Metro Train Stopping Scheme Decision Based on Multisource Data in Express-Local Train Mode

Abstract

The urban rail transit network has gradually realized grid operation with the increase in the coverage rate. Therefore, the stopping schemes in accordance with the trend of the passenger flow are more conducive to improving the attractiveness of the rail transit and improving the sharing rate of the urban public transit. Traditional data from a single source may not be sufficient to describe the overall trend of the passenger flow in a period of time, and the error is possible in the case of insufficient data. Based on the multisource data, the spatial weight function is introduced to fuse the point of interest data and real estate information data, from which one obtains the residential index and office index, and the cluster analysis is conducted to obtain the potential stop scheme. Then, the optimization model of the train operation plan is established, aiming at minimizing the passenger travel time and the generalized system cost, and is constrained by a series of driving conditions. Compared with the single data source, multisource data can better reflect passenger flow trends and land use characteristics. Compared with the traditional all-station stopping scheme, a reasonable setting of crossing stations and running express-local trains can better satisfy the demands of the passenger flow. Finally, the optimization of Changchun rail Transit Line 1 shows that the model can reduce the travel time of passengers and the operating cost of the rail transit company and improve the quality of service, so as to achieve a win-win situation between passengers and the rail transit company.