Identifying drunk driving behavior through a support vector machine model based on particle swarm algorithm

Min Li,Tao Chen,Wuhong Wang,Prakash Ranjitkar

doi:10.1177/1687814017704154

Abstract

Drunk driving is among the main causes of urban road traffic accidents. Currently, contact-type and non-real-time random inspection are methods used to verify whether drivers are drunk driving. However, these techniques cannot meet the actual demand of drunk driving testing. This study considers the following traffic parameters as inputs: speed-up, even-speed, and sharp-turn road segments; vehicle speed; acceleration and accelerator pedal position; and engine speed. Thereafter, this study adopts the support vector machine model to identify drivers’ driving behaviors to determine whether they are drunk driving, as well as the particle swarm optimization algorithm to optimize the model, thereby improving training speed. Results show that the support vector machine model based on the particle swarm optimization algorithm can immediately and accurately determine the drunk driving state of drivers, provide theoretical support to non-contact drunk driving test, and realize the foundation of safety driving assistance system toward the adoption of the corresponding measures. Therefore, this study has positive significance in improving traffic safety.

Highlights

Recent traffic accidents caused by drunk driving account for approximately 50%–60% of traffic accidents
Drunk driving is among the main causes of urban road traffic accidents, and the driving behavior characteristics of drivers in a drunk driving state are significantly different from those of drivers in a normal state
This study considers vehicle speed, acceleration and accelerator pedal position, and engine speed as input parameters and develops the support vector machine (SVM) model to identify the driving behaviors of drivers and to determine whether drivers are in a drunk driving state

Summary

Introduction

Recent traffic accidents caused by drunk driving account for approximately 50%–60% of traffic accidents. If we can collect relative parameters to identify driving behaviors and determine whether drivers are drunk driving, we can adopt appropriate warning and limiting measures provided by a traffic safety assistance system or realize a real-time, non-contact drunk driving state monitor through a smart traffic system. This study adopts the support vector machine (SVM) model based on the PSO algorithm, which can substantially improve training efficiency with high accuracy.

Results

Conclusion