Passenger Demand-Oriented High-Speed Train Stop Planning with Service-Node Features Analysis

Fanxiao Liu,Guangyuan Zhang,Gongyuan Lu,Qiyuan Peng,Luigi Dell’Olio

doi:10.1155/2020/8974315

Abstract

As a critical foundation for train traffic management, a train stop plan is associated with several other plans in high-speed railway train operation strategies. The current approach to train stop planning in China is based primarily on passenger demand volume information and the preset high-speed railway station level. With the goal of efficiently optimising the stop plan, this study proposes a novel method that uses machine learning techniques without a predetermined hypothesis and a complex solution algorithm. Clustering techniques are applied to assess the features of the service nodes (e.g., the station level). A modified Markov decision process (MDP) is conducted to express the entire stop plan optimisation process considering several constraints (service frequency at stations and number of train stops). A restrained MDP-based stop plan model is formulated, and a numerical experiment is conducted to demonstrate the performance of the proposed approach with real-world train operation data collected from the Beijing-Shanghai high-speed railway.

Highlights

In most countries, high-speed railway (HSR) is significant in daily life owing to its reliability, safety, low emissions, and energy savings
Train operation management must be maintained at an acceptable efficiency level. e train stop plan is a key element in the operation plan for satisfying the increasing passenger demand and reducing the operational costs of the railway company. e stop plan can impact the frequency of train service and the load of trains at each station, which directly influences transportation resource utilisation
As a critical part of the train operation plan, the train stop planning (TSP) problem was first proposed by Patz [2]

Summary

Introduction

High-speed railway (HSR) is significant in daily life owing to its reliability, safety, low emissions, and energy savings. Is study makes contributions in the following areas: (i) railway properties and city features are listed as input parameters to better reflect environmental influence and improve train stop plan quality, (ii) a data-mining technique is applied to explore the station level through quantitative analysis of effective features using a dataset from the BeijingShanghai high-speed railway, and (iii) a restrained Markov decision process (RMDP) is proposed to find the optimal policy to achieve a better train stop plan. Xu et al [16] focused on balancing the number of trains between major station stops and high frequency stops, aiming to minimise the total passenger time loss generated from both train stops and transfers Most of these studies focused on the optimisation model construction based on varying factors. To the best of our knowledge, limited studies have applied reinforcement learning to solve TSP

Methodologies

Findings

Experiments