a new adaptive, multi-scale traffic simulation
TRANSCRIPT
A new adaptive, multiscale traffic simulation
Michael Mahut | [email protected]
Michael Florian, Daniel Florian
AITPM, July 26-29 – Sydney, Australia
Motivation
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…
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
Example: highly congested scenario
High demand scenario
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
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
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
Example 1: High Demand
No adaptation With adaptation
Impact of adaptive mechanism on model convergence
Example 1: High Demand
Length of emergent queues aggregated to nodes
Emergent queues through model iterations
5 iterations 10 iterations 20 iterations
Example 2: Very High Demand
No adaptation With adaptation
Impact of adaptive mechanism on model convergence
Example 2: Very High Demand
Length of emergent queues aggregated to nodes
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
A new adaptive, multiscale traffic simulation
Michael Mahut | [email protected]
Michael Florian, Daniel Florian
AITPM, July 26-29 – Sydney, Australia