An integrated train service plan optimization model with variable demand

A team-based scheduling approach with dual cost information in a layered network

Lingyun Meng, Xuesong Zhou

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A well designed train timetable should fully utilize the limited infrastructure and rolling stock resources to maximize operators’ profits and passenger travel demand satisfaction. Thus, an internally coherent scheduling process should consider the three main aspects: (1) dynamic choice behaviors of passengers so as to evaluate and calculate the impact of variable passenger demand to (2) underlying train service patterns and detailed timetables, which in turn are constrained by (3) infrastructure and rolling stock capacity. This paper aims to develop an integrated demand/service/resource optimization model for managing the above-mentioned three key decision elements with a special focus on passengers’ responses to time-dependent service interval times or frequencies. The model particularly takes into account service-sensitive passenger demand as internal variables so that one can accurately map passengers to train services through a representation of passenger carrying states throughout a team of trains. The added state dimension leads to a linear integer multi-commodity flow formulation in which three closely interrelated decision elements, namely passengers’ response to service interval times, train stopping pattern planning and timetabling for conflict detecting and resolving are jointly considered internally. By using a Lagrangian relaxation solution framework to recognize the dual costs of both passenger travel demand and limited resources of track and rolling stock, we transfer and decompose the formulation into a novel team-based train service search sub-problem for maximizing the profit of operators. The sub-problem is solvable efficiently by a forward dynamic programming algorithm across multiple trains of a team. Numerical experiments are conducted to examine the efficiency and effectiveness of the dual and primal solution search algorithms.

Original languageEnglish (US)
Pages (from-to)1-28
Number of pages28
JournalTransportation Research Part B: Methodological
Volume125
DOIs
StatePublished - Jul 1 2019

Fingerprint

optimization model
scheduling
Profitability
Scheduling
demand
costs
Dynamic programming
Costs
Planning
profit
travel
resources
infrastructure
Experiments
commodity
programming
efficiency
planning
experiment
time

Keywords

  • Dynamic programming
  • Lagrangian relaxation
  • Service-sensitive demand
  • State-space-time network
  • Train timetabling

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation

Cite this

@article{9cda8958dbab481eb82e2a2d2f8e6ee6,
title = "An integrated train service plan optimization model with variable demand: A team-based scheduling approach with dual cost information in a layered network",
abstract = "A well designed train timetable should fully utilize the limited infrastructure and rolling stock resources to maximize operators’ profits and passenger travel demand satisfaction. Thus, an internally coherent scheduling process should consider the three main aspects: (1) dynamic choice behaviors of passengers so as to evaluate and calculate the impact of variable passenger demand to (2) underlying train service patterns and detailed timetables, which in turn are constrained by (3) infrastructure and rolling stock capacity. This paper aims to develop an integrated demand/service/resource optimization model for managing the above-mentioned three key decision elements with a special focus on passengers’ responses to time-dependent service interval times or frequencies. The model particularly takes into account service-sensitive passenger demand as internal variables so that one can accurately map passengers to train services through a representation of passenger carrying states throughout a team of trains. The added state dimension leads to a linear integer multi-commodity flow formulation in which three closely interrelated decision elements, namely passengers’ response to service interval times, train stopping pattern planning and timetabling for conflict detecting and resolving are jointly considered internally. By using a Lagrangian relaxation solution framework to recognize the dual costs of both passenger travel demand and limited resources of track and rolling stock, we transfer and decompose the formulation into a novel team-based train service search sub-problem for maximizing the profit of operators. The sub-problem is solvable efficiently by a forward dynamic programming algorithm across multiple trains of a team. Numerical experiments are conducted to examine the efficiency and effectiveness of the dual and primal solution search algorithms.",
keywords = "Dynamic programming, Lagrangian relaxation, Service-sensitive demand, State-space-time network, Train timetabling",
author = "Lingyun Meng and Xuesong Zhou",
year = "2019",
month = "7",
day = "1",
doi = "10.1016/j.trb.2019.02.017",
language = "English (US)",
volume = "125",
pages = "1--28",
journal = "Transportation Research, Series B: Methodological",
issn = "0191-2615",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - An integrated train service plan optimization model with variable demand

T2 - A team-based scheduling approach with dual cost information in a layered network

AU - Meng, Lingyun

AU - Zhou, Xuesong

PY - 2019/7/1

Y1 - 2019/7/1

N2 - A well designed train timetable should fully utilize the limited infrastructure and rolling stock resources to maximize operators’ profits and passenger travel demand satisfaction. Thus, an internally coherent scheduling process should consider the three main aspects: (1) dynamic choice behaviors of passengers so as to evaluate and calculate the impact of variable passenger demand to (2) underlying train service patterns and detailed timetables, which in turn are constrained by (3) infrastructure and rolling stock capacity. This paper aims to develop an integrated demand/service/resource optimization model for managing the above-mentioned three key decision elements with a special focus on passengers’ responses to time-dependent service interval times or frequencies. The model particularly takes into account service-sensitive passenger demand as internal variables so that one can accurately map passengers to train services through a representation of passenger carrying states throughout a team of trains. The added state dimension leads to a linear integer multi-commodity flow formulation in which three closely interrelated decision elements, namely passengers’ response to service interval times, train stopping pattern planning and timetabling for conflict detecting and resolving are jointly considered internally. By using a Lagrangian relaxation solution framework to recognize the dual costs of both passenger travel demand and limited resources of track and rolling stock, we transfer and decompose the formulation into a novel team-based train service search sub-problem for maximizing the profit of operators. The sub-problem is solvable efficiently by a forward dynamic programming algorithm across multiple trains of a team. Numerical experiments are conducted to examine the efficiency and effectiveness of the dual and primal solution search algorithms.

AB - A well designed train timetable should fully utilize the limited infrastructure and rolling stock resources to maximize operators’ profits and passenger travel demand satisfaction. Thus, an internally coherent scheduling process should consider the three main aspects: (1) dynamic choice behaviors of passengers so as to evaluate and calculate the impact of variable passenger demand to (2) underlying train service patterns and detailed timetables, which in turn are constrained by (3) infrastructure and rolling stock capacity. This paper aims to develop an integrated demand/service/resource optimization model for managing the above-mentioned three key decision elements with a special focus on passengers’ responses to time-dependent service interval times or frequencies. The model particularly takes into account service-sensitive passenger demand as internal variables so that one can accurately map passengers to train services through a representation of passenger carrying states throughout a team of trains. The added state dimension leads to a linear integer multi-commodity flow formulation in which three closely interrelated decision elements, namely passengers’ response to service interval times, train stopping pattern planning and timetabling for conflict detecting and resolving are jointly considered internally. By using a Lagrangian relaxation solution framework to recognize the dual costs of both passenger travel demand and limited resources of track and rolling stock, we transfer and decompose the formulation into a novel team-based train service search sub-problem for maximizing the profit of operators. The sub-problem is solvable efficiently by a forward dynamic programming algorithm across multiple trains of a team. Numerical experiments are conducted to examine the efficiency and effectiveness of the dual and primal solution search algorithms.

KW - Dynamic programming

KW - Lagrangian relaxation

KW - Service-sensitive demand

KW - State-space-time network

KW - Train timetabling

UR - http://www.scopus.com/inward/record.url?scp=85065080715&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065080715&partnerID=8YFLogxK

U2 - 10.1016/j.trb.2019.02.017

DO - 10.1016/j.trb.2019.02.017

M3 - Article

VL - 125

SP - 1

EP - 28

JO - Transportation Research, Series B: Methodological

JF - Transportation Research, Series B: Methodological

SN - 0191-2615

ER -