城市轨道交通客流量时间序列分段拟合方法

A method of fitting urban rail transit passenger flow time series

为有效指导行车调度、预防和处置轨道交通突发事件,利用曲线拟合方法挖掘客流量时间序列趋势性特征,在客流时间分布分析和数据探索的基础上,通过整体拟合、人工分段拟合和自动分段拟合,对北京市36个地铁站单日内客流量进行时间序列建模优化研究,并对比3种方法的拟合结果和R2指标。研究表明:分段拟合利用局部函数建模客流量变化的动力学过程,相较整体拟合能更好地逼近实际;在分段拟合时,采用自动分段策略,能避免人为判定分段点的主观性,实现最佳优化,进一步提高预测分析效率和精度。

In order to effectively guide traffic dispatching, prevent and deal with rail transit emergencies,the curve fitting method was used to mine the trend characteristics of passenger flow time series. Based on the analysis of time distribution law of passenger flow and the exploration of data,the time series modeling and optimization of passenger flow in 36 subway stations in Beijing in a single day were carried out by global fitting,artificial piecewise fitting and automatic piecewise fitting,and a comparison in both the fitting results and R2 index was made between the three methods. The research shows that the piecewise fitting uses the local function to model the dynamic process of passenger flow change,which can better approximate the actual situation than the global fitting,that in the case of piecewise fitting,the automatic piecewise strategy is adopted,which can avoid the subjectivity in artificially determining piecewise points,and achieve optimal optimization,further improving the efficiency and accuracy of prediction analysis.