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A new adaptive, multi-scale traffic simulation

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Michael Mahut, Michael Florian &
Daniel Florian

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A new adaptive, multi-scale traffic simulation

  1. 1. A new adaptive, multiscale traffic simulation Michael Mahut | michaelm@inrosoftware.com Michael Florian, Daniel Florian AITPM, July 26-29 – Sydney, Australia
  2. 2. Motivation
  3. 3. Current Trends • Larger-scale traffic simulation models are becoming increasing popular  Meso and micro simulation-based dynamic traffic assignment  Metropolitan to regional scale • Motivated by need for higher realism/fidelity  Temporal dynamics/resolution  More detailed network models • Facilitates modeling of complex mechanisms  Departure time choice  Real-time pricing, e.g. congestion-based tolls  Freeway management, traveller response to information, etc…
  4. 4. Challenges • Well specified scenarios:  Algorithms are very good at finding stable, converged solutions  Existence and uniqueness of solution are observed empirically • Unbalanced network demand and supply:  Models become unstable: nonlinear response of network delay to demand  Gridlock conditions  O-D impedances: no longer respond to demand changes  Non-convergent, unusable solutions • Conclusion: algorithms are not sufficiently robust
  5. 5. Example: highly congested scenario
  6. 6. High demand scenario
  7. 7. Why don’t we see this effect in static models? • In a static model (vdf)  Path travel time = function of link (+turn) v/c ratios on the path • In a simulation model  Path travel time is affected by bottlenecks that are not on the path  This is due to congestion spillback • We will refer to delay from bottlenecks off the path as secondary or indirect delay  This is the component of delay which grows in a highly nonlinear way when demand >> supply
  8. 8. A new approach - adaptive simulation • Adaptive driver response to extreme congestion  Gradual formation of emergent lanes utilizes spare turn capacity • Adaptation is triggered by a delay threshold  Exogenous parameter, can be calibrated • Impact on delay propagation  Delay in emergent lanes is not propagated upstream + 5 min
  9. 9. Additional properties • Adaptive mechanism reduces secondary delay only  Does not affect primary delay • Bottleneck (turn) capacities are easily respected  Extremely low flows can increase to become very low flows • No vehicles removed from the simulation • No vehicles lose their paths Outputs • Length of individual emergent queues (in vehicles) • Delay in individual emergent queues
  10. 10. Example 1: High Demand No adaptation With adaptation Impact of adaptive mechanism on model convergence
  11. 11. Example 1: High Demand Length of emergent queues aggregated to nodes
  12. 12. Emergent queues through model iterations 5 iterations 10 iterations 20 iterations
  13. 13. Example 2: Very High Demand No adaptation With adaptation Impact of adaptive mechanism on model convergence
  14. 14. Example 2: Very High Demand Length of emergent queues aggregated to nodes
  15. 15. Conclusions • Fast models are not enough, we need more stable models • We propose an adaptive simulation approach which responds to extreme congestion  Can be tailored to the degree of congestion inherent in the scenario  Ensures model stability even when demand significantly exceeds supply  Addresses scalability for larger networks in congested conditions  Provides a single traffic model at a consistent level of detail over the entire network
  16. 16. A new adaptive, multiscale traffic simulation Michael Mahut | michaelm@inrosoftware.com Michael Florian, Daniel Florian AITPM, July 26-29 – Sydney, Australia

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