1 A Model of Within-Households Travel Activity Decisions Capturing Interactions Between Household Heads Renni Anggraini, Dr.Theo Arentze, Prof.H.J.P. Timmermans Presented at DDSS ’06 Conference, 4-7 July 2006, Heeze, The Netherlands Urban Planning Group Faculty of Architecture, Building & Planning Technische Universiteit Eindhoven (TU/e) The Netherlands

2 Activity-based approach  Travel is derived from the demand for activities that individual needs to perform  Sequences or a pattern of behavior is the relevant unit of analysis, not individual trips  Household and other social structures influence travel and activity behavior  Spatial, temporal, transportation, and interpersonal interdependencies constrain activity-travel behavior  Activity-based approach reflect the scheduling of activities in time and space

3 ALBATROSSALBATROSS  Albatross: A Learning-based Transportation Oriented Simulation System (Arentze and Timmermans, 2000)  One of the fully operational activity-based models  Developed for the Dutch Ministry of Transportation, Public Work and Water Management as the operational system for transport demand forecasting  Two major components defining a schedule for each individual for fixed and flexible activities  Decision tree as a formalism to model the heuristic choice

4 Limitation of Albatross Household decision making process is not fully captured in terms of:  Activity allocation  Task allocation  Car allocation  Joint travel  Activity participation

5 ObjectivesObjectives To elaborate and expand Albatross in the context of household level decision making focusing on maintenance activities and related travel

6 Choice Facets  Activity generation  Task allocation  Trip-chaining  Resource allocation and Mode choice

7 Activity Generation  The model predicts the maintenance activities conducted in a household for a given day  The result is a description of activities performed at the household level for a particular day  The activities are a selection of an exhaustive list of maintenance activities considered by households

8 Task Allocation m1m1 m2m2...mnmn P1P1 x 11 x 12...x 1n RT 1 = x 11 + x 12 +…+x 1n P2P2 x 21 x 22...x 2n RT 2 = x 21 + x 22 +…+x 2n CT 1 = x 11 + x 21 CT 2 = x 12 + x CT n = x 1n + x 2n T = RT 1 +RT 2 Table 1. Matrix representation of task allocation pattern Table 2. Example of task allocation matrix pattern m1m1 m2m2 m3m3 m4m4 P1P P2P

9 Trip-chaining choices  Trip-chaining decision as a choice between yes/no linking a given pair of activities.  The existence of other activities (if any) take into account  Trip-chaining choices description: 1/3

10 Trip-chaining choices Every ij pair of trip-chaining variables must meet the following logical constraints:  c ij = c ji  If c ij =1 and c jk =1 then c ik =1 (for every 3rd activity k)  If c ij =0 and c jk =0 then c ik =0 (for every 3rd activity k) m1m1 m2m2 m3m3 m4m4 O1O1 O2O2 O3O3 m1m m2m m3m m4m O1O1 100 O2O2 10 O3O3 1 Table 3. Matrix representation of activities performed 2/3

11 Trip-chaining choices m1m1 m2m2 m3m3 m4m4 O1O1 O2O2 O3O3 m1m m2m m3m m4m O1O O2O O3O Table 4. Matrix activities performed to identify # of tours 3/3 Identify the activity combinations 1.m 1, O 1 2.m 2, m 4, O 3 3.m 3 4.m 2, m 4, O 3 5.O 1, m 1 6.O 2 7.m 2, m 4, O 3 1.m 1, O 1 2.m 2, m 4, O 3 3.m 3 4.O 2 The possible activity combinations P1P1 P2P2 P1P1 P2P2

12 Resource Allocation & Mode Choice  Households classification can be grouped into:  Drivers > Cars  Drivers = Cars  Cars > Drivers  No cars  Some variables take into account (gender role, work status, income, & complexity of the activity agenda,etc)

13  Some factors influencing resource allocation and mode choice:  The activity-travel conducted either jointly or independently  If one agent does not use car, the other agent can freely use  Both agents possible choose not to use the car  Mode choice set assigned to # of tours scheduled for the household-day: Resource Allocation & Mode Choice

14 Decision Trees Some methods oC4.5 : Information Gain oCART : Gini-Index oCHAID : Chi-Square

15 Overview properties of Tree Induction Algorithms PropertyC4.5CARTCHAID Type of splitsmultiwaybinarymultiway Allow groupings of attribute values yes Can handle continuous var. yes no Split criterionInformation gain ratioGini-indexSignificance value of chi- square Use growing/pruning strategy yes no Use different cases for pruning noyes Pruning criterionPredicted number of errors Misclassification cost complexity of the tree

16 Further Research  Involving all activities, not only maintenance activities  Expanding it into much broader choice facets such as travel participation and activity participation, duration of time, time of day, and route choice  Estimating the empirical analysis by using decision tree induction method

17 ConclusionsConclusions  The system explicitly describes what maintenance activities perform and who performs which task  The organization of activities in trip chains resulting in the number and activity composition of tours that each agent carries out during the day  Modeling resource allocation and mode choice allow a better integration of various facets in activity-travel decisions

18 Thank you for your attention

19 Review of Existing Approaches  Srinivasan, S., and Bhat, C.R., (2005). Investigating the in-home and out-of-home maintenance activity generation by examining the duration time invested by male-female household heads.  Zhang, J., Timmermans, H., Borgers, A., 2005 Investigating the task allocation and time use spent by male and female  Ettema, D., and Van der Lippe, T., 2006 Investigating the household heads interaction by using three hypotheses: traditional role expectations, higher qualified job, low accessibility  etc