@inproceedings{f145c9b1eccc4ef092ffbfba912081ca,
title = "Research on Person-Vehicle Matching Method Based on Big Data Association Analysis",
abstract = "Identification and location tracking of drivers and vehicles can be effectively performed by utilizing license plate recognition data and Wi-Fi hotspot data. However, matching collected vehicles and vehicle driver data is challenging because the process has to cope with multiple data sources. In response to this problem, we propose an association analysis algorithm to efficiently solve the Person-Vehicle matching problem in a parallel manner. Experiments in Yunnan Province show that the method effectively solves the problem of matching drivers and vehicles, and has achieved good results. The research provides valuable insights on violation identification of vehicles.",
author = "Zhimin Tao and Pengcheng Wang and Ruizhen Kang",
note = "Publisher Copyright: {\textcopyright} 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.; 21st COTA International Conference of Transportation Professionals: Advanced Transportation, Enhanced Connection, CICTP 2021 ; Conference date: 16-12-2021 Through 19-12-2021",
year = "2021",
language = "英语",
series = "CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "209--216",
editor = "Junfeng Jiao and Haizhong Wang and Heng Wei and Xiaokun Wang and Yisheng An and Xiangmo Zhao",
booktitle = "CICTP 2021",
address = "美国",
}