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

2024 , Vol. 52 >Issue 9: 131 - 141

DOI: https://doi.org/10.12141/j.issn.1000-565X.230753

Traffic & Transportation Engineering

Classification and Identification of Risky Driving Behavior Based on Hybrid Strategy Improved ASO-LSSVM

  • HE Qingling ,
  • PEI Yulong ,
  • DONG Chuntong ,
  • LIU Jing ,
  • PAN Sheng
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  • College of Civil Engineering and Transportation，Northeast Forestry University，Harbin 150040，Heilongjiang，China
何庆龄（1994—），男，博士生，主要从事智能优化算法、交通安全研究。E-mail： qinglinghe@yeah.net
裴玉龙（1961—），男，博士，教授，主要从事交通安全研究。

Received date: 2023-12-04

  Online published: 2024-02-07

Supported by

the National Key Research and Development Program of China(2018YFB1600902)

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Abstract

This paper aims to solve the problem of slow convergence rate and large error of existing intelligent algorithms in the process of optimizing support vector machine to identify risky driving behavior. Firstly, Tent mapping was used to replace the random setting of population initialization of ASO algorithm to increase the diversity and quality of atomic population. Secondly, the hybrid mechanism of dimension-by-dimension pinhole imaging reverse learning and Cauchy mutation was used to improve the diversity of preferred positions of atomic individuals and overcome the problem that ASO algorithm is easy to fall into local optimum and premature convergence. Finally, the adaptive variable spiral search strategy was introduced to improve the atomic individual position update process, so as to improve the global search ability of ASO algorithm, realize the effective balance between global search and local development, and alleviate the problem that ASO algorithm is easy to fall into local optimum and lack of convergence accuracy. Taking the vehicle trajectory data of the exit ramp of Shanghai North Cross Channel as the input, the study used the hybrid strategy to improve the ASO algorithm so as to optimize the LSSVM parameters. And it constructed the classification and identification model of the risk driving behavior of the expressway exit ramp based on IASO-LSSVM. Numerical simulation results show that the average value, standard deviation, best fitness and worst fitness of the numerical simulation results of the IASO algorithm in 12 benchmark test functions are closer to the best optimization value. Compared with ASO-LSSVM and LSSVM, the accuracy, precision, recall and F1 value of risk driving behavior classification and identification results of IASO-LSSVM model increased by 11.5~24.5, 14.1~29.0,15.1~28.6, 14.7~31.2 percentage points respectively, and the error range was the smallest in different types of risky driving behavior identification results. The accuracy and convergence rate of IASO algorithm are better than those of ASO algorithm, and the IASO-LSSVM model can be used for accurate identification of different types of risk driving behavior, which can provide data support and theoretical basis for reasonable discrimination of vehicle driving trajectory state and formulation of early warning and prevention measures of risk driving behavior.

Key words： urban transport; expressway exit ramp; risk driving behavior classification and identification; atom search optimization; hybrid strategy; LSSVM

Cite this article

HE Qingling , PEI Yulong , DONG Chuntong , LIU Jing , PAN Sheng . Classification and Identification of Risky Driving Behavior Based on Hybrid Strategy Improved ASO-LSSVM[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(9) : 131 -141 . DOI: 10.12141/j.issn.1000-565X.230753

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