Accident; analysis and prevention最新文献

In recent years, the growth of motorcycle-based ride-hailing and delivery services has led to an increase in traffic crashes involving these riders. Previous studies have indicated that the behavior of ride-hailing and delivery riders is influenced by work demands and individual characteristics. However, the extent to which risky riding behaviors depend on the type of riding and the interaction between road traffic context and risky behaviors remains unclear. Addressing these gaps, this study investigates factors influencing risky behaviors among motorcycle riders in Hanoi, Vietnam. By examining various rider traits (such as rider type, gender, and age) and aspects of the road traffic environment (such as police presence, number of road lanes, and weather), we aim to understand their contribution to risky riding behaviors. Through the observation of 9164 motorcycle riders (i.e., delivery, ride-hailing, and private motorcycle riders) at 31 intersections and decision tree analysis, the study underscores the significant impact of rider type on risky behaviors. Key findings include a higher tendency for both delivery riders and ride-hailing riders to run red lights, neglect to use turn signals, and the notable distraction of mobile phone use. Additionally, private riders are found to show a higher incidence of not wearing helmets even in locations with a police presence. These findings highlight the critical need for strategies to enhance road safety for all motorcycle riders. However, it is essential to recognize that the reasons behind risky behavior vary across different groups of motorcycle riders, from private to commercial riders. Therefore, we need more targeted strategies that address the specific factors influencing each group to effectively improve road safety for all.

In the context of future traffic systems, where automated vehicles (AVs) coexist with human-driven vehicles (HVs), ensuring road safety is of utmost importance. Existing safety assessment methods, however, are inadequate for the complex scenarios presented by mixed traffic conditions. These methods often fail to distinguish sufficiently between AVs and HVs, leading to inaccuracies in safety evaluations. To address these issues, this paper highlights the shortcomings of current surrogate safety measures (SSMs) in mixed traffic contexts and introduces a novel SSM, the Weighted Combination of Spacing and Speed Difference Rates (WS²DR). We propose a comparative analysis method to validate the effectiveness of WS²DR and to establish its safety threshold. Experiment results reveal that WS²DR outperforms traditional metrics such as time-to-collision and deceleration rate to avoid crashes, in terms of adaptability to both homogeneous and heterogeneous traffic environments and the detection of risk levels across a wider range of traffic conditions. Additionally, the paper presents a sophisticated mixed traffic modeling approach that accounts for different characteristics of AVs and HVs, incorporating factors such as errors of estimating the motion of other vehicles and the extended reaction time of HVs, as well as the perceptual and cooperative-active control capabilities of AVs. The results of the comparison analysis underscore the critical importance of considering the differences between AVs and HVs in modeling for accurate safety evaluations of mixed traffic. Simulation experiments confirm the positive impact on safety with increased AV penetration rates, emphasizing the necessity of employing refined modeling and safety assessment metrics to capture the full benefits of AV integration.

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.

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.

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.

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.

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).

The shared control authority between drivers and the steering system may lead to human–machine conflicts, threatening both traffic safety and driving experience of collaborative driving systems. Previous evaluation methods relied on subjective judgment and had a singular set of evaluation criteria, making it challenging to obtain a comprehensive and objective assessment. Therefore, we propose a two-phase novel method that integrates eye-tracking data, electromyography signals and vehicle dynamic features to evaluate human–machine conflicts. Firstly, through driving simulation experiments, the correlations between subjective driving experience and objective indices are analyzed. Strongly correlated indices are screened as the effective criteria. In the second phase, the indices are integrated through sparse principal component analysis (SPCA) to formulate a comprehensive objective measure. Subjective driving experience collected from post-drive questionnaires was applied to examine its effectiveness. The results show that the error between the two sets of data is less than 7%, proving the effectives of the proposed method. This study provides a low-cost, high-efficiency method for evaluating human–machine conflicts, which contributes to the development of safer and more harmonious human–machine collaborative driving.

The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.