Statistical Estimation of Line Congestion Delay in U.S. Freight Rail

1. Statistical Estimation of Line Congestion Delay in U.S. Freight Rail Delivered by Michael F. Gorman University of Dayton/ MFG Consulting Inc. Seattle INFORMS November 2007

2. 2 Overview Problem Statement Background Motivation Literature Contribution Methodology Development Model TPC-based variables- Train and track variables which establish Free Running Time Congestion Estimation Components Error term Equation specification Dependent, Independent variables considered Functional form Data selection: districts, train groupings, outliers Results 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 Literature: Optimization � Harrod(2007), Sahin (2006), Carey (1994), etc. No uncertainty 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 My Contribution: 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