Underreporting, crash severity and fault assignment of minor crashes in China – a study based on self-reported surveys

Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model

This study investigates the impacts of various factors on e-bike riders' injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders' illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers' speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders' injury. Moreover, both drivers and riders' illegal behaviour leads to an increased injury, while their avoidance behaviour before crashes can protect riders. In addition, types of visual obstacles, accidents occurring at night, large vehicles' involvement, and the application of sunshade canopies by riders increased the probability of severe injury, while helmet use can protect riders in accidents with motor vehicles.

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

Introduction

Electric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties.

Methods

For e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool.

Results

Between 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior.

Conclusions

Although e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.

Characterizing the effects of contributing factors on crash severity involving e-bicycles: a study based on police-reported data

Mitigating e-bicycle crash occurrence has become a great challenge across the world. It is of paramount importance for improving traffic safety to characterize the relationship between e-bicycle crash injury severities and contributing factors. This study positions itself at clarifying the roles of the factors in e-bicycle crashes from time, space, road, environment, rider and object characteristics. The partial proportional odds (PPOs) model as well as its elasticity analysis was employed to identify the influences based on 15,138 police-reported e-bicycle crashes in Shangyu District of Shaoxin City, China. The results evidenced that there were 12 factors having significant effects. Especially, the results emphasized the greater influences of rider gender, age, object hit and road type. Their maximum of the absolutes of elasticities was greater than 24%. Increased crash severity was associated with females, younger riders, and higher speed collisions. However, the remaining significant variables had minor effects (no more than 10%). The findings provide meaningful insights for advancing e-bicycle development, when making related policies and prioritizing safety countermeasures.

Road safety situation of electric bike riders: A cross-sectional study in courier and take-out food delivery population

OBJECTIVE

Almost all courier and take-out food delivery riders in China use electric bikes as the main transport mode. This study aims to understand their riding behaviors and road traffic injury information of this population.

METHODS

A cross-sectional field study focused on this population was conducted, including roadside observation and face-to-face retrospective street intercept interviews.

RESULTS

Six hundred target delivery riders were observed and 480 were interviewed. The rate of overspeeding was 91.3%, and windshield use during winter was 91.2%. Traffic violation behaviors observed included riding in the motor vehicle lane (32.8%), not waiting behind the white line at a red light (23.3%), and using cell phone when riding (21.2%). Helmet use was significantly more common during the day (73.0%) than at night (64.7%; P = .028). About 46.5% of respondents rode an e-bike more than 8 h per day. In addition, 76.5% of interviewees had been involved in a traffic crash at least once. About 13.9% of crashes happened in motor vehicle lanes and 8.2% on sidewalks. Logistic regression analysis indicated that compared with uninjured riders, injured riders showed significantly greater odds ratios of unsafe behaviors for running red lights (odds ratio [OR] = 1.75) and protective factors for wearing a helmet (OR = 0.56).

CONCLUSIONS

The courier and take-out food delivery population is a vulnerable occupational group and road traffic injuries related to e-bike use require more attention.

Exploring temporal interactions of crash counts in California using distinct log-linear contingency table models

Temporal trait of crashes has huge impact on road crash occurrence and a large proportion of research have considered different time periods to determine the causes and features of crash occurrence or frequency. Compared with other safety studies based on a single time interval, considerably less research has relied on the use of multiple time units, especially for the time intervals of less than one year. The research aims to fill the gap by investigating the temporal distribution of crash counts using multiple time spans including hour, weekday and month. To illustrate the most accurate results possible, both the Chi-square test and Cochran-Mantel-Haenzel tests were employed to explore the independence of various time units based on two-way and three-way contingency tables. Eight contingency table models were developed which can be classified into four groups including Complete Independence, Joint Independence, Conditional Independence and Homogeneous Association. Finally, a set of evaluation criteria were utilized for evaluation of the model performance. The results revealed the significant association existence in all time variables (hour, weekday, month) and the model with both main and all interactive effects of time variables provides best prediction performance. Also, the findings showed that Hour 18, weekdays 1, 6, 7 (Friday and Weekends), and month 8 (August) have the largest number of crash occurrences. It is suggested that both main and interactive effects of time variables should be included for model development, which otherwise might yield misleading information. It is anticipated that research results will benefit the safety professionals with better understanding of the temporal patterns of crashes with different time periods and allow the safety administrators to allocate the safety resources.

Is the front passenger seat always the "death seat"? An application of a hierarchical ordered probit model for occupant injury severity

Although many studies have investigated the correlations between injury severities and seat positions, few researchers explored the correlates of injury severities (e.g., seat positions) within a crash that results in multiple occupant injuries. Therefore, we examine the injury correlates within and between crashes, and study the correlations between seat positions and occupant injury severity by constructing a hierarchical ordered probit model. A total of 20,327 occupant injuries in 16,405 motor vehicle crashes in South Australia (2012 - 2016) are used. The results of this study indicate that the rear left passenger seat is associated with a 7.66% higher chance of getting injured (including moderate and severe injury), and the front left passenger seat is associated with a 2.94% higher chance of getting injured compared with the driver seat. Besides, the higher injury chances for other passenger seats including the rear right and rear middle seats are 4.97% and 4.74%, respectively, compared with the driver seat. Thus, this study offers passengers insightful suggestions about how to protect themselves by choosing the right passenger seat in a vehicle.

Work stress and health problems of professional drivers: a hazardous formula for their safety outcomes

Background

Several empirical studies have shown that professional drivers are a vulnerable occupational group, usually exposed to environmental stressors and adverse work conditions. Furthermore, recent studies have associated work-related stress with negative job performances and adverse health outcomes within this occupational group, including cardiovascular diseases and unsafe vehicle operation.

Objective

The aim of this study was to describe the working conditions and the health status of this occupational group, and to evaluate the association between the Demand-Control model of job stress and their self-reported health and safety outcomes.

Methods

A pooled sample of 3,665 Colombian professional drivers was drawn from five different studies. The Job Content Questionnaire and the General Health Questionnaire were used to measure work stress and self-reported mental health, respectively. Additionally, professional drivers self-reported health problems (hypertension, dyslipidemia, diabetes and overweight) and health-related risky behaviors (smoking and sedentary behavior).

Results

Regarding the Job Demands-Control (JDC) model, it was found that approximately a third part of Colombian professional drivers suffer from high job strain (29.1%). Correlational and multivariate analyses suggest that de JDC model of stress is associated with the professional drivers' mental health, traffic accidents and fines, but not with other physical and behavioral health-related outcomes, which are highly prevalent among this occupational group, such as hypertension, dyslipidemia, diabetes, overweight, smoking and sedentary behavior.

Conclusion

The results of this study suggest that (a) stressful working conditions are associated with health and lifestyle-related outcomes among professional drivers, and (b) that evidence-based interventions are needed in order to reduce hazardous working conditions, job stress rates and their negative impact on the health of this occupational group.