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Research paper | Open Access

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNN

Tangjian Wei1,2, Xingqi Yang3, Guangming Xu4( ), Feng Shi4
School of Transportation Engineering, East China Jiao Tong University, Nanchang, China
Institute for Transport Studies, University of Leeds, Leeds, UK
School of Economics and Management, Beihang University, Beijing, China
School of Traffic and Transportation Engineering, Central South University, Changsha, China
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Abstract

Purpose

This paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).

Design/methodology/approach

By analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.

Findings

According to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.

Originality/value

This study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

Keywords

High speed railwayPassenger flow forecastDaily passenger volumeMedium-term forecastWavelet neural network

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Railway Sciences
Volume 2 Issue 1,
April 2023
Pages 121-139
DOI: 10.1108/RS-01-2023-0003
Cite this article:
Wei T, Yang X, Xu G, et al. Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNN. Railway Sciences, 2023, 2(1): 121-139. https://doi.org/10.1108/RS-01-2023-0003

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Received: 27 January 2023
Revised: 01 February 2023
Accepted: 01 February 2023
Published: 23 March 2023
© Tangjian Wei, Xingqi Yang, Guangming Xu and Feng Shi. Published in Railway Sciences.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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