Evolutionary Multiobjective Optimization for Pedestrian Route Guidance with Multiple Scenarios

Crowd-related accidents often occur in both normal and emergency situations. To prevent these problems, it is highly suggested to investigate and simulate the risks of overcrowding in a large-scale gathering by using a multi-agent system. Such simulation enables the improvement of safe and efficient pedestrian route guidance, depending on multiple scenarios with complicated environmental and traffic conditions. In this paper, for practical safety pedestrian route guidance, we propose a multi-objective evolutionary optimization method to handle multiple scenarios in a large-scale firework event. The pedestrian dataset is obtained with a multi-agent traffic simulator, CrowdWalk. As the optimization of route guidance is a multi-objective optimization problem, we modify a natural evolution strategy based multi-objective optimization algorithm by replacing the Pareto dominance relation with the scenario dominance relation. This aims for the flexibility of pedestrian route guidance in response to traffic demands. The computational results demonstrate that the method can find a well-balanced set of solution to multiple scenarios and maintain a trade-off among multiple objectives in real world applications.