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.
WANG Juquan, WANG Wei, MA Huimin, YANG Bo, DU Wen
. Urban Passenger Flow Aggregation Risk Forecasting Based on Principal Component Regression Algorithm[J]. Journal of Applied Sciences, 2019
, 37(4)
: 529
-540
.
DOI: 10.3969/j.issn.0255-8297.2019.04.010
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