Relating mobility patterns to socio demographic profiles

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Thomas Liebig
Thomas Liebig/Technical University of Dortmund, Germany.
Topic: “Relating mobility patterns with socio-demographic profiles”

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Relating mobility patterns to socio demographic profiles

  1. 1. Relating mobility patterns to socio-demographic profilesThomas LiebigArtificial Intelligence GroupTechnical University of Dortmund
  2. 2. Agent Based SimulationModels individual mobility with artificial agentsHierarchy of motion [Hoogendoorn et.al. 2002]
  3. 3. Agent Based SimulationModels individual mobility with artificial agentsRequired data‣ Traffic Network Traffic Network‣ Points of interest Facilities‣ Description of population Plans
  4. 4. Models individual mobilityRequired data‣ Traffic Network‣ Points of interest‣ Description of populationAgent Based Simulation
  5. 5. Description of population‣ Denotes individual plan for every agent‣ Where do we get it from?‣ We present a method to derive it from census dataand analysis of sample dataset (e.g. CDC2013)
  6. 6. Different people in town‣ Who prefers which mobility behaviour?
  7. 7. Analysis Workflow‣ Process recorded raw data to sequences of annotatedlocations‣ filtering, stop detection, clustering, labeling‣ Identify most frequent sequences and theirsupporting subgroup of the populationInput data:PID, Sequence (home, work, home)|demographic attributes (gender, age, …)
  8. 8. Frequent Itemset Mining‣ We apply FP-treerequires threshold (in this example threshold=1)‣ {1, 3, 4}{2, 4, 5}{2, 4, 6}‣ 6,26,246,…
  9. 9. Subgroup Analysis‣ technique for the extraction of patterns‣ with respect to a target variable.‣ describes relations between variables and a certain valueof 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}
  10. 10. Subgroup Analysis‣ technique for the extraction of patterns‣ with respect to a target variable.‣ describes relations between variables and a certain valueof 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}
  11. 11. Test with cyclists data‣ given are trips with their purpose and person identifier‣ About 80 persons‣ purposesTo workTo 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 providedgender, age, health, employment, income, marital status(changed to binomial attributes)
  12. 12. Result - Freqent patterns‣ Threshold: 0.25‣ 69 To work and to home37 To home and to Other30 To work and to Other30 To work and to home and to other29 To Home and To Eat23 To Work and to Eat and to Home…
  13. 13. Result - Subgroups‣ 69 To work and to homeTRUE, for27-30 years=false andFullTimeEducation=false andEmployment_Other=false andSelfEmployed=false andIncome Low=false37 To home and to Other30 To work and to Other30 To work and to home and to other29 To Home and To Eat23 To Work and to Eat and to Home…
  14. 14. Result - Subgroups‣ 69 To work and to home37 To home and to OtherFALSE for23-26 years=false and35-38 years=false and55-58 years=false andEmployedPartTime=false andEmployment Other=false30 To work and to Other30 To work and to home and to other29 To Home and To Eat23 To Work and to Eat and to Home…
  15. 15. Result - Subgroups‣ 69 To work and to home37 To home and to Other30 To work and to OtherTRUE, for27-30 years=false and47-50 years=false and51-54 years=false andSelfEmployed=false andIncome Medium=false30 To work and to home and to other29 To Home and To Eat23 To Work and to Eat and to Home…
  16. 16. Result - Subgroups‣ 69 To work and to home37 To home and to Other30 To work and to Other30 To work and to home and to otherTRUE, for27-30 years=false and47-50 years=false and51-54 years=false andSelfEmployed=false andIncome Medium=false29 To Home and To Eat23 To Work and to Eat and to Home…
  17. 17. Result - Subgroups‣ 69 To work and to home37 To home and to Other30 To work and to Other30 To work and to home and to other29 To Home and To EatFALSE, forSingle=false andHealth Fair=false andEmployment Other=false andSelfEmployed=false andIncome Low=false23 To Work and to Eat and to Home…
  18. 18. Result - Subgroups‣ 69 To work and to home37 To home and to Other30 To work and to Other30 To work and to home and to other29 To Home and To Eat23 To Work and to Eat and to HomeTRUE for43-46 years=false andMarried=false andHealth-Very Good=false andFullTimeEducation=false…
  19. 19. Summary‣ Found patterns can be used to define plans for agentsbased on census of a city(e.g. for mode of transportation decisions)‣ Application to CDC2013
  20. 20. Next steps‣ Spatio-Temporal Subgroups‣ Performance analysis

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