Recognition of Abnormal Driving Behavior of Highway Vehicles Based on Data Characteristics
- 10.2991/978-94-6463-024-4_29How to use a DOI?
- highway; traffic big data; abnormal driving behavior; data identification characteristics; online calculation
Using the data characteristics of traffic big data to intelligently identify abnormal driving vehicles, it is of great significance to fight against toll evasion and improve operation management & efficiency of highway. To improve the efficiency of joint inspection of existing highway vehicle toll evasion behaviors, this paper carries out behavioral analysis and data analysis on the five existing abnormal driving behaviors, and summarizes the data characteristics of five abnormal driving behaviors: breakthrough type, alternate use of OBU, vehicle type history issues, vehicle timeout and overspeed. According to data characteristics of each type, this paper presents an online calculation method of abnormal driving behavior based on data features of threshold table, sliding window. This paper concluded that the characteristics of vehicle data can effectively identify abnormal driving behaviors on highways and improve the efficiency of inspection.
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiahao Feng AU - Jinsong Ye AU - Lei Zhou PY - 2022 DA - 2022/12/12 TI - Recognition of Abnormal Driving Behavior of Highway Vehicles Based on Data Characteristics BT - Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022) PB - Atlantis Press SP - 272 EP - 284 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-024-4_29 DO - 10.2991/978-94-6463-024-4_29 ID - Feng2022 ER -