Title: Research on abnormal monitoring of vehicle traffic network data based on support vector machine

Authors: Dahui Li; Jianzhao Cui; Qi Fan

Addresses: School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161000, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161000, China ' School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161000, China

Abstract: In order to solve the problems of low accuracy and long delay in traditional data monitoring methods of vehicle-mounted traffic network, an anomaly monitoring method based on Support Vector Machine (SVM) is proposed. The data of acceleration sensor, gyroscope and magnetic field sensor are collected and filtered. The online analysis method of driving behaviour based on support vector machine is introduced to identify various driving behaviours. By simulating the normal behaviour and abnormal behaviour based on HTTP protocol, the obtained data are analysed to construct the HTTP protocol behaviour. The neural network based on Radial Basis Function (RBF) was trained to monitor the abnormal data in driving behaviours by simulating the behaviour records generated by experiments for many times. The experimental results show that the proposed method can accurately monitor the abnormal data in driving behaviour, and the delay is short, which provides a favourable basis for relevant studies.

Keywords: driving behaviour recognition; vehicle; traffic network; data anomaly monitoring.

DOI: 10.1504/IJVICS.2020.108912

International Journal of Vehicle Information and Communication Systems, 2020 Vol.5 No.2, pp.247 - 264

Received: 25 Nov 2019
Accepted: 12 Jan 2020
Published online: 06 Aug 2020 *

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