Abstract

Travelers' route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to incorporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. A new swarm intelligence algorithm inspired by travelers' route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization (PSO) algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm (RCBA). The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.