Journal of South China University of Technology(Natural Science) >

2023 , Vol. 51 >Issue 7: 120 - 128

DOI: https://doi.org/10.12141/j.issn.1000-565X.220550

Traffic & Transportation Engineering

Reconstruction of Urban Vehicle Path Chain Based on Deep Inverse Reinforcement Learning

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  • 1.College of Civil Engineering and Architecture，Zhejiang University，Hangzhou 310058，Zhejiang，China
    2.Polytechnic Institute，Zhejiang University，Hangzhou 310058，Zhejiang，China
    3.Ocean College，Zhejiang University，Hangzhou 310058，Zhejiang，China
王福建（1969-），男，博士，副教授，主要从事交通流理论、智能交通系统等的研究。

Received date: 2022-08-26

  Online published: 2023-01-19

Supported by

the Key Program of National Natural Science Foundation of China(52131202)

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Abstract

With the improvement of urban traffic monitoring system, a large number of license plate recognition data are stored. This type of data has the advantages of strong temporal continuity, wide spatial range and multiple sample types, which provides an information foundation for studying urban traffic. However, due to the cost and technology in the process of information collection, the collected license plate data are discontinuous in time and space domains, thus limiting the application of the data. To solve this problem, a path chain extraction scheme is proposed in this paper to distinguish the complete path chain from the missing path chain for a single trip, and a reconstruction algorithm of urban vehicle travel path chain based on deep inverse reinforcement learning is proposed. This algorithm samples the complete path chain to obtain expert examples, uses deep inverse reinforcement learning to mine expert examples, and gives the potential route selection characteristics by fitting in the form of nonlinear reward function, which guides the agent to complete the missing path chain independently, and realizes the reconstruction of the missing path chain of vehicle travel. According to the experimental validation in the local road network of Xiaoshan District, Hangzhou City, it is found that the proposed reconstruction algorithm possesses good stability performance, with an average accuracy of 95%; and that the accuracy keeps more than 92% even in case of significant missing points, so that it is of significant advantages as compared with the traditional algorithms. Moreover, by analyzing the impact of the location distribution and number of expert examples on the algorithm, strong generalization ability of the proposed reconstruction algorithm is verified.

Key words： urban road network; license plate recognition; deep inverse reinforcement learning; data processing; path chain reconstruction

Cite this article

WANG Fujian, CHENG Huiling, MA Dongfang, et al . Reconstruction of Urban Vehicle Path Chain Based on Deep Inverse Reinforcement Learning[J]. Journal of South China University of Technology(Natural Science), 2023 , 51(7) : 120 -128 . DOI: 10.12141/j.issn.1000-565X.220550

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