Work

Algorithm for calculating the forecasted passenger flow of innovative railway transport based on a neural model

Year: 2025

Type: article

Abstract: Aim: to create a neural network toolkit for predicting passenger traffic by innovative (high-speed) rail transport. Materials and methods: statistical data on passenger traffic on all modes of transport in the areas of high-speed railway lines. Methods: econometric modeling, multilayer perceptron. Results: an algorithm for planning passenger traffic on high-speed railway lines based on an intelligent neural model. Conclusion: The ability of the neural network model to "learn" allows to increase the reliability of population mobility forecasts when evaluating HSRT projects.

Source: Modern Transportation Systems and Technologies

DOI: 10.17816/transsyst688684

Authors: N. A. Zhuravleva, N. V. Batalova

Institution: Petersburg State Transport University

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4414836274

FWCI:

Cites: 4

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