the safety benefits of Express Lanes and to improve their application. This study aims to rigorously evaluate the safety impact of express lanes on freeways, presenting a first-of-its-kind, in-depth analysis of their specific effects on both Express Lanes and General-Purpose Lanes (GP-Lanes) individually. The analysis utilized data from 55 miles of Express Lanes across various locations in Florida, comparing them to High Occupancy Vehicle (HOV) lanes. The results demonstrate that converting HOV lanes to Express Lanes or introducing new ones does not compromise overall freeway safety. In fact, safety within Express Lanes improves, as evidenced by a decrease in crash occurrence and Crash Modification Factors for Express lanes, which are below "1" across all crash categories. This underscores the effectiveness of Express lanes in enhancing roadway safety. In contrast, incidents in GP-Lanes have increased, indicating a shift of crashes to these lanes, and thus making Express lanes relatively safer. This underlines the importance of continued research into the safety impact of express lanes and calls for further studies to refine traffic management strategies, aiming at enhancing travel efficiency while ensuring traffic safety, especially for the GP-Lanes amid the expansion of express lanes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-end for e-bike riders by encompassing a broader range of test scenarios. Initially, large-scale rider injury response data were collected using automated Madymo simulations. A machine learning model was then trained to accurately predict the risk of rider injury under varied crash conditions. Subsequently, this model was integrated into a multi-objective optimization framework, combined with multi-criteria decision analysis, to effectively evaluate and rank various design alternatives on the Pareto frontier. This process entailed a comparative analysis of the design in a baseline scenario before and after optimization, focusing on both kinematic and injury responses of riders. Through detailed injury mechanism analysis, key design variables such as the height of the hood front and the width of the bumper were identified. This led to the proposal of specific optimization strategies for these structural parameters. The results from this study demonstrate that the proposed optimization method not only guides the design process accurately and efficiently but also balances the injury risks across different body parts. This approach significantly reduces the injury risk for riders in car-to-e-bike collisions and provides actionable insights for vehicle design enhancements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Reducing risky behaviors and traffic violations is crucial for preventing road trauma among private and commercial motorcyclists. While legal interventions such as road rules and police enforcement have been somewhat effective, there is a gap in understanding how motorcyclists perceive these deterrents and the psycho-social factors influencing their effectiveness. This research aims to explore the interplay between motorcyclists' psycho-social predispositions and their responses to legal interventions. It also compares two distinct groups of riders - private and commercial motorcyclists - within the urban environment of Da Nang, Vietnam, providing a nuanced consideration of the influence of work-related impacts on risky behavior, which can enable targeted interventions. We propose a theoretical model integrating the Theory of Planned Behavior and Deterrence Theory to analyze how psycho-social and punitive factors influence riders' traffic violations. A total of 423 delivery riders and 411 private riders in Da Nang participated in the study. The results show that attitude, perceived behavioral control, and perceptions of deterrence significantly impact riders' intentions to engage in risky behaviors, with delivery riders being particularly responsive to law enforcement and penalties. To enhance policy effectiveness, interventions should employ tailored enforcement strategies that account for the unique motivations and perceptions of each rider group. For example, increasing the visibility and consistency of law enforcement could be particularly impactful for delivery riders, who are more sensitive to immediate deterrents. Policy adjustments should also focus on reshaping riders' attitudes toward safety and adjusting their perceived control over riding situations. By tailoring interventions to effectively influence each group's behavior, we can ultimately reduce traffic incidents., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In the near future, pedestrians will face highly automated vehicles on the roads. Highly automated vehicles (HAVs) should have safety-enhancing communication tools to guarantee traffic safety, e.g., vehicle kinematics and external human-machine interfaces (eHMIs). Pedestrians, as highly vulnerable road users, depend on communication with HAVs. Miscommunication between pedestrians and HAVs could quickly result in accidents, and this, in turn, could cause severe impairments for pedestrians. Light-band eHMIs have the potential to enhance traffic safety. However, eHMIs have been less explored in Japan so far. As a first-time approach, this experimental online study shed light on the effect of a light-band eHMI on Japanese pedestrians (N=99). In short video sequences, the participants interacted with two differently sized HAVs equipped with light-band eHMI. We investigated the effect of vehicle size (small vs. large), eHMI status (no eHMI vs. static eHMI vs. dynamic eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrians' willingness to cross, trust, and perceived safety. To investigate possible side effects of eHMIs, we also included experimental conditions in which the eHMI mismatched the vehicle's kinematics. Results revealed that Japanese were more willing to cross the street and indicated higher trust- and safety ratings when they received information about the vehicle's intention and automation status (dynamic eHMI) compared to when they received no information (no eHMI) or only about the vehicle automation status (static eHMI). Surprisingly, Japanese participants tended to rely on the eHMI when there was mismatching information between eHMI and vehicle kinematics. Overall, we concluded that light-band eHMIs could contribute to a safe future interaction between pedestrians and HAVs in Japan under the requirement that the eHMI is in accordance with vehicle kinematics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways' horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model's attributes with the Harris Hawks Optimization (HHO) algorithm-referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of "region-road-segment" for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Statistical analysis of traffic crash frequency is significant for figuring out the distribution pattern of crashes, predicting the development trend of crashes, formulating traffic crash prevention measures, and improving traffic safety planning systems. In recent years, the theory and practice for traffic safety management have shown that road crash data have characteristics such as spatial correlation, temporal correlation, and excess zeros. If these characteristics are ignored in the modeling process, it may seriously affect the fitting performance and prediction accuracy of traffic crash frequency models and even lead to incorrect conclusions. In this research, traffic crash data from rural two-way two-lane from four counties in Pennsylvania, USA was modeled considering the spatiotemporal effects of crashes. First, a negative binomial Lindley spatiotemporal effect model of crash frequency was constructed at the micro level; Simultaneously, the characteristics and problems of excess zeros and potential heterogeneity of the crash data were resolved; Finally, the effects of road characteristics on crash frequency were analyzed. The results indicate a significant spatial correlation between the crash frequency of adjacent road sections. Compared with the negative binomial model, the negative binomial Lindley model can better handle the excess zeros characteristics in traffic crash data. The model that considers both spatial correlation and temporal conditional autoregressive effects has the best fit for the observed data. In addition, for road sections that allow passing and have a speed limitation of not less than 50 miles per hour, the crash frequency corresponding to these sections is lower due to their good visibility and road conditions. The increase in average turning angle and intersection density on the horizontal curve of the road section corresponds to an increase in crash frequency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The City of Toronto adopted a Vision Zero strategy in 2016 that aims to eliminate deaths and serious injuries from vehicular collisions. The strategy includes policies to improve lighting to reduce collision risks, and past research has suggested lighting as a road safety factor. We apply Bayesian spatial analysis (including Poisson log-normal regression modelling, shared component spatial modelling, and Bayesian spatiotemporal modelling) to publicly available data on traffic collisions where persons are killed or seriously injured (KSI) based on Day/Dark conditions. We assess (1) links between KSI risk and socioeconomic and built environment factors, (2) spatial distributions of relative Day & Dark KSI risk, and (3) area-specific trends in space and time for Day-Dark KSI risk change across Toronto neighbourhoods. Our analysis does not find significant associations between socioeconomic/built environment factors and KSI risk, but we uncover neighbourhoods with heightened Dark KSI risk and pronounced Day-Dark KSI changes compared to Toronto's mean area trend. Findings highlight the need for increased policy attention for impacts of lighting on collisions and provide insight for focus regions for improved Vision Zero policy development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models. An analytical framework was proposed, further illustrated through a detailed case study. In the case study, real vehicle trajectory data from HighD were processed and applied, which can be aggregated to extract both traffic state variables and corresponding conflict data during a specific time interval. As for the conflict detection, Time-to-Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) indicators were utilized to identify risky conditions. Based on the proposed approach, the selection for the optimal prediction model was conducted, and the variable importance in conflict prediction within the optimal models derived from the SM-OPMS was also investigated. Finally, a comparative analysis with the enumeration-based optimal prediction model selection (E-OPMS) approach was conducted to validate the superiority of the proposed approach. Results indicate that SM-OPMS outperforms E-OPMS in optimal model selection, notably enhancing computational efficiency by up to 94.03%, while maintaining prediction precision within a maximum reduction of only 7.91%. The significance of the SM-OPMS approach is revealed by its comprehensive selection of the optimal prediction models for specific traffic scenarios, taking into account both prediction efficiency and precision simultaneously. The proposed approach is expected to contribute to the development of real-time conflict prediction in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Elderly pedestrians are involved in disproportionately more vehicle-pedestrian crashes than younger age groups. Training programs have been found to be effective in training children in pedestrian behaviours that improve their safety, however there is no consensus on whether older adults benefit from training. This systematic review aimed to identify whether training is effective for older adult pedestrians through analysis of training type, modalities, and the lasting effects of training. A systematic search of Medline, PsycINFO, and Scopus was conducted in March 2022 and updated in September 2023. Eight studies met the criteria all of which were high quality. Four distinct training types were found: physical (e.g., training physical strength or balance), behavioural (e.g., training specific pedestrian safety behaviours), cognitive (e.g., training reaction time and executive functioning), and educational (training knowledge about pedestrian safety behaviours). Physical training types were found to be most effective, followed by behavioural, cognitive, and educational respectively. Twelve pedestrian behaviours were measured across the eight studies. Reaction time was the most effectively trained outcome, followed by missed crossing opportunities. Errors of stimuli, median accepted time gap, initiation time and crossing were not effectively trained. The effects of training were maintained at follow-up for missed crossing opportunities only. There was preliminary evidence of potential efficacy of training for specific pedestrian safety behaviours, however, the long-term efficacy of training was not promising. Theory-driven research is needed to better understand why some behaviours are more trainable than others. More research is also needed to determine the real-world generalisability if training is to be recommended for older adult pedestrians., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne's zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into three levels (i.e., slight, ordinary, severe) based on point deduction, and explore the patterns of serious traffic violations (i.e., ordinary, severe) using multi-source data. This paper designed an interpretable machine learning framework, in which four popular machine learning models were enhanced and compared. Specifically, adaptive synthetic sampling method was applied to overcome the effects of imbalanced data and improve the prediction accuracy of minority classes (i.e., ordinary, severe); multi-objective feature selection based on NSGA-II was used to remove the redundant factors to increase the computational efficiency and make the patterns discovered by the explainer more effective; Bayesian hyperparameter optimization aimed to obtain more effective hyperparameters combination with fewer iterations and boost the model adaptability. Results show that the proposed interpretable machine learning framework can significantly improve and distinguish the performance of four popular machine learning models and two post-hoc interpretation methods. It is found that six of the top ten important factors belong to multi-scale built environment attributes. By comparing the results of feature contribution and interaction effects, some findings can be summarized: ordinary and severe traffic violations have some identical influencing factors and interactive effects; have the same influencing factors or the same combinations of influencing factors, but the values of the factors are different; have some unique influencing factors and unique combinations of influencing factors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Driving risk prediction emerges as a pivotal technology within the driving safety domain, facilitating the formulation of targeted driving intervention strategies to enhance driving safety. The driving safety undergoes continuous evolution in response to the complexities of the traffic environment, representing a dynamic and ongoing serialization process. The evolutionary trend of this sequence offers valuable information pertinent to driving safety research. However, existing research on driving risk prediction has primarily concentrated on forecasting a single index, such as the driving safety level or the extreme value within a specified future timeframe. This approach often neglects the intrinsic properties that characterize the temporal evolution of driving safety. Leveraging the high-D natural driving dataset, this study employs the multi-step time series forecasting methodology to predict the risk evolution sequence throughout the car-following process, elucidates the benefits of the multi-step time series forecasting approach, and contrasts the predictive efficacy on driving safety levels across various temporal windows. The empirical findings demonstrate that the time series prediction model proficiently captures essential dynamics such as risk evolution trends, amplitudes, and turning points. Consequently, it provides predictions that are significantly more robust and comprehensive than those obtained from a single risk index. The TsLeNet proposed in this study integrates a 2D convolutional network architecture with a dual attention mechanism, adeptly capturing and synthesizing multiple features across time steps. This integration significantly enhances the prediction precision at each temporal interval. Comparative analyses with other mainstream models reveal that TsLeNet achieves the best performance in terms of prediction accuracy and efficiency. Concurrently, this research undertakes a comprehensive analysis of the temporal distribution of errors, the impact pattern of features on risk sequence, and the applicability of interaction features among surrounding vehicles. The adoption of multi-step time series forecasting approach not only offers a novel perspective for analyzing and exploring driving safety, but also furnishes the design and development of targeted driving intervention systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Harsh driving events such as harsh brakings (HBs) and harsh accelerations (HAs) are promising Surrogate Safety Measures, already extensively utilised in road safety research. However, their occurrence relative to normal driving conditions has not been the explicit target of research, as they are typically used as inputs for crash prediction. The present study addresses this research gap by investigating factors influencing HB and HA occurrence using real-time naturalistic driving telematics data recorded from smartphones, traffic data and road geometry & network characteristics data. These multisource data were matched in order to capture the specific circumstances under which HBs and HAs occur. The utilized telematics dataset included trips from 314 anonymous drivers in an urban arterial of Athens, Greece. Subsequently, Synthetic Minority Oversampling TEchnique (SMOTE) was applied due to class imbalance and then binary classification was conducted to detect factors leading to HB and HA occurrence. Imbalanced Machine Learning (ML) XGBoost algorithms predicted over 75% of HBs and over 84% of HAs for the test dataset, indicating suitability for real-time monitoring. The algorithms were also augmented with SHapley Additive exPlanation (SHAP) values, aiming to increase outcome explainability. Results reveal strong nonlinear effects on harsh event occurrence, with individual speed and traffic flow parameters showing the highest influence, followed by exposure parameters such as segment length and pass count. Network characteristics such as number of lanes, and speed limit had limited influence on HA and HB occurrence, as did behaviors such as mobile phone engagement and speeding., Competing Interests: Declaration of competing interest The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Crash counts are non-negative integer events often analyzed using crash frequency models such as the negative binomial (NB) distribution. However, due to their random and infrequent nature, crash data usually exhibit unique characteristics, such as excess zero observations that the NB distribution cannot adequately model. The negative binomial-Lindley (NBL) and random parameters negative binomial-Lindley (RPNBL) models have been proposed to address this limitation. Despite addressing the issues of excess zero observations, these models may not fully account for unobserved heterogeneity resulting from temporal variations in crash data. In addition, many variables, such as traffic volume, speed, and weather, change with time. Therefore, the analyst often requires disaggregated data to account for their variations. For example, it is recommended to use monthly crash datasets to better account for temporally varying weather variables compared to yearly crash data. Using temporally disaggregated data not only adds the complexity of the temporal variations issue in data but also compounds the issue of excess zero observations. To address these issues, this paper introduces a new variant of the NBL model with coefficients and Lindley parameters that vary by time. The derivations and characteristics of the model are discussed. Then, the model is illustrated using a simulation study. Subsequently, the model is applied to two empirical crash datasets collected on rural principal and minor arterial roads in Texas. These datasets include several time-dependent variables such as monthly traffic volume, standard deviation of speed, and precipitation and exhibit unique characteristics such as excess zero observations. The results of several goodness-of-fit (GOF) measures indicate that using the NBL model with time-dependent parameters enhances the model fit compared to the NB, NBL, and the NB model with time-dependent parameters. Findings derived from crash data collected from both rural minor and principal arterial roads in Texas suggest that the variables denoting the median presence and wider shoulder width are associated with a potential decrease in crash occurrences. Moreover, higher variations in speed and wider road surfaces are linked to a potential increase in crash frequency. Similarly, a higher monthly average daily traffic (Monthly ADT) positively correlates with crash frequency. We also found that it is important to account for temporal variations using time-dependent parameters., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios., Background: Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events., Method: Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured., Results: Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment., Conclusion: Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios., Application: Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events.

The present analysis used full-trip naturalistic driving data along with driver behavioral and psychosocial surveys to understand the individual and contextual predictors of speeding. The data were collected over a three-week period from 44 drivers and contain 3,798 full trips, with drivers speeding 7.8 % of the time. Speeding events were identified as periods when participants traveled at a velocity greater than five mph over the speed limit for at least five seconds. Data were analyzed using the Comprehensive Driver Profile (CDP) framework which uses principal component analysis (dimensionality reduction), random forest (predictive modeling), k-means clustering (grouping and profiling), and bootstrapping (profile stability) to decompose the predictive variables and driver characteristics. The final dataset included 188 candidate independent variables from the CDP framework and one dependent variable (speeding). Nine variables emerged as significant predictors of speeding onset with an AUC of 0.88, including the percent of trip time spent idling and speeding, highway driving in low traffic conditions, and positive attitudes toward phone use. Percent of trip speeding was associated with a higher likelihood of speeding by up to 42 percent, and percent trip idling was associated with it by up to 30 percent. Driver profile clusters revealed four types: Traffic & Idling Speeders, Infrequent Speeders, Frequent Speeders, and Situational Speeders. The present analysis demonstrates the importance of situational factors and individual differences in motivating speeding behavior. Countermeasures targeting speeding may be more effective if they address the root causes of the behavior in addition to the behavior itself., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Pedestrian fatalities comprise a quarter of all traffic deaths in Low-and-Middle-Income Countries (LMICs). The use of safer modes of transport such as buses can reduce road trauma as well as air pollution and traffic congestion. Although travelling by bus is safer than most other modes, accessing bus stops can be risky for pedestrians. This paper systematically reviews factors contributing to the safety of pedestrians near bus stops in countries of differing income levels. The review included forty-one studies from high (20), upper-middle (13) and lower-middle income countries (8) during the last two decades. The earliest research was conducted in high-income countries (HICs), but research has spread in the last decade. The factors influencing pedestrian safety fell into three groups: (a) characteristics of road users, (b) characteristics of bus stops and (c) characteristics of the road traffic environment. Pedestrians near bus stops are frequently exposed to a high risk of collisions and fatalities due to factors such as unsafe pedestrian behaviours (e.g., hurrying to cross the road), lack of bus stop amenities such as safe footpaths, high traffic speeds and traffic volumes, multiple lanes, and roadside hazards (e.g., parked cars obscuring pedestrians). Road crash statistics are commonly used to identify unsafe bus stops in HICs but the unavailability and unreliability of data have prevented more widespread use in LMICs. Future research is recommended to focus on surrogate safety measures to identify hazardous bus stops for pedestrians., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers' viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To validate active human body models for investigating occupant safety in autonomous cars, it is crucial to comprehend the responses of vehicle occupants during evasive maneuvers. This study sought to quantify the behavior of midsize male and small female passenger seat occupants in both upright and reclined postures during three types of vehicle maneuvers. Volunteer tests were conducted using a minivan, where vehicle kinematics were measured with a DGPS sensor and occupant kinematics were captured with a stereo-vision motion capture system. Seatbelt loads, belt pull-out, and footrest reaction forces were also documented. The interior of the vehicle was 3D-scanned for modeling purposes. Results indicated that seatback angles significantly affected occupant kinematics, with small female volunteers displaying reduced head and torso movements, except during emergency braking with a upright posture seatback. Lane-change maneuvers revealed that maximum lateral head excursions varied depending on the maneuver's direction. The study concluded that seatback angles were crucial in determining the extent of occupant movement, with notable variations in head and torso excursions observed. The collected data assist in understanding occupant behavior during evasive maneuvers and contribute to the validation of human body models, offering essential insights for enhancing safety systems in autonomous vehicles.

With the arrival of Industry 4.0, intelligent construction sites have seen significant development in China. However, accidents involving digitized tower cranes equipped with smart systems continue to occur frequently. Among the main causes of these accidents is human unsafe behavior. To assess the human factors reliability of intelligent construction site tower cranes, it is necessary to shift the safety focus to the human-machine interface and identify patterns of human error behaviors among tower crane drivers through text mining techniques (TF-IDF-TruncatedSVD-ComplementNB). Based on the SHEL model, the behavioral factors influencing human factors reliability in the human-machine interface are categorized and a Performance Shaping Factors (PSF) system is constructed. Building on the foundation of constructing an indicator system for human factors error influence in the driver interface of intelligent construction site tower cranes, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is combined with the Interpretive Structural Modeling (ISM) to analyze the importance of various factors in causing human errors and to analyze the logical structure among these factors. Simultaneously, a Bayesian network is constructed using a multi-level hierarchical structural model, thus establishing a new evaluation method for the human-machine interface. The effectiveness of the proposed method is validated through Bayesian network causal inference based on real case studies. The results demonstrate that the evaluation process of this method aligns with the operational scenarios of tower crane drivers in intelligent construction sites. It not only allows for quantifying the likelihood of human errors but also enables the development of targeted measures for controlling unsafe behaviors among tower crane drivers in intelligent construction sites., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lixia, Si. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region., (© 2024. The Author(s).)

The Safe System Approach is an evidence-based strategy committed to eliminating fatalities and serious injuries among all road users. The Safe System Approach as developed in Sweden, acknowledges that human errors will occur, but the cost of these mistakes should not be death or serious injury. Technology is an integral component of the Safe System Approach and can address equity and reduce human error among other safety benefits. A literature review will be conducted to compare high-income countries leveraging the Safe System Approach and assess opportunities for technology interventions in low- and middle-income contexts. Evidence will be analyzed, as well as implementation considerations of the recent National adoption of the Safe System Approach in the United States. As SSA evolves in a global context, further evaluation is needed on the role of technology and how government policies can restrict or advance its implementation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Booker, Dolce and Obi.)

Introduction and Objective: Road Traffic Injuries (RTIs) represent the predominant cause of morbidity and mortality globally. Thus, impactful interventions and trustworthy authorities are imperative to mitigate traffic accidents. The present study seeks to assess public trust in traffic accident diminution policies and measures within the country of Iran., Method: This cross-sectional study utilized a questionnaire comprising 31 questions distributed across eight dimensions. The questionnaire's design was informed by a literature review and semi-structured interviews with experts specializing in traffic accidents. The target population consisted of Tabriz city residents, who were selected via convenience sampling. The collected data were then analyzed using SPSS-16 software, employing the T-test, One-way ANOVA, and Pearson correlation coefficient to present the study's results. structural equation modeling (SEM) was conducted using R 4-4-2 programming language., Results: A total of 681 participants completed the questionnaire, yielding a commendable response rate of 88.6%. The majority of participants were male (60.8%), and 86.2% identified as drivers, with 61.8% having experienced driving accidents. The overall score for public trust in RTIs policies was calculated to be 46.9%. The individual scores for specific domains of trust in traffic safety were as follows: safer roads (43.1%), safe vehicle (43.3%), safety laws (48.8%), safe user (51.1%), safe technology (51.5%), road safety management (46.8%). All the latent variables except education are significantly effective on public trust., Conclusion: The study findings indicate relatively low levels of public trust in RTIs policies among the Iranian population. These results emphasize the need for targeted interventions to enhance public confidence in specific aspects of traffic safety. Policymakers can use these insights to implement effective measures, thus contributing to the RTIs and the promotion of road safety in the country., (© 2024. The Author(s).)

Purpose: Night-time driving is dangerous, with increased crash rates, particularly involving vulnerable road users. A Night-Time Hazard Visibility Test (NHVT) was developed and validated by exploring the effects of refractive and cataract blur on performance., Methods: The NHVT comprised video clips of night-time roads from the driver's perspective, including different hazards (pedestrians, cyclists, and vehicles). Participants responded when they first recognized hazards requiring them to take evasive action to avoid a collision. In experiment 1, there were 16 young visually normal drivers (mean age = 22.3, standard deviation [SD] = 2.2 years) who completed 2 NHVT sets, viewed separately through best-correction and refractive blur (+1.00 diopter sphere [DS]). In experiment 2, a refined version of the NHVT was administered to an additional 16 young visually normal drivers (mean age = 21.1, SD = 1.2 years) with best-correction and cataract blur. The order of visual conditions and NHVT sets were counterbalanced., Results: In experiment 1, refractive blur significantly reduced photopic visual acuity (VA) compared to best-corrected vision (+0.09 vs. -0.21 logMAR, P < 0.001) and delayed response times by 0.69 seconds (3.10 vs. 2.41 seconds, P < 0.001) compared to best-corrected vision. In experiment 2, cataract blur reduced VA compared to best-corrected vision (+0.03 vs. -0.17 logMAR, P < 0.001) and delayed response times by 0.63 seconds (2.92 vs. 2.29 seconds, P < 0.001)., Conclusions: The NHVT was sensitive to refractive and cataract blur, providing preliminary support of its validity as a measure of night-time hazard visibility performance., Translational Relevance: The NHVT has potential as an off-road assessment of visibility for night driving and application for assessment of drivers with different refractive corrections and ocular diseases.

Introduction: Immersive simulation is used increasingly in medical education, and there is increasing awareness of the impact of simulation scenarios on emotional state and cognitive load and how these impact learning. 1 There is growing awareness of the requirement to equip veterinarians with skills for managing high-pressure environments and provide training on human factors., Methods: Veterinary students participated in a high-fidelity immersive simulation of a road traffic collision involving multiple casualties. The students took part in the same simulation twice, the second time after a debrief. Each participant's emotional state and cognitive load were assessed after participating in each simulation. Each participant was asked to score the effect of pressure on their performance., Results: One hundred twenty-five students participated and demonstrated a higher cognitive load with more positive emotional states during the second scenario after the completion of a structured debrief and discussion focusing on pressure relief techniques (cognitive load - ¯ μ Scenario run 1 = 4.44 ± 1.85 [SD], ¯ μ Scenario2 = 5.69 ± 1.74 [SD]). Most (63%) participants described being in a low-performance state of frazzle during the first scenario compared with most (61%) who described being in a high-performance state of flow during the second., Conclusion: Immersive simulation scenarios, with structured debriefing, may allow the measurement of emotional state and cognitive load in participants. Furthermore, this study suggests that curriculum training in human factors and pressure relief techniques, coupled with immersive simulation and debrief, may improve future performance in high-stakes and high-pressure scenarios., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Society for Simulation in Healthcare.)

Various safety enhancements and policies have been proposed to enhance pedestrian safety and minimize vehicle-pedestrian accidents. A relatively recent approach involves marked sidewalks delineated by painted pathways, particularly in Asia's crowded urban centers, offering a cost-effective and space-efficient alternative to traditional paved sidewalks. While this measure has garnered interest, few studies have rigorously evaluated its effectiveness. Current before-after studies often use correlation-based approaches like regression, lacking effective consideration of causal relationships and confounding variables. Moreover, spatial heterogeneity in crash data is frequently overlooked during causal inference analyses, potentially leading to inaccurate estimations. This study introduces a geographically weighted difference-in-difference (GWDID) method to address these gaps and estimate the safety impact of marked sidewalks. This approach considers spatial heterogeneity within the dataset in the spatial causal inference framework, providing a more nuanced understanding of the intervention's effects. The simplicity of the modeling process makes it applicable to various study designs relying solely on pre- and post-exposure outcome measurements. Conventional DIDs and Spatial Lag-DID models were used for comparison. The dataset we utilized included a total of 13,641 pedestrian crashes across Taipei City, Taiwan. Then the crash point data was transformed into continuous probability values to determine the crash risk on each road segment using network kernel density estimation (NKDE). The treatment group comprised 1,407 road segments with marked sidewalks, while the control group comprised 3,097 segments with similar road widths. The pre-development program period was in 2017, and the post-development period was in 2020. Results showed that the GWDID model outperformed the spatial lag DID and traditional DID models. As a local causality model, it illustrated spatial heterogeneity in installing marked sidewalks. The program significantly reduced pedestrian crash risk in 43% of the total road segments in the treatment group. The coefficient distribution map revealed a range from -22.327 to 2.600, with over 95% of the area yielding negative values, indicating reduced crash risk after installing marked sidewalks. Notably, the impact of crash risk reduction increased from rural to urban areas, emphasizing the importance of considering spatial heterogeneity in transportation safety policy assessments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The trucking industry urgently requires comprehensive methods to evaluate driver safety, given the high incidence of serious traffic accidents involving trucks. The concept of a "truck driver persona" emerges as a crucial tool in enhancing driver safety and enabling precise management of road transportation safety. Currently, the road transport sector is only beginning to adopt the user persona approach, and thus the development of such personas for road transport remains an exploratory endeavor. This paper delves into three key aspects: identifying safety risk characteristic parameters, exploring methods for constructing personas and designing safety management interventions. Initially, bibliometric methods are employed to analyze safety risk factors across five domains: truck drivers, vehicles, roads, the environment, and management. This analysis provides the variables necessary to develop personas for road transportation drivers. Existing methods for constructing user personas are then reviewed, with a particular focus on their application in the context of road transportation. Integrating contemporary ideas in persona creation, we propose a framework for developing safety risk personas specific to road transportation drivers. These personas are intended to inform and guide safety management interventions. Moreover, the four stages of driver post-evaluation are integrated into the persona development process, outlining tailored safety management interventions for each stage: pre-post, pre-transit, in-transit, and on-post. These interventions are designed to be orderly and finely tuned. Lastly, we offer optimization recommendations and suggest future research directions based on safety risk factors, persona construction, and safety management interventions. Overall, this paper presents a safety management-oriented research technology system for constructing safety risk personas for truck drivers. We argue that improving the design of the persona index system, driven by big data, and encompassing the entire driver duty cycle-from pre-post to on-post-will significantly enhance truck driver safety. This represents a vital direction for future development in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Analyzing crash data is a complex and labor-intensive process that requires careful consideration of multiple interdependent modeling aspects, such as functional forms, transformations, likely contributing factors, correlations, and unobserved heterogeneity. Limited time, knowledge, and experience may lead to over-simplified, over-fitted, or misspecified models overlooking important insights. This paper proposes an extensive hypothesis testing framework including a multi-objective mathematical programming formulation and solution algorithms to estimate crash frequency models considering simultaneously likely contributing factors, transformations, non-linearities, and correlated random parameters. The mathematical programming formulation minimizes both in-sample fit and out-of-sample prediction. To address the complexity and non-convexity of the mathematical program, the proposed solution framework utilizes a variety of metaheuristic solution algorithms. Specifically, Harmony Search demonstrated minimal sensitivity to hyperparameters, enabling an efficient search for solutions without being influenced by the choice of hyperparameters. The effectiveness of the framework was evaluated using two real-world datasets and one synthetic dataset. Comparative analyses were performed using the two real-world datasets and the corresponding models published in literature by independent teams. The proposed framework showed its capability to pinpoint efficient model specifications, produce accurate estimates, and provide valuable insights for both researchers and practitioners. The proposed approach allows for the discovery of numerous insights while minimizing the time spent on model development. By considering a broader set of contributing factors, models with varied qualities can be generated. For instance, when applied to crash data from Queensland, the proposed approach revealed that the inclusion of medians on sharp curved roads can effectively reduce the occurrence of crashes, when applied to crash data from Washington, the simultaneous consideration of traffic volume and road curvature resulted in a notable reduction in crash variances but an increase in crash means., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Navigating through complex road geometries, such as roundabouts, poses significant challenges and safety risks for drivers. These challenges may be exacerbated when drivers are distracted by mobile phone conversations. The interplay of road geometry, driving state, and driver characteristics in creating compound risks remains an underexplored area in existing literature. Proper understanding of such compound crash risk is not only crucial to improve road geometric design but also to educate young drivers, who are particularly risk-takers and to devise strict penalties for mobile phone usage whilst driving. To fill this gap, this study examines crash risks associated with gap acceptance manoeuvres at roundabouts in the simulated environment of the CARRS-Q driving simulators, where 32 licenced young drivers were exposed to a gap acceptance scenario in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A parametric random parameters survival modelling approach is adopted to understand safety margins-characterised by gap times-during gap acceptance scenarios at roundabouts, concurrently uncover driver-level heterogeneity with mobile phone distraction and capture repeated measures of experiment design. The model specification includes the handheld phone condition as a random parameter and hands-free phone condition, acceleration noise, gap size, crash history, and gender as non-random parameters. Results suggest that the majority of handheld distracted drivers have smaller safety margins, reflecting the negative consequences of engaging in handheld phone conversations. Interestingly, a group of drivers in the same handheld phone condition have been found to exhibit cautious/safer behaviour, as evidenced by longer gap times, reflecting their risk compensation behaviour. Female distracted drivers are also found to exhibit safer gap acceptance behaviour compared to distracted male drivers. The findings of this study shed light on the compound risk of mobile phone distraction and gap acceptance at roundabouts, requiring policymakers and authorities to devise strict penalties and laws for distracted driving., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers' decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants' final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Extreme value theory (EVT) models have been frequently utilized to estimate crash risk from traffic conflicts with the peak over threshold commonly used to identify conflict extremes. However, a common problem for the peak over threshold method is the selection of a suitable threshold to distinguish general and extreme conflicts. Subjective and arbitrary selection of the threshold in peak over threshold method can result in bias and unstable estimation results. The primary objective of the study is to propose a hybrid modelling approach for the threshold determination in peak over threshold method. The hybrid model consists of a joint gamma distribution and generalized Pareto distribution (GPD). The gamma distribution is used to fit general conflicts while the GPD is used to fit extreme conflicts. Specially, discontinued, continued and differentiable gamma-GPD models are developed with the threshold being treated as a model parameter. Traffic conflict data collected from three signalized intersections in the city of Surrey, British Columbia were used for the study. The modified time to collision (MTTC) was employed as conflict indicator. The Bayesian approach was employed to estimate the threshold as well as other hybrid gamma-GPD model parameters. The results show that the discontinued gamma-GPD model is superior to the continued and differentiable gamma-GPD models for determining the threshold in terms of crash estimation accuracy and model fit. The crash estimates using the threshold determined by the hybrid gamma-GPD model outperform those estimated based on the traditional quantile plots method, indicating that the superiority of the proposed threshold determination approach based on gamma-GPD hybrid model. The proposed hybrid gamma-GPD model could determine the threshold parameter in peak over threshold method for traffic conflicts extremes automatically in an objective and quantitative way. It contributes to existing peak over threshold method for producing reliable crash estimation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A major safety hazard for e-bike riders crossing an intersection is encountering heavy vehicles turning right in the same direction, which often results in severe casualties. Recently, some cities in China have implemented right-turn safety improvement treatments (i.e., right-turn yielding rules and right-turn warning facilities) at intersections to reduce the occurrence of such accidents. However, the risk perception and behavior of e-bike riders and heavy vehicle drivers dynamically change during the right-turn interaction process, and the safety effects of different right-turn safety measures remain unclear. This study aims to investigate the safety effect of right-turn safety measures on E-Bike-Heavy Vehicle (EB-HV) right-turn conflicts at signalized intersections. The right-turn conflicts and potential influencing factors are extracted from aerial video data, including characteristics of right-turn warning facilities, characteristics and behavior of e-bike riders and heavy vehicle drivers, environmental factors, and traffic-related factors. Moreover, traffic conflict indicators such as the Time to Collision (TTC), Post Encroachment Time (PET), and Jerk are selected and calculated. Multinomial and binary logit models are used to estimate and analyze the EB-HV right-turn conflict severity and drivers yielding behavior. The results reveal that: (a) right-turn warning facilities can decrease the probability of slight and severe EB-HV right-turn conflicts, while the presence of law enforcement cameras could prompt heavy vehicle drivers to comply with the yielding rules and adopt more cautious behavior; (b) increased heavy vehicle speed and acceleration before turning right have strong correlation to illegitimate yielding behavior of the driver and higher EB-HV right-turn conflict severity; and (c) aggressive behavior of e-bike rider increases the severe conflict probability, especially at intersections without right-turn warning facilities. Based on the study findings, several practical implications are suggested to reduce the risk of EB-HV right-turn conflicts, enhance the effectiveness of right-turn safety measures, and improve crossing safety for e-bike riders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The rising prevalence of e-bikes and shared bikes in transportation modes adds complexity to pedestrian movement at intersections. The conflict technique is a substitute for collisions in analyzing pedestrian safety at digital countdown signal intersections. Pedestrian and two-wheeler trajectories were obtained using Unmanned Aerial Vehicle (UAV) and T-Analyst software. The severity of pedestrian-two-vehicle conflicts was assessed using indicators such as Time to Collision (TTC), Post Encroachment Time (PET), and Yaw Rate Ratio (YRR), along with the fuzzy C-mean clustering method. An analysis of the impact of pedestrian characteristics, cyclist characteristics, and road conflict factors on severity was conducted using a random parameter ordered logit model. A total of 630 valid conflicts were identified, comprising 105 potential conflicts, 242 minor conflicts, and 283 serious conflicts. More minor and serious conflicts occurred in Signal 1 and Signal 2. Serious conflicts mainly occurred in road Zone 2, Zone 3, and Zone 5, while minor conflicts were more frequent in Zone 4 and Zone 5. Pedestrian crossing at Signal 2 increased the conflict severity, and the refuge island had a similar effect. Cyclists passing the conflict point first reduced the probability of serious conflicts. Older adults are safer at countdown signal intersections than young people. It is essential to enhance the awareness of digital countdown signals among youth. Managers should consider diverting two-wheelers during peak hours and encourage cyclists to walk through crosswalks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Driving behaviors are important cause of expressway crash. In this study, method based on modified time-to-collision (MTTC) to identify risky driving behaviors on an expressway diverge area is proposed, thus investigating the impact of velocity and acceleration features of risky driving behavior. Firstly, MTTC is applied to judge whether the behavior is risky. Then, the relationships between velocity, acceleration and different driving behavior on the expressway diverge area were fit by binary logistic regression models (BLR) with L2 regularization and random forests (RF) models, and the models were interpreted by feature importance plots and partial dependency plots. The results show that the AUC metric of 4 RF models for 4 types of driving behaviors, namely, left lane change, right lane change, acceleration and deceleration, are 0.932, 0.845, 0.846 and 0.860 separately. The interpretation of models demonstrates that velocity and absolute value of acceleration greatly affect the risk of the driving behaviors. Different driving behaviors with a certain acceleration have a range of safety speed range. The range will get narrower with the growth of maximum absolute value of acceleration rate, and will be nearly non-exist when the acceleration is over 5 m/s 2 . In conclusion, this study provided a methodology to measure the risk of driving behaviors and establish a model to recognize of risky driving behaviors. The results can lay the foundation for making countermeasures to prevent risky driving behaviors by managing the vehicle speed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Police road crash and injury data in low-income and middle-income countries are known to under-report crashes, fatalities and injuries, especially for vulnerable road users. Local record keepers, who are members of the public, can be engaged to provide an additional source of crash and injury data., Methods: This paper compares the application of a local record keeper method to capture road crash and injury data in Bangladesh and Nepal, assesses the quality of the data collected and evaluates the replicability and value of the methodology using a framework developed to evaluate the impact of being a local record keeper., Outcome: Application in research studies in both Bangladesh and Nepal found the local record keeper methodology provided high-quality and complete data compared with local police records. The methodology was flexible enough to adapt to project and context differences. The evaluation framework enabled the identification of the challenges and unexpected benefits realised in each study. This led to the development of an 11-step process for conducting road crash data collection using local record keepers, which is presented to facilitate replication in other settings., Conclusion: Data collected by local record keepers are a flexible and replicable method to understand the strengths and limitations of existing police data, adding to the evidence base and informing local and national decision-making. The method may create additional benefits for data collectors and communities, help design and assess road safety interventions and support advocacy for improved routine police data., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Although motorcycle helmets can save lives in case of a crash, no helmet use data are available for many countries. When data is available, it is often only analysed as a global average, preventing targeted road safety education and legislative action. In this study, we conducted a detailed analysis of motorcycle helmet use in the capital of Madagascar., Methods: Using a cross-sectional observational field survey framework, we observed 17 230 individual motorcycles. We systematically recorded motorcycle riders' helmet use, position on the motorcycle, rider numbers and gender., Results: We found a general helmet use of 76.1%. Observed drivers had a significantly higher helmet use (84.6%) than passengers (47.7%), and subsequently helmet use per motorcycle decreased significantly when the number of riders per motorcycle increased. Female drivers had significantly higher helmet use than male drivers, and female passengers had significantly higher helmet use than male passengers. That is, on the same position of the motorcycle, female riders behaved safer than male riders. However, since female riders were more often passengers than drivers, their average helmet use was lower than that of male riders overall. Contrary to findings from other countries, motorcycle helmet use did not differ significantly throughout the day but was relatively constant., Conclusion: Our results show the potential for injury and fatality prevention in Madagascar through increased passenger helmet use. This increase would also proportionally benefit female riders more than male riders. Findings regarding road safety legislation's applied impact, education, enforcement and future research needs are discussed., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Virtual reality (VR) simulation offers a proactive, cost effective, immersive, and low risk platform for studying pedestrian safety. Within immersive virtual environments (IVEs), existing and alternative design conditions and intelligent transportation systems (ITS) technologies can be directly compared, prior to real-world implementation, to assess the impacts alternatives may have on pedestrian safety, perception, and behavior. Environmental factors can be controlled within IVEs so that test trials are replicable and directly comparable. Coupled with stated preference feedback, participants' observed preferences and behavior provide a comprehensive understanding of the impacts of proposed design alternatives. This research presents a case study of pedestrian behavior with three different mid-block crossing safety treatments modeled within a one-to-one scale IVE replication of a real-world location in Charlottesville, Virginia. The three safety treatments consider both passive and active collision avoidance designs and technologies, including (1) the existing painted crosswalk, (2) the addition of rectangular rapid flashing beacons (RRFBs), and (3) a pedestrian to everything (P2X) ITS phone application. Additionally, this paper demonstrates a VR simulation experimental design and framework for testing pedestrian safety treatments within naturalistic and replicable IVEs to assess both stated and observed preferences and behaviors of pedestrians. Repeated measures ANOVA indicated changes in both accepted gap size (p = 0.001) and crossing speed (p < 0.001) with alternative safety treatments. Generalized mixed models showed that pedestrians waited for statistically larger gap sizes (p = 0.02) without the assistance of alternative safety technologies (RRFBs and P2X application) and pedestrians crossed the street significantly faster (p = 0.001) without the alternative safety technologies, leading to unsafe dashing behavior. Through post-experiment surveys, it was found that participants perceived the As Built environment to be the least safe of the three treatments and that their sense of risk within the IVE was realistic. Considering both the observed crossing behavior and stated feedback, pedestrians exhibited intentionally unsafe darting behavior without assistive safety technology. This study demonstrates how VR simulation may be leveraged to study both stated preferences and observed behavior for understanding the safety implications of alternative roadway designs, providing a proactive approach for assessing and designing for pedestrian safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

With the development of driving behavior monitoring technologies, commercial transportation enterprises have leveraged aberrant driving event detection results for evaluating crash risk and triggering proactive interventions. The state-of-the-art applications were established based upon instant associations between events and crash occurrence, which assumed crash risk surged with aberrant events. Consequently, the generated crash risk monitoring results merely contain discrete abrupt changes, failing to depict the time-varying trend of crash risk and posing challenges for interventions. Given the multiple types of aberrant events and their various temporal combinations, the key to depict crash risk time-varying trend is the analysis of multi-type events' temporal coupling influence. Existing studies employed event frequency to model combined influence, lacking the capability to differentiate the temporal sequential characteristics of events. Hence, there is an urgent need to further explore multi-type events' temporal coupling influence on crash risk. In this study, the temporal associations between multi-type aberrant driving events and crash occurrence are explored. Specifically, a contrastive learning method, fusing prior domain knowledge and empirical data, was proposed to analyze the single event temporal influence on crash risk. After that, a novel Crash Risk Evaluation Transformer (RiskFormer) was developed. In the RiskFormer, a unified encoding method for different events, as well as a self-attention mechanism, were established to learn multi-type events' temporal coupling influence. Empirical data from online ride-hailing services were employed, and the modeling results unveiled three distinct time-varying patterns of crash risk, including decay, increasing, and increasing-decay pattern. Additionally, RiskFormer exhibited remarkable crash risk evaluation performance, demonstrating a 12.8% improvement in the Area Under Curve (AUC) score compared to the conventional instant-association-based model. Furthermore, the practical utility of RiskFormer was illustrated through a crash risk monitoring sample case. Finally, applications of the proposed methods and their further investigations have been discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)