Solving Road-Network Congestion Problems by a Multi-objective Optimization Algorithm with Brownian Agent Model
The past decades witnessed a big effort in solving road-network congestion problem through routing optimization approaches. With a multi-objective optimization perspective, this paper proposed a new method which solved the road-network congestion problem by combining two objectives of shortest routing and congestion avoidance. Especially, we applied the approach of Brownian agents to find the next intersection of road network to avoid congestion. Vehicles were simulated as Brownian agents with automatic movements in the road-network, and the entire network congestion distribution were optimized at the same time. We tried to find out the relationship between the moving strategies of the vehicles and the network congestion. By means of computer simulation, we implemented our proposed method with a predefined road-network topological structure. We tested the parameters sensitivity by scaling the proportion of agent with two moving strategies: the shortest path strategy and a mix strategy combining two objectives of shortest routing and congestion avoidance. Furthermore, we analyzed the various network congestions under a mix strategy by changing the weights to represent different focus on two moving strategies. The simulation results proved the applicability and efficiency of our proposed method for alleviating the network congestion distribution, and the intersections within a higher vehicle density were observed decreased.
Keywordsroad-network congestion multi-objective optimization Brownian agent
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