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Location Choice Modeling for Shopping and Leisure Activities with MATSim:
Status Update & Next Steps
A. Horni
IVT, ETH Zurich
…Next Steps
Done …
Local search based on time geography
First validation steps
Competition on activity infrastructure
Disaggregation level of multi-agent models vs. data base
General predictability of leisure activities f (person attributes)
Estimate
Choice set generation (& F.P.?)
Existence & Uniqueness of scheduling equilibrium
(& D.C.?)
Leisure: Integrate …
- Social networks
- Detailed psychological models
Activity differentiation combined w/ random assignment
Ring-shaped PPA (leisure)
Shopping UTF extensions arbitrary
Further measures (e.g. link speeds ← GPS)
TRB 08/09 (TRR) TRB 09/10?
Computational issues Realism of planning tool MATSim Theoretical fundament +
realism of planning tool MATSim
Intro
3
Modify activity timing, routes and activity locations of agents‘ plans
initial demand
analysesexecution scoring
replanning
Trip generation/attraction Trip distribution
Location choice
Location Choice in MATSim
crucial!
> 1 million facilities!
4
Location Choice in MATSim: Local Search – WHY?
Relaxed state (i.e. scheduling equilibrium … (not network eqilibrium (Wardrop I/II), Nash? )
Huge search space prohibitively large to be searched exhaustively or even worse by global random search
Dimensions (LC):# (Shopping, Leisure) alternatives (facilities)# Agents+ Time dimension→ agent interactions
Local search + escape local optima Existence and uniqueness of equilibrium
5
Local Search in Our Coevolutionary System – HOW?
Day plansFixed and flexible activities
Travel time budget
Relatively small set of locations per iteration step
Time Geography Hägerstrand
610 % ZH Scenario: 60K agents
7
Competition on the Activites Infrastructure
Load-dependent decrease of score
Reduces number of implausibly overloaded facilities
0
5000
10000
15000
20000
25000
1 2 3 4
Load category
Vis
ito
rs it_0_config2/3
it500_config2
it500_config3
Load category1: 0 – 33 %2: 33 - 66 %3: 66 - 100 %4: > 100%10 % ZH Scenario: 60K agents
Realism
Stability of algorithm
8
First Validation Steps
Count data (avg. working day)
Micro census (shopping and leisure)
Starting point
Larger volume of more disaggregated data necessary …- GPS- FCD- M Cumulus, Supercard, …- License plate- GSM- …
9
Leisure location choice modeling – ring-shaped PPA
Leisure travel <= models of social interaction and sophisticated utility function
Not yet productiveMATSim longterm goal
First goal: model shopping location choice=> Activity-based models (chains) → reasonable shopping location choice model requires sound leisure location choice modeling (aggregate level)
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 activity
activity chains ← f(agent attributes)
10
Utility Function Extension
Consider potential for application/testing of estimated utility maximization models
→ hypothesis testing w/ data basis ≠ used for model estimation
MATSim utility maximization framework
Improve simulation results
Store sizeStores density
SituationAlternative Person
11
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)
12
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)
13
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)
Config 0
Config 1 Config 3.1
Results – Count Data – 18-19 h
15
Results – Count Data – 24 h
Config daily [%]
0
1
2
3.1
3.2
Weighting by shopping traffic work: (#trips * trip length)
≈ 7 % (excl. back to home trips)
Small effects
(i,j) [%]
23.82
0.07
0.46
0.45
-60.25
-36.43
-36.36
-35.90
-35.91
Worksaggregated model
No improvement w/ respect to spatial distribution of trips
Retest:- ... more disaggregated data!- ... more stations (now 300 stations for CH)- … time dimension- … compare with variance(year)- … Reject hypothesis
Estimate (Shopping) Utility Function Parameters
rho = f(robserved) ?
Shopping round trips by car → mode, → chaining, …
Choice set generation & F.P. dominance attributes
robserved
rho
csreal(t)distance
Model
csreal ~ csho ?
= f(rho)rho arbitrary → i arbitrary
Estimate (Shopping) Utility Function Parameters
i
Unawareness set
csreal
Awareness set= csreal(t –t)
Inept set (-)
Bias?
csreal(t)
Where is the relevant cut? choice(t)
Narayana and Markin 1975
Evoked set (+) Inert set (0)
Survey(s) in 2010?
MATSim measures?• Travel distance distribution• Travel time distribution• Link loads• Winner-loser statistics (WU)• Number of visitors of type xy…
18
Activity-based Demand Modeling
Problem to solve
Activity differentiation (shopping → grocery ↔ non-grocery) + random assignment
Neglectable effect
Facilities info
Model
Input Output
Iinput
Imodel (+ Iemergence)Ioutput ~ Iinput × (Imodel + Iemergence)
no info!
Ioutput = level ×
Level: e.g. count data vs. avg. trip lengthThe closer we look the larger the error (Ioutput fixed)? our hope!
define level and Necessary information (data)
Research …• Ioutput = level × for MATSim• Structure of data (variance of behav.)(explicable + random part) → reachable level and in principle → range of solvable problems
little info!
Activity-based Demand Modeling – e.g., Location Choice
S
S
H
Uni
HB
Same flow, different people
Facilities informationErrors at different levels
Different flow Comparison w/ aggregated models:Gravity models: trip length distribution
→ information about heterogeneity
superior?
Agent attributes information (e.g. income)
Our hope:Reduction of error at „coarser“ level?
„Averaging“ of local decisions and effects (traffic jams)?
Activity-based demand modeling
Model quality (level × error)
Data (volume and level)
Aggregated models
Disaggregated models
GSM?
Always superior?Saturation behavior?
Is there error propagation and thus error accumulation in the chains?
Predictability of leisure activities
Life path: Reducing leisure travel to a cross-sectional sample (e.g. 1 day)?
Leisure behavior
ConstraintsPossibilities← Environment
Person attributes Unobservable personal life path(friends etc.)
Shopping behavior
Descr. statistics
Reduction of complexity (by statistics)? Integration of Social
Networks and Detailed Psych. Models of Individuals
Starting w/ combining MATSim with rule-based models etc.
24
Results – Count Data – 24 h
(i, j) (i,j) [%] dist(i,j) [%]
2, 3.1 0.46
2, 3.2 0.45
Car shopping trips
Retest:- ... more disaggregated data!- ... more stations (now 300 stations for CH)- … time dimension- … compare with variance(year)- … H0
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
Reject hypothesis
No improvement w/ respect to spatial distribution of trips
0.62
0.39
Activity-based Demand Modeling – e.g., Initial Demand
Census: • Population• h, w (chain anchors)
Micro census: • Chains (chain structure)
Assignment ofchains → populationf(agent attributes)
Activity-based Demand Modeling – e.g., Initial Demand
7‘500‘000 chainsSample „inflating“?
MC: 30‘000 chains representative sample (of persons and also chains?)
12 6
9 9
18
18
= …
- level 1: 0
- level 0: 3+3 = 6
Real chain distribution
Random assignment
Initial demand: Assignment ofchains → populationf(agent attributes)
Region 1 Region 2
Missing information at level 0:• Systematic partexplicable by f(agent attributes)
• Random part observable but not explicable
Underlying distribution?
Interpolation?