评估预测模型以改进印度铁路客运市场管理和机车车辆规划

IF 2.3 Q3 REGIONAL & URBAN PLANNING Foresight Pub Date : 2024-03-20 DOI:10.1108/fs-09-2022-0105
Vinod Bhatia, K. Kalaivani
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引用次数: 0

摘要

目的印度铁路公司(IR)是世界上最大的铁路网络之一。作为其战略发展举措的一部分,需求预测是不可或缺的活动之一,因为它可以为客车生产、新列车规划、客车扩充和配额再分配等各种活动的规划和控制提供基本投入。本研究的目的是为 IR 管理提出一种需求预测方法。我们进行了一项案例研究,测试了几种模型,即自动自回归综合移动平均法(auto-ARIMA)、三角回归法(TBATS)、霍尔特-温特斯加法模型、霍尔特-温特斯乘法模型、简单指数平滑法和简单移动平均法。根据投资者关系管理的要求,所采用的研究方法主要是辨证法,并采用了过去五年中最具代表性的列车服务五年内的乘客预订模式。相对误差矩阵和 Akaike 信息标准被用来比较各种模型的性能。研究结果根据最合适的自动-ARIMA 模型提出了客车生产策略。在过去 3 年中,IR 每年生产约 6,000 辆铁路客车。根据 2023-2024 年的客车生产计划,暂定生产 6551 辆不同类型的客车。从本文中获得的启示可促进基于需求的客车生产和库存的优化利用。原创性/价值本研究为铁路票务需求预测方面的文献做出了贡献,并为机车车辆管理过程增加了价值。所提出的模型可作为一种综合决策工具,用于规划新的列车服务和评估任何铁路系统的机车车辆生产需求。该分析有助于对旺季的需求进行预测,管理层可对服务定价做出重要决策。
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Evaluation of forecasting models for improved passenger market management and rolling stock planning on Indian railways

Purpose

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

Design/methodology/approach

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

Findings

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

Originality/value

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

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来源期刊
Foresight
Foresight REGIONAL & URBAN PLANNING-
CiteScore
5.10
自引率
5.00%
发文量
45
期刊介绍: ■Social, political and economic science ■Sustainable development ■Horizon scanning ■Scientific and Technological Change and its implications for society and policy ■Management of Uncertainty, Complexity and Risk ■Foresight methodology, tools and techniques
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