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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 Time_clusterkm,n 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

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