Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

Zhu, Tao; Zhu, Zishuo; Zhang, Jie; Yang, Can

doi:10.3390/ijerph182111131

Cited by 4 publications

(4 citation statements)

References 52 publications

(89 reference statements)

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“…The peak time indicator increased the probability of a fatal crash and the probability of an injury crash for electric motorcycles by 15.94% and 5.38%, respectively. The peak times indicators may increase the likelihood of serious crashes involving electric motorcycles because of the higher traffic volume and more complex traffic scenarios compared with flat times ( 36 ). The riding speed of electric motorcycles is greater than that of electric bicycles, so the peak time indicator is only statistically significant for electric motorcycle crashes.…”

Section: Resultsmentioning

confidence: 99%

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Wang

Jin

Chen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Exploring the heterogeneity of factors influencing the severity of electric bicycle crashes and electric motorcycle crashes can help target accident prevention policies to improve traffic safety. Therefore, this paper establishes a mean heterogeneity random parameter logit model using crash data from 2016 to 2020 in Guangxi to explore the different factors influencing the severity of crashes involving electric motorcycles and electric bicycles. The results show that the key influences on crash severity differ in electric motorcycle crashes and electric bicycle crashes. At the same time, some common factors affect the two types of crashes to different degrees. In addition, the complex interaction effects of unobserved heterogeneity were considered to explore the random parameters of the two types of crashes. The effect of unobserved heterogeneity on the distribution characteristics of the random parameters was then determined. For example, in electric motorcycle crashes, street lighting at night has a random parameter characteristic. The likelihood of serious crashes decreased when both street lighting at night and vehicle left turn were involved, and decreased when both street lighting at night and no signal control were involved. In electric bicycle crashes, large trucks have a random parameter characteristic. The likelihood of serious crashes increased when both large trucks and motor vehicle lights not turned on were involved, and increased when both large trucks and visibility less than or equal to 200 meters were involved. The results provide a basis for improving the road safety of electric motorcycles and electric bicycles.

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Section: Resultsmentioning

confidence: 99%

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Wang

Jin

Chen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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show abstract

“…In the study of [29], the multivariate random-parameters Tobit model was applied to analyze the accident rate by road injury severity, and in the study of Ijaz et al [30], factors that affect the accident severity of motorcyclists were derived by considering variables with heterogeneity. Existing studies have used the random parameter methodology to argue that traffic accidents are mainly caused by drivers' traffic behavior, such as speeding and changing lanes, and these factors have heterogeneity, so they show various influences according to road sections or temporal characteristics [20,22,31].…”

Section: Literature Reviewmentioning

confidence: 99%

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Kim,

Hong

2023

Sustainability

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As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

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“…2,3 Motor vehicles and conventional bicycle users are changing their shortdistance travel patterns and there are 59 electric bicycles per 100 households in China by 2019. 4 Electric bicycles not only bring convenience to the riders' lives but also bring serious pressure to the efficiency of mixed traffic and traffic safety. 5 Electric bicycles significantly differ from conventional bicycles both in terms of traffic characteristics and microbehavioral characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…2,3 Motor vehicles and conventional bicycle users are changing their short-distance travel patterns and there are 59 electric bicycles per 100 households in China by 2019. 4…”

Section: Introductionmentioning

confidence: 99%

Risk prediction and factor analysis of rider’s injury severity in passenger car and E-bike accidents based on interpretable machine learning

Wei

Zhu

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

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Casualties and property losses caused by the passenger car and electric bicycle crash accidents increased year by year. Assessment of the relevant risk factors of injury severity in passenger car and electric bicycle crashes could help to mitigate crash severity. This study uses an emerging machine learning method to predict the relationship between the risk factors and bicyclist accident injury severity in passenger car-electric bicycle collision accidents. The model’s performance is compared and evaluated based on accuracy, precision, recall, F1-Score, area under curve (AUC), and receiver operating characteristic curve (ROC). An interpretable machine learning framework Shapley additive explanations (SHAP) is used to further analyze the relationship between risk factors and bicyclist injury severity. It is found that we can adopt the light gradient boosting machine (LightGBM) algorithm after hyper-parameter optimization to get the highest accuracy (94.85%), precision (95.2%), recall (94.9%), F1-Score (95%), and AUC (0.993) based on the accident data of electric bicycles and passenger cars in the China in-depth accident study dataset from 2014 to 2018. The model can be used to assess new accident cases based on the model learning rate. There are some new findings in the aspects of bicyclists’ physical factors and electric vehicle characteristics. The throwing distance of the bicyclist has a positive impact on the injury severity. The bicyclist is more likely to suffer more serious injuries in a crash accident when the bicyclist is male, or shorter. Electric bicycles have a smaller handlebar width. In general, the lower handlebar height is, or lower saddle height is, the more serious the bicyclist’s injury is. Safety training for drivers can help reduce the injury severity in crash accidents and improve traffic safety.

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Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

Cited by 4 publications

References 52 publications

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

Risk prediction and factor analysis of rider’s injury severity in passenger car and E-bike accidents based on interpretable machine learning

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