基于流量预测的高速公路收费员动态配置模型

Dynamic Equilibrium Model of Expressway Toll Collector Based on Traffic Flow Prediction

基于神经网络对不完全信息具有良好的适应性和遗传算法具有较强的全局搜索能力的优良特性,将遗传算法和神经网络有机结合起来对高速公路短期流量进行预测.采用精英选择、按比例适应度分配和基于排序的适应度分配相结合的选择方法,以及自适应的交叉、变异概率改进遗传算法,通过使用自适应学习速率来改进BP算法,并提出使用新的结合方式获取新一代种群,提高获取全局最优解的搜索速度,构建符合高速公路短期流量特点的预测模型.同时,采用排队论模拟高速公路收费过程,构建高速公路收费站的排队模型.结合短期流量预测模型及收费站排队模型,根据车道与收费员的配备,预测短期各时段的收费员需求,从而实现收费员的动态最优配置.最后结合实例,证明了模型的有效性.

Based on the excellent characteristics of neural network such as favorable adaptability to incom- plete information and of genetic algorithm with relatively strong and comprehensive search ability, these two objectives are combined to predict the short-term traffic flow of expressway. In this paper, the methods of elite＇ s choices, fitness assignment by proportion and by sort are combined, as well as self-adaptive crossing and mutation probability are used to improve genetic algorithm. To enhance the comprehensive optimal search speed and develop the prediction model for expressway short-term traffic flow, self-adaptive learning rates are introduced to improve BP algorithm and new combing methods are presented to get new species. Meanwhile, queuing theory is adopted to simulate toll process of expressways, and then queuing model of ex- pressways＇ toll stations is formulated. By combining short-term traffic flow prediction model and toll station queuing model, the demands of toll collectors are predicted according to the allocation of lanes and toll col- lectors, which realizes the dynamic optimal equilibrium of toll collectors. The validity of the model is finally testified by field tests.