Structural and temporal analysis of metro passenger traffic
Abstract
The article is devoted to one approach to the analysis of traffic flows. The initial data for analysis are the so-called correspondence matrices, which describe the number of trips per unit of time between two points. The specific dataset that was analyzed in the work is a matrix of Moscow metro correspondence (passenger trips between metro stations) for February 2018. The purpose of the analysis is a structural-temporal analysis of passenger traffic (how and when passengers move). The paper proposes a method for analyzing transport traffic based on a combination of singular value decomposition and machine learning clustering methods. Singular value decomposition is used here for dimensionality reduction. The concept of using these tools in conjunction is not new, it has been used in other areas, but in this work, it has been successfully adapted specifically for the transport sector. The article presents a library software module for the implementation of each stage of the development of the proposed model. The module is capable of processing large amounts of data and has the potential for easy scaling and expansion. The paper presents an example of the implementation of the proposed methodology in relation to historical data on the passenger flow of the Moscow metro.
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