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A Dynamic Macroscopic model integrated into Dynamic
Traffic Assignment: advantages and disadvantages
Martijn Breen & Jordi...
Overview
• Motivation
• Model description
• Isolated examples
• Case study
• Conclusion
Motivation
• Travel Demand models require O/D travel times
• Current static models do not capture congestion/queues
spillb...
Where does it stand?
Model – Link model
• Continuous flow model
• Conservation equation:
dρ
dt
+
dq
dx
= 0
• Flux rate function:
q = ϕ ρ
Model – link model (ii)
Forward Wave Backward Wave
U t −
L
γ
= V t U t − V t −
L
ω
= KL
Mark P.H. Raadsen, Michiel C.J. Bl...
Node model
• Generic
• Maximizing flows w.r.t
constraints.
• Conservation of turn
fractions
• Invariance principle.
Tampèr...
Path propagation (integration with DTA)
MACRO
MESO
MICRO
Static assignment
Dynamic user
equilibrium
or
Stochastic route choice
OD Matrix
Network data base
Pathsan...
Network input / calibration parameters
• Geometry
• Section
– Free flow speed
– Capacity
– Jam density
• Turn
– Capacity
•...
Isolated examples - spillback
Isolated examples – traffic lights
Isolated examples – Give-way node
Case Study – M4 model
• 476 zones
• 1500 km section length
• 5:00 – 10:00 am
• 600.000 vehicles
Case study – Travel Times
OD Travel Time
Meso vs Macro Dynamic 6:00 – 7:00
OD Travel Time
Meso vs Macro Dynamic 7:00 – 8:00
Case study – Travel Times
OD Travel Time
Meso vs Macro Static 6:00 – 8:00
Case study – Flows
Computational performance
Simulator Link actualization
threshold [%]
Network Loading
[seconds]
Mesoscopic n/a 362
Macro dy...
Density view mode
Conclusions
• Dynamic Macroscopic model integrated in Dynamic
Traffic Assignment
• Travel times comparable under free flow...
Future developments
• Improve traffic signal treatment
• Improve computation speed
• Add actions like:
– Metering
– Force ...
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A Macroscopic Dynamic model integrated into Dynamic Traffic Assignment: advantages and disadvantages

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Martijn Breen, Perarnau J., Lim T., Torday A., Casas J.

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A Macroscopic Dynamic model integrated into Dynamic Traffic Assignment: advantages and disadvantages

  1. 1. A Dynamic Macroscopic model integrated into Dynamic Traffic Assignment: advantages and disadvantages Martijn Breen & Jordi Casas
  2. 2. Overview • Motivation • Model description • Isolated examples • Case study • Conclusion
  3. 3. Motivation • Travel Demand models require O/D travel times • Current static models do not capture congestion/queues spillback • Vehicle-based dynamic models are more complex
  4. 4. Where does it stand?
  5. 5. Model – Link model • Continuous flow model • Conservation equation: dρ dt + dq dx = 0 • Flux rate function: q = ϕ ρ
  6. 6. Model – link model (ii) Forward Wave Backward Wave U t − L γ = V t U t − V t − L ω = KL Mark P.H. Raadsen, Michiel C.J. Bliemer, Michael G.H. Bell, An efficient and exact event-based algorithm for solving simplified first order dynamic network loading problems in continuous time
  7. 7. Node model • Generic • Maximizing flows w.r.t constraints. • Conservation of turn fractions • Invariance principle. Tampère C.M.J., Corthout R., Cattrysse D., Immers, L.H. (2011). A Generic Class of First Order Node Models for Dynamic Macroscopic Simulation of Traffic Flows. Transportation Research Part B: methodological. Volume 45B issue 1, 2011, pp289-309
  8. 8. Path propagation (integration with DTA)
  9. 9. MACRO MESO MICRO Static assignment Dynamic user equilibrium or Stochastic route choice OD Matrix Network data base Pathsand pathflowsdatabase Traffic flow representationTraffic assignment HYBRID Integration in Dynamic Traffic Assignment S
  10. 10. Network input / calibration parameters • Geometry • Section – Free flow speed – Capacity – Jam density • Turn – Capacity • Traffic lights control plan
  11. 11. Isolated examples - spillback
  12. 12. Isolated examples – traffic lights
  13. 13. Isolated examples – Give-way node
  14. 14. Case Study – M4 model • 476 zones • 1500 km section length • 5:00 – 10:00 am • 600.000 vehicles
  15. 15. Case study – Travel Times OD Travel Time Meso vs Macro Dynamic 6:00 – 7:00 OD Travel Time Meso vs Macro Dynamic 7:00 – 8:00
  16. 16. Case study – Travel Times OD Travel Time Meso vs Macro Static 6:00 – 8:00
  17. 17. Case study – Flows
  18. 18. Computational performance Simulator Link actualization threshold [%] Network Loading [seconds] Mesoscopic n/a 362 Macro dynamic 5 144 Macro dynamic 10 133 Macro dynamic 20 123
  19. 19. Density view mode
  20. 20. Conclusions • Dynamic Macroscopic model integrated in Dynamic Traffic Assignment • Travel times comparable under free flow and congested situations • O/D travel times are more sensitive to errors for coarse (higher threshold) simulation • Dynamic Macroscopic model is easily calibrated due to few calibration parameters • Dynamic Macroscopic doesn’t replace the Mesoscopic
  21. 21. Future developments • Improve traffic signal treatment • Improve computation speed • Add actions like: – Metering – Force turn – Capacity reduction

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