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Destination Choice Modelingof Discretionary Activities inTransport Microsimulations
Andreas Horni
destination choice modeling for transport microsimulations
This Thesis
problem: implementation of a MATSim destination choice module for shopping and leisure activities efficiently applicable for large-scale scenarios and easily adoptable by other simulation models
• consistent and efficient computation of quenched randomness
• destination choice utilityfunction estimation
• agent interactions • infrastructure competition modeling• CA cruising-for-parking simulation
• results variability • analysis of temporal variability andaggregation and variability
• choice sets specification• analysis
contribute to microsimulation destination choice modeling
• efficiency and consistency
Basic Procedure
instantiationinstantiation
microsimulation coremicrosimulation core OutputOutputinputinput
feedback
Umax (day chains)Umax (day chains)
populationpopulation
situation(e.g. season, weather)
situation(e.g. season, weather)
choice modelchoice model
generalized costs
generalized costs
censuscensus travel surveystravel surveys infrastructure datainfrastructure data
estimation e.g., network constraints, opening hours
e.g., socio-demographcis
network load simulation
network load simulation
constraintsconstraints
Basic Procedure
microsimulation coremicrosimulation core
feedback
choice modelchoice model
network load simulationnetwork load simulation
(usually non-linear) system of equations
fixed point problem(== UE)
Evolutionary algorithm
optimized plans
optimized plans
Initial plansInitial plans
scoringscoring
replanningreplanning
executionexecution
agent1..n
optimized plans
optimized plans
initial plansinitial plans
scoringscoring
replanningreplanning
executionexecution
MATSim
agent0
interaction
species1..n
optimized populationoptimized population
initial population
initial population
recombinationrecombination
mutationmutation
survivor selectionsurvivor selection
parent selectionparent selection
parentsparents
offspringsoffsprings
fitness evaluation
fitness evaluation
species0
optimized populationoptimized population
initial population
initial population
recombinationrecombination
mutationmutation
survivor selectionsurvivor selection
parent selectionparent selection
parentsparents
offspringsoffsprings
fitness evaluation
fitness evaluation
interaction
Co-
Destination Choice & Other Frameworks
TRANSIMS
ALBATROSS
PCATS
search space
space
draw from discrete choice model
hierarchical destination choice (zone and intra-zonal choice)
various constraints
draw from decision trees
time geography
draw from discrete choice model
MATSim Destination Choice Approaches
time-geographic space-time prisms hollow prisms
PPA
time
space
t1
destination
t0
origin
distance
rin,out = f(act dur)
min (ctravel) min (ctravel) with r < ctravel< r i
Unobserved Heterogeneity
adding heterogeneity: conceptually easy, full compatibility with DCM framework
MATSim:
discrete choice modeling:
but: technically tricky for large-scale application
Repeated Draws: Quenched Randomness
• fixed initial random seed• freezing the generating order of ij
• storing all ij
destinations
persons
00
nn
10
iji
personi(actq)
store seed ki store seed kj
regenerate ij on the fly with random seed f(ki,kj)
one additional random number can destroy «quench»
i,j ~ O(106) -> 4x1012Byte (4TByte)
alternativej
Search for Umax
global optimum
local optimum
space
traveldisutility
→ restrain search spaceexhaustive search
i,j
U
Search for Umax : Search Space Boundary
approximate by distance
realized utilities with Gumbel distribution
pre-process once for every person
max– ttravel = 0
search space boundary dmax := ?
dmax := distance to destination with max
A0 = πr2 A1 = π(2r)2 - πr2 = 3πr2
A2 = π(3r)2 - 4πr2 = 5πr2 A3 = π(4r)2 - 9πr2 = 7πr2
A
r
Search for Umax in Search Space
tdeparture tarrival
Dijkstra forwards 1-n Dijkstra backwards 1-n
approximation
probabilistic choice
search space
work homeshopping
exact calculation of tt for choice
Results 10% Zurich Scenario
shopping
leisure
70K agentsiteration: 10 days 5 minutes
link volumes
Conclusions
ZH scenario: 10 days 5 minutes (iteration)but: module still needs to be faster for CH scenarioimprove sampling, sample correction factor
more validation data with more degrees of freedom
procedure for quenched randomness important in all iterative stochastic frameworks