Predicting motorcycle riding behavior using vehicle density variation

Abstract

Recently, motorcycle accidents are increasing in developing countries. One of the main reasons for this is the increase in traffic volume due to an increased number of four-wheeled vehicles. This brings about a heterogeneous (mixed) traffic flow consisting of two-wheeled vehicles and four-wheeled vehicles, which can result in the occurrence of sideswipe collisions. We carried out a survey of two-wheeled vehicle driving in heterogeneous traffic flow by considering vehicle density, acceleration, and pore (lateral gap), among other factors. Based on the results of this survey, we aim to predict motorcycle riding that carries high risk of collision, and to prevent such accidents from occurring. In this paper, we describe a novel algorithm which is capable of predicting two-wheel driving using vehicle detection and pore consideration. The performance of the proposed algorithm is verified and its associated issues are described. In addition, an example of this prediction algorithm is preliminarily implemented as a smartphone application.