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