2. Overview. Problem Statement Background MotivationLiteratureContributionMethodology DevelopmentModelTPC-based variables- Train and track variables which establish Free Running TimeCongestion Estimation ComponentsError termEquation specificationDependent, Independent variables considered
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1. Statistical Estimation of Line Congestion Delay in U.S. Freight Rail Delivered by Michael F. Gorman
University of Dayton/
MFG Consulting Inc.
2. 2 Overview Problem Statement Background
TPC-based variables- Train and track variables which establish Free Running Time
Congestion Estimation Components
Dependent, Independent variables considered
Data selection: districts, train groupings, outliers
Final regression equation
Assessment/comparison to prior research
Regression Results Usage in Practice
Predicted run times and equation tracking
Marginal congestion impact
3. 3 Motivation Railroads are running at capacity in many lanes –
Billions being spent on track
Impact of marginal traffic is critical to assess
What are the congestion implications of an additional train?
Simulation of many train districts can be cumbersome
Calibrating is time consuming
We develop an alternative method to gain these critical insights without excessive labor requirements
Optimization – Harrod(2007), Sahin (2006), Carey (1994), etc.
Simulation tests of contributors to congestion: (Vromans, Gibson, etc.)
Representation of the operating environment
Parametric Models: Prokopy and Rubin (1975), Krueger (1990)
Statistical analysis of simulated environment
The first to statistical evaluate congestion impacts on train operations
Important to research: Which variables do contribute to congestion?
Important to practice: What is the congestion impact of trains
4. 4 This Research Establish appropriate dependent variable
Total train running time (not delay time or deviation from schedule)
Establish appropriate functional form
Prior research hypothesizes log-linear; we find no evidence of exponential delay
We specify an autoregressive estimation equation to capture correlatio between successive trains in the district
Establish appropriate regression equations
Different regression equations by train priority (high, medium, low)
Hypothesize set of explanatory variables for train running time
Test these variables for their significance
Apply the explanatory equation to eight districts
Evaluate claims of prior research
Evaluate equation usefulness:
Estimate the marginal congestion impact of a train
Apply explanatory equation to districts for purposes of forecasting district run times
Evaluate forecast accuracy
Can we predict train running time, given these explanatory variables?
5. 5 Subdivisions Under Study