A Highway Abnormal Event Discrimination and Detection Method
DOI:
https://doi.org/10.54097/6t4gyw49Keywords:
Highway, Blind Spots, Abnormal events, characteristic parameter, k-means clusteringAbstract
With the continuous increase in highway traffic volume, abnormal events occur frequently, seriously affecting traffic fluidity and safety. This study aims to develop an efficient abnormal event detection method. Based on simulation data generated by the VISSIM software, the K-means clustering algorithm is applied, with speed, density, and occupancy rate as key feature parameters, aiming to effectively identify abnormal events on highways. The research results show that, after comparing the performance of models using different combinations of feature parameters, the K-means model using speed and occupancy as input features exhibits the best performance, achieving an accuracy of 90.4% and a false negative rate of only 6.47%. The model proposed in this study can effectively identify abnormal events on highways, thereby providing strong support for improving the safety management level of highways.
Downloads
References
[1] Payne, Howard J, Tignor, Samuel C. Freeway incident-detection algorithms based on decision trees with states [C]. Annual Meeting of the Transportation Research Board.1978:30-37.
[2] Cohen S, Ketselidou Z. Microcomputers in Transportation. A Calibration process for Automatic Incident Detection Algorithms [C]. International Conference on Microcomputers in Transportation. 1993:506-515.
[3] C.L. Dudek, C.J. Messer, and N.B. Nuckles. Incident Detection on Urban Freeways [C]. Transportation Research Record. Washington DC: TRB, 1974.12-24.
[4] Martin P T, Perrin J, Hansen B, et al. Incident detection algorithm evaluation [J]. Prepared for Utah Department of Transportation, 2001.
[5] Srinivasan D, Cheu R L, Poh Y P, et al. Development of an intelligent technique for traffic network incident detection [J]. Engineering Applications of Artificial Intelligence, 2000, 13(3): 311-322.
[6] Zhang K, Taylor M A P. Effective arterial road incident detection: a Bayesian network based algorithm [J]. Transportation Research Part C: Emerging Technologies, 2006, 14(6): 403-417.
[7] Wan Binbin. Research on Highway Traffic Abnormal Event Detection Methods Based on Fuzzy Theory and Incremental Discriminant Method [D]. Kunming University of Science and Technology, 2014.
[8] Aliari S, Sadabadi K F. Automatic detection of major freeway congestion events using wireless traffic sensor data: machine learning approach [J]. Transportation Research Record, 2019, 2673(7): 436-442.
[9] Kinoshita A, Takasu A, Adachi J. Real-time traffic incident detection using probe-car data on the Tokyo Metropolitan Expressway [C], Big Data 2014 IEEE International Conference on IEEE, 2014: 43-45.
[10] Qin Tao. Study on Rapid Recognition Methods for Incidental Local Congestion on Urban Roads [D]. Changsha University of Science & Technology, 2014.
[11] Chen Huiru. Research and Application of Real-Time Traffic State Discrimination Methods for Basic Sections of Expressways [D]. Chang'an University, 2015.
[12] Du Chong. Study on Real-Time Traffic State Discrimination Methods for Expressways [D]. Beijing Jiaotong University, 2017.
[13] Pi Xiaoliang, Wang Zheng, Han Hao, et al. Application Research on Traffic State Classification Method Based on Information Collected by Inductive Loop Detectors [J]. Journal of Highway and Transportation Research and Development, 2006, (04): 115-119.
[14] Li Xiaolu. Traffic State Discrimination Based on Fuzzy C-Means Clustering for Urban Road Traffic [J]. Journal of Transportation Science and Economics, 2016, 18 (04): 32-36+42. 2016.04.009.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
