Stability Evaluation of Route Assignment Strategy by a Foresight-Route under a Decentralized Processing Environment

Abstract

We propose a foresight-route-providing method based on anticipatory stigmergy for collecting near-future traffic positions. Even though traffic flow information is better than no traffic information, drivers still face dangerous congested traffic flow. Several approaches have addressed short-term traffic information in which a stigmergy-based approach is employed as an indirect communication method for cooperation among distributed agents and for managing traffic congestion. Recently, advances in Intelligent Transport Systems suggest a future in which vehicles handle their own positions. Our foresight-route-providing method combines a previous method that utilized the past travel times of vehicles for decentralized traffic congestion management. We assume that all vehicles collect dynamic traffic conditions in decentralized processing environments as real implementation environments during the simulations. In our simulations, we demonstrate that our assignment route strategies have the potential for significant efficiency and stability for traffic flow in decentralized processing environments that can collect dynamic traffic conditions from the previous few minutes and estimate the vehicle positions after a few minutes in only limited links. In this paper, we evaluate the differences between centralized and decentralized processing environments and investigate how the ratio of probe vehicles that are equipped with such devices as navigation systems is related to efficient and stable traffic flow. We confirmed that a route assignment strategy contributes to the stable efficiency of the total travel time. With more probe vehicles, assignment route strategies becomes efficient in our approach.