A first look at bridging choice modeling and agentbased microsimulation in MATSim
Sebastian Hörl (presenting)
Milos Balac
Kay W. Axhausen
ABTMRANS Workshop 2018
Best paper award
Porto, 11 May 2018
Initial Idea
 Much work and effort put into choice modeling at IVT
 Discrete choice models are readily available
 Microcensus on Transport and Mobility
 Autonomous vehicles
 How to make use of them in MATSim?
Example: Multinomial logit model
Making discrete choices
 Score (utility) for each available
choice with deterministic and
random component
 Choice model
 Choice sampling
Making discrete choices
Making discrete choices
 Walking
 Travel Time:
 Travel Time:
 Cycling
 Travel Time:
 Travel Time:
 Car
 Travel Time:
 Cost:
 Public Transport
 Travel Time:
 Access Time:
 Waiting Time:
 Number of transfers:
 Cost:
Making discrete choices
Making discrete choices
 Probabilistic choice interpretation
 More complex models available
 Nested Logit
 Mixed Logit
 ...
Mode choice in MATSim
Almost purely random proposition of new plan
Scorebased selection of plan from memory
Simulation
Scoring
Cleanup of worst plans until N are left
Initial execution is
necessary before
selection makes sense
First idea of integration
 Selection between chains
 Two components:
 Choice set generation
 A priori mode choice based on estimated travel characteristics
Choice set generation
 Obtain the set of all possible chains of modes for a given chain of trips with origin and destination
 Constrained by agentlevel attributes (e.g. car availability)
 Constrained by continuity constraints (e.g. vehicle location)
 Maximum set:
 Feasible set:
Choice set generation
Start
Choice set generation
Start
Choice set generation
Start
Selection procedure
 Problem: How to use a tripbased mode choice model for a whole chain?
 Three (imperfect) approaches
Best response selection
Total chain utility sampling
Naive chain sampling
with
and
First simulation results
 Teleportationbased simulation
 Bestresponse is upper bound
 Fast convergence for tested approaches vs SMC
First simulation results
 Networkbased simulation
 Bestresponse is not upper bound
Conclusions from our experiment
 Questionable to draw conclusions from tripbased model in chainbased environment (MATSim)
 Choice model makes life easier  we can argue to skip some calibration work, faster convergence
 Choice model makes life harder  we need to come up with good estimates for the trip characteristics
 Which one is right?
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
 Imagine a MATSim run without innovation
 Choice set per agent is changed continuously
 Worst plan removal strategy has biggest influence on the procession of the choice set
 Choice set collapses for high number of iterations
 Finally, M plans with momentarily best score for the agent = (Stochastic) User Equilibrium
 Choice set per agent is changed continuously
Some thoughts on choice making in MATSim
 Except, there is innovation
 Random new plan in X% of iterations (e.g. every 20th)

Early stopping after N iterations to maintain variability
 So ...
 MATSim is a global search algorithm for the UE
 MATSim stops optimizing early to maintain a notion of a discrete plan choice
Short detour: Oneagent toy examples
 Numerical experiment: One agent, four plans with predefined utilities
 Run MATSim selection / innovation procedure
Short detour: Oneagent toy examples
 Now without innovation after 300 iterations
The best of both worlds?
Modelbased proposition of plans
MATSim scoring and selection
Estimates (model input)
Sampling of relevant alternatives

Improves convergence

Avoids "innovation turnoff"
 May introduce bias through estimates

Maintains stability
 Compensates for estimation bias
The best of both worlds?
Modelbased proposition of plans
MATSim scoring and selection
Estimates (model input)
Sampling of relevant alternatives
 Condition: Any feasible can be sampled with nonzero probability
 The code is there (for mode choice)
 Chain sampler is available, scoring framework is available
 MATSim choice procedure is available
Can we do better?
 Why evolutionary algorithm? Notouriously hard to treat theoretically.
 We have:
 Prior score (during plan sampling)
 Posterior score (after simulation)
 Apply any rejection sampling approach (simulated annealing, Metropolis Hastings) on one plan per agent
 Natural convergence measure: Prior score vs. posterior score
Propose plan
Simulate plan
Score plan
Accept / Reject plan
Thanks!
Questions?
TRB 2019
By Sebastian Hörl