Improving traffic system performance by combining tolling and intention-based prediction: an agent-based model

Abstract

A traffic system in which all drivers optimise their individual efficiency does not always lead to the most efficient use of a road network. Rather, ”selfish-routing” can lead to user equilibrium, in which individual travel time is optimised. This is not necessarily equal to the system optimum, in which the average travel time is minimised. This study uses an agent-based traffic model to examine the effects of ∆-tolling and intention-based prediction on the average travel time in a traffic system. It is found that intention-based prediction can sharply improve the performance of an imperfectly implemented tolling scheme.