用遗传算法解决固定需求交通平衡分配问题

为了提高交通量预测模型的可靠性，利用遗传算法的结构并行性将其用于求解固定需求交通平衡分配问题中。算法设计中采用多维并行交叉、变化的交叉率与变异率、优先策略及目标函数加惩罚项等改进措施，从而大大提高计算速度，减少了交通分配的时间，降低了分配的复杂性，为交通分配问题开创了一条新的途径，同时显示出遗传算法在交通规划中潜在的实用前景。

Solving Traffic Equilibrium Assignment Problem with Genetic Algorithm

This paper proposes a parallel processing method of genetic algorithm for the traffic equilibrium assignment problem with fixed traffic demand in order to forecast the traffic volume accurately. In the algorithm design, some improved steps such as the multi dimensional parallel crossover, variational rates of crossover and mutation, the elitism and the punishment of objective function are taken, so that the computing speed is greatly improved; the computation time and the assignment complexity are substantially reduced. It founds a new way for traffic assignment problems, and at the same time shows a potential practical prospect for the genetic algorithm to be used in the traffic programming.