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The Moving Dynamic Nature of Progression Curves for Freeway Incident Related Congestion

The Moving Dynamic Nature of Progression Curves for Freeway Incident Related Congestion. Neveen Shlayan PhD Student Transportation Research Center University of Nevada, Las Vegas. Introduction . Incidents on urban freeways Causing congestion and delays in both directions

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The Moving Dynamic Nature of Progression Curves for Freeway Incident Related Congestion

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  1. The Moving Dynamic Nature of Progression Curves for FreewayIncident Related Congestion NeveenShlayan PhD Student Transportation Research Center University of Nevada, Las Vegas

  2. Introduction • Incidents on urban freeways • Causing congestion and delays in both directions • Secondary impact has been poorly defined by using static time and length thresholds • Does not cover the full range of effects, • Resulting in erroneous data http://www.youtube.com/watch?v=q0FIO775hEE

  3. Why Does it Matter? • Incident Management • Policy Making • Studying the overall impact of an incident • Financial • Fatalities • Productivity • Proper Definition of Secondary Incidents • Secondary incidents compose 20 percent of all nonrecurring events (Federal Highway Administration, FHWA-OP-04-052, 2004)

  4. Outline • Thorough study of freeway incidents and the progression curve • Classical progression curves • Case study in the Seattle, Washington • VISSIM simulations • Proposed Novel Progression Curve • Conclusion

  5. FACTSTexas Transportation Institute, FHWA-HOP-09-005, 2008 • Incidents cause 33% to 60% of all delays • The capacity of the facility is reduced by up to • 17% (shoulder only) • 63% one lane obstruction • 77% two lane obstructions • 50% due to “rubbernecking” effect

  6. NHP Average Arrival, Management, and Clearance times for incidents on the I15 interchange in the Las Vegas Area

  7. Static Thresholds for Secondary Congestion • The maximum queue clearance length and clearance time for the incident Carlos Sun and Venki Chilukuri Secondary Accident Data Fusion for Assessing Long Term Performance of Transportation Systems. US Department of Transportation, (MTC Project 2005-04):1–38, 2007.

  8. Dynamic Thresholds for Secondary Congestion • It was found that static and dynamic thresholds can vary in incident definitions by 30 percent. Carlos Sun and Venki Chilukuri Secondary Accident Data Fusion for Assessing Long Term Performance of Transportation Systems. US Department of Transportation, (MTC Project 2005-04):1–38, 2007.

  9. Case StudyThe I-5 and I-405 interchange near Linwood north of Seattle, WA An accident occurred at 2:55pm Queue length of 2.3 “rubbernecking”

  10. Progression of the queue after the incident clearance extends to twelve miles even after an hour from clearance

  11. VISSIM Simulations Artificially Creating Accidents by Lane Obstructions and Speed Reduction

  12. Simulations Scheme Tracking the locations of front and back of the queue In the direction of the accident • Four, three, and two lane obstructions In the opposite direction of the accident “rubbernecking” • Low traffic volume • (3000 vph) • Moderate traffic volume • (5000 vph) • High traffic volumes • (8000 vph)

  13. Moving Dynamic Progression Curve

  14. Conclusion • Secondary congestion is highly dynamic • Secondary Incidents definition must be case specific

  15. Future Work • Development of detailed models that will study all types of secondary congestion based on Shock wave analysis • A software is being built that will process data using the above analysis that will identify secondary incidents mapped to their primary ones

  16. Thank You!

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