Preferred citation style Horni A. (2013) MATSim Issues … suitable for a car trip discussion, Group...

Preferred citation style

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