location choice modeling for shopping and leisure activities with matsim: utility function extension...
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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)