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?