A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems

Abstract

Abstract-Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.

Equipoising multi-modal traffic environments using vehicular ad-hoc networks

“…Claes et al [24] is another research work which has been an influential factor in our study. In this paper the authors propose an anticipatory vehicle routing solution based on multi-agent systems [25] concepts.…”

Access Time of Emergency Vehicles Under the Condition of Street Blockages After a Large Earthquake

“…This method enables us to detect the rule of congestion by controlling conditions pertaining to time, place, and so forth. Also, Claes et al (2011) described a routing strategy based on forecast information. This strategy forecasts the locations of vehicles using the delegate multi-agent system.…”

Clonal plasticity

“…MASs are typically used in situations where distributed, autonomous decision-making is a natural fit. These situations include modeling city traffic [8], socio-technical systems [22], smart-grids [14,20], smart-parking systems [24], cooperative control [25], etc. Most multi-agent system based adaptation relies on the distributed and autonomous nature of the constituent agents [11], but there is little literature on how to configure the agents themselves.…”