Accident; analysis and prevention最新文献

Future automated vehicles (AVs) are anticipated to feature innovative exteriors, such as textual identity indications, external radars, and external human-machine interfaces (eHMIs), as evidenced by current and forthcoming on-road testing prototypes. However, given the vulnerability of pedestrians in road traffic, it remains unclear how these novel AV appearances will impact pedestrians' crossing behaviour, especially in relation to their multimodal performance, including subjective perceptions, gaze patterns, and road-crossing decisions. To address this gap, this study pioneers an investigation into the influence of AVs' exterior design, in conjunction with their kinematics, on pedestrians' road-crossing perception and decision-making. A video-based eye-tracking experimental study was conducted with 61 participants who were exposed to video stimuli depicting a manipulated vehicle approaching a predefined road-crossing location on an unsignalized, two-way road. The vehicle's kinematic pattern was manipulated into yielding and non-yielding, and its external appearances were varied across five conditions: with a human driver (as a conventional vehicle), with no driver (as an AV), with text-based identity indications, with roof radar sensors, with dynamic eHMIs adjusted to vehicle kinematics. Participants' perceived clarity, crossing initiation time (CIT), crossing initiation distance (CID), and gaze behaviour during interactions were recorded and reported. The results revealed that AVs' yielding patterns play a dominant role in pedestrians' road-crossing decisions, supported by their subjective evaluations and CID. Furthermore, it was found that both textual identity indications and roof radar sensors had no significant effect on pedestrians' CIT and CID but did negatively impact their visual attention, as evidenced by heightened fixation counts and prolonged fixation durations. In contrast, the deployment of eHMIs helped mitigate the visual load and perceptual confusion associated with AV's identity features, expedite road-crossing decisions in terms of both time and space, and thus improve overall communication efficiency. The practical and safety implications of these findings for future external interaction design of AVs are discussed from the perspective of vulnerable road users.

This research investigates the effectiveness of nudge presentation on Hazard Perception (HP) during a computerized Hazard Perception Test (HPT). Three types of nudges were examined: Reminder, Social Norm, and Negative Reinforcement. Their effects on drivers' reaction times, hazard misidentifications (errors), and hazard recognition failures (misses) were analyzed. Additionally, the study explored how demographic and personality factors relate to individual differences in nudge responses. Results indicated that nudge presentation, regardless of type, improved reaction times and reduced errors. Reduction in errors was uniquely associated with personal characteristics, showing a positive correlation with age. Specifically, female participants and individuals low in conscientiousness exhibited fewer errors following the Social Norm nudge, while males and highly conscientious individuals showed reduced errors after the Reminder nudge. However, misses were unaffected by nudge presentation. All tested dependent variables were influenced by the order of hazard presentation, reflecting both contextual and nudge presentation effects. To further investigate the order's impact, a follow-up study examined specific hazards sensitive to nudge presentation. Findings revealed that some hazards were more influenced by nudge/contextual factors, while others were unaffected, highlighting the need to consider complex contextual dynamics in HP research. Overall, the study supports the conclusion that nudge presentation can positively influence HP without distracting drivers, offering a promising strategy for improving road safety.

Autonomous driving systems (ADS), leveraging advancements in learning algorithms, have the potential to significantly enhance traffic safety by reducing human errors. However, a major challenge in evaluating ADS safety is quantifying the performance uncertainties inherent in these black box algorithms, especially in dynamic and complex service environments. Addressing this challenge is crucial for maintaining public trust and promoting widespread ADS adoption. In this work, we propose a Polynomial Chaos Expansion (PCE) approach, utilizing High Definition (HD) maps to quantify positional uncertainties from an ADS object detection algorithm. The PCE-based approach also offers the flexibility for online self-updating, accommodating data shifts due to changing operational conditions. Tested in both simulation and real-world experiments, the PCE method demonstrates more accurate uncertainty quantification than baseline models. Additionally, the results highlight the importance and effectiveness of the self-updating capability, particularly when encountering weather changes.

Accurate prediction and causal analysis of road crashes are crucial for improving road safety. One critical indicator of road crash severity is whether the involved vehicles require towing. Despite its importance, limited research has utilized this factor for predicting vehicle towing probability and analyzing its causal factors. This study addresses this gap by predicting the probability of vehicle towing in road crashes based on road scene features and identifying key causal factors. Utilizing the Transportation Injury Mapping System (TIMS) dataset from California, USA, encompassing 12 years, 14 relevant features, and over 2 million road crash records, research team developed a prediction model using advanced gradient boosting techniques. Our model outperforms Random Forest, GBDT, and XGBoost in predictive accuracy. Employing the Shapley Additive Explanation (SHAP) method, researchers elucidate seven key factors influencing towing necessity. These findings introduce a novel predictive approach and offer valuable insights for road crash risk assessment and road safety planning.

Understanding the impacts of traffic crashes is essential for safety management and proactive safety protection. Current studies often hold the assumption of linearity and spatial dependence, which may lead to underestimated results. To address these gaps, this study considers both nonlinear and spatiotemporal spillover effects to explore the intricate relationships between vehicular crashes and their influencing factors at a macro level. Spatiotemporal spillover effects are captured by creating exogenous variables from neighboring zones and their historical status through a geographically and temporally weighted method. Then, the extracted spillover factors are combined with factors from internal zones to construct independent variables. Their nonlinear characteristics are modeled by the gradient boosting decision trees model and interpreted through accumulated local effect plots. A case study was conducted in New York City spanning four years from 2016 to 2019, considering six categories of influencing factors: street view imagery, exposure, land use, points of interest, traffic network, and socioeconomic attributes. The experimental results demonstrate that model performance is improved by incorporating nonlinear and spatiotemporal spillover effects. Additionally, the proposed model highlights the significant nonlinear effects of factors including mixed land uses, sidewalks, and junction density, and emphasizes the presence of spatiotemporal spillover effects, such as building density, bike parking density, and education attainment. These findings offer insightful implications for transportation practitioners and policymakers to devise safety countermeasures and policies, emphasizing the importance of collaboration across neighboring urban regions.

Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environment design also has an impact on autonomous vehicles (AVs), resulting in that current evaluation models on road readiness for AVs (RRAV) do not consider road environment aesthetics. Therefore, this study aims to explore the relationship between road environment aesthetics and risky driving behavior of AVs (RDBAV) and propose an RRAV evaluation model from the new perspective of road environment aesthetics. Using real autonomous driving data, 1,491 longitudinal RDBAV events and 225 lateral RDBAV events are acquired together with corresponding road environment images. A novel quantitative model of road environment aesthetics is developed and 38 relevant feature variables are extracted from four aspects, including Naturalness, Vividness, Variety, and Unity. Then, an explainable machine learning that combines XGBoost (eXtreme Gradient Boosting) with SHAP (SHapley Additive exPlanation) is employed to establish an evaluation model of RRAV, by treating the occurrence of RDBAV as the dependent variable and feature variables of road environment aesthetics as independent variables. The results show that this XGBoost-based RRAV evaluation model performs better than other commonly-used methods, with accuracies of 96.9% and 91.8% for longitudinal and lateral RDBAV prediction, respectively. Due to the advantages of SHAP, the influence degrees of aesthetic features of road environments on RDBAV are calculated and explained based on global and individual feature contributions. In addition, a random parameters multinomial logit model with heterogeneity in means and variances reveals that the indicator of left visual curve length in the "middle scene" and the indicator of dominant color have significant heterogeneity for the analyses of longitudinal RDBAV. The findings of this study might contribute to the accurate evaluation of RRAV from the new viewpoint of aesthetics, the development of human-like visual perception systems of AVs, and the optimization of aesthetics-based road environment design.

Background: Acknowledging the significance of both subjective and objective safety in promoting cycling, there is a need for effective measures aimed at improving cycling skills among a broader population. Hence, the aim of the current study is to evaluate and investigate the impact of online cycling training targeted at adults.

Methods: An online cycling training consisting of three modules was developed to train safe behaviour in seven prototypical safety-relevant situations. 10,000 individuals were invited to participate, with 700 individuals completing the training. The effectiveness of the training was evaluated using a mixed-methods approach combining self-report measures with behavioural measures. Self-report measures were collected using four items of the Cycling Skills Inventory and knowledge-based questions. On a behavioural level, effectiveness was investigated using a virtual reality cycling simulator.

Results: Participants' self-reported cycling skills were evaluated before and after participation in the online training. Three out of four self-reported skills (i.e. predicting traffic situations, showing consideration, knowing how to act) improved on average, across participants. Moreover, participants who cycle less frequently benefited more from the training as they indicated their ability to recognise hazards, to predict traffic situations and to know how to appropriately after completion of the online training. Finally, all participants indicated that they felt more comfortable while cycling after completing the training. In the training evaluation, it was found that the treatment group navigated through traffic more safely on a behavioural level, and/or possessed the required knowledge-based skills in three out of five evaluated situations.

Conclusion: These promising findings indicate that online cycling training is one potential avenue to develop cycling skills within a target audience of adult cyclists: not only on a knowledge level, but also on a behavioural level. Notwithstanding limitations, we conclude that an online cycling training can contribute to safer cycling and the promotion of cycling in general.

Walking is the primary means of mobility and a daily activity for the elderly. Despite the need to ensure pedestrian safety given their physical limitations, elderly pedestrian traffic accidents in South Korea occur at a rate 7.7 times higher than in OECD member countries. In preparation for an aging society, there is a growing need to create a safe walking environment for the elderly. This study focuses on Seoul, analyzing the factors that compromise pedestrian safety for the elderly and identifying the characteristics of vulnerable areas. By using elderly pedestrian traffic accident data provided by the Road Traffic Authority and applying factors influencing accident occurrence to the MaxEnt model, the study identified priority elements for ensuring pedestrian safety. Additionally, the study predicted the regional vulnerability of elderly pedestrian accidents with the increasing elderly population in the future and reviewed possible measures to mitigate the risks. The study indicates that areas where elderly pedestrian safety is vulnerable tend to have lower budget allocations for road management, suggesting a need for future policy support. The prediction of elderly pedestrian accident occurrences through this study is expected to be useful in identifying areas with vulnerable pedestrian safety in Seoul, which can be utilized in prioritizing road improvement projects.

There has been an increase in the use of the extreme value theory (EVT) approach for conflict-based crash risk estimation and its application such as conducting the evaluation of safety countermeasures. This study proposes a cross-sectional approach for evaluating the effectiveness of a right-turn safety treatment using a conflict-based EVT approach. This approach combines traffic conflicts of different sites at the same period and develops the generalized extreme value (GEV) models. It introduces treatment as a dummy variable for estimating the treatment effects and adds traffic-related and conflict severity-related variables to account for unobserved confounding factors between sites. The approach was applied to a case of right-turn safety treatment at two signalized intersections in Nanjing, China. Conflict indicators (i.e., TTC, PET) and potential influencing factors of E-bike-heavy vehicle (EB-HV) right-turn interactions were extracted from aerial video data. A series of GEV models were developed considering different combinations of covariates and their link to the model parameters. Moreover, site GEV models were developed separately for each site to compare the treatment effects across different models. Based on the best-fit models, the results indicate significant safety improvements after implementing the right-turn safety treatment. In addition, the results also show that the cross-sectional GEV models indicate a significant reduction in the number of high-severity conflicts and lowering overall crash risk attributed to the treatment highlighting the applicability of the GEV cross-sectional models in evaluation safety treatments.

Due to the substantial mass disparity between trains and highway vehicles, crashes at Highway-Rail Grade Crossings (HRGCs) are often severe. Therefore, it is essential to develop systematic frameworks for allocating federal and state funds to improve safety at the highest-risk grade crossings. Common techniques for hazard prioritization at HRGCs include the hazard index and the collision prediction formula. A few research projects and state departments of transportation (DOTs) have employed hybrid models that integrate crash hazard indices with prediction models to create comprehensive safety decision-making frameworks. In addition, ranking grade crossings based on their forecasted crash severity likelihood remains largely unexplored, partly due to the complexity of integrating crash severity outputs with hazard indices. This research introduces a new mixed hazard ranking model, the Analytic Hierarchy Process Hazard Index (AHP-HI), which serves as a decision-making tool for ranking grade crossings based on their potential for crash severity. The AHP-HI model combines the analytic hierarchy process (AHP) and the competing risk model (CRM), a prediction model that estimates the likelihood of crash severity for crossings. Risk analysis using the AHP-HI model categorizes public grade crossings in North Dakota into four risk levels, with 4.73% of the crossings identified as high risk.