Hamed Pouryousef ; Pasi Lautala, Ph.D, P.E. Hamed Pouryousef ; Pasi Lautala, Ph.D, P.E. Michigan Tech. University Michigan Tech. University PhD Candidate.

Slides:



Advertisements
Similar presentations
Capacity Studies on Transportation Network Presented by Rakesh Ambre ( ) Under Guidance Of Prof. Narayan Rangaraj.
Advertisements

RELATION BETWEEN DEMAND AFTER TRAIN PATHS AND CAPACITY OF RAILWAY TRANSPORT INFRASTRUCTURE Ing. Josef Bulíček, Ph.D. Ing. Lukáš Fiala University of Pardubice,
Real Time Train SNCF. 1 Agenda Essentials Basic Model Applications Traffic density is getting very high in several networks and management.
SCORT/TRB Rail Capacity Workshop - Jacksonville Florida1 1  A Primer on Capacity Principles  New Technologies  Public Sector Needs 22 September
The travel solution for our time 1 High-Speed Rail at Amtrak Frank Vacca, Chief Engineer June 24, 2011.
Abstract The SEPTA Regional Rail system serves as an important network for the Philadelphia region, moving many commuters during the peak hours on suburb-to-city.
Train platforming problem Ľudmila Jánošíková Michal Krempl University of Žilina, VŠB-Technical University of Ostrava, Slovak Republic Czech Republic.
Carter Danne, PE, PTOE Parametrix, Inc. Assessment of Point Defiance Bypass for High-Speed Intercity Passenger Rail.
1.  NCHRP Report 657 is the general guidebook for implementing passenger service on shared corridors  This guidebook “drills down” on the issue of service.
1 Amtrak’s Vision for the Northeast Corridor PRIIA 305 Annual General Meeting – June 14 th, PRIIA305 DMU Approval Meeting Andrew Wood Chief, Next.
Bottleneck Identification and Calibration for Corridor Management Planning Xuegang (Jeff) Ban Lianyu Chu Hamed Benouar California Center for Innovative.
INFORMS 2012 Shared Corridor Railway Maintenance Scheduling Brennan M. Caughron Graduate Research Assistant Rail Transportation and Engineering Center.
Route 17 Corridor Study Public Workshop II – November 29, 2012 Orange / Sullivan County 1.
Analysis and Multi-Level Modeling of Truck Freight Demand Huili Wang, Kitae Jang, Ching-Yao Chan California PATH, University of California at Berkeley.
1 February 2009 Analysis of capacity on double-track railway lines Olov Lindfeldt February 2008.
10 December J/ESD.204J Lecture 13 Outline Real Time Control Strategies for Rail Transit Prior Research Shen/Wilson Model Formulation Model Application.
Ing. Tomáš Vicherek, Ing. Vlastimil Polach, Ph.D. Research and development Automatic Route Setting According to Train Paths in Anticipated Time Schedule.
Transportation Data Palooza Washington, DC May 9, 2013 Steve Mortensen Federal Transit Administration Data for Integrated Corridor Management (ICM) Analysis,
Route 28 South of I-66 Corridor Safety and Operations Study Technical Committee Meeting #2 June 25,
©David F. Thurston, 2012, All rights reserved Capacity Implications of PTC now and in the Future David Thurston, Ph.D., P.E., FIRSE Vice President – Rail.
ARISTOTELION UNIVERSITY OF THESSALONIKI SCHOOL OF TECHNOLOGY FACULTY OF RURAL AND SURVEYING ENGINEERING DEPARTMENT OF TRANSPORTATION AND HYDRAULIC ENGINEERING.
Operation-driven Scheduling Approach for Fast, Frequent and Reliable Railway Services Alfons A.M. Schaafsma, Vincent A. Weeda ProRail, Department of Traffic.
Rail Zürich, Train scheduling based on speed profiles © ETH Zürich | M. Fuchsberger Martin Fuchsberger, ETH Zurich RailZurich, 11. February 2009.
Randy Wade TRB Intercity Passenger Rail Committee Tuesday January 25, 2011 Washington, DC.
Optimal Design of Timetables to maximize schedule reliability and minimize energy consumption, rolling stock and crew deployment.
CE 515 Railroad Engineering
Slide 1 ILLINOIS - RailTEC Capacity of Single-Track Railway Lines with Short Sidings to Support Operation of Long Freight Trains Ivan Atanassov, C. Tyler.
Rail Related Research at IIT Madras
3rd Street Light Rail Process and Challenges of Developing Transit Signal Priority Javad Mirabdal, Jack Fleck & Britt Thesen Department of Parking and.
Caltrain Modernization Program CA Passenger Rail Summit April 29, 2015.
RAILWAY INDUSTRY TRAIN PLANNING LEVEL 2 TRAINING Module 2 - Who, What Why?
Analysis of the High Speed Rail in California Daniel Montanez 1, Antonio Castro 1 1 Napa Valley College, Napa, CA Abstract: San Francisco and Los Angeles.
Program Update Baltimore MPO November 25, Internal Draft AGENDA  Program Overview  Alternatives Development  Stakeholder and Public Outreach.
1 Segment Level Analysis of Travel Time Reliability Meead Saberi K., Portland State University I-5 SB, San Diego, CA.
RAILWAY INDUSTRY TRAIN PLANNING LEVEL 2 TRAINING Module 9 - The TOCs and Network Rail.
Evaluating InSync Performance in Microsimulation Aleksandar Stevanovic, PhD, PE Florida Atlantic University Transpo 2012 Bonita Springs, FL October 29,
Evaluating Robustness of Signal Timings for Conditions of Varying Traffic Flows 2013 Mid-Continent Transportation Research Symposium – August 16, 2013.
Computational Experiments Algorithm run on a Pentium IV 2.4 GHz Instances from “Rete Ferroviaria Italiana” For each station: - minimum interval between.
 Classes of trains  Fundamental principles of track authority  Impact of power/ton ratios  Drivers of dispatch priority 22 September 2010 SCORT/TRB.
First Regional Conference Energy Efficiency of Railways in terms of Sustainable Development November Belgrade, Serbia Mr. Saša Živković, B.Sc.Eng.
Integration of Transportation System Analyses in Cube Wade L. White, AICP Citilabs Inc.
TRB 88th Annual Meeting, Washington DC January, 2009 Huan Li and Robert L. Bertini Transportation Research Board 88th Annual Meeting Washington, DC January.
Accuracy in Real-Time Estimation of Travel Times Galen McGill, Kristin Tufte, Josh Crain, Priya Chavan, Enas Fayed 15 th World Congress on Intelligent.
A Genetic Algorithm Based Microscopic Simulation To Develop The Evacuation Plan For Multi-institutional Centers Fengxiang Qiao, Ph.D., Assistant Professor,
KIT – University of the State of Baden-Wuerttemberg and National Laboratory of the Helmholtz Association INSTITUTE FOR ECONOMIC POLICY RESEARCH (IWW),
1 Northeast Corridor - Overview Regional Plan Association Spring 2010.
Railway Operations: Issues and Objectives Capacity management Infrastructure planning Timetable preparation Management of day-to-day movement of trains.
Portland North Small Starts Alternatives Analysis Coordination Meeting June 15, 2009.
Measuring Travel Time Reliability of Transportation Systems Abstract When traveling people want to be on time and avoid any traveling delays. We worked.
Ames Research Center 1 FACET: Future Air Traffic Management Concepts Evaluation Tool Banavar Sridhar Shon Grabbe First Annual Workshop NAS-Wide Simulation.
WORK ZONE DELAY ESTIMATION Work Zone Management, Accelerated Construction, and Smart Work Zones TEAM Monthly Meeting November 16, 2004 Luis Porrello, Ph.D.,
Jack is currently performing travel demand model forecasting for Florida’s Turnpike. Specifically he works on toll road project forecasting to produce.
Amtrak Gov’t Affairs October 19, 2007 Amtrak Key Facts Summary November 12, 2007.
Transit Signal Priority: The Importance of AVL Data David T. Crout Tri-County Metropolitan Transportation District of Oregon (TriMet) Presented at Transportation.
December 17, 2010 Developing Transit Performance Measures for Integrated Multi-Modal Corridor Management.
© APTA and AREMA D Introduction to Railway Capacity C. Tyler Dick, P.E. University of Illinois at Urbana-Champaign.
Q/.r NSRZKLA4-P1 Timetable planning Leo Kroon Sept 9, 2003.
Summary of the WILMAPCO Congestion Management Process Prepared for T3 Webinar September 18, 2007.
Commuter Rail Studies Summary of MAG High Capacity Transit Study June 2003 Commuter Rail Summary.
Kanok Boriboonsomsin, Guoyuan Wu, Peng Hao, and Matthew Barth
Alternative ways to address growth
Public Conference December
NCHRp Report 773 Capacity Modeling Guidebook FOR SHARED-USE PASSENGER AND FREIGHT RAIL OPERATIONS A project to develop guidebook for DOT’s, Public Agencies.
Macro / Meso / Micro Framework on I-395 HOT Lane Conversion
Light Rail Enhancement Project
Analysis of capacity on double-track railway lines
Michigan Update Michigan Association of Rail Passengers
System Optimizations Work Group Update
Region 8: What is Possible? Topic : Systems Optimization
Analysis of the High Speed Rail in California
Presentation transcript:

Hamed Pouryousef ; Pasi Lautala, Ph.D, P.E. Hamed Pouryousef ; Pasi Lautala, Ph.D, P.E. Michigan Tech. University Michigan Tech. University PhD Candidate Assistant Professor 2014 INFORMS Annual Meeting; November 9-12, 2014; San Francisco, CA Capacity Evaluation along Baltimore-DC Based on Directional vs. Non-directional Scenarios of Operation

Introduction, Background Review of Case Study: Baltimore-DC Capacity Evaluation on Directional vs. Non- directional Scenarios Conclusions and Next Steps 2 Outline

Almost 80% of the U.S. rail network are single track corridors Double and multiple-track corridors for shared freight and passenger/commuter traffic (NEC, California, Midwest) Double and Multiple-track Corridors: Directional Operation Approach (Europe, Asia) Non-directional Operation Approach (North America) Directional approach and capacity Directional operation approach offers higher level of capacity (Tolliver, 2010; Hansen, 2008) Research Questions: Use rescheduling/rerouting to convert a multiple-track corridor under “Non- directional” operation pattern to “Directional” operation pattern? What are the impacts of changes on the Capacity and LOS? 3 Introduction, Background

Capacity and LOS analysis vary based on the techniques and methodologies Simulation is a common tool to evaluate Capacity and LOS Commercial Railway Simulation Timetable-based vs. Non-timetable based 4 Introduction, Background

5 “Combined” Simulation Steps Amtrak RTC Database & Output Replicating RTC in RailSys & OpenTrack Timetable/ Capacity Analysis (RailSys & OpenTrack) Why to take advantage of multiple simulation tools? RTC: Predefined database of U.S. signaling and rolling stock systems (Conflict-free Schedule) RailSys & OpenTrack: Advanced capacity and timetable management features and outputs

Introduction, Background Review of Case Study: Baltimore-DC Capacity Evaluation on Directional vs. Non- directional Scenarios Conclusions and Next Steps 6 Outline

7 Baltimore – Washington Case Study Database, replicated in RailSys and OpenTrack based on RTC’s database: -Infrastructure: 40.6 miles of Northeast Corridor (Baltimore-DC) Signaling: Cab signaling system, integrated with absolute permissive block (APB) -Trains: 136 daily trains (Acela, Commuter, Long distance and Regional Amtrak) -Operation rules: Speed limits, train priority, stop patterns, dwell times, arrival-departure times Washington DC Baltimore

RTC Output - Initial Timetable Northeast Corridor Timetable in RTC (Initial Timetable)

RailSys Input - Replicated Timetable Initial Timetable in RailSys

OpenTrack Input - Replicated Timetable Initial Timetable in OpenTrack

11 Validating Developed Timetable in RailSys & Opentrack Initial Timetable in RTC Replicated Timetable in RailSys Same pattern with minor differences Same order and schedule of trains Same stop pattern 1-3 minutes deviation in some arrivals / departures or dwell times Replicated Timetable in Opentrack

12 Summary of Developed/Validated Timetables Evaluation CriteriaInitial Timetable Replicated Timetable RTCRailSysOpenTrack Version of Software67 Z (2013) (2013)1.7.5 (2014) Running Time of Simulation 35 sec18 sec301 sec No. of Daily Trains Successfully Simulated 136 Timetable Duration24 h Total Delay of All Trains56.6 min103.5 min83.4 min Avg Delay per Train25 sec45 sec37 sec Correlation with Initial Timetable Initial Timetable Sufficient, could be improved via database adjustments Good; could be improved via minor adjustments

Introduction, Background Review of Case Study: Baltimore-DC Capacity Evaluation on Directional vs. Non- directional Scenarios Conclusions and Next Steps 13 Outline

14 A Non-directional Multiple-Track Case Study Baltimore-D.C. is operated under “Non-directional” pattern: Providing access to station platforms Preventing train conflicts by providing more flexible routing options What would be capacity effects of directional operation to Delay, Average Speed, Track Occupancy Limitations Baltimore – D.C. considered a “stand-alone” segment Would require platform construction at intermediate stations Washington DC Baltimore Northbound Southbound

15 Several Routing Patterns Along Baltimore-DC Using Single Track (Directional / “No Crossovers) Using Multiple Tracks (Non-directional /“Crossovers”) Some of the Common Routings SBNB SB

16 Research Steps, Scenarios

17 Initial Schedule (Non-directional) Northbound (NB) 31.2% of Acela trains, mostly northbound, use crossovers All NB Regional trains Southbound (SB) No commuter trains Three Acela trains Breakdown of trains using crossovers Number of Trains

18 Scenario 1- “Rerouting Only” a a b b

19 Scenario 2- Rerouting/Rescheduling (Fully Directional) c c b b

20 Analysis on Trains’ Schedule/Route Changes Summary of rerouting and rescheduling changes to provide a fully directional operation

21 Analysis on Track Occupancy Level Average Occupancy Level of Tracks (per day) Maximum Occupancy Level of Tracks (in an hour) Washington DC Baltimore

22 Analysis on Average Speed & Delay Speed (mph) Train Delays Analysis in Different Scenarios Train Speed Analysis in Different Scenarios

“Normalized Speed-Delay” Parameter A new combined parameter, defined as “Speed-Delay” normalized parameter for evaluating the trade-off between increased speeds and delays

Introduction, Background Review of Case Study: Baltimore-DC Capacity Evaluation on Directional vs. Non- directional Scenarios Conclusions and Next Steps 24 Outline

Summary and Conclusions 25 Evaluation Criteria Initial Schedule Scenario1- Rerouting Scenario2- Rescheduling/rerouting Speed-Delay Total delay of all Trains min min117.4 min Avg delay per train 45.6 sec 45.7 sec51.8 sec Longest delay of a train180 sec 161 sec Avg speed of all trains70.4 mph71.3 mph 71.9 mph Sum of “Speed-Delay” normalized parameters Track Occupancy Level Avg Occupancy level of tracks per day (%) Track #110.5%12.2%10.8% Track #26.6%9.8%11.6% Track #3 5.7%3.5%0.0% Track #4 7.0%0.3%0.0% Max. Occupancy level of tracks per hour (%) Track #1 50.7% Track #236.9%44.6%45.5% Track #3 34.4% 0.0% Track #4 19.2%8.5%0.0%

Summary and Conclusions Research used rescheduling/rerouting to convert a multiple- track corridor under “Non-directional” operation pattern to “Directional” operation pattern Combined simulation approach to take advantage of advanced capacity and timetable management features Achieved fully directional operations through rerouting/rescheduling Increased average speeds Slightly increased delays and track occupancy Traffic removed from Tracks #3 and #4 Improved normalized Speed-Delay (SD) parameter 26

Next Steps of Research Next Step: Develop a modular approach for automatic rescheduling and timetable improvements? Hybrid Optimization of Train Schedule (HOTS) Model 27 The initial timetable of NEC corridor (Top figure) was reschedule and compressed using “Same-Order” approach of HOTS model (Bottom figure)

28 Thanks for Your Attention! Question or Comment? Hamed Pouryousef Pasi Lautala Acknowledgment: Amtrak (Davis Dure) Berkeley Simulation-RTC (Eric Wilson) OpenTrack (Daniel Huerlimann) RMCon GmbH- RailSys (Sonja Perkuhn, Gabriele Löber) This research was supported by National University Rail (NURail) Center, a US DOT-OST Tier 1 University Transportation