Analyzing Hybrid Track Configurations Through Design of Experiments Techniques

1. Analyzing Hybrid Track Configurations Through Design of Experiments Techniques INFORMS 2012 Samuel L. Sogin, C. Tyler Dick,Yung-Cheng Lai, & Christopher P.L. Barkan

2. Outline Overview Previous Findings Design of experiments (DOE) Hybrid track capacity factors Response surface model Preliminary findings Questions

3. Capacity Upgrade Map One track baseline Siding extensions Fill-in Sidings Increased Capacity Increased Cost Connect Sidings Full Two-Mainline-Track

4. Single Track Sequence Single Track Baseline B A Siding Extension B A B A Fill-in Sidings Partial Second Track B A A Second Track B

5. Comparing Single Track & Double Track Double Track • Most delays are caused by overtaking trains or by crossovers movements • Likely that trains will perform close to the minimum run time • Delays can follow a exponential distribution • Speed differential between trains is a major factor by causing more overtaking situations Single Track Most delays are caused by trains taking turns using the single track bottleneck sections Very unlikely that trains will perform close to the minimum run time Delays can follow a log-normal distribution Speed of the passenger train is not a major factor

6. Freight Train Delay Distributions Double Track Single Track Freight trains interacting with 79 mph passenger trains

7. Freight Delay Distributions Double Track Freight trains interacting with 79 mph passenger trains Freight trains interacting with 110 mph passenger trains Single Track

8. DOE – More Information From Less Data • Design of Experiments (DOE): • Statistical tool to systematically determine runs in a experiment design matrix • Gain more information than simply varying 1 factor at a time • Consider 3 factors at low, medium, and high levels • 27 () runs are needed to cover all permutations • (Full Factorial) • Could we gain knowledge of the key drivers of the process without doing all 27 runs? • (Partial Factorial – 16 runs) • Could be used to analyze more factors effecting train performance than typical 2-3 factor analyses

9. Hybrid Track Response Surface Design 50 runs to test all interactions and curvature factors

10. Hybrid Track Configurations DOE Full Factorial: 952 Runs JMP Partial Factorial: 50 Runs

11. Double Track Progression Extend Sidings Alternate Double Track Sections (Alternate) Build In (Split) Build Out (Grouped)

12. Turnout & Crossover Management

13. Rail Traffic Controller Developed by Eric Wilson from Berkeley Simulation Software Emulates a dispatcher controlling train movements across a network based on train priority Integrated train performance calculator Inputs: track, signals, trains, and schedule Output: delay, average velocity, on time performance

14. Route Characteristics • 245 miles long • 10,000 ft. sidings • 10 miles between siding centers (8 miles between turnouts) • 2.0 miles between signals • 2-block, 3-aspect signaling • 1 Origin-Destination Pair • 0% grade & curvature

15. Train Characteristics • Train schedules are randomized over the 24 hour period • All delays are due to mainline train interactions

16. Analysis Guide The subsequent analysis shows snapshots of the profiles from the response surface models for both freight and passenger delay Each panel shows the change in delay for each variable 12 panels total Steeper lines indicate the trains are more sensitive to a change in the factor level Parallel lines indicate independent effects Non-parallel lines indicate interaction effects

17. % Double Track Traffic Level Freight Speed Passenger Speed Grouping % Freight Passenger Delay Freight Delay 50 mph 110 mph 48TPD 75 % Alternate 50% 73%

18. % Double Track Traffic Level Freight Speed Passenger Speed Grouping % Freight Passenger Delay Freight Delay 45 mph 110 mph 40 TPD 75 % Alternate 50% 60 mph

19. Freight Speed Passenger Speed Traffic Level % Double Track % Freight Grouping Passenger Delay Freight Delay 45 mph 110 mph 48 TPD 75 % Extend 75% Alternate

20. Freight Speed Passenger Speed Traffic Level % Double Track % Freight Grouping Passenger Delay Freight Delay 45 mph 110 mph 48 TPD 75 % Extend 75% Split

21. Freight Speed Passenger Speed Traffic Level % Double Track % Freight Grouping Passenger Delay Freight Delay 45 mph 110 mph 48 TPD 75 % Extend 75% Group

22. Preliminary Findings • Major Effects • Traffic Level • % Double Track • Freight Train Speed • Minor Effects • Progression strategy • Passenger Train Speed • Traffic Composition • Interesting Results • By being the high priority train, the passenger trains are less sensitive to the identified factors than freight trains • Adding double track improves train delay in a near-linear manner • Progression strategy effect is modest • Priority trains can see significant delay reductions by improving non-priority train speeds

23. Future Work 23 Refine the response surface model to incorporate only significant terms Validate the model against data not used in fitting the model Analyze the delay distributions to explore the transition from Lognormal to Exponential Expand the ranges covered in the identified factors to improve confidence limits Add resolution to the progression from single to double track while holding some minor variables constant

24. Questions? Samuel L. Sogin 847-899-2711 ssogin2@illinois.edu