Demand-Driven Timetabling for a Metro Corridor Using a Short-Turning Acceleration Strategy

Author:

Schettini Tommaso1ORCID,Jabali Ola1ORCID,Malucelli Federico1ORCID

Affiliation:

1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano 20133, Italy

Abstract

The efficient management of metro lines is a major concern for public transport operators. Traditionally, metro lines are operated through regular timetables, that is, timetables where trains have a constant headway between all stations. In this paper, we propose a demand-driven metro timetabling strategy and elaborate exact solution methods for the case of a two-directional metro corridor. In doing so, we avoid imposing any predetermined structure to the timetable, and instead control the trains individually to best match passenger demand. We consider that trains may short turn, that is, trains that are not required to serve the line from terminal to terminal, but instead may reverse direction before reaching the terminal. We present a mixed integer linear programming formulation for the demand-driven timetabling problem of a two-directional metro corridor with short turning. Furthermore, we develop an efficient exact algorithm using cut generation for an alternative formulation with an exponential number of constraints, and derive two classes of valid inequalities. We evaluate the proposed formulation and algorithm considering seven possible cut generation strategies on a number of test instances from artificially generated lines and on two test beds derived from real-world lines. Through the computational experiments, we demonstrate the effectiveness of the developed algorithm and the added value of the proposed strategy in terms of passengers’ waiting time.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Transportation,Civil and Structural Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3