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 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.
Highlights
The success of urbanization in China has brought some problems as follows: urban traffic congestion in megacities is becoming more and more serious, and the focus of urban life is shifting to suburbs, and the passenger flow between suburbs increases
Initialize AFC passenger flow database according to the mining condition with the cleaned and preprocessed data as initial data for correlation rule mining, scanning the transaction database TID, and finding all the item sets whose length is k = 1, forming candidate 1-item set (C1), substituting C1 into (5) and (6), and calculating the support of each item, comparing with the minimum support one by one, and forming the frequent 1-item set if the support is greater than the minsupport
Passenger transport organizations are involved in key indicators such as the temporal passenger flow, passenger OD, and speed car ratio of the express/slow trains
Summary
The success of urbanization in China has brought some problems as follows: urban traffic congestion in megacities is becoming more and more serious, and the focus of urban life is shifting to suburbs, and the passenger flow between suburbs increases. Bowman and Ben-Akiva established a model to predict the travel occurrence from the traveler’s activities as the starting point [3] These foreign scholars made a more in-depth study in the passenger flow prediction and distribution models, which laid a theoretical basis. Based on the passenger flow data of AFC inbound and outbound traffic from the operators, this paper studies the information tracking based on passengers’ transportation card numbers and travel time and calculates the travel time of passengers by combining relevant time parameter data, so as to construct the model of mining passenger aggregation mechanism It is of great help on train operating plan making and temporary adjustment and can provide theoretical and methodic references for supplementation of passenger flow forecasting and emergency relief model
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