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Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

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TIL/T&P Masterclass presentation by Thomas Schreiter on his PhD project in cooperation with Rotterdam Harbor Authority and about project management. December 2011.

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Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

  1. 1. Dynamic Traffic Management:Class-specific Control at de A15Thomas Schreiter, Hans van Lint, Serge Hoogendoorn, ZlatanMuhurdarević, Ernst Scheerder Goal: 40 km in 38 min Delft University of Technology Challenge the future
  2. 2. A15 during evening peak Delft University of Technology Challenge the future
  3. 3. Class-specific Vehicle Length•  More jam ßà longer trucks (in relative terms)•  Worsening effect•  Person-car equivalent (pce) value •  Effective density = pce * density •  Dynamic, dependent on traffic state! Thomas Schreiter: “Dynamisch Verkeersmanagement” 3/16
  4. 4. Truck percentage•  A lot more trucks than on other highways Thomas Schreiter: “Dynamisch Verkeersmanagement” 4/16
  5. 5. Outline•  The model BOS HbR •  Control Loop •  3 Components•  Examples of class-specific Control•  Conclusion•  Review Thomas Schreiter: “Dynamisch Verkeersmanagement” 5/16
  6. 6. BOS HbR Traffic System A15 Actuators Sensors Real-time Real-time Real-time Control Prediction Estimation BOS-HbR ( Beslissingsondersteunend Systeem voor het Havenbedrijf Rotterdam ) Network Traffic model ∂ku ∂qu + =0 Goalfunction ∂t ∂x Travel time <= 38min Vehicle properties Historic inflows / outflows l = 20 m l=6m vmax =85 km/u vmax =110 km/u Thomas Schreiter: “Dynamisch Verkeersmanagement” 6/16
  7. 7. Estimation: traffic state now•  Given: induction loops •  Flow [veh/uur], Speed •  Every ~500 m and 60 sec•  Needed: 1.  Density [vtg/km] every 100 m •  Apply filter Check 2.  Traffic composition •  Historic microscopic loop data 5:30 8:00 10:30 Past now Thomas Schreiter: “Dynamisch Verkeersmanagement” 7/16
  8. 8. Prediction: traffic state during next 1 hour •  Traffic Flow Model: Fastlane •  Road segmented into cells of 100 m, time step 3 sec •  Density(t+1) = Density(t) + Inflow(t) – Outflow(t) •  Simulation of incidents Incident 10% Intensiteit200 veh/h Dichtheid Inflow Fundamental Diagram Turnfraction •  Class-specific: trucks and cars Thomas Schreiter: “Dynamisch Verkeersmanagement” 8/16
  9. 9. Prediction: traffic state during next 1 hour• Results Prediction •  Density, flow, speed •  Location of congestion •  Travel times 5:30 8:00 now Prediction 10:30 Past Thomas Schreiter: “Dynamisch Verkeersmanagement” 9/16
  10. 10. Control: Optimization of Traffic for each vehicle class•  Model predictive control (MPC) •  Predict effect of DTM measurement •  Choose best DTM measurement •  In realtime •  Example: class-specific route guidance during incident: Thomas Schreiter: “Dynamisch Verkeersmanagement” 10/16
  11. 11. Class-specific Route Guidance•  Experiment with simple network •  à less total delay [veh*h]•  Possible Application for A15: Thomas Schreiter: “Dynamisch Verkeersmanagement” 11/16
  12. 12. Class-specific Ramp Metering •  Prioritize trucks à shorter travel time trucks à fewer spillback at on-ramp •  Prioritize cars à Less total delay Thomas Schreiter: “Dynamisch Verkeersmanagement” 12/16
  13. 13. Possible locations for class-specific ramp metering A15 Thomas Schreiter: “Dynamisch Verkeersmanagement” 13/16
  14. 14. Conclusion Traffic System A15 Actuators Sensors Real-time Real-time Real-time Control Prediction Estimation BOS-HbR ( Beslissingsondersteunend Systeem voor het Havenbedrijf Rotterdam ) •  Dynamic Traffic Management •  Goal: improve traffic state during incidents •  By prediction of expected traffic situation •  Predict jam locations •  Class-specific control improves traffic state Thomas Schreiter: “Dynamisch Verkeersmanagement” 14/16
  15. 15. My Review Planning RealityEstimation 1st year 1.5 yearsPrediction 2nd year Still busy with calibrationControl 3rd year Mid of 3rd to beginning of 4th yearDissertation 4th year start 3 months later Thomas Schreiter: “Dynamisch Verkeersmanagement” 15/16
  16. 16. My Review•  Good •  Culture: open, freedom, honesty, relaxed •  Theory and application •  Exciting topic •  Helicopter flights J•  Tough •  Culture •  Dutch at TUD and sponsors •  Getting distracted by other interesting research topics Thomas Schreiter: “Dynamisch Verkeersmanagement” 16/16
  17. 17. A15 haven-uit: bij Charlois Delft University of Technology Challenge the future
  18. 18. A.Homepage met resultaten in realtimewww.regiolab-delft.nl/boshbr Thomas Schreiter: “Dynamisch Verkeersmanagement” 18/16
  19. 19. www.regiolab-delft.nl/boshbr•  BOS-HbR op computer bij TU Delft•  Vlekkenkaarten •  Snelheid, intensiteit •  A15, beide richtingen •  Schatting, voorspelling Thomas Schreiter: “Dynamisch Verkeersmanagement” 19/16
  20. 20. Space (30km) à Screenshots – Schatting Current Speed Time (4h) à Space (30km) à Current Flow Thomas Schreiter: “Dynamisch Verkeersmanagement” 20/16
  21. 21. Space (30km) à Screenshots – Voorspelling Time (1h) àCurrent Speed Space (30km) à Current Flow Thomas Schreiter: “Dynamisch Verkeersmanagement” 21/16
  22. 22. B.Resultaten met incident Thomas Schreiter: “Dynamisch Verkeersmanagement” 22/16
  23. 23. Resultaten: Incident simulaties•  Voorbeeld: 26 jan 2011 om 16.10 X Thomas Schreiter: “Dynamisch Verkeersmanagement” 23/16
  24. 24. Resultaten: Incident simulaties•  Voorbeeld: 26 jan 2011 om 16.10 •  incident Thomas Schreiter: “Dynamisch Verkeersmanagement” 24/16
  25. 25. Resultaten: Incident simulaties•  Voorbeeld: 26 jan 2011 om 16.10 •  Herrouteren: Wat gebeurd, als het verkeer over het onderliggende wegennet geherrouteerd wordt? Thomas Schreiter: “Dynamisch Verkeersmanagement” 25/16

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