Cross-industry Analysis And Application Based On Mobile Communication Data

As the structure of urban rail transit network is increasingly complex and its scale is huge,the investment and operation costs of construction are often high,and it usually takes more than one investor and operator to undertake the construction and operation.However,in order to facilitate passenger travel,the number of cities to take one ticket transfer principle,"one ticket transfer" has brought ticket distribution of income problems for the various operators.In addition,there was a serious imbalance in the distribution of passenger flow between peak and flat periods of travel.In order to improve the fairness of income distribution and to improve passenger experience,two classical research topics in rail transit are derived,that is,the research of subway income distribution model and subway timetable optimization.At present,the research on these two issues is mainly based on the data collected by passengers in and out of stations by the AFC(Automatic Fare Collection)system.By means of traffic surveys and other means can not get the actual route,so there will be some difference between the findings and the real situation.As an important carrier of communication in today's society,mobile phones play an irreplaceable role in people's lives.When people call,send text messages,and surf the Internet,they will communicate with nearby base stations,and base stations can collect data such as the user's cell phone number,communication time and cell number.This type of mobile communication data has the characteristics of large amount,wide range coverage,high implementation,strong authenticity and so on.The use of mobile communication data for practical problems in the field of rail transit can largely compensate for the lack of authenticity of the studies that rely solely on the data provided by the AFC system.Therefore,this paper takes mobile communication data as a starting point to provide new ideas for the study of two classical topics in the field of urban transit.The first step in the research of this paper is to identify the user's riding path in rail transit from the mobile communication data.Before trajectory identification,in order to reduce the workload of the post-order research and increase the accuracy of the algorithm,a series of data preprocessing operations,such as data merging,unification,screening,error removal and compression,need to be performed on the mobile communication data.In this paper,the recognition of the subway track includes the underground part and the underground part of the subway.Then this paper uses grey Markov model and weighted Markov model to study the forecasting method of passenger flow distribution in urban rail transit.For special scenes of rail transit,a series of special treatments are performed during modeling.In this paper,the gray Markov model and the weighted Markov chain model are respectively used to predict the short-term passenger flow and the individual movement position of the passengers identified in the first step,and the feasibility and effectiveness of the algorithm are verified by an example.According to the prediction of passenger flow and the prediction of population flow,the forecast method of population passenger flow is finally obtained.Finally,the application of mobile communication data in urban rail transit is put forward.Based on the mobile communication data and the identified user trajectories,and with the help of individual position prediction methods and subway operation schedules,an income distribution method and timetable optimization method are proposed based on mobile communication data.