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| Prediction of Fake Toll-free Logistic Vehicles Based on Historical Traffic Data |
| LIU Yu-gang1,2,4, ZHENG Shuai1,2, XU Xu-dong1,2, WANG Tian-bi1,2, YE Jin-song3 |
1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
2. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
3. China Academy of Transportation Science, Beijing 100088, China;
4. Institute of Transportation Development Strategy and Planning of Sichuan Province, Chengdu Sichuan 610001, China |
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Abstract At present, the "green channel" policy is fully implemented. However, the detection is relatively lagging, and truck drivers are susceptible to fake the toll-free logistics vehicles (TFLVs), causing huge losses to the operation. In order to improve the accuracy and efficiency of TFLVs inspection, this paper establishes a toll evasion prediction model for fake TFLVs based on the historical TFLVs traffic dataset derived from the highway network toll collection system. First, according to the importance and reliability, we used the data mining technology to differentiate and extract the data attributes. And the spatiotemporal characteristics and other characteristics of fake TFLVs through-traffic entering and exiting toll booths are studied and analyzed based on the preprocessed data. Then use the Borderline-SMOTE oversampling method to balance the pass data set, use the ChiMerge algorithm to discretize continuous attributes. In order to ensure the effective matching and the correlation between the large contribution attributes and the results, correlation item test and collinearity test are used for discrete related attributes. Finally, adopting the processed discrete TFLVs data which passed correlation item test and collinearity test, and using the decision tree to establish the prediction model. The classification results of the escape behavior prediction model and other models are compared by using the historical TFLVs data set. The results show that the accuracy of the escape behavior prediction model proposed in this paper is 83.4%, which is higher than that of the Logistic regression model (61.8%) and the Random forest model (81%).This research model can effectively warn of fake TFLVs, and on the basis of simplifying the inspection process of TFLVs, reducing the probability of the occurrence of fake TFLVs evasion, which has practical application significance.
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Received: 07 February 2021
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| Fund:Supported by the National Natural Science Foundation of China (Nos.51774241, 71704145); Sichuan Youth Science and Technology Innovation Research Team Project under Grant (No.2020JDTD0027); Humanity and Social Science Foundation of Ministry of Education of China (No.18YJCZH138) |
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Corresponding Authors:
LIU Yu-gang
E-mail: liuyugang@swjtu.edu.cn
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2021, Vol. 15 
