Location Choice Modeling for Shopping and Leisure Activities with MATSim: Utility Function Extension and Validation Results

A. Horni

IVTETHZurich

• MATSim: Overview• Local search based on time geography• Utility function extension and validation results• Future research

dynamic, disaggregated

Measures:• Travel distance distribution• Travel time distribution• Link loads (?)• Winner-loser statistics (WU) ?• Catchment area ?• Number of visitors of type xy ?…

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MATSim: Model Purpose

Possible level of disaggregation

Clustering of populationf(person attributes)

?

MATSim model purpose: Transport planning simulationModel patterns of people’s activity scheduling and participation behavior at high level of detail.

Planning goal: Average working day of Swiss resident population (> 7.5 M) in „reasonable“ time

→ end of 2009 (KTI project)

Method: Coevolutionary, agent-based algorithm

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Execution:Traffic simulation

Replanning

Scoring:Utility function f(t)t→ activity participation, travel

Share x of agents (usually 10%):Time choiceRoute choiceLocation choice

Physical layer

Strategic layer

Agent population

MemoryDay plans

Initial demand: Fixed attributes e.g. (home location) from census data

Plan Selection

MATSim: Structure

Exit conditon:„Relaxed state“, i.e. equilibrium

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Location Choice in MATSim

Relaxed state (i.e. scheduling equilibrium … (not network eqilibrium (Wardrop I/II), Nash? )

Huge search space prohibitively large to be searched exhaustively

Dimensions (LC):# (Shopping, Leisure) alternatives (facilities)# Agents+ Time dimension→ agent interactions

Local search + escape local optima

Existence and uniqueness of equilibrium

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Local Search in Our Coevolutionary System

Tie together location choice and time choice (t) p(accept bad solutions) > 0

Day plan

Aktivity i - WorkLocationStart time, duration…

Aktivity i+1 - ShoppingDuration

Aktivity i+2 - HomeLocationStart time, duration…

Location Set:Locations consistent with time choice (ttravel ≤ tbudget)

Travel time budget

Time GeographyHägerstrand

Based on PPA-Algorithm Scott, 2006

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STRC 2008ZH Scenario: 60K agents

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First Validation Steps & Utility Function Extensions

Improve sim results

Consider potential for application of estimated utility maximization models → hypothesis testing

MATSim utility maximization framework

Starting point for development and introduction of mental modules (such as e.g. location choice)

Score → verification

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Utility Function Extensions (Shopping Activities)

Utility function

SituationAlternative Person

Strictly time-based → extension (parameters, structure)

• Store size• Stores density in given neighborhood

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On the Way to Results – ring-shaped PPA

Leisure travel <= models of social interaction and sophisticated utility function

Not yet productiveMATSim longterm goal

Activity-based models (chains) → Reasonable shopping location choice model requires sound leisure location choice modeling

trip generation/distribution → activity-based multi-agent framework

Trip distance distribution MC → act chains (ring-shaped potential path area)

Agent population

Assignment of travel distances

crucial and non-trivial for multi-agent models!

Leisure

Predictability of leisure travel based on f(agent attributes)?

Leisure trip distance ↔ -desired leisure activity duration-working activityactivity chains ← f(agent attributes)

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Results – Avg. Trip Distances

• Config 0: base case

• Config 1: leisure PPA

• Config 2: + shopping activity differentiation(grocery – non-grocery; random assignment)

• Config 3.1: config 2 + store size• Config 3.2: config 2 + stores density

Shopping trips (car)

Leisure trips (car)

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Results – Avg. Trip Durations

Strong underestimation in general!

-Missing intersection dynamics-Access to (coarse) network (parking lots etc)-Freight traffic essentially missing

Shopping trips (car)

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Microcensus bin size ratio (bin0/ bin1) = 4.22

Config 0 bin size ratio (bin0/ bin1) = 19.41

Config 1 bin size ratio (bin0/ bin1) = 7.08

Config 2 bin size ratio (bin0/ bin1) = 7.00

Config 3.1 bin size ratio (bin0/ bin1) = 6.41

Config 3.2 bin size ratio (bin0/ bin1) = 6.44

Results – Shopping Trip Distance Distributions (Car)

Results – Count Data 18:00 -19:00

Config 0

Config 1 Config 3.1

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Results – Count Data – 24 h

(i, j) (i,j) [%] dist(i,j) [%]

0, 1 23.82 …

1, 2 0.06 …

2, 3.1 0.46

2, 3.2 0.45

Car shopping trips Config 0 daily: -60.3%

Config 1 daily: -36.4%

Retest:- ... more disaggregated data!- ... more stations (now 300 stations for CH)

General underestimation of traffic volume

dist = upper bound for reduction of error due to increased traffic volume (increased avg. distances)

Utf. extensions productive → spatial distribution of trips

Weighting by shopping traffic work (#trips * trip length)≈ 7 % (excl. back to home trips)

Reject hypothesis

No improvement w/ respect to spatial distribution of trips

0.62

0.39

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Conclusions

Open research questions ...

Starting point for validation

Strictly time-based utility function → strong underestimation of traffic volume(as expected)

Extension of utility function shows expected effects but …

• Effects very small & difficult to evaluate• H0: blue eyes

→ disaggregated evaluation level

• Reject hypothesis?• MATSim hypothesis testing tool?

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Shopping utility function estimation

Future work

Choice set generation (boundaries)→ survey: homo oeconomicus vs. real person

Further validation steps

Disaggregation level of agent-based models

Evaluation and modelinglevel = f (data base)

Existence and uniqueness of scheduling equilibrium

Inductively: different initial states

Predictability of leisure travel

Reducing leisure travel to a cross-sectional sample (e.g. 1 MATSim day)