基于非参数回归的城轨实时进出站客流预测

为准确预测城轨实时进出站客流,构建基于非参数回归的实时进出站客流预测模型。首先,对不同特征日分时进出站客流量进行对比分析,据此构建历史数据库;其次,通过计算历史分时数据的相关系数,并设置阈值对分时客流数据间的相关性进行判断,从而确定合适的非参数模型状态向量;再次,根据K近邻样本与预测目标的客流量差异性,设计基于权重加权的预测算法;最后利用广州市城轨客流数据对预测模型进行精度分析,对全网站点多天的预测结果显示:全天平均绝对百分比误差均在2%以下,分时平均绝对百分比误差均在14%以下,表明模型具有较高的预测精度和良好的适用性。

Real-time Forecasting of Entrance and Exit Passenger Flows for Urban Rail Transit Station: A Non-parametric Regression Approach

The short-term fluctuations of passenger flows should be responded quickly with the help of real-time forecasts to guarantee safe transportation.A non-parametric regression model is established to accurately forecast the real-time entrance and exit passenger flows in urban rail transit stations.Firstly,the time-sharing data for entrance and exit passenger flows of different days are compared and analyzed to lay a foundation for the construction of historical database.Secondly,the appropriate state vector for the non-parametric model is defined by calculating the self-correlation coefficient of historical time-share passenger flow data and setting the threshold value of correlation to judge the data dependency.Thirdly,the forecasting algorithm is designed according to the entrance and exit passenger flows' difference between K-nearest neighbor samples and prediction objectives.Finally,the data of entrance and exit passenger flows collected from Guangzhou metro system is used for the case study,and the result shows that the mean absolute percentage errors for the day and time-sharing passenger flows are successfully limited to 2％ and 14％ respectively,which demonstrates that the forecasting accuracy of the proposed model is satisfactory.