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• W2955287842 abstract "The speeding violation has become a key concern in the traffic safety management, as it increases the risk of traffic crashes, as well as the severity of these crashes. This uncivilized phenomenon is prominent and presents an increasing trend in Wujiang in recent years, which severely endangers the road traffic safety. This study is approved by the Traffic Police Brigade of Wujiang Public Security Bureau and aims to explore the characteristic of the speeding violation behaviour and attempt to make an effective prediction about it. This study proposes a speeding violation type (including type 1 and type 2) prediction method using electronic law enforcement data obtained from the public security administration of Wujiang. Before the prediction, a speeding violation influence factor analysis based on the binary logical regression model is proposed. The binary logical regression analysis identifies that the license plate, season, speeding area, position, and rainfall are the influence factors of Wujiang’s speeding violation. Then a decision tree method is used to predict the speeding violation type according to the influence factors, and from which the speeding violation situations can be determined. The prediction results demonstrate that under the hypothetical conditions, the high speeding violation level (i.e., type 2) tends to occur under high rainfall environment, and the foreign license plate and autumn present a larger probability of high speeding violation level than the local license plate and other seasons (i.e., spring, summer, and winter), respectively. Finally, a model comparison between the proposed method and other tree-based approaches is conducted. The comparison results show that the decision tree method outperforms other methods in prediction performance (including accuracy, precision, recall, and classification error), runtime, and ROC curve, which indicates that the decision tree method is feasible in predicting the speeding violation type of Wujiang. Based on the findings, the traffic managers can macroscopically grasp the speeding violation situation of the whole road networks, which can be referred for making the related polices and taking intervention measures." @default.
• W2955287842 created "2019-07-12" @default.
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• W2955287842 date "2019-06-27" @default.
• W2955287842 modified "2023-10-17" @default.
• W2955287842 title "Speeding Violation Type Prediction Based on Decision Tree Method: A Case Study in Wujiang, China" @default.
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• W2955287842 doi "https://doi.org/10.1155/2019/8650845" @default.
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