基于主成分回归算法的城市客流聚集风险预测

Urban Passenger Flow Aggregation Risk Forecasting Based on Principal Component Regression Algorithm

为解决特大城市突发公共事件客流聚集预警准确度不高的问题,采用主成分回归算法对固定区域内运营商提供的手机用户数据和真实客流数据进行回归拟合,并根据多种统计检验方法对模型进行检验与评估.通过主成分分析,有效克服了运营商提供的手机用户数据的多重共线性问题,充分利用了手机用户数据的各个维度信息,并且降低了算法的复杂度,有效提高了城市客流聚集风险预测的精准度.

In order to solve the problem of the low accuracy of early warning of public events in mega-cities,this paper proposes a principal component regression algorithm to fit the mobile user data and real passenger flow data of fixed regions provided by operators,and uses a variety of statistical methods to test and evaluate the model.The principal component analysis can effectively overcome the multicollinearity problem of the mobile phone user data provided by the operators.By making full use of all-dimension information of the mobile phone user data,the complexity of the algorithm is reduced,and the accuracy of the prediction of the urban passenger flow aggregation risk is effectively improved.