基于时空特征的城市轨道交通客流量预测方法

随着城市轨道交通网络运营里程的不断增长及网络承载客流量的不断提高,特定站点客流量极易发生急剧变化,这种变化引起整个网络客流量的不均匀分布,从而增加运营调度的难度和运营事故的发生概率.本文以城市轨道交通实际运营中采集的大量客流数据为基础,从时间和空间两个维度分析城市轨道交通客流分布的特点,并进一步提出基于贝叶斯网络的客流量预测方法,实现对特定站点的客流量预测.本实验完全基于实际数据,结果表明:预测客流量平均绝对百分比误差基本在0.1以下,预测准确程度较高.

A passenger volume prediction method based on temporal and spatial characteristics for urban rail transit

With the expanding of urban rail transit network revenue length and the increasing of its passenger volume,the passenger volume for some subway stations is susceptible to rapid chan-ges,which can easily incur uneven distribution of the entire network traffic.It could therefore in-crease the difficulty of rail transit operations and probability of operational incidents.On the basis of passenger volume data collected from practical operation,this paper analyzes the temporal and spatial characteristics of the passenger volume.It also proposes a passenger volume prediction method using the Bayesian network to predict the passenger volume of certain subway stations. Based on practical data,these numerical experiments demonstrate that the proposed method can achieve an mean absolute percentage error below 0.1 when predicting,proving the model is highly accurate.