Bus Fleet Accident Prediction Based on Violation Data: Considering the Binding Nature of Safety Violations and Service Violations

Abstract

The number and severity of bus traffic accidents are increasing annually. Therefore, this paper uses the historical data of Chongqing Liangjiang Public Transportation Co., Ltd. bus driver safety violations, service violations, and road traffic accidents from January to June 2022 and constructs road traffic accident prediction models using Extra Trees, BP Neural Network, Support Vector Machine, Gradient Boosting Tree, and XGBoost. The effects of safety and service violations on vehicular accidents are investigated. The quality of the prediction models is measured by five indicators: goodness of fit, mean square error, root mean square error, mean absolute error, and mean absolute percentage error. The results indicate that the XGBoost model provides the most accurate predictions. Additionally, simultaneously considering safety and service violations can improve the accuracy of the model’s predictions compared to a model that only considers safety violations. Bus safety violations, bus service violations, and bus safety operation violations significantly influence traffic accidents, which account for 27.9%, 20%, and 16.5%, respectively. In addition to safety violations, the service violation systems established by bus companies, such as bus service codes, can be an effective method of regulating the behavior of bus drivers and reducing accidents. They are improving both the safety and quality of public transportation.

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

“…In addition, a previous study conducted by Ding et al [33] proposed that the intensity and number of bus road accidents increase yearly. Ding et al's [33] study employed data from the public transportation company of Chongqing Liangjiang; the data was related to road accidents, service and driver safety traffic mistakes from January 2022 to June 2022. Ding et al's [33] study used SVM, XGBoost, BP neural network, extra trees and gradient boosting tree as the prediction framework to investigate traffic safety violations.…”

“…Ding et al's [33] study employed data from the public transportation company of Chongqing Liangjiang; the data was related to road accidents, service and driver safety traffic mistakes from January 2022 to June 2022. Ding et al's [33] study used SVM, XGBoost, BP neural network, extra trees and gradient boosting tree as the prediction framework to investigate traffic safety violations. Furthermore, twenty-seven-point nine percent of traffic accidents were impacted by safety violations, whereas vehicle safety operations accounted for twenty percent of traffic accidents.…”

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

“…Supervised learning is an important training method in machine learning, which is the process of tuning the parameters of a classifier to achieve the required performance using a set of samples of a known class [24,25]. Most scholars analyzing the prediction process tend to compare the performance of different popular machine learning algorithms on test sets, in order to find the optimal prediction model suitable for the respective dataset and the actual problem [26,27]. These prediction methods mainly include linear regression [28,29], support vector machines [30], various neural networks [27], various types of integrated tree modeling algorithms [31,32], improved hybrid models [2], and so on.…”

Research on Active Safety Situation of Road Passenger Transportation Enterprises: Evaluation, Prediction, and Analysis

“…Moreover, when no accident occurs, the safety is actually not exactly the same, and it is difficult to accurately describe the safety when no accident occurs if only accident data is relied upon. Ding et al [ 6 ] found a large correlation between the occurrence of safety violations and traffic accidents. Rahman et al [ 7 ] found that fatigue can have a large impact on traffic safety.…”