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Title:Influence of traffic complexity and schedule flexibility on railway classification yard capacity and mainline performance
Author(s):Dick, C. Tyler
Director of Research:Barkan, Christopher P.L.
Doctoral Committee Chair(s):Barkan, Christopher P.L.
Doctoral Committee Member(s):Ouyang, Yanfeng; Work, Daniel B.; Clarke, David B.
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):railroad
operations
network
efficiency
blocking
train delay
hump yard
marshalling yard
precision scheduled railroading
train plan
manifest
freight
Abstract:This dissertation presents research to understand and quantify the relationships governing railway classification yard capacity and performance, and their connection to the capacity, performance and infrastructure planning of railway mainlines under flexible operations. Understanding how schedule flexibility influences the capacity and performance of classification yards and mainlines is critical to understanding how the railway network responds to service disruptions and changes in rail traffic patterns. To improve upon current volume-based representations of yard capacity, the concept of classification yard traffic complexity was introduced. An original exploratory simulation model of a hypothetical yard operation was developed and used to create a yard capacity constraint involving railcar volume, number of blocks, and number of outbound trains. Traffic complexity and schedule flexibility in hump classification yards were further investigated through the first academic research implementation of YardSYM, a discrete-event simulation model of yard operations. The model was used to conduct controlled simulation experiments in which combinations of railcar throughput volume, number of blocks, number of outbound trains, block size distribution, train departure patterns, arriving train schedule flexibility, and inbound block variability were varied for the eastbound operation at the Belt Railway Company of Chicago Clearing Yard. Regression analysis yielded a yard capacity model that quantified trade-offs between throughput volume and number of blocks as a function of maximum allowable average railcar dwell. To better understand and explain the mechanics of the yard operation, the concept of “adjusted number of blocks” was developed. Adjusted number of blocks was then used to describe the effect of poorly matched block sizes and classification track lengths on yard performance and capacity. The results of the yard simulation experiments demonstrate the need for a network perspective in developing railway operating plans. Efforts to manage outbound trains in a manner that improves performance at one yard may create inbound train conditions that are even more detrimental to destination yards. Since schedule flexibility is more detrimental to yard performance than volume variability, practitioners have an incentive to prioritize on-time originations to reduce arrival time variability at destination yards. In general, classification yards are disruptive to efforts to operate trains on precise schedules and with consistent train sizes. Rail Traffic Controller mainline operation simulation software was used to conduct the first controlled simulation experiments examining the influence of schedule flexibility on single-track mainlines with different numbers of passing sidings and double-track segments. Starting from an ideal structured operation on a mainline corridor, small amounts of schedule flexibility rapidly lead to increasing train delay and the need for additional track infrastructure to maintain a given level of service. Combining the yard and mainline portions of this research provides a more complete understanding of the railway network efficiency cycle, and how schedule flexibility and volume variability can propagate through classification yards and mainlines. Understanding this phenomenon will enable design of yard and mainline capacity expansion projects that are more robust to the effects of schedule flexibility and other stochastic elements, and improve network reliability and efficiency by reducing network-level congestion events.
Issue Date:2019-04-15
Type:Text
URI:http://hdl.handle.net/2142/105024
Rights Information:Copyright 2019 C. Tyler Dick
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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