Set of parameters used to control how a Simulation is run.
{
"learning_model": {
"type": "Exponential",
"value": {
"alpha": 0.9
}
},
"network": {
"road_network": {
"contraction": {
"complexity_quotient_weight": 2.0,
"edge_quotient_weight": 2.0,
"hierarchy_depth_weight": 1.0,
"num_threads": 8,
"thin_profile_interval_hop_limit": 16,
"unpacked_edges_quotient_weight": 1.0
},
"overlay_simplification": {
"type": "Raw"
},
"recording_interval": 60.0,
"search_space_simplification": {
"type": "Raw"
},
"simulated_simplification": {
"type": "Raw"
},
"weight_simplification": {
"type": "Raw"
}
}
},
"period": [
21600.0,
43200.0
],
"random_seed": 13081996,
"stopping_criteria": [
{
"type": "MaxIteration",
"value": 100
},
{
"type": "DepartureTime",
"value": [
2.0,
3600.0
]
}
],
"update_ratio": 1.0
}
Learning model used to update the values between two iterations.
Exponential learning model.
An exponential learning model.
The average value at iteration T, x_T, is a mean of the update values y_t at each iteration t, where the coefficient of the update value y_t is (1 - alpha) * alpha^(T - t) / (1 - alpha^T).
When T is large, the exponential learning model is such that x_{t+1} = alpha * x_t + (1 - alpha) * y_{t+1}
Weight of the old value, between 0 and 1.
Value must be greater or equal to 0.0 and lesser or equal to 1.0
Linear learning model: x_{t+1} = (t / (t + 1)) * x_t + (1 / (t + 1)) * y
Genetic learning model: x_{t+1} = (x_t^t * y)^(1 / (t + 1))
Quadratic learning model: x_{t+1} = (w / (w + 1)) * x_t + (1 / (w + 1)) * y where w = t^(1/2)
Set of parameters for the network.
Parameters specific to the road network.
Parameters controlling how a hierarchy overlay is built from a road network graph.
Number of threads to use for the parallelized parts of the code.
Value must be greater or equal to 0.0
Value must be greater or equal to 0.0
How to simplify the edges TTFs after the hierarchy overlay is built.
No simplification is done.
Allow for a given error bound.
Representation of time duration or timestamp, expressed in seconds.
Compute the values at fixed intervals.
Representation of time duration or timestamp, expressed in seconds.
Interval in time for which the bottleneck and road segment travel times are aggregated.
How to simplify the TTFs of the forward and backward search spaces.
No simplification is done.
Allow for a given error bound.
Representation of time duration or timestamp, expressed in seconds.
Compute the values at fixed intervals.
Representation of time duration or timestamp, expressed in seconds.
How to simplify the edges TTFs at the end of the within-day model.
No simplification is done.
Allow for a given error bound.
Representation of time duration or timestamp, expressed in seconds.
Compute the values at fixed intervals.
Representation of time duration or timestamp, expressed in seconds.
How to simplify the edges TTFs at the beginning of the iteration.
No simplification is done.
Allow for a given error bound.
Representation of time duration or timestamp, expressed in seconds.
Compute the values at fixed intervals.
Representation of time duration or timestamp, expressed in seconds.
Time interval used to restrict the travel-time functions of the edges.
The departure-time intervals of the agents must be included in this interval.
Agents can still travel on the network when the period is exceeded but the edges' travel times are no longer recorded. The departure time chosen by any agent must be such that the expected arrival time is earlier than the end of the period.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
Representation of time duration or timestamp, expressed in seconds.
Random seed used for all the draws.
If null, the seed is generated by entropy.
Value must be greater or equal to 0.0
Set of stopping criteria used to decide when the iterative process should stop.
Criterion that is used to check if a simulation must be stopped.
Stop when the number of iterations has reached a given value.
Value must be greater or equal to 0.0
Stop when the mean departure-time shift from one iteration to another is below a threshold value.
The first value represents the threshold value. The second value represents the backup value to use when an agent switch from a mode to another mode.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
Representation of time duration or timestamp, expressed in seconds.
Representation of time duration or timestamp, expressed in seconds.
Share of agents that can update their pre-day choices at each iteration.
Value must be greater or equal to 0.0 and lesser or equal to 1.0