Algorithm for calculating the forecasted passenger flow of innovative railway transport based on a neural model
- 作者: Zhuravleva N.A.1, Batalova N.V.1
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隶属关系:
- Emperor Alexander I Petersburg State Transport University
- 期: 卷 11, 编号 3 (2025)
- 页面: 474-485
- 栏目: Original studies
- URL: https://journal-vniispk.ru/transj/article/view/328157
- DOI: https://doi.org/10.17816/transsyst688684
- ID: 328157
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全文:
详细
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.
作者简介
N. Zhuravleva
Emperor Alexander I Petersburg State Transport University
编辑信件的主要联系方式.
Email: zhuravleva_na@mail.ru
ORCID iD: 0000-0003-3566-9225
SPIN 代码: 8599-5636
Scopus 作者 ID: 56583893700
Dr Sci. (Economics), Professor
俄罗斯联邦, St. PetersburgN. Batalova
Emperor Alexander I Petersburg State Transport University
Email: natalyabatalova@yandex.ru
ORCID iD: 0000-0002-5948-7226
SPIN 代码: 4027-4771
Senior Lecturer
俄罗斯联邦, St. Petersburg参考
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