Multiple Lane Road Car-Following Model using Bayesian Reasoning for Lane Change Behavior Estimation: A Smart Approach for Smart Mobility

Abstract

Car-following modeling is one of the most used approaches for road traffic modeling. It ensures a detailed overview of vehicles behavior at microscopic traffic modeling level, taking into account some primary parameters like velocity, acceleration/deceleration, the distance between vehicles etc. A big disadvantage of this model is that is single-lane oriented, studying the current vehicle behavior based only on vehicle ahead behavior. The purpose of this paper is to deliver a new car-following model capable to adapt to multiple lanes roads, where the followed vehicle can be changed at any time. In this case, a big challenge will be the integration of a new vehicle in the established car-following model. This study attempts to estimate these different cases of lane-change based on a Bayesian reasoning estimation, facilitating the new vehicle integration on the current lane. Results will show the advantage of having a multiple lanes road traffic overview in adopting a proper traffic strategy, from the possible routes that can be reached point of view, based on lane change drivers' decisions.