Anticipation based on a bi-level bi-objective modeling for the decision-making in the car-following behavior

Abstract

Safety distance models are in a continuous state of improvement due to their important role in the micro-simulation of traffic, intelligent transport systems and safety engineering models. Indeed, many existing models of car-following behavior do not explicitly consider a real link between the increase of the speed of movement and the guarantee of the road safety. In this context, this paper presents a decision-making model for determining velocity and safety distance values basing-on a bi-level bi-objective modeling and that allows simulation parameters anticipation. In fact, our model addresses drivers that circulate in urban zones with normative behaviors. The first objective of the model is to allow agent drivers to have a smooth transition between acceleration and deceleration behaviors according to the leading vehicle actions. Simultaneously, the model intends, as a second objective, to reduce the circulation time by increasing the speed of movement. Agent technology and Tabu search algorithm are used respectively to model drivers and to find the best solution during decision-making. The paper provides first a theoretical background of the research. Then it describes the agent driver decision-making model and the resolution algorithm. Finally it presents and discusses a first simulation and experimentations.