Relating mobility patterns to socio-demographic profiles

Thomas Liebig

Artificial Intelligence Group

Technical University of Dortmund

Agent Based Simulation

Models individual mobility with artificial agents

Hierarchy of motion [Hoogendoorn et.al. 2002]

Agent Based Simulation

Models individual mobility with artificial agents

Required data

‣ Traffic Network Traffic Network

‣ Points of interest Facilities

‣ Description of population Plans

Models individual mobility

Required data

‣ Traffic Network

‣ Points of interest

‣ Description of population

Agent Based Simulation

Description of population

‣ Denotes individual plan for every agent

‣ Where do we get it from?

‣ We present a method to derive it from census data

and analysis of sample dataset (e.g. CDC2013)

Different people in town

‣ Who prefers which mobility behaviour?

Analysis Workflow

‣ Process recorded raw data to sequences of annotated

locations

‣ filtering, stop detection, clustering, labeling

‣ Identify most frequent sequences and their

supporting subgroup of the population

Input data:

PID, Sequence (home, work, home)|demographic attributes (gender, age, …)

Frequent Itemset Mining

‣ We apply FP-tree

requires threshold (in this example threshold=1)

‣ {1, 3, 4}

{2, 4, 5}

{2, 4, 6}

‣ 6,26,246,…

Subgroup Analysis

‣ technique for the extraction of patterns

‣ with respect to a target variable.

‣ describes relations between variables and a certain value

of the target variable.

Frequent pattern {2,4}

‣ {1, 3, 4,x=0} male, student

{2, 4, 5,x=1} female, worker

{2, 4, 6,x=1} male, worker pattern: worker,{2,4}

Subgroup Analysis

‣ technique for the extraction of patterns

‣ with respect to a target variable.

‣ describes relations between variables and a certain value

of the target variable.

Frequent pattern {2,4}

‣ {1, 3, 4,x=0} male, student

{2, 4, 5,x=1} female, worker

{2, 4, 6,x=1} male, worker pattern: worker,{2,4}

Test with cyclists data

‣ given are trips with their purpose and person identifier

‣ About 80 persons

‣ purposes To work

To visit (friends, etc);

To work related task;

To Food shopping;

To Non-food shopping;

To School (Student);

To Entertainment;

To Eat (Lunch, etc);

To Home;

Other (any other not mentioned)]

‣ For the persons several attributes are provided gender, age, health, employment, income, marital status

(changed to binomial attributes)

Result - Freqent patterns

‣ Threshold: 0.25

‣ 69 To work and to home

37 To home and to Other

30 To work and to Other

30 To work and to home and to other

29 To Home and To Eat

23 To Work and to Eat and to Home

…

Result - Subgroups

‣ 69 To work and to home

TRUE, for

27-30 years=false and

FullTimeEducation=false and

Employment_Other=false and

SelfEmployed=false and

Income Low=false

37 To home and to Other

30 To work and to Other

30 To work and to home and to other

29 To Home and To Eat

23 To Work and to Eat and to Home

…

Result - Subgroups

‣ 69 To work and to home

37 To home and to Other

FALSE for

23-26 years=false and

35-38 years=false and

55-58 years=false and

EmployedPartTime=false and

Employment Other=false

30 To work and to Other

30 To work and to home and to other

29 To Home and To Eat

23 To Work and to Eat and to Home

…

Result - Subgroups

‣ 69 To work and to home

37 To home and to Other

30 To work and to Other

TRUE, for

27-30 years=false and

47-50 years=false and

51-54 years=false and

SelfEmployed=false and

Income Medium=false

30 To work and to home and to other

29 To Home and To Eat

23 To Work and to Eat and to Home

…

Result - Subgroups

‣ 69 To work and to home

37 To home and to Other

30 To work and to Other

30 To work and to home and to other

TRUE, for

27-30 years=false and

47-50 years=false and

51-54 years=false and

SelfEmployed=false and

Income Medium=false

29 To Home and To Eat

23 To Work and to Eat and to Home

…

Result - Subgroups

‣ 69 To work and to home

37 To home and to Other

30 To work and to Other

30 To work and to home and to other

29 To Home and To Eat

FALSE, for

Single=false and

Health Fair=false and

Employment Other=false and

SelfEmployed=false and

Income Low=false

23 To Work and to Eat and to Home

…

Result - Subgroups

‣ 69 To work and to home

37 To home and to Other

30 To work and to Other

30 To work and to home and to other

29 To Home and To Eat

23 To Work and to Eat and to Home

TRUE for

43-46 years=false and

Married=false and

Health-Very Good=false and

FullTimeEducation=false

…

Summary

‣ Found patterns can be used to define plans for agents

based on census of a city

(e.g. for mode of transportation decisions)

‣ Application to CDC2013

Next steps

‣ Spatio-Temporal Subgroups

‣ Performance analysis