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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 3819304, 7 pages
https://doi.org/10.1155/2017/3819304
Research Article

The Aggregation Mechanism Mining of Passengers’ Flow with Period Distribution Based on Suburban Rail Lines

College of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, China

Correspondence should be addressed to Xiaobing Ding

Received 31 December 2016; Revised 21 March 2017; Accepted 8 May 2017; Published 22 June 2017

Academic Editor: Seenith Sivasundaram

Copyright © 2017 Xiaobing Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Stranding too many passengers at the stations will reduce the service level; if measures are not taken, it may lead to serious security problems. Deeply mining the time distribution mechanism of passenger flow will guide the operation enterprises to make the operation plans, emergency evacuation plans, and so on. Firstly, the big data theory is introduced to construct the mining model of temporal aggregation mechanism with supplement and correction function, then, the clustering algorithm is used to mine the peak time interval of passenger flow, and the passenger flow time aggregation rule is studied from the angle of traffic dispatching command. Secondly, according to the rule of mining traffic aggregation, passenger flow calculation can be determined by the time of train lines in the suburbs of vehicle speed ratio, to match the time period of the uneven distribution of passenger flow. Finally, an example is used to prove the superiority of model in determining train ratios with the experience method. Saving energy consumption improves the service level of rail transit. The research can play a positive role in the operation of energy consumption and can improve the service level of urban rail transit.