Improvement of Multi-agent Routing Guidance with an Intelligent Traffic Light Scheduling and the Ability to Select Intermediate Destinations

Abstract

Traffic congestion and route guidance are integral parts of urban development in large cities. How cars are routed and the traffic flowing have a direct impact on each other. Therefore, the first step is to determine a criterion for assessing the traffic situation. The type of vehicles should also be considered in routing. Emergency vehicles must arrive at their mission site as soon as possible. Public transportations must also travel according to their plans. Ordinary vehicle drivers can either choose a road as an intermediate destination (out of interest, pick up someone, etc.). In this paper, two new algorithms are proposed to 1) route with intermediate destination selection for ordinary vehicles, and 2) schedule traffic lights to decrease traffic density and routing delay. The first algorithm proposed an agent-based route guidance model that in addition to finding the least expected travel time (LET) routes, drivers could select a part of the route as intermediate destinations according to their interests, to raise their satisfaction level. The second algorithm considers the density of traffic flow and the presence of emergency vehicles. This algorithm evaluates the status of the traffic flow by fuzzy logic. The evaluation is conducted by considering traffic flow speed and density. The output of fuzzy logic is used by the Gradational Search Algorithm (GSA). GSA regards the status of the flow, the priority of the traffic flow, and the distance of the emergency vehicles to the traffic light. The simulation results prove that the proposed algorithms have better performances.