Evolutionary Synthesis of High-Capacity Reconfigurable Multilayer Road Networks Using a Multiagent Hybrid Clustering-Assisted Genetic Algorithm

Abstract

Modern requirements for urban traffic management and control call for the design of high-capacity reconfigurable multilayer road networks (RMRNs). This paper discusses the proposed evolutionary synthesis approach, a promising method for finding the best configurations of RMRNs, aiming to create road networks with optimized layouts that maximize vehicle outflow. As the complexity of RMRNs increases, due to the addition of overpasses and tunnels, the expenses for building these road networks also rise significantly. Therefore, it is essential to find a balance when choosing the optimal topological solution for an RMRN. These solutions need to maximize traffic flow while minimizing the complexity of the RMRN. To achieve this goal, a new multiagent hybrid clustering-assisted genetic algorithm (MA-HCAGA). The proposed algorithm combines the use of binary-coded crossovers and mutations as genetic operators, and biobjective discrete particle swarm optimization (BODPSO) techniques to improve the evolutionary search process. In addition, the algorithm combines the use of finite-state machines (FSMs) to control the transitions between the states of agent-processes and the fuzzy clustering technique (FCA) to estimate the swarm and select clusters for interaction among the groups of agent-processes and particle swarms. The superior performance of the MA-HCAGA algorithm in evolutionary synthesis of RMRNs has been demonstrated through comparisons with other well-known multiobjective optimization methods. MA-HCAGA has been successfully applied in the evolutionary synthesis of RMRNs, allowing a decision maker to select the optimal RMRN topologies along the approximate Pareto front by selecting specific solutions. A traffic flow simulation model, aggregated with the MA-HCAGA algorithm, has been developed to simulate vehicle flow at various configurations of RMRNs. The results of this study show the effectiveness of the proposed method for configuring RMRNs in order to optimize vehicle outflow and reduce the complexity of RMRNs.