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Horni A. (2013) MATSim Issues … suitable for a car trip discussion, Group seminar VPL, IVT, Zurich, September 2013.
MATSim Issues …
Andreas Horni
IVTETHZürich
September 2013
executionexecution
replanningreplanning
scoringscoring
controlercontroler
• Basic procedure• Equilibrium-based and rule-
based simulations• Modeling Horizon and
Temporal Variability• Disaggregation• UTF Estimation
… suitable for a car trip discussion
Basic Procedure
instantiationinstantiation
microsimulation (model)microsimulation (model) 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 (model)microsimulation (model)
choice modelchoice model
network load simulationnetwork load simulation
(usually non-linear) system of equations
fixed point problem(== UE)
h
h
Basic Procedure
Numerics: Root finding problem ↔ fixed point problem
3
032)(2
2
xxx
xxxf
32
32
32
32
2
xx
xx
xx
xxx
2
32
3
)2(3
3)2(0
320
32
2
xx
xx
xx
xx
xx
xxx
2
3
32
3
2
2
2
xx
xx
xxx
(ans: x = 3 or -1)
321 ii xx
1. x0 = 4
2. x1 = 3.31662
3. x2 = 3.10375
4. x3 = 3.03439
5. x4 = 3.01144
6. x5 = 3.00381
2
31
ii x
x2
32
1
ii
xx
1. x0 = 4
2. x1 = 1.5
3. x2 = -6
4. x3 = -0.375
5. x4 = -1.263158
6. x5 = -0.919355
7. x6 = -1.02762
8. x7 = -0.990876
9. x8 = -1.00305
1. x0 = 4
2. x1 = 6.5
3. x2 = 19.625
4. x3 = 191.070
convergence
slow convergence
divergence
Basic Procedure
instantiationinstantiation
microsimulation (model)microsimulation (model) 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
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-
planomat, dc.br
share →Charypar
?
Equilibrium-based vs. Rule-based Models
t0
t1t0
t1
Transition process
Equilibrium models
Needs to be efficient butnot behaviorally sound
Characteristics need to be defined
(not under-determined)
Computational process models
Both need to be behaviorally sound
Resonable but essentially does not matterboundary conditions accurate (chains)
Equilibrationprocess
q0
q1
t0t1
t0
t1 Simulated period
Simulated period
s1
s0
Non-iterativeIterative
Useful for longitudinal models
?warmstart
Modeling Horizon and Temporal Variability
?
Modeling Horizon and Temporal Variability
avg(0x0, … , nxn) + 0
(b)
(a)
(c)
f(.)f(.)
input model output
0x0 + 0 f(.)f(.) f (0x0 + 0)
… f(.)f(.) f (… )
nxn + n f(.)f(.) f (nxn + n)
0x0 + 0 , … , nxn + n F(.)F(.) F (0x0 + 0 , … , nxn + n)
averaging
averaging
averaging
… f(.)f(.)
f (avg(0x0, … , nxn) + 0 )
…
avg(0x0, … , nxn) + n f(.)f(.) f (avg(0x0, … , nxn) + n )
averaging
endogenous correlations
results = avg
Modeling Horizon and Temporal Variability
(b) Project Suprice
(a) MATSim standard
(c) weekplans
Mon Sun
executionexecution
replanningreplanning
scoringscoring
controlercontroler
executionexecution
replanningreplanning
scoringscoring
controlercontroler
executionexecution
replanningreplanning
scoringscoring
controlercontroler
Wed
replanningreplanning
scoringscoring
executionexecution
?
Disaggregation
A B C D
A
B
C
D
e.g., freight, cross-border traffic
censuscensus
travel surveystravel surveys
h
h
agent0
h
h
agent1
h
h
agentn
population
FA
FB
agentn+1
disaggregation
FA
FC
FB
agentn+2
disaggregate assignmentcorrelations in plans+ side effects
w
ss
? improve
Utility Function Estimation
n
iiitrav
n
iiactplan UUU
2,1,
1,
Utility Function Estimation
iitravtraviitrav tU ,1,,1,
hEurotrav /12
Balmer 2005
Utility Function Estimation
iwaitiact UU ,,
hEurowait /6
Utility Function Estimation
iarlateiwaitiact UUU ,.,,
hEuroarlate /18.
Utility Function Estimation
idpearlyiarlateiwaitiact UUUU ,.,.,,
hEurodpearly /6.
Utility Function Estimation
idurshortidpearlyiarlateiwaitiact UUUUU ,.,.,.,,
hEurodurshort /6.
Utility Function Estimation
iduridurshortidpearlyiarlateiwaitiact UUUUUU ,,.,.,.,,
hEurodur /6
iidurduridur tttU ,0,*
, ln
240 4 8 12 16 20
1 2 1T T
tshortest.dur,2 = 4 h
0
50
100
150
200
250
300
350
-50
400
Upl
an [E
uro
]
time of day [h]
Utility Function Estimation – Example (Home-Work-Home)
Non-linear Udur
Relative vs. absolute utilitiesComprehensive model estimation due to e.g., activity dropping ?Balmer 2005