Post on 28-Dec-2015
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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 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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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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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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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