Mo So

A. Horni

IVTETH Zürich

Juli 2012

Simulation einer Woche mit MATSim

http://synonyme.woxikon.de/

Gemeinschaftsprojekt KTH, ETH, EPFL, DTUEingebettet:

(Entfernte) Verwandtschaft mit Diss-Thema Zielwahl für (etwas) grösseren Zeithorizontzeitliche Variabilität → e in MATSim UTF

Ordóñez et al. 2012

Szenario aufgesetzt, grobe Idee für Experimente, noch keine Resultate

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Project Surprice (sic) - Kontext

MATSim week

temporal variation

Project Surprice (sic) - Problem

VOT

income

individual preferences

trip context

CC equity

MATSim week

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output

execution

replanning

scoring

controler

analyses

input

config

input:

• plans (demand)

• config (parameters)

• network (supply)

planagent population with day plans

Implementation in MATSim: MATSim Principle

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output

execution

replanning

scoring

controler

analyses

input

config

plan

execute plans

mobility simulation: event-based queue model

modes:• motorized individual traffic • public transport• bike (teleported)• walk (teleported)• ride (experimental)

Implementation in MATSim: MATSim Principle

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output

execution

replanning

scoring

controler

analyses

input

config

utility functiongeneralized costs:

plan

evaluate plans

Implementation in MATSim: MATSim Principle

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output

execution

replanning

scoring

controler

analyses

input

config

share (usually 10%)

decision dimensions:• time choice (local random mutation)• route choice (best response)• mode choice (random mutation)• destination choice (experimental)

plan

change plans

Implementation in MATSim: MATSim Principle

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output

execution

replanning

scoring

controler

analyses

input

config

• statistics• counts

• plans• events → post-processing e.g., in visualizer

Implementation in MATSim: MATSim Principle

exit conditon:„relaxed state“, i.e. equilibrium

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output

execution

replanning

scoring

controler

input Evolutionary algorithm

Implementation in MATSim: MATSim Principle

More Precisely: A Co-Evolutionary Algorithm

Interpretation

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Agent i

Plan 0

Plan 1

Plan 2

Plan 3

Agent j

Plan 0

Plan 1

Plan 2

Plan 3

species iIndivid. i,0

Plan 0

Individ. i,1

Plan 1

Individ. i,2

Plan 2

Individ. i,3

Plan 3

species jIndivid. j,0

Plan 0

Individ. …

Plan ..Competition on the infrastructure

→ Score (fitness)

The weakest die→ Generations

person.removeWorstPlan()Agent …

Plan ..

Coevolution: „The evolution of two or more interdependent species, each adapting to changes in the other. […]“The American Heritage Dictionary of the English Language

Evolution: "Change in the gene pool of a population from generation to generation by such processes as mutation, natural selection, and genetic drift. […]“ „www.dictionary.com“

instantiation

microsimulation (model) outputinput

Umax (day chains)

population

situation(e.g. season,

weather)

choice model

generalized costs

census travel surveys infrastructure data

estimation e.g., network constraints, opening hours

e.g., socio-demographcis

network load simulation

constraints

(«demand/supply equilibration»)fixed point problem solved with co-evolutionary algorithm

Implementation in MATSim: MATSim Principle

feedback

Implementation in MATSim: Approach Q & D

Mon SunSatshop leisure

MATSim integrationT. Dubernet

Q&D: no opening of MATSim 24h-cycle

execution

replanning

scoring

controler

execution

replanning

scoring

controler

execution

replanning

scoring

controler

endogenous:time, route and modechoice

exogenous:chain and destination choice

lagged variables

base: ZH scenario (state WU)population: census 2000; demand: MZ 2000/2005; infrastructure: IVTCH, BZ 2001

chains (Thurgau, 231 respondents, 6 weeks)h-*-h-chainsMATSim activity types

locations (h, w from census; s, l, e with neighborhood search Balmer) 13

Implementation in MATSim: Scenario

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Implementation in MATSim: Scenario cont’dpopulation: census 2000

demand: MZ 2000/2005

s/l destinations: nb search Balmer

net

execution

replanning

scoring

controler

Ut = SUact,t + SUtrav,t

person characteristics/preferences

Ut-1 = SUact,t-1 + SUtrav,t-1

lagged vars

modemain,t-1

fincome bi + epref

Implementation in MATSim: UTF in More Detail

agentmemory

Estimated UTF: 148 parameters!

Reduce for MATSim application:

Implementation in MATSim: UTF in More Detail

Estimation of MATSim UTF for iterative context?

Constrained preferences!

De Palma et al. (2006), Discrete choice models with capacity constraints: an empirical analysis of the housing market of the greater Paris region

“ex ante and ex post demand”

Dependency on spatial configuration and CC scheme (in MATSim: distance, cordon, area)

toll road

free road

net losers with CC

higher tt with CC

net winners with CC

lower tt with CC

Road Pricing: Equity Effects …where and how?DC = a Dtt + g m a ~ income; g ~ 1/income; m: e.g., tollrich: a large, g small → DC potentially larger with CCif a ~ income less strong (trip context, variable prefs) → DC more dispers == hypotheses

• equity in terms of C not (DC), tt?• trip context averages out?

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Temporal Variability, Counts 17-18 Uhr

aggregate results variability (e.g., link volumes)Var(X0+X1) = Var(X0) + Var(X1) + 2 Cov(X0, X1)

Input

bx + e

input sets

Output

output sets

chains, destination time, route, mode

Modelbx + e

input variability(exogenous)

model variability(endogenous)

total variability o

measured variability(spatio-temporal)

Temporal Variability and Correlations

week chains lagged variables

temporal variability

equidirectional rhythm of life

Modeling of observed (“real”) variability or uncertainty? meaning of e?

meaning of measures of dispersion?meaningless → Sampling method with sampling error

confidence intervals

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Variabilität – Interpretation of Results

search space extent → curse of dimensionality, combinatorial explosion

Discussion: Week vs. Day Optimization

Mon Sun

planning horizon ofdecision makers?

dependent on choicedimension (e.g., chain vs. time)

24h:

Zurich scenario WU: 10%, 1 dayKTI: 25%, 4 days (more choice dimensions and modes)Herbie: 10%, 5h (more threats)

Zurich scenario with destination choice: 1 day

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Discussion: Week vs. Day Optimization – Computation Times

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Questions