View
214
Download
0
Category
Preview:
Citation preview
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
2
Project Surprice (sic) - Kontext
MATSim week
temporal variation
Project Surprice (sic) - Problem
VOT
income
individual preferences
trip context
CC equity
MATSim week
4
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
5
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
6
output
execution
replanning
scoring
controler
analyses
input
config
utility functiongeneralized costs:
plan
evaluate plans
Implementation in MATSim: MATSim Principle
7
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
8
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
9
output
execution
replanning
scoring
controler
input Evolutionary algorithm
Implementation in MATSim: MATSim Principle
More Precisely: A Co-Evolutionary Algorithm
Interpretation
10
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
14
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?
18
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
20
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
22
Discussion: Week vs. Day Optimization – Computation Times
23
Questions
Recommended