modeling destination choice in matsim andreas horni ivt eth zürich july 2011

Modeling Destination Choice in MATSim

Andreas Horni

IVTETHZürich

July 2011

Destination Choice in MATSim

2

initial demand

analysesexecution scoring

replanning

I. Search Method & Capacity Restraints II. Adding Unobserved Heterogeneity -> Adapted Search Method

III. Variability Analyses

IV. Model Estimation

3

I. Local Search in Our Coevolutionary System

day plansfixed and discretionary activities

travel time budget

relatively small set of locations per iteration step

time geography Hägerstrand

4

r = tbudget/2 * v

check all locationsttravel ≤ tbudget

→ choice set

check ∑ttravel ≤ tbudget

random choice

510 % ZH Scenario: 60K agents

6

I. Competition on the Activites Infrastructureload-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

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II. Adding Unobserved Heterogeneity: Scoring Function

V + eimplicit

+ eexplicit

II. Adding Unobserved Heterogeneity: Search Space

II. Adding Unobserved Heterogeneity: Search Space

costs(location(emax))

estimate by distance

realized utilities

preprocess once for every person

emax– bttravel = 0

search space boundary dmax = …distance to loc with emax

dmax

10

shopping leisure

II. Adding Unobserved Heterogeneity: Results

ei

j

personi alternativej

seed seed Random draws from DCM

microsimulation results = random variables X

III. Variability Analysis

-> microsimulations are a sampling tool

estimation of parameters for X (=statistic) with random sampling

Results should be given as interval estimation

Standard error of sampling distribution= sampling error

# of runs

20 runs (goal: 30 for TRB)time, route, destination choice, 200 iterations

Comparison OVER runs -> var = random var

Scores

population level

average executed plans:184.36: avg0.166: s0.09: s in percent of avg

III. Variability Analysis

agent level

Aggregation reduces var (applied in different fieldse.g. filtering (moving average))

III. Variability Analysis

daily link volumes

Previous studies confirmed (...?)

Castiglione TAZ levelup to 6% std.dev.

III. Variability Analysis

Hourly link volumes

high!Var = f(spatial resolutionstemporal resolutionchoice dimensions)

Previous studies at lowerres levels or less choicedimensions!-> difficult to compare

high!

III. Variability Analysis

Large intra-run var

1. Large var through replanning modules (20% replanner)(iteration 201: 100% select best)

2. Not in equilibrium3. Utility plateau -> genetic drift

ideas?

-> future research

16

Modeling Temporal Variability in MATSim

intrapersonal (temporal) variability

UMATSim = V + e MATSim cross-sectional model(average working day)

Correlations!

drawing from

?

(best case)

17

Correlations

t

y

General rhythm of life

Avoidance behavior

+

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model estimation

IV. Model Estimation

+ eexplicit – penaltycap

correlations tastescoefficients b