Risky behaviors, psychological failures and kinematics in vehicle-to-powered two-wheeler accidents: Results from in-depth Chinese crash data

Accustomed or Regulated: Influencing factors of two-wheeler riders' illegal Lane-Transgressing behavior when overtaking

Illegal lane-transgressing is a typical aberrant riding behavior of riders of two-wheelers, i.e., motorcycles, bicycles, and e-bikes, which is highly frequent in accident reports. However, there is insufficient attention to this behavior at present. This study aims to explore the socio-psychologic factors that influence the illegal lane-transgressing behavior of two-wheeler riders when overtaking. For this purpose, a questionnaire was first composed. The questionnaire included the behavioral intention of two-wheeler riders towards illegal overtaking behavior and five influencing factors: safety knowledge, descriptive norms, injunctive norms, perceived behavior control, and risk perception. Second, a survey was conducted on different two-wheeler riders in Xi'an. Third, various types of two-wheelers were analyzed jointly and separately by structural equation models and analyses of variance. Results show that e-bike riders were more similar to motorcycle riders in behavioral intentions, with their risk perception weaker than other riders. Descriptive norms and perceived behavior control played the most significant roles in the structural equation model. It was also found that two-wheeler riders with a car license had better traffic safety performance. Based on the above results, it is recommended that attention be paid to illegal lane-transgression in the process of law enforcement and education, and a higher level of safety training should be provided for two-wheeler riders.

Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users

Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.

Exploring the differences in rider injury severity in vehicle-two-wheelers accidents with dissimilar fault parties

Objective: The division of responsibility in vehicle-two-wheelers accidents reflects the extent to which different fault parties contributed to the occurrence of the accident, with significant differences in the injuries sustained by the riders in accidents where diverse parties were primarily responsible. We want to explore the difference in the severity of injury of riders in different fault parties of accidents so that we can make targeted protection improvements.Methods: In this study, three generalized ordered logit models were established for the total sample (n = 1204), the sample with drivers as the primary fault party (n = 607), and the sample with riders as the primary fault party (n = 597), respectively, to explore the differential impact factors on rider injury severity in vehicle-two-wheelers accidents involving different fault parties. Inter-group difference tests were conducted on the mean rider injury severity caused by differential factors in different accidents. Combining the impact effect trends and mean differences in the model, the differences in rider injury severity in accidents involving different fault parties were analyzed from the standpoints of human, vehicle, and road factors.Results: It was found that the effects of curve on injury severity was sheerly opposite in accidents with different fault parties and that factors, such as visual obstruction, road surface condition, gender, and helmet wearing differed in their effects on rider injury severity under different fault parties accidents. This reveals the driving tendencies and states of both parties in different environments.Conclusion: Based on the differential impact factor analysis and rider injury characteristics in accidents involving different fault parties, suggestions for improvement were made from the perspectives of road facilities, and safety awareness of drivers and riders, which are beneficial for improving rider safety and providing a theoretical reference for future regulations on liability allocation.

Comparison of injury severity between E-bikes-related and other two-wheelers-related accidents: Based on an accident dataset

This study aims to compare the accident injury severity of e-bikes with that of other types of two-wheelers based on accident data and to analyze the factors influencing them. Using 1015 police accident records from Zhangjiakou City in 2020 and 2021, the accident injury severity of e-bikes was firstly compared with that of other two-wheelers based on five levels of accident injury severity classified according to the records. Two ordered Probit regression models were secondly used to compare the factors influencing the accident injury severity of e-bikes with that of other two-wheelers and the magnitude of their effects. At the same time, the contributions of each influential factor to the degree of accident injury of two-wheelers were estimated with the assistance of classification trees. Results show that e-bikes are closer to bicycles than motorcycles in terms of injury severities and the factors influencing them, in which the factors "accident configuration," "division of responsibility for the accident," and "collision with a heavy vehicle or four-wheeled vehicle" are significant. Based on the findings, potential measures are suggested to reduce e-bike accident casualties, such as improving rider education, ensuring speed limit enforcement, promoting safety equipment wearing, and making road design friendly to non-motorized and elderly riders. The results of this study can provide an essential reference for traffic management and rider education measures on e-bikes.

Risk of motorcycle collisions among patients with type 2 diabetes: a population-based cohort study with age and sex stratifications in Taiwan

AIMS

To investigate the overall and sex-age-specific absolute and relative risks of motorcycle collisions at road traffic accidents among patients with type 2 diabetes.

METHODS

A cohort study in Taiwan was conducted by following 989,495 patients with type 2 diabetes and the same number of matched controls recruited between 2010 and 2012 to the end of 2016. Collision events by motorcycle driver victims were identified from the Police-reported Traffic Accident Registry. Overall and sex-age-specific incidence rates of collision involving motorcycle driver victims were estimated under Poisson assumption. The Cox proportional hazard regression models were performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of collision in association with type 2 diabetes.

RESULTS

Over an up to 7 years of follow-up, patients with type 2 diabetes had a higher incidence rate of motorcycle collision than controls at 1.16 and 0.89 per 100 person-years, respectively, which represented a significantly elevated HR of 1.28 (95% CI 1.27-1.30) after adjusting for potential confounders including various diabetic complications. The elevated HR was similarly seen in both men and women patients, and was significantly decreasing with increasing age regardless of sex. Little evidence supported the dose-response relationship between duration of type 2 diabetes and motorcycle collision risk.

CONCLUSIONS

After adjustment for common diabetic complications and comorbidities that could impair driving performance, patients with type 2 diabetes still suffered from increased risk of motorcycle collisions, regardless of sex, but was more evident in younger than in older patients.

Causation analysis of crashes and near crashes using naturalistic driving data

Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework's road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.

Autonomous driving testing scenario generation based on in-depth vehicle-to-powered two-wheeler crash data in China

A reliable critical-scenario-based safety assessment of autonomous vehicles in China requires a thorough understanding of complex crash scenarios in Chinese background traffic. Based on actual crashes between a vehicle and a powered two-wheeler (PTW) in China, this study generated the autonomous driving testing scenarios from functional, logical and concrete levels. First, 239 video-recorded crash cases were selected from the China In-depth mobility Safety Study - Traffic Accident (CIMSS-TA) database. Using the k-medoids clustering method, six functional scenarios were generalized according to seven crash characteristics (time of day, road type, road surface, obstruction, motion of vehicle, motion of PTW, relative moving direction and position of PTW with respect to vehicle), which contained two straight road scenarios, two T-junction scenarios and two intersection scenarios. Then, using a trajectory analysis program written by Python, the dangerous time instant of each crash was extracted based on the relative trajectory. According to five dynamic parameters of dangerous time instant, namely vehicle velocity (Vehicle_V), PTW X'-coordinate velocity (PTW_VX'), PTW Y'-coordinate velocity (PTW_VY'), PTW X'-coordinate relative position (PTW_LocX') and PTW Y'-coordinate relative position (PTW_LocY'), a crash trigger scheme was built to remain a case challenging when the involved vehicle is replaced by an autonomous vehicle with completely different maneuvers. Using the kernel density estimation (KDE), the logical scenarios were evolved by calculating the distribution of these dynamic parameters in each cluster. The results showed that there were differences in the distribution of dynamic parameters between six functional scenarios. For instance, the Vehicle_V in the scenario where a vehicle turning right impacts with a right/right rear PTW traveling straight ahead was higher than that in the scenario where a vehicle changing to the left lane impacts with a left/left rear PTW traveling straight ahead, with ranges of (10 km/h, 30 km/h) and (5 km/h, 15 km/h), respectively. Finally, considering the correlation of dynamic parameters, a virtual crash generation approach based on the independent component analysis (ICA) representing the original crashes with independent parameters was proposed to obtain sufficient concrete testing scenarios. The results showed that the statistical characteristics of virtual crashes were consistent with those of original crashes. Therefore, the virtual crash generation approach was effective. And a concrete crossing testing scenario with the crash trigger conditions of Vehicle_V = 26.272 km/h, PTW_VX' = 15.567 km/h, PTW_VY' = -1.670 km/h, PTW_LocX' = -27.265 m and PTW_LocY' = 52. 149 m was especially demonstrated. This study provides a theoretical basis for generating autonomous driving testing scenarios and data support for establishing relevant testing schemes tailored to the traffic environment in China.

The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

The driver is one of the most important factors in the safety of the transportation system. The driver's perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver's brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver's brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.

A Focus Group Study to Explore Risky Ridership among Young Motorcyclists in Manipal, India

Road crash injuries have emerged as a significant public health issue in many low and middle-income countries in recent years. In India, motorized two-wheelers comprise 70% of the vehicle population and are considered the most vulnerable road users. Road crash injury is common among the young-aged population leading to premature deaths. It is essential to understand risky riding behaviors to develop accurate, evidence-based risk reduction programmes that fit the target population’s characteristics and the intervention setting. The current study aims to improve the understanding of the typical characteristics of motorcycle crashes among young riders in India, primarily focusing on the prevalence and role of risky riding behaviors. Five focus group discussions with eight to ten participants in each group (N = 35) were conducted in Manipal, in the Karnataka state of Southwestern India. A thematic analysis was completed using MAXQDA software to identify, analyze, and report on themes within the data. Speeding, riding under the influence of alcohol, and the poor maintenance of motorcycles were indicated as leading causes of crashes. Furthermore, using mobile phones while riding, violations of the traffic rules, and helmet non-use were identified as other risky behaviors among young riders. Future research can be taken up in other settings for the target population. Generational awareness with the involvement of young riders, government authorities, university officials, and the Regional Transport Office can be initiated. Engaging young riders, government authorities, university officials, and the Regional Transport Office through behavioral interventions such as persuasive communication techniques, an active experimental approach (such as the use of a simulator), and regulating the licensing procedure can reduce the number of road crashes.

A novel approach to investigate effects of front-end structures on injury response of e-bike riders: Combining Monte Carlo sampling, automatic operation, and data mining

Transportation safety related to e-bikes is becoming more problematic with the growing popularity in recent decade years, however, rare studies focused on the protection for e-bike riders in traffic accidents. This paper aimed to investigate the relationship between vehicle front-end structures and rider's injury based on a novel approach including modeling, sampling, and analyzing. Firstly, a parametrized model for front-end structures of the vehicle was developed with nine parameters to realize the standardization of multi-body models of car to e-bike collision considering three stature riders and different impacting velocities. Secondly, a framework, combining Monte Carlo sampling for twelve initial variables and automatic operation for 1000 impact simulations, was built to obtain valid results automatically and then to construct a big dataset. Finally, according to the sensitive variables to riders' vulnerable regions, the decision tree algorithm was further adopted to develop the decision or prediction model on injuries. The novel approach achieved the stochastical generation of vehicle shapes and the automatic operation of multi-body models. The results showed that the rider's head, pelvis, and thighs were more vulnerable to being injured in the car to e-bike perpendicular accidents. The three decision tree models (HIC₁₅, lateral force of pelvis, bending moment of upper leg) were validated to be accurate and reliable according to the confusion matrix with the precision of more than 80% and the receiver operating characteristic curves (ROC) with the under area more than 85%. Based on decision tree models, not only the effects of front-end structural parameters on the corresponding injury but also the interaction mechanism between various variables can be clearly interpreted. Each route from the same root node to hierarchical middle nodes then to various leaf nodes represented a decision-making process. And the different branches under the same decision node directly illustrated the correlation between variables, which is highly readable and comprehensible. During the safety performance design of front-end structures, the rational value of variables could be decided according to decision routes that resulted in lower injury levels; Even if the accident was inevitable, the collision parameters could be controlled within a certain range for the least injury according to the prediction rules. Based on the novel framework coupling Monte Carlo sampling and automatic operation, it's foreseeable to apply the parametric and standard car-to-e-bike collision models to develop the virtual test system and to optimize front-end shapes for rider's protection.