A first look at bridging choice modeling and agent-based microsimulation in MATSim
Sebastian Hörl (presenting)
Milos Balac
Kay W. Axhausen
ABTMRANS Workshop 2018
Best paper award
Porto, 11 May 2018
Initial Idea
Example: Multinomial logit model
Making discrete choices
Making discrete choices
Making discrete choices
Making discrete choices
Making discrete choices
Mode choice in MATSim
Almost purely random proposition of new plan
Score-based selection of plan from memory
Simulation
Scoring
Clean-up of worst plans until N are left
Initial execution is
necessary before
selection makes sense
First idea of integration
Choice set generation
Choice set generation
Start
Choice set generation
Start
Choice set generation
Start
Selection procedure
Best response selection
Total chain utility sampling
Naive chain sampling
with
and
First simulation results
First simulation results
Conclusions from our experiment
Mode choice in MATSim
If memory size exceeds limit Then
- Remove plan with worst score in memory
If the removed plan was selected Then
- Select random plan from the remaining ones
End If
End If
- Choose a strategy by probability (given whether innovation is already turned off)
If chosen strategy is a selection strategy:
- Select new plan from existing ones according to strategy
Else (it is an innovation strategy)
- Copy a random plan from the existing ones and set as selected plan
- Apply mutation strategy to the newly selected plan
End If
Some thoughts on choice making in MATSim
Some thoughts on choice making in MATSim
Short detour: One-agent toy examples
Short detour: One-agent toy examples
The best of both worlds?
Model-based proposition of plans
MATSim scoring and selection
Estimates (model input)
Sampling of relevant alternatives
The best of both worlds?
Model-based proposition of plans
MATSim scoring and selection
Estimates (model input)
Sampling of relevant alternatives
Can we do better?
Propose plan
Simulate plan
Score plan
Accept / Reject plan
Thanks!
Questions?