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Dynamic Traffic Management:
Class-specific Control at de A15
Thomas Schreiter, Hans van Lint, Serge Hoogendoorn, Zlatan
Muhurdarević, Ernst Scheerder



                               Goal: 40 km in 38 min




        Delft
        University of
        Technology


        Challenge the future
A15 during evening peak




   Delft
   University of
   Technology


   Challenge the future
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
Truck percentage




•  A lot more trucks than on other highways



                         Thomas Schreiter: “Dynamisch Verkeersmanagement”   4/16
Outline

•  The model BOS HbR
   •  Control Loop
   •  3 Components

•  Examples of class-specific Control

•  Conclusion

•  Review




                          Thomas Schreiter: “Dynamisch Verkeersmanagement”   5/16
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
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
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%
                        Intensiteit




200 veh/h
                                      Dichtheid


            Inflow                      Fundamental Diagram                         Turnfraction
            •  Class-specific: trucks and cars


                                                  Thomas Schreiter: “Dynamisch Verkeersmanagement”   8/16
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
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
Class-specific Route Guidance

•  Experiment with simple network
   •  à less total delay [veh*h]




•  Possible Application for A15:




                            Thomas Schreiter: “Dynamisch Verkeersmanagement”   11/16
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
Possible locations for
    class-specific ramp metering A15




              Thomas Schreiter: “Dynamisch Verkeersmanagement”   13/16
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
My Review

               Planning                     Reality

Estimation     1st year                     1.5 years

Prediction     2nd year                     Still busy with
                                            calibration
Control        3rd year                     Mid of 3rd to beginning
                                            of 4th year
Dissertation   4th year                     start 3 months later




                Thomas Schreiter: “Dynamisch Verkeersmanagement”   15/16
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
A15 haven-uit: bij Charlois




   Delft
   University of
   Technology


   Challenge the future
A.
Homepage met resultaten in realtime


www.regiolab-delft.nl/boshbr




             Thomas Schreiter: “Dynamisch Verkeersmanagement”   18/16
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
Space (30km) à
                                             Screenshots –
                                                 Schatting

                                                  Current Speed
                   Time (4h) à
 Space (30km) à




                                                 Current Flow
                          Thomas Schreiter: “Dynamisch Verkeersmanagement”   20/16
Space (30km) à
                                                            Screenshots –
                                                                Voorspelling

                                   Time (1h) à
Current Speed
                 Space (30km) à




 Current Flow                       Thomas Schreiter: “Dynamisch Verkeersmanagement”   21/16
B.
Resultaten met incident




             Thomas Schreiter: “Dynamisch Verkeersmanagement”   22/16
Resultaten: Incident simulaties

•  Voorbeeld: 26 jan 2011 om 16.10




                            X


                        Thomas Schreiter: “Dynamisch Verkeersmanagement”   23/16
Resultaten: Incident simulaties

•  Voorbeeld: 26 jan 2011 om 16.10
   •  incident




                        Thomas Schreiter: “Dynamisch Verkeersmanagement”   24/16
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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Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

  • 1. Dynamic Traffic Management: Class-specific Control at de A15 Thomas Schreiter, Hans van Lint, Serge Hoogendoorn, Zlatan Muhurdarević, Ernst Scheerder Goal: 40 km in 38 min Delft University of Technology Challenge the future
  • 2. A15 during evening peak Delft University of Technology Challenge the future
  • 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. Truck percentage •  A lot more trucks than on other highways Thomas Schreiter: “Dynamisch Verkeersmanagement” 4/16
  • 5. Outline •  The model BOS HbR •  Control Loop •  3 Components •  Examples of class-specific Control •  Conclusion •  Review Thomas Schreiter: “Dynamisch Verkeersmanagement” 5/16
  • 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. 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. 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% Intensiteit 200 veh/h Dichtheid Inflow Fundamental Diagram Turnfraction •  Class-specific: trucks and cars Thomas Schreiter: “Dynamisch Verkeersmanagement” 8/16
  • 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. 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. 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. 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. Possible locations for class-specific ramp metering A15 Thomas Schreiter: “Dynamisch Verkeersmanagement” 13/16
  • 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. My Review Planning Reality Estimation 1st year 1.5 years Prediction 2nd year Still busy with calibration Control 3rd year Mid of 3rd to beginning of 4th year Dissertation 4th year start 3 months later Thomas Schreiter: “Dynamisch Verkeersmanagement” 15/16
  • 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. A15 haven-uit: bij Charlois Delft University of Technology Challenge the future
  • 18. A. Homepage met resultaten in realtime www.regiolab-delft.nl/boshbr Thomas Schreiter: “Dynamisch Verkeersmanagement” 18/16
  • 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. Space (30km) à Screenshots – Schatting Current Speed Time (4h) à Space (30km) à Current Flow Thomas Schreiter: “Dynamisch Verkeersmanagement” 20/16
  • 21. Space (30km) à Screenshots – Voorspelling Time (1h) à Current Speed Space (30km) à Current Flow Thomas Schreiter: “Dynamisch Verkeersmanagement” 21/16
  • 22. B. Resultaten met incident Thomas Schreiter: “Dynamisch Verkeersmanagement” 22/16
  • 23. Resultaten: Incident simulaties •  Voorbeeld: 26 jan 2011 om 16.10 X Thomas Schreiter: “Dynamisch Verkeersmanagement” 23/16
  • 24. Resultaten: Incident simulaties •  Voorbeeld: 26 jan 2011 om 16.10 •  incident Thomas Schreiter: “Dynamisch Verkeersmanagement” 24/16
  • 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