Integrating discrete choice models with MATSim scoring
Sebastian Hörl
25 March 2021
ABMTRANS 2021
Discrete choice models vs. MATSim
Discrete choice model
Choicemaking in MATSim
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Discrete choice models vs. MATSim
Discrete choice model
Choicemaking in MATSim
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Discrete choice modeling
Discrete choice modeling
Discrete choice modeling
Discrete choice modeling
 Definition of utility v for a choice situation i with travel characteristics X and utility parameters beta
 Observed choice in each situation i
 Concept of utility
maximization
Find such that
Discrete choice modeling
 Definition of utility v for a choice situation i with travel characteristics X and utility parameters beta
 Observed choice in each situation i
 Concept of utility
maximization
Problem: Usually cannot be solved!
Find such that
Discrete choice modeling
 Random utility model adds
stochastic component to the
systematic utility
 Random utility maximization (RUM)
Discrete choice modeling
 If we choose the error to be EV / Gumbeldistributed ...
 ... there is a closed form expression of the choice probabity!
 Two alternatives:
Binary logit model
 More alternatives:
Multinomial logit model
Discrete choice modeling
 Closedform expression allows to derive maximum likelihood estimate
Discrete choice modeling
 Models can be estimated from survey data, also for nonexistant modes!
Discrete choice modeling
Discrete choice modeling
Discrete choice modeling: Simulation
 Given X, we have two options for predicting or simulating a choice
 Probabilitybased, sampling one alternative
 Maximizationbased, sampling one error term
Discrete choice modeling
Summary
 Utility maximization principle
 Utilities affected by error / taste component to reflect uncertainty / noise in the data
 Generalization to nonexistant modes is possible
 But: Solve very specific problem (e.g. mode choice)
Choice data
Utility model
Estimation
Simulation
Scoringbased choice making in MATSim
Mobility simualtion
Scoring
Replanning
 Daily plans of agents are simulated and scored in parallel
 Performing activities brings positive score
 Travling brings negative score
 After, some agents replan
 Either they choose from plans they have seen before (selection)
 Or they make random modification on an existing plan (innovation)
Scoringbased choice making in MATSim
MATSim
Comparison
New parameters
 We can make the simulation fit to reality by calibration
New parameters
 We can only fit simulation to baseline / historical cases
 We can only construct future scenarios of new modes of transport
Scoringbased choice making in MATSim
Summary
 Score maximization
 Complex activity chain possible
 Offers large flexibility
 But how to incorporate consistently future modes?
Scoringbased choice making in MATSim
Summary

Score maximization
 Complex activity chain possible
 Offers large flexibility
 But how to incorporate consistently future modes?
Is it possible to make use of a discrete choice model in MATSim?
Scoringbased choice making in MATSim

Integrating discrete choice models directly as a replanning strategy
 Available as discrete_mode_choice contrib (next presentation)
 As DMC, very specific use case: Mode choice!
 Not clear how to interact with other choice dimensions

Pragmatic solution

Making use of scoring to resemble a DMC
 This presetation!
 More theoretical analysis
 May lead to better insights and
compatibility in the future
Choice process in detail
 M: Maximum memory size
 ρ: Innovation rate
Choice process in detail
 Selection and deletion steer plans in memory towards higher scores
 Innovation explores all the potential plans
Hypotheses

If we run this process infinitely, the memory of each agent should become populated with the same plan with maximum score

Whenever an agent performs innovation, there is one nonoptimal plan generated in memory
 The selection process resembles score (utility) maximization except for some cases where we have innovation
Experimental setup
 10,000 agents; one leg each
 Two modes (A and B; e.g., car vs. pt)
Defaults
 Mode A leads to score 1 for the plan
 Mode B leads to score 2 for the plan
 Memory of size 3
 Innovation rate 10%
Tests  Choice strategy
 Innovation rate
 Change of score for mode B
A/B
Experimental results
Frozen errors
 MATSim is score maximizing
 But it is affected by innovation
 We can use frozen errors to simulate the error terms we have in the discrete choice model
 Has been used for location choice, but not from a generic perspective
 Idea: For each combination of (Person, Trip Index, Mode), we need to determinstically create an error term
Cryptographic hash functions
 Cryptographic hash functions are used, e.g. to encode passwords
 In binary representation
flower123
abd5142fab24ef15
01001001
00110110001000101011001010
Fixed size, depending on hash function, e.g. SHA512
Cryptographic hash functions
 Avalanche effect: "If one bit in the input changes, at least 50% of bits in the output must change"
 This leads to the fact that if the input is changed (systematically), we get a (approx.) uniformly distributed output over the value range of the hash function!
01001001
00110110001000101011001010
abc
abd
abe
...
Cryptographic hash functions
 The error term stays fix for each combination, but over all trips in the population, the term is uniformly distributed!
 We can use Inverse Transform Sampling to create any distribution using the inverse CDF
01001001
00110110001000101011001010
(Person, Trip, Mode)
F can be Gumbel, Normal, ...
Implementation
 Straightforward implementation as additive scoring function
Experiments
 Model is now sensitive to score!
 However, we would expect
Experiments
 Choice probability is affected by innovation strategy!
Random mode
ChangeTripMode
Experiments
Conclusion
 On a conceptual level choice model parameters cannot be translated directly into scoring parameters
 MATSim as a utility maximizer can replicate the dynamics of an estimated discrete choice model
 We can systematically quantify the differences in a toy example
 Outlook
 What does this mean for stability of the simulation?
 What to do with innovation turnoff?
 How to generalize to other choice dimensions?
Thank you!
Questions ?
Contact: sebastian.horl@irtsystemx.fr
Integrating discrete choice models with MATSim scoring
By Sebastian Hörl
Integrating discrete choice models with MATSim scoring
ABMTRANS 2021, 25 March 2021
 814