fundp namur 19 avril 2004 la modélisation de la demande de transport: méthodes avancées...

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FUNDP Namur 19 Avril 2004 FUNDP Namur 19 Avril 2004 La modélisation de la La modélisation de la demande de transport: demande de transport: méthodes avancées appliquées méthodes avancées appliquées aux chaînes d’activités. aux chaînes d’activités. Cinzia Cirillo Cinzia Cirillo Facultes Universitaires Notre Dame de la Paix – Facultes Universitaires Notre Dame de la Paix – FUNDP FUNDP Transportation Research Group – GRT Transportation Research Group – GRT Namur BELGIUM Namur BELGIUM [email protected] [email protected]

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Page 1: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

La modélisation de la La modélisation de la demande de transport:demande de transport:

méthodes avancées appliquées méthodes avancées appliquées aux chaînes d’activités.aux chaînes d’activités.

Cinzia CirilloCinzia CirilloFacultes Universitaires Notre Dame de la Paix – FUNDPFacultes Universitaires Notre Dame de la Paix – FUNDP

Transportation Research Group – GRTTransportation Research Group – GRT

Namur BELGIUMNamur BELGIUM

[email protected]@math.fundp.ac.be

Page 2: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

The activity based approachThe activity based approach

The analysis of transport demand in Belgium The analysis of transport demand in Belgium (MOBEL)(MOBEL)

Page 3: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Modeling frameworkModeling framework

The scheduling model system for workersThe scheduling model system for workers Pattern, tour and stop models.Pattern, tour and stop models. The Mode choice modelThe Mode choice model Value of Time (VOT) study.Value of Time (VOT) study. The destination choice modelThe destination choice model Combining temporal and spatial aspects in the Combining temporal and spatial aspects in the

mobility analysis.mobility analysis. Advanced models to measure travel behaviorAdvanced models to measure travel behavior

Mixed Logit & AMLET.Mixed Logit & AMLET.

Page 4: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

An econometric simulator An econometric simulator for daily activity travel patternsfor daily activity travel patterns

Individual &Household

Demographics(forecast year)

SyntheticPopulationGenerator

AggregateDemographics(forecast year)

Transportationsystem

Characteristics(forecast year)

Activity-Environment

Characteristics(forecast year)

Activity-travelsimulator

Modelparameters

Modelparameters

Medium-termChoice

Simulator

IndividualMedium-term

Decision(forecast year)

IndividualActivity-travel

Patterns(forecast year)

Page 5: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Daily pattern simulation for Daily pattern simulation for each individual of the householdeach individual of the household

fo r each in d ivid u a l in th e H H

S top -leve lm od e l sys tem

w orkers

Tou r-leve lm od e l sys tem

w orkers

P a tte rn -leve lm od e l sys tem

w orkers

S top -leve lm od e l sys temn on -w orkers

Tou r-leve lm od e l sys temn on -w orkers

P a tte rn -leve lm od e l sys temn on -w orkers

G en era tion -a lloca tionm od e l sys tem in th e H H

A c tivit ies an d ca rs

Page 6: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

The scheduling model system The scheduling model system

Tou r-leve l su b -sys tem

H om e-S tay D u ra tionb e fo re tou r

Tou r D u ra tion

L ocationD es tin a tion ch o ice

M od e

A c tivity typ e

S top -leve l su b -sys tem

A c tivity s top d u ra tionan d trave l t im e

N u m b er an d typ eS econ d ary ac tivity

P atte rn -leve l su b -sys tem

D ep artu re T im e

N u m b er an d sch ed u lin gP rin c ip a l d a ily ac tivity

Page 7: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Scheduling model system for workersScheduling model system for workersPattern model system alternativesPattern model system alternatives

11

22

33

44

55

66

77

88

99

WW

BWBW

WMWM

WPWP

BWMBWM

WMPWMP

BWPBWP

BWMPBWMP

> 4> 4

only work tour only work tour

before work pattern and work tours before work pattern and work tours

work pattern and midday tours work pattern and midday tours

work pattern and post-work tours work pattern and post-work tours

before, work and midday tours before, work and midday tours

work, midday and post-work tours work, midday and post-work tours

before, work and post-work tours before, work and post-work tours

before, work midday and post-work tours before, work midday and post-work tours

more than 4 toursmore than 4 tours

Page 8: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

    

AASCSC

11

  22

  33

  44

  55

  Work scheduling varsWork scheduling vars

66

  77

  88

  99

  1010

  HHLD variablesHHLD variables

1111

  1212

  IND variablesIND variables

1313

  1414

  1515

  1616

  1717

  1818

  Inertia variablesInertia variables

1919

  2020

  2121

W (as base)W (as base)

  BWBW

  WMWM

  WPWP

  BWPBWP

  MWPMWP

  

WPWP

  BWBW

  BWP-MWPBWP-MWP

  WP-BWP-MWPWP-BWP-MWP

  Only WOnly W

  

WP-BWP-MWPWP-BWP-MWP

BW-WPBW-WP

only Wonly W

WP-BWP-MWPWP-BWP-MWP

MWMW

BWBW

BWBW

only Wonly W

oonly Wnly W

  BW-MW-WPBW-MW-WP

  BWP-MWPBWP-MWP

Parameters Parameters

Work tourWork tour

Before and Work toursBefore and Work tours

Work and Midday toursWork and Midday tours

Work and Post toursWork and Post tours

Before, Work and Post toursBefore, Work and Post tours

Midday, Work and Post toursMidday, Work and Post tours

  

Arrival at work before 9:00 a.m.Arrival at work before 9:00 a.m.

Arrival at work betArrival at work betww 9/10:00 a.m. 9/10:00 a.m.

Dep from work betw 4/6:00 p.m.Dep from work betw 4/6:00 p.m.

Dep from work after 6:00 p.m.Dep from work after 6:00 p.m.

Arrival at work betArrival at work betww 9/10:00 a.m. 9/10:00 a.m.

  

Household incomeHousehold income

NN.. of children between 12 and 18 of children between 12 and 18

  

Age 26-35Age 26-35

Age over 60Age over 60

Not marriedNot married

Married with child/renMarried with child/ren

Female and part TimeFemale and part Time

NN.. of working hours per week of working hours per week

  

act ch =act ch = pre choice = work onlypre choice = work only

acactt ch = ch = pre choice = 1 extra actpre choice = 1 extra act

act ch =act ch = previous choice = previous choice =

2 extra activiti2 extra activititiesties

EstimEstim..

  

-3.769-3.769

-3.333-3.333

-0.932-0.932

-4.291-4.291

-2.863-2.863

  

1.3441.344

0.8970.897

0.9700.970

-1.889-1.889

-1.438-1.438

  

0.0730.073

0.2190.219

  

0.4220.422

-2.165-2.165

1.3491.349

1.3431.343

0.5880.588

0.0160.016

  

0.8680.868

-0.168-0.168

0.7060.706

s.e.s.e.

  

0.2950.295

0.2610.261

0.2070.207

0.3150.315

0.2080.208

  

0.1170.117

0.3050.305

0.2630.263

0.2080.208

0.1780.178

  

0.0200.020

0.0820.082

  

0.2080.208

0.5280.528

0.2410.241

0.2830.283

0.2920.292

0.0030.003

  

0.1530.153

0.0800.080

0.1210.121

t-statst-stats

  

-12.8-12.8

-12.8-12.8

-4.5-4.5

-13.6-13.6

-13.7-13.7

  

11.411.4

2.92.9

3.73.7

-9.1-9.1

-8.1-8.1

  

3.63.6

2.72.7

  

2.02.0

-4.1-4.1

5.65.6

4.84.8

2.02.0

6.06.0

  

5.75.7

-2.1-2.1

5.85.8

Page 9: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Scheduling model system for workersScheduling model system for workersTour model system alternativesTour model system alternatives

11

22

33

44

55

66

MM

LL

OO

MLML

MOMO

LOLO

Maintenance Maintenance

LeisureLeisure

OtherOther

Maintenance and leisureMaintenance and leisure

Maintenance and othersMaintenance and others

Leisure and othersLeisure and others

Page 10: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

    

AASCSC

11

  22

  33

  44

  55

  66

Work actWork activity varsivity vars

77

  88

  99

  1010

  1111

  1212

HHHLD HLD varsvars

1313

  1414

  1515

  1616

  IINDND vars vars

1177

  1188

  1919

  2200

  2211

  Inertia variablesInertia variables

2222

  2233

  2244

AltAltss

WorkWork

MM

LL

OO

MLML

MOMO

LOLO

1 act.1 act.

2 act.2 act.

LL

MM

OO

LOLO

  

11 act. act.

2 act.2 act.

1 act.1 act.

2 act.2 act.

  

1act. & 2act.1act. & 2act.

1act. & 2act.1act. & 2act.

1act. & 2act.1act. & 2act.

1act. & 2act.1act. & 2act.

1act. & 2act.1act. & 2act.

  

0 act.0 act.

1act.1act.

2act.2act.

Parameters Parameters

  (as base)(as base)

MaintenanceMaintenance

LeisureLeisure

OtherOther

MaintMaint.. and Leisure and Leisure

Maint and OtherMaint and Other

Leisure and OtherLeisure and Other

  

NN of stop of stop inin the commute pattern the commute pattern

NN of stop in the commute pattern of stop in the commute pattern

Distance to work locationDistance to work location

DurDur. . of work activity (min/100)of work activity (min/100)

DurDur.. of work activity (min/100) of work activity (min/100)

DurDur.. of work activity (min/100) of work activity (min/100)

  

Number of children under 12Number of children under 12

Number of children under 12Number of children under 12

NN.. of children between 12 and 18 of children between 12 and 18

N.N. of children between 12 and 18 of children between 12 and 18

  

Female and part-time jobFemale and part-time job

Age between 25-50Age between 25-50

Age over 50Age over 50

Married with childrenMarried with children

Not marriedNot married

  

Inertia no extra actInertia no extra act

Inertia 1 activityInertia 1 activity

Inertia 2 activitiesInertia 2 activities

EstimEstim

  

-1.459-1.459

-0.260-0.260

-0.464-0.464

-3.606-3.606

-3.887-3.887

-1.704-1.704

  

0.0630.063

0.0160.016

-0.110-0.110

-0.149-0.149

-0.092-0.092

-0.221-0.221

  

0.2510.251

0.3090.309

0.5680.568

0.9790.979

  

0.6570.657

-0.459-0.459

-0.885-0.885

-0.525-0.525

-0.475-0.475

  

0.4610.461

0.6250.625

0.2060.206

s.e.s.e.

  

0.2910.291

0.2040.204

0.2270.227

0.2770.277

0.2960.296

0.3360.336

  

0.0530.053

0.0900.090

0.0170.017

0.0640.064

0.0390.039

0.0760.076

  

0.0880.088

0.1550.155

0.0950.095

0.1410.141

  

0.1300.130

0.1500.150

0.1770.177

0.1650.165

0.1510.151

  

0.0990.099

0.2330.233

0.1220.122

t-stat-statt

  

-5.0-5.0

-1.3-1.3

-2.0-2.0

-13.0-13.0

-13.1-13.1

-5.1-5.1

  

1.21.2

0.20.2

-6.3-6.3

-2.3-2.3

-2.3-2.3

-2.9-2.9

  

2.82.8

2.02.0

6.06.0

6.96.9

  

5.05.0

-3.1-3.1

-5.0-5.0

-3.2-3.2

-3.1-3.1

  

4.74.7

2.72.7

1.71.7

Page 11: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Scheduling model system for workersScheduling model system for workersStop model system alternativesStop model system alternatives

11

22

33

44

55

66

nonenone

1 stop TW1 stop TW

2 stop TW2 stop TW

1 stop FW1 stop FW

2 stop FW2 stop FW

TWFWTWFW

no stopno stop

1 stop on the way to work1 stop on the way to work

2 stops on the way to work2 stops on the way to work

1 stop on the way back home from work1 stop on the way back home from work

2 stops on the way back home from work2 stops on the way back home from work

one or more stop(s) on the way to work andone or more stop(s) on the way to work and

one or more stop(s)on the way back home one or more stop(s)on the way back home from workfrom work

Page 12: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

    

AASCSC

11

  22

  33

  44

  55

  LOS variablesLOS variables

  66

  77

  Household variablesHousehold variables

88

  99

  1010

  1111

  1212

  Individual variablesIndividual variables

1313

  1414

  1515

  1616

  1717

  1818

  1919

  2020

  Inertia variablesInertia variables

2121

  2222

  2233

  2424

AAlternativeslternatives

    

1 stop TW1 stop TW

  2 stop TW2 stop TW

  1 stop FW1 stop FW

  2 stop FW2 stop FW

  TWFWTWFW

  

NoneNone

NoneNone

    2 stop TW2 stop TW--2 stop FW2 stop FW NoneNone

  NoneNone

   stop TWFWstop TWFW

  1 stop FW1 stop FW

1 stop FW1 stop FW

  2 stop FW2 stop FW

  1 stop TW1 stop TW

  2 stop TW2 stop TW

  NoneNone

  2 stop TW2 stop TW-2-2 stop FW stop FW

   stop TWFWstop TWFW

   stop TWFWstop TWFW

  nonenone

  1 stop TW1 stop TW-2-2 stop TW stop TW

  1 stop FW1 stop FW--2 stop FW2 stop FW

   stop TWFWstop TWFW

  

none (as base)none (as base)

1 stop to work1 stop to work

2 stop to work2 stop to work

1 stop from work1 stop from work

2 stop from work2 stop from work

1 stop to work1 stop to work 1 stop from work1 stop from work

  

  To work in vehicle timeTo work in vehicle time

  To work out of vehicle timeTo work out of vehicle time

  

Number of vehiclesNumber of vehicles

Number of adultsNumber of adults

Urban Household locationUrban Household location

Suburban Household locationSuburban Household location

Suburban Household locationSuburban Household location

MaleMale

MaleMale

Female and part-timeFemale and part-time

Female and part-timeFemale and part-time

Not MarriedNot Married

Age25-35Age25-35

Age35-50Age35-50

Age over 65Age over 65

  

Inertia no stopInertia no stop

Iner to stop on the way to workIner to stop on the way to work

Iner to stop on theIner to stop on the way from workway from work

Inertia to stop bothInertia to stop both ways ways

EEstimstim..

  

-2.657-2.657

-5.149-5.149

-1.811-1.811

-3.571-3.571

-6.350-6.350

  

-0.019-0.019

-0.028-0.028

  

0.6070.607

0.1810.181

-0.831-0.831

1.9631.963

0.7690.769

  

-0.724-0.724

-0.346-0.346

1.0161.016

1.0431.043

0.7620.762

0.6620.662

1.9611.961

1.2281.228

  

0.4960.496

0.4940.494

0.2060.206

2.1152.115

s.e.s.e.

0.2780.278

0.4110.411

0.3390.339

0.4020.402

0.8140.814

  

0.0060.006

0.0070.007

  

0.1200.120

0.0710.071

0.2110.211

0.6390.639

0.2320.232

  

0.1370.137

0.1820.182

0.2190.219

0.4300.430

0.1130.113

0.2810.281

0.5450.545

0.5910.591

  

0.1480.148

0.1210.121

0.0560.056

0.3790.379

t-t-ststatat

  

-9.6-9.6

-12.5-12.5

-5.3-5.3

-8.9-8.9

-7.8-7.8

  

-3.1-3.1

-3.9-3.9

  

5.15.1

2.52.5

-3.9-3.9

3.13.1

3.33.3

  

-5.3-5.3

-1.9-1.9

4.64.6

2.42.4

6.76.7

2.42.4

3.63.6

2.12.1

  

3.33.3

4.14.1

3.73.7

5.65.6

Page 13: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Mode choice model: Mode choice model: MobidriveMobidrive data data

Type of tourType of tour

Main Main modemode

Walking Walking  CyclingCycling

VehicleVehicle

driverdriver

VehicleVehicle

PassengerPassenger

Public Public

transporttransport

TotalTotal

AllAll

modesmodes

% shares% shares

Non-workersNon-workers

    Morning tourMorning tour

    Principal tourPrincipal tour

    Evening tourEvening tour

  WorkerWorker

    Morning tourMorning tour

    Midday tourMidday tour

    Work tourWork tour

    Evening tourEvening tour

  

All tour typesAll tour types

(Share in %)(Share in %)

286286

250250

5151

99

2020

213213

112112

941 941

16.3%16.3%

328328

203203

3131

  

1010

5353

474474

144144

12431243

21.4%21.4%

638638

541541

8989

  

3131

3333

561561

181181

20742074

35.8%35.8%

182182

264264

2525

  

11

33

7676

170170

721721

12.4%12.4%

138138

207207

2525

  

44

2424

379379

3939

816816

14.1%14.1%

1572 1572

14651465

221 221

  

55 55

133 133

1703 1703

646 646

5795 5795

  

27.1%27.1%

25.3%25.3%

3.8%3.8%

  

0.9%0.9%

2.3%2.3%

29.4%29.4%

11.2%11.2%

  

100.0%100.0%

Page 14: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Mode choice model: Mode choice model: variablesvariablesLevelLevel

HouseholdHousehold

IndividualIndividual

PatternPattern

LOSLOS

VariablesVariables

House hold locationHouse hold location

  

Age Age

  

  

Marital statusMarital status

Professional StatusProfessional Status

  

Use of carUse of car

Use of Public TransportUse of Public Transport

  

Time budget [min/100]Time budget [min/100]

  

  Sum of travel time [min]Sum of travel time [min]

Tour Duration [min Tour Duration [min 

Number of stops Number of stops

Time [min]Time [min]

Cost [DM]Cost [DM]

CategoriesCategories

UrbanUrban

SuburbanSuburban

  Age 18-25Age 18-25

Age 26-35Age 26-35

Age 51-65Age 51-65

Married with childrenMarried with children

Full Time workerFull Time worker

Female and employed part-time Female and employed part-time

Main car userMain car user

Total annual mileage by carTotal annual mileage by car

Number of season ticketsNumber of season tickets

24 hours – time spent on previous activities 24 hours – time spent on previous activities (home stay included) and previous travel(home stay included) and previous travel

Sum of time spent travelingSum of time spent traveling

Sum of tour travel time and activity duration. Sum of tour travel time and activity duration.

Number of secondary activities observed within Number of secondary activities observed within each toureach tour  

Including any parking feesIncluding any parking fees

Page 15: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

Goodness of fitGoodness of fit

n. of obs.n. of obs.

LL (0) (0)

LL (C) (C)

LL ( (ββ))

KK

ρρ22 adjusted adjusted

MultinomialMultinomial

Logit (MNL)Logit (MNL)

57955795

- 8179.88- 8179.88

- 7503.82- 7503.82

- 6465.11- 6465.11

2121

0.20700.2070

MNL with interactions MNL with interactions with socio-economic with socio-economic parametersparameters

57955795

- 8179.88- 8179.88

- 7503.82- 7503.82

- 6559.23- 6559.23

2121

0.19550.1955

Mixed LogitMixed Logit

57955795

- 8179.88- 8179.88

- 7503.82- 7503.82

- 6446.88- 6446.88

2626

0.20860.2086

Mixed logit Mixed logit on panel on panel data (day)data (day)

57955795

- 8179.88- 8179.88

- 7503.82- 7503.82

- 6039.21- 6039.21

2626

0.25850.2585

Page 16: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

FUNDP Namur 19 Avril 2004FUNDP Namur 19 Avril 2004

VOT: Value Of Time studyVOT: Value Of Time study

Confidence interval Confidence interval (Armstrong et al., 2001)(Armstrong et al., 2001)

22

c

22c

22t

2ct

2

t

c

c

t22

c

2ct

t

c

c

tI,S

tt

ttttttt

t

t

tt

ttt

t

tV

VariableVariable

Upper limitUpper limit

Lower limitLower limit

VTTS point VTTS point estimateestimate

rhorho

tt

Number of Number of observationsobservations

All All purposespurposes

14.4714.47

8.608.60

11.0611.06

0.0930.093

- 4.76- 4.76

57955795

Work or Work or educationeducation

13.5713.57

4.504.50

8.238.23

0.0610.061

- 4.65- 4.65

18661866

ShoppingShopping

757.09757.09

13.0213.02

26.9926.99

0.0770.077

- 6.03- 6.03

12521252

LeisureLeisure

80.6180.61

7.077.07

15.3015.30

0.090.09

- 3.24- 3.24

13841384

OtherOther

50.7450.74

9.099.09

17.9417.94

0.0570.057

- 6.78- 6.78

12931293

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VOT by socio-economic characteristicsVOT by socio-economic characteristicsParameterParameter

ASC Car passengerASC Car passenger

ASC Public TransportASC Public Transport

ASC WalkASC Walk

ASC BikeASC Bike

Annual mileage by carAnnual mileage by car

Time BudgetTime Budget

Tour DurationTour Duration

Sum of Travel TimeSum of Travel Time

TimeTime

CostCost

Interaction variablesInteraction variables

Time * urban household locationTime * urban household location

Time * Age18-25Time * Age18-25

Time * Age26-50Time * Age26-50

Time * Age51-65Time * Age51-65

Time * Full-time workerTime * Full-time worker

Time * Female part-timeTime * Female part-time

Time * Married with childrenTime * Married with children

Time * Number of stopsTime * Number of stops

Time * Season TicketTime * Season Ticket

Time * Main Car userTime * Main Car user

AlternativeAlternative

CPCP

PTPT

WW

BB

CDCD

CD, CPCD, CP

PTPT

BB

AllAll

AllAll

  

AllAll

AllAll

AllAll

AllAll

AllAll

AllAll

AllAll

AllAll

AllAll

AllAll

ββ

-1.379-1.379

-1.495-1.495

0.2390.239

-0.491-0.491

0.0460.046

-0.043-0.043

0.0030.003

-0.005-0.005

-0.023-0.023

-0.113-0.113

  

0.0180.018

0.0210.021

-0.001-0.001

0.0030.003

-0.001-0.001

-0.032-0.032

-0.027-0.027

0.0150.015

0.0020.002

-0.006-0.006

t-stat.t-stat.

-22.6-22.6

-11.6-11.6

1.71.7

-3.8-3.8

12.512.5

-2.9-2.9

17.217.2

-2.8-2.8

-9.4-9.4

-10.5-10.5

  

8.08.0

5.75.7

-0.1-0.1

1.31.3

-0.2-0.2

-8.4-8.4

-9.2-9.2

6.36.3

0.90.9

-2.5-2.5

VOTVOT

12.0412.04

  

  

2.352.35

0.820.82

12.4812.48

10.3510.35

12.3112.31

29.1929.19

26.5226.52

4.194.19

10.7910.79

14.9814.98

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VOT per tour tipeVOT per tour tipe

VOT distribution for non-workers per tour typeVOT distribution for non-workers per tour type

VOT distribution for workers per tour typeVOT distribution for workers per tour type

9595thth percentile VTTS [DM] percentile VTTS [DM]

7575thth percentile VTTS [DM] percentile VTTS [DM]

Average VTTS [DM] Average VTTS [DM]

Share of % negative Share of % negative VTTS VTTS

Morning Morning patternpattern

32.232.2

18.818.8

13.513.5

6%6%

Principal Principal patternpattern

55.2555.25

28.428.4

15.415.4

25%25%

Evening Evening patternpattern

14.614.6

8.28.2

5.45.4

14%14%

9595thth percentile VTTS [DM] percentile VTTS [DM]

7575thth percentile VTTS [DM] percentile VTTS [DM]

Average VTTS [DM] Average VTTS [DM]

Share of % negative Share of % negative VTTS VTTS

Morning Morning patternpattern

n.a.n.a.

n.a.n.a.

n.a.n.a.

n.a.n.a.

Commute Commute patternpattern

15.315.3

9.39.3

7.67.6

0%0%

Evening Evening patternpattern

64.764.7

35.035.0

21.421.4

19%19%

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Travel timeTravel time

0

0.2

0.4

0.6

0.8

1

-0.060 -0.050 -0.040 -0.030 -0.020 -0.010 0.000 0.010 0.020 0.030

Parameter value: travel time

Cu

mu

lati

ve p

rob

abil

ity

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Travel costTravel cost

0

0.2

0.4

0.6

0.8

1

-0.200 -0.180 -0.160 -0.140 -0.120 -0.100 -0.080 -0.060 -0.040 -0.020 0.000

Parameter value: travel cost

Cu

mu

lati

ve p

rob

abil

ity

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VOTVOT

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 40

Value of time (GM)

Cu

mu

lati

ve

pro

ba

bili

ty

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TT: BPATT: BPA

0

0.2

0.4

0.6

0.8

1

-0.070 -0.060 -0.050 -0.040 -0.030 -0.020 -0.010 0.000 0.010 0.020 0.030

Parameter value: travel time Before Principal Activity - non-workers

Cu

mu

lati

ve p

rob

abil

ity

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TT: Principal pattern NWTT: Principal pattern NW

0

0.2

0.4

0.6

0.8

1

-0.150 -0.100 -0.050 0.000 0.050 0.100 0.150

Parameter value: travel time Principal pattern - non-workers

Cu

mu

lati

ve p

rob

abil

ity

Page 24: FUNDP Namur 19 Avril 2004 La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires

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TT: Evening pattern NWTT: Evening pattern NW

0

0.2

0.4

0.6

0.8

1

-0.035 -0.030 -0.025 -0.020 -0.015 -0.010 -0.005 0.000 0.005 0.010 0.015 0.020

Parameter value: travel time Evening pattern - non-workers

Cu

mu

lati

ve p

rob

abil

ity

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TT: commute pattern WTT: commute pattern W

0

0.2

0.4

0.6

0.8

1

-0.030 -0.025 -0.020 -0.015 -0.010 -0.005 0.000

Parameter value: travel time Commute pattern - workers

Cu

mu

lati

ve p

rob

abil

ity

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TT: Evening pattern WTT: Evening pattern W

0

0.2

0.4

0.6

0.8

1

-0.150 -0.100 -0.050 0.000 0.050 0.100

Parameter value: travel time Evening pattern - workers

Cu

mu

lati

ve p

rob

abil

ity

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Destination choice model.Destination choice model.

Discrete choice methods to model out-of-Discrete choice methods to model out-of-home and out-of-work activity location home and out-of-work activity location choicechoice

Alternative sizeAlternative size :: Statistical Sector Statistical Sector

Sampling of alternatives : Sampling of alternatives : Action Space Action Space **

(*) Dijst and Vidakovic (1997)(*) Dijst and Vidakovic (1997)

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DataData

Data sources:Data sources:

MOBELMOBEL, the Belgian National Mobility , the Belgian National Mobility Survey (1999)Survey (1999)

1484 geocoded daily activity chains 1484 geocoded daily activity chains achieved in the Flemish Region achieved in the Flemish Region

1950 out-of-home and out-of-work 1950 out-of-home and out-of-work activitiesactivities

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Alternatives generation Alternatives generation processprocess

Action space theoryAction space theory

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Action Space EquationAction Space Equation

(*)(*)

where, where, T : time-budget;T : time-budget; V : travel speed;V : travel speed; L : distance between bases (home-work);L : distance between bases (home-work);

ττ : travel time ratio ;: travel time ratio ;

x, y : coordinates of points belonging to the action-space.x, y : coordinates of points belonging to the action-space.

(*) Dijst and Vidakovic (1997)(*) Dijst and Vidakovic (1997)

staytravel

travel

TT

T

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Worker’s daily activity chainWorker’s daily activity chain Morning commuteMorning commute Midday tourMidday tour Evening commuteEvening commute After tourAfter tour

Non worker’s daily activity chainNon worker’s daily activity chain Before tourBefore tour Main tour (main activity)Main tour (main activity) After tourAfter tour

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Worker’s action spacesWorker’s action spaces

Non worker’s action spacesNon worker’s action spaces

S

H W

S

W H

S S

W

SS

H

1 stop 2 stops 1 stop 2 stops

s sH H

ss

2 stops main activity (ma) + 2 stops 2 stops

ma s

H s

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For each tour and commute, building an action For each tour and commute, building an action spacespace

For each observed stop, creating a set of max For each observed stop, creating a set of max 19 alternatives:19 alternatives:

nine randomly selected destinationsnine randomly selected destinations++

actual destination chosen by the individualactual destination chosen by the individual++

the other destination chosen in the activity chainthe other destination chosen in the activity chain

all in action spaceall in action space

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Group I: Group I: HomeHome Work (workers)Work (workers) Principal activity stop (non-workers) Principal activity stop (non-workers)  Group IIGroup IIMain stop in the morning tourMain stop in the morning tourMain stop in the evening tourMain stop in the evening tour Group IIIGroup III 1 Secondary stop in the morning tour1 Secondary stop in the morning tour 1 Secondary stop in the evening tour2 Secondary stops 1 Secondary stop in the evening tour2 Secondary stops

in principal tourin principal tour

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Before main tour action spaceBefore main tour action space

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Morning commute action spaceMorning commute action space

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Variables descriptionVariables description

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LOS variablesLOS variables

Impedance variables:Impedance variables: in-vehicle travel time;in-vehicle travel time; costcost; ;

Impedance Impedance = IVTT += IVTT + COSTCOST

(VOT = value of time = 7 Euro/hour)*(VOT = value of time = 7 Euro/hour)** Recent model developped for the Walloon Region* Recent model developped for the Walloon Region

VOT

1

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Land use variablesLand use variables Statistical sector area (total geographical area of the sector [m²]) ;Statistical sector area (total geographical area of the sector [m²]) ;

densely-built housing ;densely-built housing ; built-up housing ;built-up housing ; housing and other developments ;housing and other developments ;

industrial / commercial / port area ;industrial / commercial / port area ;

agriculture and meadowland (agriculture and open space, meadowland and agriculture and meadowland (agriculture and open space, meadowland and orchards). orchards).

green / nature area (broad-leaved, coniferous and/or mixed forests, green / nature area (broad-leaved, coniferous and/or mixed forests, municipal parks, heath land and moors, dunes and beaches, water);municipal parks, heath land and moors, dunes and beaches, water);

infrastructure (highways, district roads, airport and/or railway infrastructure infrastructure (highways, district roads, airport and/or railway infrastructure and so on);and so on);

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Activity variablesActivity variables

shopping variable;shopping variable; financial variable (banks);financial variable (banks); hotel / restaurant / café;hotel / restaurant / café; cinemas;cinemas; sport activities;sport activities; cultural, recreational and leisure activities cultural, recreational and leisure activities

(museum, library, school of music, zoo, nature (museum, library, school of music, zoo, nature reserve, theatres, casino and so on);reserve, theatres, casino and so on);

car retail;car retail; personal service (beauty center and so on).personal service (beauty center and so on).

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Random utilityRandom utility Utility functionUtility function

yyz z : land use zone-specific variables,: land use zone-specific variables, λλ :: coefficients fixed across all zones; coefficients fixed across all zones;

: size measure of alternative : size measure of alternative z, z,

MMzkzk : : kkth size variable for zone th size variable for zone zz, , ββkk : corresponding coefficient, : corresponding coefficient, μμ : positive scale parameter;: positive scale parameter;

xxizjizj : exogenous accessibility variables for individual : exogenous accessibility variables for individual ii in zone in zone z ,z , γγ : vector of random parameters;: vector of random parameters; εεiziz : error terms: error terms independently and identically Gumbel distributed.independently and identically Gumbel distributed.

zk

s

kz MeM k

1

izizzzT

iz xMyU lnln

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Model resultsModel results

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VariableVariable Multinomial Multinomial logitlogit

EstimateEstimate T-statT-stat

Mixed logitMixed logit

EstimateEstimate T-statT-stat

LOS VariableLOS Variable

Impedance (Mean)Impedance (Mean)

Impedance (s.d.)Impedance (s.d.)

ImpedanceImpedance

Socio-demografic interaction Socio-demografic interaction with impedancewith impedance

FemaleFemale

Number of HHLD actives = 1 Number of HHLD actives = 1

Number of HHLD actives = 2Number of HHLD actives = 2

Age 18-25Age 18-25

Age 25-55Age 25-55

Age > 55Age > 55

Number of cars 0-1Number of cars 0-1

Number of cars =2Number of cars =2

-0.0307-0.0307

0.00820.0082

-0.0172-0.0172

-0.0182-0.0182

-0.0199-0.0199

-0.0182-0.0182

-0.0103-0.0103

0.01520.0152

0.02510.0251

-3.1-3.1

2.62.6

-3.4-3.4

-3.5-3.5

-3.0-3.0

-2.2-2.2

-2.1-2.1

2.12.1

2.72.7

-0.1157-0.1157

-0.0671-0.0671

--

--

--

--

--

--

--

--

-5.4-5.4

-4.8-4.8

--

--

--

--

--

--

--

--

Land use variablesLand use variables

Ln (population)Ln (population)

Ln (agricultural area) Ln (agricultural area)

Ln (infrastructure)Ln (infrastructure)

Ln (housing)Ln (housing)

Ln (built area)Ln (built area)

Ln (densely built area)Ln (densely built area)

Ln (surface) Ln (surface)

-0.4951-0.4951

-0.2817-0.2817

0.06450.0645

0.11170.1117

0.06910.0691

0.14920.1492

0.68840.6884

-9.7-9.7

-8.3-8.3

2.12.1

2.82.8

3.53.5

3.83.8

14.814.8

-0.3567-0.3567

-0.3933-0.3933

-0.0514-0.0514

0.16690.1669

0.11640.1164

0.20030.2003

1.07401.0740

-5.9-5.9

-6.8-6.8

1.01.0

2.42.4

3.53.5

2.82.8

12.412.4

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Size variablesSize variables

Industry/Shopping areaIndustry/Shopping area

RecreationRecreation

SpectaclesSpectacles

RestaurantsRestaurants

ShoppingShopping

SportsSports

11

7.0777.077

5.2055.205

4.4934.493

4.5194.519

--

--

9.79.7

5.65.6

5.25.2

9.09.0

--

11

4.0354.035

--

1.7531.753

1.9121.912

2.8512.851

--

5.35.3

--

2.22.2

5.85.8

2.92.9

Error componentsError components

Evening Main + Stop EveningEvening Main + Stop Evening

Morning Prin. + Stop1 Prin.Morning Prin. + Stop1 Prin.

Evening Main + Stop1 Prin. Evening Main + Stop1 Prin.

+ Stop2 Prin.+ Stop2 Prin.

--

--

--

--

--

--

-0.4072-0.4072

0.25590.2559

0.40160.4016

-3.4-3.4

3.63.6

3.93.9

Number of observationsNumber of observations

Log-likelihood at zeroLog-likelihood at zero

Log-likelihood at convergenceLog-likelihood at convergence

Degrees of FreedomDegrees of Freedom

Adjusted ρAdjusted ρ

15821582

-3424.85-3424.85

-2961.68-2961.68

2121

0.1290.129

15821582

-3424.85-3424.85

-2676.33-2676.33

1616

0.2140.214

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Estimation of mixed logitEstimation of mixed logit

Probability choiceProbability choice

where,where,

: normally distributed random vector;: normally distributed random vector;

θθ : means and standard deviation of ; : means and standard deviation of ;

LLiziz : logit formula.: logit formula.

dfLdPLLEP izizizPiz ,,,

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Maximizing the log-likelihood functionMaximizing the log-likelihood function

Monte-Carlo SimulationMonte-Carlo Simulation

where where RR is the number of random draws is the number of random draws δδrr, taken , taken from the distribution function of from the distribution function of δδ..

I

1iiizPln

I

1maxLLmax

R

rriz

Riziz

iii L

RSP)(P

1

,1

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Computing Computing θθ as the solution of the as the solution of the simulated log-likelihood problem:simulated log-likelihood problem:

I

1i

Riin

R SPlnI

1maxSLLmax

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Software to estimate mixed logitSoftware to estimate mixed logit

Gauss (special routine written by K. Train)Gauss (special routine written by K. Train) Biogeme (M. Bierlaire)Biogeme (M. Bierlaire) Alogit (A. Daly)Alogit (A. Daly) LIMDEP (W. Greene)LIMDEP (W. Greene) AMLET (F. Bastin)AMLET (F. Bastin)