【摘要】 随着城市轨道交通的迅速发展,地铁短期断面客流的预测有利于运营部门观测客流的实时变化,从而调整调度策略。客流具有时空特征,在10 min粒度时间片下,客流变化存在周期性,在空间上客流波形存在差异性。使用凝聚层次聚类算法对不同站点在一周内的客流进行聚类分析,得到贴近站点特征的客流分类结果。根据分类结果,对不同类别客流时间片分别进行相关性分析,提出一种基于SVM的预测模型,将强相关性的时间片序列作为模型输入。同时,提出一种基于协同自适应调整的双种群萤火虫算法以寻优模型参数,算法中引入混沌吸引度来提高算法的全局搜索能力,避免由于初始值陷入局部最优;加入自适应搜索步长,以加快算法的收敛速度并提高求解精度。与其他模型和优化算法的对比表明,本模型具有较好的预测精度、稳定性和鲁棒性。更多还原

【Abstract】 With the rapid development of urban rail transit,the short-term passenger flow forecast of the subway is conducive to the operation department to observe the real-time changes in passenger flow and adjust the scheduling strategy.This paper studied the temporal and spatial characteristics of passenger flow.Under the 10-minute granular time slice,there is a periodicity of passenger flow changes,and there are differences in the waveform of passenger flow in space.This paper used agglomerative hierarchical clustering algorithm to analyze the passenger flow of different stations for a week,and obtained the results of passenger flow close to the characteristics of the station.According to the results of classification,correlation analysis was performed on time slices of different types of historical passenger flow,and prediction models based on Support Vector Machine were proposed,regarding time slice sequences with strong correlation as input.Besides,a parameter optimization model for double-population firefly algorithm based on cooperative self-adaptive adjustment was proposed,in which the chaotic attraction was introduced to improve the global search ability,avoiding the initial value being trapped into a local optimum.The adaptive search step length was added to improve the convergence speed and solution accuracy.Compared with other models and optimization algorithms,the proposed model has better prediction accuracy,stability and robustness.更多还原