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The relative role of spatial, temporal and interpersonal flexibility on the activity scheduling process

Sean T. DohertyWilfrid Laurier University

Kouros MohammadianUniversity of Illinois at Chicago

2nd MCRI/GEOIDE PROCESUS International Colloquium, June 11-15, Toronto, Canada.

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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.

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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.

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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?

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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)

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Data Collection

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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

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CHASE: Main Screen (Blank)Instructions to User Login once a day Add activities anywhere

in your schedule Review and modify Respond to prompts

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CHASE: Add/Modify Dialog Box

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CHASE: Example Partial Schedule

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CHASE: Example Completed Schedule

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CHASE Planning Time Horizon Dialog Box

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Prompting for the Spatial Flexibility of Observed Activities

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Prompting for the Temporal Flexibility of Observed Activities

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Analytical Methods

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Models

MNL models are developed to predict when an activity is scheduled.

Universal choice set: ImpulsiveSame DayDays AgoWeeks/months AgoRoutine

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Explanatory Variables

Activity characteristicsObservable: Duration, frequency, etc. Spatial, temporal, duration, and interpersonal

flexibilities Individual and household characteristics Generic activity labels used for

comparison purposes only

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Analysis

Descriptive Analysis Highlights

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Planning Time Horizon (Dependent variable)

Planning time horizon

0

10

20

30

%

30.77%

19.21%

15.19%16.0%

10.71%

8.11%

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Time Flexibility

0

10

20

30

40%

29.48%

32.18%

22.45%

5.94%

9.94%

Grouped together as “Very Variable” (38.3%)

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Spatial Flexibility

1 2 3 4 5 6 7 8 9 10+

# Locations activity could occur at

0

3,000

6,000

9,000

12,000

15,000

Co

un

tLocation

At-home

Out-of-home

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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

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Analysis

Modelling Estimation result highlights

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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

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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.

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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

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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.

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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

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Model ComparisonModel 1 Model 2 Model 3

Log-Likelihood at Convergence -28003.97 -26457.75 -26298.23 -2[L(0) - L()] 5704.35 8796.79 9115.83

3769.87 6862.31 7181.35

0.09 0.14 0.15

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.

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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

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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

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Acknowledgements