Parallel Bayesian Optimization of Agent-based Transportation Simulation
- URL: http://arxiv.org/abs/2207.05041v1
- Date: Mon, 11 Jul 2022 17:49:29 GMT
- Title: Parallel Bayesian Optimization of Agent-based Transportation Simulation
- Authors: Kiran Chhatre, Sidney Feygin, Colin Sheppard, Rashid Waraich
- Abstract summary: MATSim is an open source large-scale agent-based transportation planning project applied to various areas like road transport, public transport, freight transport, regional evacuation, etc.
The agents from the BEAM simulation exhibit'mode choice' behavior based on multinomial logit model.
In our study, we consider eight mode choices viz. bike, car, walk, ride hail, driving to transit, walking to transit, ride hail to transit, and ride hail pooling.
We present a parallel Bayesian optimization method with early stopping rule to achieve fast convergence for the given multi-in-multi-out problem to its optimal
- Score: 0.4129225533930965
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: MATSim (Multi-Agent Transport Simulation Toolkit) is an open source
large-scale agent-based transportation planning project applied to various
areas like road transport, public transport, freight transport, regional
evacuation, etc. BEAM (Behavior, Energy, Autonomy, and Mobility) framework
extends MATSim to enable powerful and scalable analysis of urban transportation
systems. The agents from the BEAM simulation exhibit 'mode choice' behavior
based on multinomial logit model. In our study, we consider eight mode choices
viz. bike, car, walk, ride hail, driving to transit, walking to transit, ride
hail to transit, and ride hail pooling. The 'alternative specific constants'
for each mode choice are critical hyperparameters in a configuration file
related to a particular scenario under experimentation. We use the
'Urbansim-10k' BEAM scenario (with 10,000 population size) for all our
experiments. Since these hyperparameters affect the simulation in complex ways,
manual calibration methods are time consuming. We present a parallel Bayesian
optimization method with early stopping rule to achieve fast convergence for
the given multi-in-multi-out problem to its optimal configurations. Our model
is based on an open source HpBandSter package. This approach combines hierarchy
of several 1D Kernel Density Estimators (KDE) with a cheap evaluator
(Hyperband, a single multidimensional KDE). Our model has also incorporated
extrapolation based early stopping rule. With our model, we could achieve a 25%
L1 norm for a large-scale BEAM simulation in fully autonomous manner. To the
best of our knowledge, our work is the first of its kind applied to large-scale
multi-agent transportation simulations. This work can be useful for surrogate
modeling of scenarios with very large populations.
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