Abstract
In this paper, we investigate the possibility of applying various approaches to solving the problem of medium-term forecasting of daily passenger traffic volumes in the Moscow metro (MM): 1) on the basis of artificial neural networks (ANN); 2) using the singular-spectral analysis implemented in the package “Caterpillar”-SSA; 3) sharing the ANN and the “Caterpillar”-SSA approach. We demonstrate that the developed methods and algorithms allow us to conduct medium-term forecasting of passenger traffic in the MM with reasonable accuracy.
Highlights
The Moscow metro is the main transport system in Moscow, carrying up to 10 million passengers a day
Our analysis has shown [1] that the intensity of passenger traffic varies greatly between working days and weekend-holidays
The corresponding distributions of passenger traffic practically do not overlap [1]. This observation made it possible to break the initial series into two series: 1) working days, 2) weekend-holidays
Summary
The Moscow metro is the main transport system in Moscow, carrying up to 10 million passengers a day. Given the dynamics of the MM development and the construction of modern transport infrastructure in Moscow, the role of the MM in passenger transportation will grow. To ensure effective functioning of the MM, constant monitoring of passenger traffic and its near- and medium-term forecasting are necessary
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