Accident; analysis and prevention最新文献

The safety of motor scooters used to deliver food has come under scrutiny due to the growing popularity of food delivery services in Republic of Korea. Policymakers have been tasked with investigating and identifying the factors associated with scooter safety to prevent accidents and develop mitigating strategies. A comprehensive analysis of the components of road traffic influencing the safety of motor scooters has received little attention to date. This study aims to identify the road- and traffic-related factors that affect the safety of such vehicles through GIS-based geographically weighted regression (GWR) analysis. First, it assesses safety by analyzing the riding characteristics of delivery scooters using naturalistic study data, including speed, acceleration, and direction. Second, it evaluates safety through the hazardous riding behavior rate, offering a proactive measure for preventing accidents. Third, it uses GWR analysis to examine safety factors at the scale of the individual road segments (referred to as 'links'), identifying hazardous road segments and proposing customized measures. The results show that number of lanes, signal density, speed limit, and average speed on road segments are key factors influencing motor scooter safety. A thorough interpretation of the geographical regression coefficients for the two most hazardous links suggests useful policy implications. Notably, the effects of speed limits and riding speeds on safety vary by link. We propose effective speed-management strategies by analyzing the relationship between speed limit and the average speed of delivery motor scooters. Our research provides valuable insights on how to improve the safety of delivery motor scooters.

Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insights into the accidents on the read roads where Human-driven Vehicles (HV) coexist. To address such an issue, The New Car Assessment Program (NCAP) is currently in progress, and scenario-based approaches have been spotlighted. Scenario approaches offer a unique advantage by evaluating AV driving safety through carefully designed scenarios that reflect various real-world situations. While most scenario studies favor the data-driven approach, the studies have several shortcomings, including perspectives of data, AI models, and scenario standards. Hence, we propose a holistic framework for generating functional, logical, and concrete scenarios. The framework composes explainable scenarios (X-Scenarios) based on real-driving LiDAR data, and visual trend interpretation using eXplainable AI (XAI). The framework consists of four components as follows: (1) voxelization of LiDAR PCD and extraction of kinematic features; (2) classification of critical situations and generation of attention maps using visual XAI and Vision Transformer (ViT) to generate range values of elements in logical scenarios; (3) analysis of the importance and correlations among input data features using SHapley Additive exPlanations (SHAP) for selecting scenarios based on the most relevant criteria; and (4) composition of AV safety assessment scenarios. X-scenarios generated from our framework involve the parameters of ego vehicles and surrounding objects on the highways and urban roads. With our framework highly trustworthy AV safety assessment scenarios can be created. This novel work provides an integrated solution to generate trustworthy scenarios for AV safety assessment by explaining the scenario selection process.

Pedestrians use visual cues (i.e., gaze) to communicate with the other road users, and visual attention towards the surrounding environment is essential to be situationally aware and avoid oncoming conflicts. However, multi-tasking activities compromise visual attention behaviour. Average Fixation Duration (AFD) was captured in six Areas of Interest (AOI) when engaged in activities like texting, talking, listening to music (LM) and gazing at billboards (GBB) while crossing the road. Quantification of situational awareness is accomplished using Weibull Accelerated Failure Time (AFT) model with AFD as a duration variable. This approach helps to understand ongoing cognitive attention required for the user to process the information conveyed by the AOI. The survival rate obtained from Weibull AFT model is defined as the probability of continuing gaze fixation on an AOI at a given time instance. The study demonstrated thatthe continuation of gaze fixation increased greatly when texting compared to other multi-tasking activities, which was attributed to a decrease in situational awareness. Talking, LM and GBB-involved pedestrians shifted their gaze to another AOI within a maximum of 300 ms, except for vehicle AOI. The LM activity, perceived as less task-intensive and less risky, compensated for their gaze fixation behaviour by spending less time on different AOIs. In addition, billboards near pedestrian crossing locations impact gaze fixation behaviour similar to talking on the phone. The study suggested mitigative policies and strategies to curb distracted walking. Additionally, the aim is to design human-computer interaction-based incident warning systems for real-world situations using augmented reality glasses.

A common and challenging data and modeling aspect in crash analysis is unobserved heterogeneity, which is often handled using random parameters and special distributions such as Lindley. Random parameters can be estimated with respect to each observation for the entire dataset, and grouped across segments of the dataset, with variable means, or variable variances. The selection of the best approach to handle unobserved heterogeneity depends on the data characteristics and requires the corresponding hypothesis testing. In addition to dealing with unobserved heterogeneity, crash frequency modeling often requires explicit consideration of functional forms, transformations, and identification of likely contributing factors. During model estimation, it is important to consider multiple objectives such as in- and out-of-sample goodness-of-fit to generate reliable and transferable insights. Taking all of these aspects and objectives into account simultaneously represents a very large number of modeling decisions and hypothesis testing. Limited testing and model development may lead to bias and missing relevant specifications with important insights. To address these challenges, this paper proposes a comprehensive optimization framework, underpinned by a mathematical programming formulation, for systematic hypothesis testing considering simultaneously multiple objectives, unobserved heterogeneity, grouped random parameters, functional forms, transformations, heterogeneity in means, and the identification of likely contributing factors. The proposed framework employs a variety of metaheuristic solution algorithms to address the complexity and non-convexity of the estimation and optimization problem. Several metaheuristics were tested including Simulated Annealing, Differential Evolution and Harmony Search. Harmony Search provided convergence with low sensitivity to the choice of hyperparameters. The effectiveness of the framework was evaluated using three real-world data sets, generating sound and consistent results compared to the corresponding published models. These results demonstrate the ability of the proposed framework to efficiently estimate sound and parsimonious crash data count models while reducing costs associated with time and required knowledge, bias, and sub-optimal solutions due to limited testing. To support experimental testing for analysts and modelers, the Python package "MetaCountRegressor," which includes algorithms and software, is available on PyPi.

Driving in highway tunnel groups necessitates frequent adaptation to drastic changes in the traffic environment, thereby increasing driving difficulty and risk. This study integrates drivers' preferences for rhythmic information with the inherent rhythmic characteristics of tunnel group structures to propose a new and adaptive method to mitigate driving risks using rhythmic visual guidance (RVG) technology. Unlike traditional visual guidance systems, which often rely on static signals, RVG utilizes dynamic, rhythmically varying cues to capture drivers' attention and improve situational awareness more effectively. By employing principles of fuzzy mathematics, the study quantifies the applicability of various rhythmic forms in visual guidance technology and establishes priority application principles for undulating and staggered rhythms. After verifying the accuracy of the simulation model, the effectiveness of RVG technology in mitigating driving risks in highway tunnel groups was analyzed using lateral offset, driving speed, and vehicle acceleration as evaluation metrics. The findings reveal that RVG technology significantly reduces vehicle lateral offset and enhances drivers' perception and control of tunnel sidewalls and driving trajectories. This effect is particularly pronounced under limited lighting conditions or in large tunnel groups with extended driving distances. Regardless of whether the lighting level is set at 0% or 100% of the standard brightness, the implementation of RVG results in reduced vehicle driving speeds. The variation in the 25th to 75th percentile distribution of driving speeds was insignificant, demonstrating that RVG technology effectively regulates driving speed and is not significantly affected by lighting conditions. Furthermore, when the lighting level is set at 100% of the standard brightness, the 25th to 75th percentile distribution interval of driving speeds is [89.576, 102.416], indicating the highest and least stable driving speeds suggests that blindly increasing tunnel lighting levels not only raises operating costs but may also adversely affect driving safety. This study provides novel insights into applying dynamic visual cues for highway tunnel groups' traffic operation and safety management. It has significant practical engineering value for guiding the low-carbon design of tunnel groups.

Traffic signals, while reducing conflicts within intersections, often lead to stop-and-go behaviors in approaching vehicles, negatively impacting traffic flow in terms of safety, efficiency, and fuel consumption. Aimed at minimizing the traffic oscillations caused by traffic signals through Connected and Autonomous Vehicles (CAVs) and meeting real-time operational needs, this paper proposes a Risk-Based Adaptive Cruise Control (RACC). RACC designs the constraints of approaching a signalized intersection as expected risks, enabling compliance with all constraints while being adaptable to basic road scenarios. Theoretical analysis indicates that RACC, under specific parameter conditions, achieves string stability and overdamped characteristics while maintaining high throughput efficiency. Simulations confirm RACC's sensitivity to risks, allowing it to timely adjust to return to a stable state, thus ensuring platoon safety under high throughput conditions. At signalized intersections, RACC enables CAVs to cross stop lines with smooth trajectories, significantly reducing risk, delays, and fuel consumption for all downstream vehicles. Further simulations demonstrate that RACC significantly reduces average travel time delay and fuel consumption across various traffic volumes and Market Penetration Rates (MPRs), with reductions of up to 87.1% in delays and 54.8% in fuel consumption, showcasing substantial computational efficiency improvements over benchmarks. Furthermore, extending this study to scenarios with higher traffic conflicts, such as multi-lane roads or intersections, while considering the impact of lane-changing behavior, is a promising direction for future research.

Estimating reliable causal estimates of road safety interventions is challenging, with a number of these challenges addressable through analysis choices. At a minimum, developing reliable crash modification factors (CMFs) needs to address three critical confounding factors, i.e., 1) the regression-to-the-mean (RTM) phenomenon, 2) the effect of traffic volume, and 3) the time trend for the occurrence of crashes. The current preferred crash analysis method is the empirical Bayes (EB) before-after analysis but requires complex bespoke analysis and may not be the best performing method. We compare in a simulation experiment various EB methods to a more straightforward negative binomial generalized linear mixed model (NB-GLMM) with an interaction term between treatment group and time for analysing treatment effects in crash data. Data were simulated using two broad scenarios: 1) an idealized randomized controlled design, and 2) a moderately biased site-selection scenario commonly encountered in road safety crash analyses. Both scenarios varied treatment effects, overdispersion, and sample sizes. The NB-GLMM performed best, maintaining type I error rate and providing least biased estimates across most analyses. Most standard EB methods were too liberal or generally more biased, with the exception of the EB method that incorporated a varying dispersion parameter. Incorporating mixed-effects modelling into the EB procedure improved bias. Overall, we found that using a "standard" NB-GLMM with an interaction term is sufficient for crash analysis, reducing complexity compared to bespoke EB solutions. Chosen methods should also be the least biased and possess the marginal error rates under both ideal and selection-bias conditions. Mixed-effects approaches to analysis of road safety interventions satisfy these criteria outperforming standard or other empirical Bayes approaches tested here.

Freeway continuous merging areas in a short distance exist continuous multiple ramps. In these areas, traffic flow and vehicle interactions are more complex, and traffic crashes and congestion are more frequent, which has been a major concern influencing traffic operation of freeways. Active traffic management (ATM) measures can improve traffic efficiency and reduce traffic risks in merging areas. Previous studies have focused on variable speed limit (VSL) control or ramp metering (RM) to address traffic problems in merging areas, whereas the problem of comprehensively ameliorating for traffic risks on mainlines and ramps by coordinating VSL and RM control strategies has rarely been explored. This study introduces a Bi-level Programming Model capable of coordinating controls of traffic risks (e.g., Crash Risk and Congestion Risk) in freeway continuous merging areas. The upper-level model aims to minimize the crash risk, the congestion risk, and vehicle energy consumption by VSL control. While the lower-level model focuses on the ramp control by minimizing the congestion risk and energy consumption of the ramp. Then an extended Cell Transmission Model (CTM) (it is based on VSL and RM control) is utilized to simulate the traffic flow of merging areas, based on which a traffic risk evaluation model and a Bi-level coordinated control model for the continuous merging areas are developed. The results demonstrate the constructed method outperforms other control strategies for improving the safety and efficiency of freeways. Specifically, the proposed control framework in the continuous merging areas of freeways reduces the average crash risk (ACR), average mainline congestion risk (AMCI), and average energy consumption (AEC) by 14.10%, 19.52%, and 8.86%, respectively. The research results could be potentially applied to active and coordinated traffic management of freeways.

Proactive and holistic safety management approaches should consider multi-modal crash risk. Cyclist crash risk should be prioritized given the high-severity of vehicle-cyclist crashes. Cyclist crash risk is difficult to quantify given the sparse nature of cyclist collisions and collisions in general. There is thus a need to develop a more proactive approach for multi-modal road-safety management by leveraging new technologies. This study proposes a conflict-based methodology to estimate cyclist crash risk using autonomous vehicle data, extrapolating from observed conflicts to real-time dynamic crash risk. Using 87 hours of data from an autonomous vehicle dataset from downtown Boston (nuPlan), traffic conflicts were identified. A Bayesian Hierarchical Extreme Value model was created representing driver and cyclist crash risk over short time intervals. This allows for identifying the real-time crash risk of various intersections and mid-blocks, enabling route-level safety metrics. The spatiotemporal characteristics of crash risk were examined in this study. Routes with cyclist facilities were found to be safer for cyclists, on average, than those with shared facilities. However, substantial fluctuations in crash risk were observed at different time intervals, with the shared facilities sometimes being safer than those with painted or buffered bicycle lanes. This highlights the need for real-time safety monitoring. At the user-level, a safest route application was also proposed, allowing for an impedance function to be developed based on real-time crash risk and the comparison of any number of nodes and links along a particular route.

Blind spot collisions are a critical and often overlooked threat to pedestrian safety, frequently resulting in severe injuries. This study investigates the impact of automated vehicles equipped with external human-machine interfaces (eHMIs) on pedestrian crossing behavior and safety, focusing on scenarios where AVs create mutual blind spots between pedestrians and adjacent traffic. A virtual reality experiment with 51 participants simulated crossing situations in front of yielding trucks with obstructed pedestrian visibility, featuring three eHMIs: 'Walk,' 'Don't Walk,' and 'Caution! Blind Spots'. Vehicles within the truck's blind spot exhibited proactive and reactive braking behaviors toward pedestrians. The results indicate that eHMI designs based on color, text, and symbols enhance pedestrian understanding. However, the 'Walk' eHMI, which ignores blind spot risks, may lead to dangerous crossing behaviors. In contrast, the 'Don't Walk' eHMI effectively reduced unsafe crossing behaviors, though yielding trucks sometimes caused pedestrian confusion. The 'Caution! Blind Spots' eHMI increased alertness but was not significantly more effective than the direct 'Don't Walk' instruction. This study provides empirical evidence for integrating dynamic environmental perception and hazard warnings into eHMI designs to raise road users' awareness of blind spots. The findings emphasize the importance of comprehensive strategies, including policy-making, education, and VR-based training, to ensure the effective deployment and public understanding of eHMIs in blind spot environments.