Chaotic particle swarm optimization algorithm based on tent mapping for dynamic origin-destination matrix estimation

Due to the disadvantage of slow convergence and local best of particle swarm optimization (PSO), based on the ergodicity, randomicity and disciplinarian of chaos as well as the advantages of Tent mapping, Tent mapping was used as a chaotic optimization searching and introduced into PSO to avoid PSO getting into local best and appearing premature convergence. This modified and novel PSO was called chaotic particle swarm optimization algorithm (CPSO). This algorithm is applied to solve the maximum entropy model, estimating OD matrix from traffic link flows. Through a test on a specific road intersection, the results show that CPSO is feasible and effective for OD matrix estimation, and has much higher capacity of optimization than basic particle swarm algorithm.