University of Illinois Urbana-Champaign

Capacity evaluation and infrastructure planning techniques for heterogeneous railway traffic under structured, mixed, and flexible operation

Shih, Mei-Cheng

Content Files
SHIH-DISSERTATION-2017.pdf

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https://hdl.handle.net/2142/98121

Description

Title
Capacity evaluation and infrastructure planning techniques for heterogeneous railway traffic under structured, mixed, and flexible operation
Author(s)
Shih, Mei-Cheng
Issue Date
2017-07-14
Director of Research (if dissertation) or Advisor (if thesis)
Barkan, Christopher P.L.
Doctoral Committee Chair(s)
Barkan, Christopher P.L.
Committee Member(s)
  • Ouyang, Yangfeng
  • Lai, Yung-Cheng
  • Work, Daniel B.
  • Dick, C. Tyler
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2017-09-29T16:37:57Z
Keyword(s)
  • Linear regression
  • Mixed integer programming
  • Jobshop modeling
Abstract
North American railroads have a strong business incentive to match rail line capacity to traffic demand. Since insufficient capacity reduces level of service and excess capacity represents inefficient use of capital, either one of these situations is undesirable. Various processes, models, and tools have been developed to assist the railroads in determining appropriate infrastructure projects and operational plans to balance network capacity. In North America, these approaches have typically been tailored to operating conditions on rail corridors that are dominated by freight trains that do not run according to a precise schedule. Changes in the composition of rail traffic have resulted in new operating conditions that require new approaches to rail capacity evaluation. The long-term growth of freight rail traffic (with particular increases in premium intermodal traffic) and recent interest in the expansion of passenger service on freight corridors have increased rail traffic volume and heterogeneity, while altering the level of randomness involved in train departure and trip times. The single-track lines that comprise the majority of the North American rail network have limited capacity and can frequently become congested under these new rail traffic demands. The combined impact of traffic volume, heterogeneity, and level of randomness in train plans has not always been fully considered by previous approaches to the study of rail line capacity. This dissertation develops new capacity evaluation and infrastructure planning techniques for single-track lines that consider the impact of relationships between infrastructure layout, train operating plans including train-specific levels of service, and train characteristics on line capacity. In this study, the randomness involved in a train operating plan is described by “schedule flexibility” and “operating style”. In chapter 1, the concepts of operating style and schedule flexibility are proposed and defined. In chapters 2 and 3, a capacity evaluation and alternative comparison process are proposed to assist the capacity evaluation and planning of single-track lines under mixed or flexible operation. In chapter 4, an optimization model is developed to determine the optimal number and locations of passing sidings for single-track lines under structured operation. In chapter 5, the concept of traffic conflict analysis is introduced as a research direction to address rail infrastructure and operational planning problems. The methods developed in this dissertation can help to better assess mainline capacity under current operating conditions and determine more effective infrastructure expansion projects or changes in operational strategy for railroads and passenger rail agencies in North America. Use of these methods can help railroads improve their service quality and maximize returns to their stakeholders.
Graduation Semester
2017-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/98121
Copyright and License Information
Copyright 2017 Mei-Cheng Shih

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