dynamic traffic management: class specific control at the a15; thomas schreiter

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Challenge the future Delft University of Technology 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

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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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Page 1: Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

Challenge the future Delft University of Technology

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

Page 2: Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

Challenge the future Delft University of Technology

A15 during evening peak

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3/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

Class-specific Vehicle Length

• More jam ßà “longer” trucks (in relative terms) • Worsening effect

•  Person-car equivalent (pce) value •  Effective density = pce * density •  Dynamic, dependent on traffic state!

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4/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

Truck percentage

• A lot more trucks than on other highways

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5/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

Outline

• The model BOS HbR

•  Control Loop •  3 Components

•  Examples of class-specific Control

• Conclusion

• Review

Page 6: Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

6/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

BOS-HbR (“Beslissingsondersteunend Systeem voor het Havenbedrijf Rotterdam”)

BOS HbR

Traffic System A15

Sensors Actuators

Real-time Control

Real-time Prediction

Real-time Estimation

Network

Historic inflows / outflows

0u uk qt x

∂ ∂+ =

∂ ∂

Traffic model

Goalfunction Travel time <= 38min

Vehicle properties

l = 20 m vmax =85 km/u

l = 6 m vmax =110 km/u

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7/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

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

10:30 5:30 8:00 now Past

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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

Inflow Turnfraction Fundamental Diagram

10%

200 veh/h

Incident

•  Class-specific: trucks and cars

Dichtheid

Inte

nsite

it

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Prediction: traffic state during next 1 hour

• Results Prediction •  Density, flow, speed •  Location of congestion •  Travel times

10:30 5:30 8:00 now Past Prediction

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10/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

• Model predictive control (MPC) •  Predict effect of DTM measurement •  Choose best DTM measurement •  In realtime

•  Example: class-specific route guidance during incident:

Control: Optimization of Traffic for each vehicle class

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Class-specific Route Guidance

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

•  Possible Application for A15:

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Class-specific Ramp Metering

•  Prioritize trucks à shorter travel time

trucks à fewer spillback at

on-ramp

•  Prioritize cars à Less total delay

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13/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

Possible locations for class-specific ramp metering A15

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Conclusion

• Dynamic Traffic Management • Goal: improve traffic state during incidents

•  By prediction of expected traffic situation •  Predict jam locations •  Class-specific control improves traffic state

BOS-HbR (“Beslissingsondersteunend Systeem voor het Havenbedrijf Rotterdam”)

Traffic System A15

Sensors Actuators

Real-time Control

Real-time Prediction

Real-time Estimation

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My Review

start 3 months later 4th year Dissertation

Mid of 3rd to beginning of 4th year

3rd year Control

Still busy with calibration

2nd year Prediction

1.5 years 1st year Estimation

Reality Planning

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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

Page 17: Dynamic Traffic Management: Class specific control at the A15; Thomas Schreiter

Challenge the future Delft University of Technology

A15 haven-uit: bij Charlois

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18/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

A.

Homepage met resultaten in realtime www.regiolab-delft.nl/boshbr

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www.regiolab-delft.nl/boshbr

• BOS-HbR op computer bij TU Delft

• Vlekkenkaarten •  Snelheid, intensiteit •  A15, beide richtingen •  Schatting, voorspelling

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Screenshots – Schatting

Current Speed

Current Flow

Spac

e (3

0km

) à

Sp

ace

(30k

m) à

Time (4h) à

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Screenshots – Voorspelling

Spac

e (3

0km

) à

Sp

ace

(30k

m) à

Time (1h) à Current Speed

Current Flow

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B.

Resultaten met incident

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Resultaten: Incident simulaties

• Voorbeeld: 26 jan 2011 om 16.10

X

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24/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

• Voorbeeld: 26 jan 2011 om 16.10 •  incident

Resultaten: Incident simulaties

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25/16 Thomas Schreiter: “Dynamisch Verkeersmanagement”

• Voorbeeld: 26 jan 2011 om 16.10 •  Herrouteren: Wat gebeurd, als het verkeer over het onderliggende wegennet

geherrouteerd wordt?

Resultaten: Incident simulaties