关键词: 智能交通, 串车, 克隆选择, 调度, 反向学习

Abstract:

In order to avoid the tandem traffic problem, a real-time vehicle tandem scheduling algorithm with different station intervals on multiple routes is studied. Based on the data analysis and prediction of AVL data, an Opposition-learning Clonal Selection Algorithm (OCSA) with opposition-learning ability is proposed，the scheduling sequence is used to avoiding tandem traffic and guide vehicle scheduling. In the algorithm, a reverse antibody library is designed, which stores the information of several inferior antibodies during the population iteration. The location information of the inferior genes is used to guide the reverse learning, in order to pull them out of the local optimal region quickly. The opposition- learning process can rapidly improve the diversity of antibodies, so that the algorithm has a strong global optimization ability in a short time, and the scaling factor of local learning can be dynamically adjusted with the iterative process to improve the accuracy of the algorithm. The experimental results show that the scheduling sequence obtained by OCSA algorithm has better adaptability than the classical scheduling algorithm, and the scheduling sequence obtained by OCSA algorithm can effectively reduce the tandem traffic problem in real-time.

Key words: intelligent transportation, tandem, clone selection, scheduling, opposition-learning