An original synthetic population tool applied to Belgian ... · An original synthetic population...

An original synthetic population tool applied to Belgian case:VirtualBelgium

Dr Eric Cornelis Johan Barthelemy Laurie Hollaert Philippe Toint

naXys - GRT, University of Namur, Belgium

NTTS 2013, Brussels, March 5

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 1 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 2 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 3 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUT

Impossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUT

Impossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUTImpossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulationAgents based modelsDisaggregated spatial meshing

BUTImpossible or too expensive to get an exhaustive data set if the number of agents is largePrivacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUT

Impossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUTImpossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUTImpossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Motivation

Micro-simulation

Agents based models

Disaggregated spatial meshing

BUTImpossible or too expensive to get an exhaustive data set if the number of agents is large

Privacy rules

Need for SYNTHETIC POPULATION

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 4 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 5 / 21

Classical approaches

IPFP: Iterative Proportion Fitting Process

Idea: Integrating an aggregate dataset (target) with a disaggregate dataset (seed).

+ ...Significant

sample

Set of consistent margins

Synthetic Population

”Cloning” entities from the sampleProblems for bi-level approach (e.g. individuals and households) but solved by (Guo and Bhat,2007)

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 6 / 21

Classical approaches

IPFP: Iterative Proportion Fitting Process

Idea: Integrating an aggregate dataset (target) with a disaggregate dataset (seed).

+ ...Significant

sample

Set of consistent margins

Synthetic Population

”Cloning” entities from the sampleProblems for bi-level approach (e.g. individuals and households) but solved by (Guo and Bhat,2007)

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 6 / 21

Classical approaches

IPFP: Iterative Proportion Fitting Process

Idea: Integrating an aggregate dataset (target) with a disaggregate dataset (seed).

+ ...Significant

sample

Set of consistent margins

Synthetic Population

”Cloning” entities from the sampleProblems for bi-level approach (e.g. individuals and households) but solved by (Guo and Bhat,2007)

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 6 / 21

Classical approaches

IPFP: Iterative Proportion Fitting Process

Idea: Integrating an aggregate dataset (target) with a disaggregate dataset (seed).

+ ...Significant

sample

Set of consistent margins

Synthetic Population

”Cloning” entities from the sample

Problems for bi-level approach (e.g. individuals and households) but solved by (Guo and Bhat,2007)

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 6 / 21

Classical approaches

IPFP: Iterative Proportion Fitting Process

Idea: Integrating an aggregate dataset (target) with a disaggregate dataset (seed).

+ ...Significant

sample

Set of consistent margins

Synthetic Population

”Cloning” entities from the sampleProblems for bi-level approach (e.g. individuals and households) but solved by (Guo and Bhat,2007)

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 6 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 7 / 21

Why classical approaches are not feasible for the Belgian case?

Generic problem of categories not present in the sample

No representative sample available for the Belgian case

Inconsistencies in the margins

Contingency table Source Margins Prop.municipality × gender × age GeDAP, 2001 405.491 1,00municipality × hh type GeDAP, 2001 380.653 0,94municipality × education level GeDAP, 2001 426.372 1,05municipality × status GeDAP, 2001 396.594 0,97district × hh type × age INS, 2001 357.884 0,88district × education level INS, 2001 398.582 0,98

Example of data inconsistencies for the district of Charleroi

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 8 / 21

Why classical approaches are not feasible for the Belgian case?

Generic problem of categories not present in the sample

No representative sample available for the Belgian case

Inconsistencies in the margins

Contingency table Source Margins Prop.municipality × gender × age GeDAP, 2001 405.491 1,00municipality × hh type GeDAP, 2001 380.653 0,94municipality × education level GeDAP, 2001 426.372 1,05municipality × status GeDAP, 2001 396.594 0,97district × hh type × age INS, 2001 357.884 0,88district × education level INS, 2001 398.582 0,98

Example of data inconsistencies for the district of Charleroi

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 8 / 21

Why classical approaches are not feasible for the Belgian case?

Generic problem of categories not present in the sample

No representative sample available for the Belgian case

Inconsistencies in the margins

Contingency table Source Margins Prop.municipality × gender × age GeDAP, 2001 405.491 1,00municipality × hh type GeDAP, 2001 380.653 0,94municipality × education level GeDAP, 2001 426.372 1,05municipality × status GeDAP, 2001 396.594 0,97district × hh type × age INS, 2001 357.884 0,88district × education level INS, 2001 398.582 0,98

Example of data inconsistencies for the district of Charleroi

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 8 / 21

Why classical approaches are not feasible for the Belgian case?

Generic problem of categories not present in the sample

No representative sample available for the Belgian case

Inconsistencies in the margins

Contingency table Source Margins Prop.municipality × gender × age GeDAP, 2001 405.491 1,00municipality × hh type GeDAP, 2001 380.653 0,94municipality × education level GeDAP, 2001 426.372 1,05municipality × status GeDAP, 2001 396.594 0,97district × hh type × age INS, 2001 357.884 0,88district × education level INS, 2001 398.582 0,98

Example of data inconsistencies for the district of Charleroi

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 8 / 21

Why classical approaches are not feasible for the Belgian case?

Generic problem of categories not present in the sample

No representative sample available for the Belgian case

Inconsistencies in the margins

Contingency table Source Margins Prop.municipality × gender × age GeDAP, 2001 405.491 1,00municipality × hh type GeDAP, 2001 380.653 0,94municipality × education level GeDAP, 2001 426.372 1,05municipality × status GeDAP, 2001 396.594 0,97district × hh type × age INS, 2001 357.884 0,88district × education level INS, 2001 398.582 0,98

Example of data inconsistencies for the district of Charleroi

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 8 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 9 / 21

Objectives

A Virtual population for Belgium

± 11.000.000 individuals

± 4.350.000 households

589 municipalities (LAU2)

Individuals and households interacting

Characterization by attributes influencing the travel behaviour

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 10 / 21

Objectives

A Virtual population for Belgium

± 11.000.000 individuals

± 4.350.000 households

589 municipalities (LAU2)

Individuals and households interacting

Characterization by attributes influencing the travel behaviour

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 10 / 21

Objectives

A Virtual population for Belgium

± 11.000.000 individuals

± 4.350.000 households

589 municipalities (LAU2)

Individuals and households interacting

Characterization by attributes influencing the travel behaviour

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 10 / 21

Objectives

A Virtual population for Belgium

± 11.000.000 individuals

± 4.350.000 households

589 municipalities (LAU2)

Individuals and households interacting

Characterization by attributes influencing the travel behaviour

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 10 / 21

Characteristics of our method

no need to be fed with a representative sample of the population

feasible for case where that could exist incoherencies amongst the margins

allowing building a synthetic population of individuals gathered in households according toinformation (margins) related to one level (individuals) or the other one (households).

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 11 / 21

Characteristics of our method

no need to be fed with a representative sample of the population

feasible for case where that could exist incoherencies amongst the margins

allowing building a synthetic population of individuals gathered in households according toinformation (margins) related to one level (individuals) or the other one (households).

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 11 / 21

Characteristics of our method

no need to be fed with a representative sample of the population

feasible for case where that could exist incoherencies amongst the margins

allowing building a synthetic population of individuals gathered in households according toinformation (margins) related to one level (individuals) or the other one (households).

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 11 / 21

Characteristics of our method

no need to be fed with a representative sample of the population

feasible for case where that could exist incoherencies amongst the margins

allowing building a synthetic population of individuals gathered in households according toinformation (margins) related to one level (individuals) or the other one (households).

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 11 / 21

Virtual Belgium: a new synthetic population generator

...

Source 1

Source 2

Inconsistencies

processing

Source n

Synth Pop Generator

Goal

A synthetic population generator han-dling the data inconsistencies due to theuse of various sources

Data sources:

INS, GeDAP (UCL), MOBEL, . . .

2 levels of aggregation:- Municipality- District

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 12 / 21

Virtual Belgium: a new synthetic population generator

...

Source 1

Source 2

Inconsistencies

processing

Source n

Synth Pop Generator

Goal

A synthetic population generator han-dling the data inconsistencies due to theuse of various sources

Data sources:

INS, GeDAP (UCL), MOBEL, . . .

2 levels of aggregation:- Municipality- District

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 12 / 21

Virtual Belgium: a new synthetic population generator (2)Main principles

General philosophy

Construct individuals and households by drawing their characteristics or members randomly

within the relevant distributions at the most disaggregate level available;

while maintaining known correlation structures.

A 3-steps procedure applied to each municipality

1 Generation of Ind : a pool of individuals.

2 Estimation of Hh: the households’ attributes joint-distribution.

3 Construction of the synthetic households by drawing individuals from Ind .

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 13 / 21

Virtual Belgium: a new synthetic population generator (2)Main principles

General philosophy

Construct individuals and households by drawing their characteristics or members randomly

within the relevant distributions at the most disaggregate level available;

while maintaining known correlation structures.

A 3-steps procedure applied to each municipality

1 Generation of Ind : a pool of individuals.

2 Estimation of Hh: the households’ attributes joint-distribution.

3 Construction of the synthetic households by drawing individuals from Ind .

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 13 / 21

Virtual Belgium: a new synthetic population generator (2)Main principles

General philosophy

Construct individuals and households by drawing their characteristics or members randomly

within the relevant distributions at the most disaggregate level available;

while maintaining known correlation structures.

A 3-steps procedure applied to each municipality

1 Generation of Ind : a pool of individuals.

2 Estimation of Hh: the households’ attributes joint-distribution.

3 Construction of the synthetic households by drawing individuals from Ind .

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 13 / 21

Example of outcomes

VirtualBelgiumNAXYS - 2011

0 20 40 6010

Km

12.3 to 19.2 %

19.3 to 20.8 %

20.9 to 22.1 %

22.2 to 23.5 %

23.6 à 31.8 %

60+ years old individuals

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 14 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 15 / 21

Temporal evolution for the synthetic population

Data source: Statbel

On a year per year basis

using

known fecundity and mortality rates, . . .

transition matrices

discrete choice models

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 16 / 21

Temporal evolution for the synthetic population

Data source: Statbel

On a year per year basis

using

known fecundity and mortality rates, . . .

transition matrices

discrete choice models

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 16 / 21

Temporal evolution for the synthetic population

Data source: Statbel

On a year per year basis

using

known fecundity and mortality rates, . . .

transition matrices

discrete choice models

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 16 / 21

Contents

1 Motivation

2 Classical approaches

3 Why classical approaches are not feasible for the Belgian case?

4 VirtualBelgium, an innovative method

5 Temporal evolution for the synthetic population

6 Application and perspective

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 17 / 21

Application to travel behaviours: Activity-based model

Activity chains assignment

1 Random draw of an activity chain A ∈ Ai = set of activity chains for the individual type i

2 Spatial and temporal localization ∀a ∈ A

NB: activity chains start and end at the individual’s house

Data source: MOBEL (2001)

10.000 different activity chain patterns and 12 activity purposes

192 individual types

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 18 / 21

Application to travel behaviours: Activity-based model

Activity chains assignment

1 Random draw of an activity chain A ∈ Ai = set of activity chains for the individual type i

2 Spatial and temporal localization ∀a ∈ A

NB: activity chains start and end at the individual’s house

Data source: MOBEL (2001)

10.000 different activity chain patterns and 12 activity purposes

192 individual types

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 18 / 21

Application to travel behaviours: Activity-based model

Activity chains assignment

1 Random draw of an activity chain A ∈ Ai = set of activity chains for the individual type i

2 Spatial and temporal localization ∀a ∈ A

NB: activity chains start and end at the individual’s house

Data source: MOBEL (2001)

10.000 different activity chain patterns and 12 activity purposes

192 individual types

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 18 / 21

Example of outcomes

Assignment of traffic due to activity chains

Namur case

using MATSIM framework for assignment

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 19 / 21

Example of outcomes

Assignment of traffic due to activity chains

Namur case

using MATSIM framework for assignment

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 19 / 21

Perspective

Virtual Belgium

Residential choices

Activity Chains :

- assignment

- localization (statistical sectors)

Social Networks

Dynamic evolution

Epidemiology

New variables

...

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 20 / 21

Thanks for your attention

Cornelis et al. (naXys -GRT, UNamur) VirtualBelgium NTTS2013 21 / 21