基于ALINEA算法的城市快速路匝道控制方法

发布时间：2018-04-22 22:30

本文选题：城市快速路 + 短时交通流预测　；参考：《西南交通大学学报》2017年05期

【摘要】：为解决传统的ALINEA(asservissement linéaire d'entrée autoroutière)匝道控制算法未考虑城市快速路入口匝道排队溢出,造成关联交叉口交通拥堵等问题,在经典的ALINEA匝道控制算法的基础上,提出了一种新的基于主干道车流量预测的城市快速路入口匝道控制方法.该方法采用遗传算法优化的小波神经网络来预测城市快速路交通流量;引入主干道车流可插入间隙和匝道排队分级控制原则,实现了对城市快速路入口匝道控制率的动态调节.通过微观仿真实验比较两种算法的控制效果.结果表明:与传统的ALINEA匝道控制算法相比,新的控制方法不仅能够有效保证主线交通通行能力,同时还使匝道平均旅行时间减少了24.8%.
[Abstract]:In order to solve the problem that the traditional ALINEA(asservissement lin 茅 aire d'entr 茅 e autorouti 猫 re ramp control algorithm does not take into account the queue overflow of the urban expressway ramp, resulting in traffic congestion at the associated intersections, the classical ALINEA ramp control algorithm is used to solve the problem. This paper presents a new approach to control the on-ramp of urban expressway based on the traffic flow prediction of the main road. In this method, the wavelet neural network optimized by genetic algorithm is used to predict the traffic flow of urban expressway, and the principle of interchangeable gap and ramp queuing grading control is introduced to realize the dynamic regulation of the on-ramp control rate of urban expressway. The control effects of the two algorithms are compared by microscopic simulation experiments. The results show that compared with the traditional ALINEA ramp control algorithm, the new control method can not only effectively guarantee the main line traffic capacity, but also reduce the average ramp travel time by 24. 8%.
【作者单位】：西南交通大学交通运输与物流学院;西南交通大学综合交通运输智能化国家地方联合工程实验室;
【基金】：国家自然科学基金资助项目(51578465,71402149) 重庆市应用开发计划重点资助项目(cstc2014yykfB30003,2015H01373)
【分类号】：U491

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