关键词: 智能交通系统, OD矩阵, 极大熵模型, 粒子群优化, 免疫算法

Abstract: Due to the disadvantage of slow convergence and local optimum of particle swarm algorithm，introducing relative distances among particles to improve probability selection formula，an improved particle swarm optimization with immune mechanism is proposed.Particles update their velocity and position not only by individual and global optima，but also by individual optima of a specific particle selected by roulette method according to certain probability，to keep the variety of the population and avoid precocity and stagnation.This algorithm is applied to solve the maximum entropy model，estimating OD matrix from traffic link flows.Through a test on a specific crossroad in Chongqing City，the experimental results show that particle swarm algorithm is feasible and effective for OD matrix estimation，overcomes the shortcoming of Newton’s method that strictly depends on initial values，and the particle swarm algorithm has much higher capacity of optimization than basic particle swarm algorithm and basic genetic algorithm.

Key words: intelligent transportation system, Origin-Destination（OD） matrix, maximum-entropy model, particle swarm optimization, immune algorithm