A Study of Multi-agent Based Metropolitan Demand Responsive Transport Systems

Abstract

Multi-agent based modeling has been looked as an efficient tool for large-scale system, it can be integrated with other AI-based and conventional approaches, be greatly enhanced. The resulting hybrid systems offer a flexible modeling environment that exploits the benefits of each individual in a synergistic fashion, like urban transport system. This research presents a multi-agent based demand responsive transport (DRT) services model, which adopts a multi-agents planning approach for metropolis traffic services control. In the proposed model, there are three layers: transport-admin agent layer, node-station agent layer and taxi agent layer. The agent for station and each vehicle have a planning domain, and select a route by a cooperation of agents in its planning domain. Based on this model, a simplified multi-agent based demand responsive transportation services system can be developed that is effective for reducing traffic congestion and air pollution. By computational experiments, we examine an effectiveness of the proposed method.