1. 1 The relative role of spatial, temporal and interpersonal flexibility on the activity scheduling process Sean T. Doherty Wilfrid Laurier University Kouros Mohammadian University of Illinois at Chicago 2nd MCRI/GEOIDE PROCESUS International Colloquium, June 11-15, Toronto, Canada.

2. 2 Introduction Observed travel patterns are the result of an underlying activity scheduling process Activities are scheduled/planned on varying time horizons In practice, simplifying assumptions are most often adopted in the specification of planning time horizon The validity of this assumption is of some concern.

3. 3 Scheduling Process Models Key Assumptions SCHEDULER (Gärling et al., 1994) and SMASH (Ettema et al., 1993)  schedule is formed by adding activities with the highest priority followed by attempts to fit less prioritized activities into open time slots. Albatross (Arentze and Timmermans, 2000)  decision sequencing rule assumes that mandatory activities are completed before discretionary ones, and out of-home before in- home activities TASHA (Miller and Roorda, 2003)  selection of activity types for scheduling in a fixed order (work, joint other, joint shopping, individual other; individual shopping). CEMDAP, FAMOS, etc.

4. 4 Key Questions Activity flexibility believed to be major factor in scheduling and modification of activities Very little empirical measurement done Most often assume static levels of flexibility by activity type How can we go about measuring activity flexibility? What effect does it have on scheduling?

5. 5 Motivation The relative flexibility/fixity of certain activity types is also evolving and does not hold for all people in all circumstances It is important to develop a model or rule for the scheduling time horizon of activities that is dependent upon the nature of the activity not simply the activity type This will make the model more amenable to a variety of people and situations Also makes the model more sensitive to emerging policies that inherently effect activity flexibility and subsequent scheduling (e.g. telework)

6. 6 Data Collection

7. 7 Data Toronto CHASE survey 2002-2003 One-week observed activity-travel patterns and scheduling decisions 271 households, including 452 people Raw data includes information on 35,753 observed activities and 66 specific activity types  Only 19,836 selected for analysis In addition to various attributes of activities, information on planning time horizon (when planned) obtained in the survey

8. 8 CHASE: Main Screen (Blank) Instructions to User  Login once a day  Add activities anywhere in your schedule  Review and modify  Respond to prompts

9. 9 CHASE: Add/Modify Dialog Box

10. 10 CHASE: Example Partial Schedule

11. 11 CHASE: Example Completed Schedule

12. 12 CHASE Planning Time Horizon Dialog Box

13. 13 Prompting for the Spatial Flexibility of Observed Activities

14. 14 Prompting for the Temporal Flexibility of Observed Activities

15. 15 Analytical Methods

16. 16 Models MNL models are developed to predict when an activity is scheduled. Universal choice set:  Impulsive  Same Day  Days Ago  Weeks/months Ago  Routine

17. 17 Explanatory Variables Activity characteristics  Observable: Duration, frequency, etc.  Spatial, temporal, duration, and interpersonal flexibilities Individual and household characteristics Generic activity labels used for comparison purposes only

18. 18 Analysis Descriptive Analysis Highlights

19. 19 Planning Time Horizon (Dependent variable)

20. 20 Time Flexibility Grouped together as “Very Variable” (38.3%)

21. 21 Duration Flexibility

22. 22 Spatial Flexibility

23. 23 Involved Persons, Average (indicator of interpersonal flexibility)

24. 24 Other Explanatory Variables Activity Characteristics  Frequency per Week (LN)  Avg Duration (LN)  Weekend Activity  Morning Activity  Mid-Day Activity  Afternoon Activity  Evening Activity  Choosen for Modification Household Characteristics  # of Adults in HH  Household Size  No of Automobiles in HH  Duration at residence (LN)  Duration in City (LN) Individual Characteristics  Total # of Activities in Schedule  Total No of Trips in Schedule  Cellphone User  Children Under Care  High School or Less Ed.  Non-University Certificate  College Degree  Graduate Degree  LN (Income)  Retired  Full Time Employed  Female  LN (Age) Other flexibility measures  Duration  Interpersonal

25. 25 Analysis Modelling Estimation result highlights

26. 26 Variables A wide variety of variables contributed significantly to the model, including:  Basic activity-travel characteristics duration, frequency, location, time of day, modifications  New measures of activity flexibility Duration, space, time, and involved persons flexibilities,  Household characteristics size, # autos, residence duration  personal characteristics cellphone, education, income, gender, etc.  Characteristics of a persons weekly agenda total # activities, total # trips

27. 27 Model 1 An MNL model developed  Choice set: Impulsive, Same Day, Days Ago, Weeks Ago, Routine Explanatory Variables (28 variables, 4 ASC, 62 parameters)  Activity Labels  Individual and household characteristics The best model: -2[L(  0 ) - L(  )] 5704.35  3769.87  0.09

28. 28 Model 1 All parameters explaining individual and household characteristics are meaningful and statistically significant activity label variables introduced to the model include:  Impulsive activities: meals, drop off/pick-up, shopping, entertainment, HH obligations  Same Day shopping, services, entertainment, social  Days ago drop off/pick-up, recreation, shopping, entertainment, social  Weeks/months/years ago Work, school  Routine Sleep, meals.

29. 29 Model 2 MNL model Explanatory Variables (33 variables, 4 ASC, 87 parameters)  Activity labels are replaced with Activity characteristics Observable: Duration, frequency, etc. Flexibilities Variables: temporal, spatial, etc.  Individual and household characteristics Model fit improved by 54% over Model 1 The best model: -2[L(  0 ) - L(  )] 8796.79  6862.31  0.14

30. 30 Model 2 HH and individual characteristics almost similar to Model 1 Activity characteristics:  Impulsive (+): very time flexible, duration flex., spatial flex., weekend, mid-day or evening (-): interpersonal flexibility, activity duration, morning act.  Same Day (+): very time flex, duration flex., spatial flex., weekend, mid-day, PM, or evening (-): high frequency, activity duration, morning act.  Days ago (+): spatial flexibility, out of home act., act. duration, mid-day or evening act. (-): fixed and SW variable time flexibility  Weeks/months ago (+): out of home activity, frequency, morning act. (-): duration flexible, interpersonal flexible, weekend act., mid-day act.  Routine (+): very & SW variable time flex., frequency, duration, weekend or evening (-): duration flexibility.

31. 31 Model 3 MNL model Explanatory Variables (43 variables, 4 ASC, 102 parameters)  Activity labels  Activity characteristics (Observable, Flexibilities)  Individual and household characteristics Model fit improved just 3.6% over Model 2 The best model: -2[L(  0 ) - L(  )] 9115.83  7181.35  0.15

32. 32 Model Comparison Using activity type alone (Model 1) or in combination with other activity characteristics (Model 3) did NOT improve model performance Model 2 presents much better fit compared to Model 1  flexibility measures and activity characteristics improve the model Model 3 performs only slightly better than Model 2  adding activity labels did not improve model 2 as much as expected.

33. 33 Discussion (on no activity type model) Effect of explanatory variables (highlights):  More time, duration, and spatial flexibility tends to lead to more impulsive planning  Higher frequency and longer duration led to more preplanning  Weekend activities are more impulsive  Presence of auto led to less preplanning  Longer duration in city led to more routine planning  Larger households plan more  Busy people compensate by doing more same-day days-before planning  Cell phone use tends to lead to less impulsive planning  Those with children do more impulsive and same day planning  Older people have more routine plans  Females do more weeks and days ago planning

34. 34 Conclusions This paper provided many firsts:  first empirical examination of temporal, spatial and interpersonal activity flexibility  First MNL of activity planning time horizon  first model accounting for Routine activities Effect of flexibility variables made sense Flexibility alone not sufficient in explaining planning time horizon  Household and individual characteristics important Variables reflecting activity type did not improve the model, further challenging past assumptions The results could be used as a rule for prioritizing selection of activities for scheduling in emerging process models:  Avoid static assumption by activity type  Make model more behavioural realistic and applicable to a wider range of peoples and situations  BUT, will require simulation of new explanatory variables

35. 35 Acknowledgements