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

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.