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Travel/Activity Panel Surveys in the Toronto and Quebec City Regions:Comparison of Methods and Preliminary Results

Matthew J. Roorda, University of TorontoMartin Lee-Gosselin, Université LavalSean T. Doherty, Wilfrid Laurier UniversityEric J. Miller, University of TorontoPierre Rondier, Université Laval

PROCESSUS Second International Colloquium on the Behavioural Foundations

of Integrated Land-use and Transportation Models: Frameworks, Models andApplications, Toronto June 12 – 15, 2005

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Introduction• Travel Activity Panel Survey

– 3 year, 3 wave in-depth panel survey– Concurrently running in 2 areas: Toronto and Quebec City– Total initial sample: 270 in Toronto, 250 in Quebec City– Uses a “reflexive” approach: survey method allowed to

change over time• We, as researchers, learn about the right questions to ask• Respondents experience panel conditioning… we can try methods

that require previous respondent experience

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Outline• Survey Objectives

• Survey Methods

• Some Preliminary Results

• Conclusions

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Substantive Objectives• Understand the process by which people schedule and

reschedule activities and travel

• Observe how activities, travel and the underlying scheduling process change or remain stable over time

• Compare decision processes in two different study areas, Quebec City and Toronto

• Provide an empirical basis for modelling

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Methodological Objectives• Compare computerised versus non-computerised

survey methods

• Compare semi-structured qualitative interviews versus systematic quantitative survey methods

• Compare telephone-survey to face-to-face interview

• Test GPS units

• New measures of data quality

• Test CHASE on a medium-sized random sample

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Methods used

ALL WAVES2–day detailed

activitydiary

WAVE 1Detailed

Scheduling Info,7 days

WAVE 2Stated Preference

SchedulingConflicts

CORE SURVEY DATA

WAVE 3GPS route

tracking

ADDITIONAL SURVEY DATA

ALL WAVES2–day detailed

activitydiary

WAVE 1Detailed

Scheduling Info,7 days

WAVE 2Stated Preference

SchedulingConflicts

CORE SURVEY DATA

WAVE 3GPS route

tracking

ADDITIONAL SURVEY DATA

Assess Routines,

GPS tracking

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WAVE 1

Detailed scheduling

info

7 days

TorontoCHASE

•7 day activity diary

•Computerized

•Detailed planning process

•Detailed questions aboutflexibility of activity in time,space, interpersonal, mode

Quebec CityOPFAST

•7 day activity diary

•Paper & pencil – daily fax-back

•Detailed planning process

•In-depth qualitative post interview on: fixity, projects, negotiations, ICT, holistic interpretations of scheduling

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WAVE 2

2-day diary

Stated Adaptation:

Scheduling

Conflicts

Toronto•CATI used

Quebec City•Post-coding with CATI software

ILUTEWave 2: 2-day Diary - Toronto

Stated Adaptation Questions:

“What would have happened ifyou had an unexpectedone-hour delay ingetting to this activity?”

“What would you have doneif the ___mode were notavailable to get to thatactivity?”

How would it have affected:

•other activities the same day

•activities on other days

•other household members

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Wave 2 diary – Quebec City

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WAVE 3

Routine Weekly

Schedules,

GPS tracking

Toronto•Routine weekly scheduleon a single 17x22 sheet

Quebec City•Routine activities entered in a memory jogger

What activities/trips do you normally do every week?

ILUTEToronto Wave 3:

Routine Weekly Schedule

• 2-day diary of activities

• 7-day routine weekly schedule

• Follow-up Interview– Details of the 2-day

diary– How old are the routine

activities?– Flexibility of activity

times, modes, location– Commitments to other

people

ILUTEExample day

Time in week with no routineRoutine travel

Routine activity

Colour coded symbols indicating time/ space/ interpersonal flexibility

Toronto Wave 3:

Routine Weekly Schedule

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Sample• Toronto 270

hhldsInitial response rate 16.6%Wave 2 retention 84%Wave 3 (in progress)

• Quebec City 250 hhlds

Initial response rate 20.6%Wave 2 retention 80%Wave 3 (to start in July ‘05)

ILUTEAverage Weekday Activity Durations (n.inc. basic needs or travel)

Work/School

Drop-off/pick-up

Shopping

Services

Recreation/ Entertainment

Social Other

Household Obligations

Work/School

Drop-off/pick-up

Shopping

Services

Recreation/ Entertainment

Social Other

Household Obligations

Work/School

Drop-off/pick-up

Shopping

Services

Recreation/ Entertainment

Social

Other

Household Obligations

Work/School

Drop-off/pick-up

Shopping

Services

Recreation/ Entertainment

Social Other

Household Obligations

TORONTO

Wave 1

Wave 2

QUEBEC CITY

ILUTEActivity Preplanning

“When did you originally make the decision to add/change this activity?”

TORONTO (CHASE) QUEBEC CITY (OPFAST)

Impulsive25%

Same day14%

Days before12%

Wks/mths/yrs ago15%

Routine11%

Unknown/ can’t recall/

missing23%

Impulsive21%

Same day10%

Days before7%

Wks/mths/yrs ago

4%

Routine58%

Unknown/ can’t recall/

missing0%

If a CHASE respondent makes changes to a routine activity, it is no longer considered routine

An OPFAST respondent simply reports their perception of whether an activity is routine

ILUTEActivity Preplanning in Toronto

“When did you originally make the decision to add/change this activity?”

Work / School Activities Shopping Activities

Impulsive10%

Same day12%

Days before18%

Wks/mths/yrs ago23%

Routine11%

Unknown/ can’t recall/

missing26%

Impulsive29%

Same day35%

Days before15%

Wks/mths/yrs ago

3%

Routine2%

Unknown/ can’t recall/

missing16%

ILUTEQuebec City - Use of Telecommunications

to plan spontaneous activities(planned < 1hr in advance)

Only about 1/3 of spontaneous activities involve any contact with others

Only 6% involve telecommunications

No strong indication that cellular phones enable “last minute coordination” with others

Pager0%

Cellular1%

E-mail0%

Telephone5%

Unknown2%

In person25%

No communication

67%

ILUTEToronto Wave 2 – Stated Adaptation

Effect of a one hourdelay in getting to an activity

2/3 of the time people anticipate that they can accommodate delays by changing the timing of the next activity within the same day

We also capture effects onother activities, other people, other days.

(not shown)Split0.5%

No effect3%

Move toanother day

12%

Skip17%

Shift36%

Shift &shorten

4%

Modify withinthe same day

68%

Changelocation

2%

Shorten duration

25%

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Conclusions• Unprecedented combination of in-depth surveys in a

longitudinal panel framework

• Many survey methods have been developed and tested

• We have sacrificed the ability to do clear “trend analysis” by changing the survey instrument.

• Opportunities for data analysis– Better understand the process of activity scheduling– Understand long & short term dynamics of activity/travel behaviour– Provide a better behavioural base for development of activity based

models

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Q

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ADDITIONAL SLIDES

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CHASE ProcessChase Scheduling Diary Interview Household

Household keeps schedules on laptopfor 7 daysFollow-up

Interview

Retrieve datafrom laptop

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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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Add/modify dialogue box

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Example Partial Schedule

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Example Completed Schedule

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At the end of the week, respondents asked questions about flexibility of activitieswith respect to:

•Start time

•Duration

•Location

•Frequency

•Other people

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Other Add-on Surveys• 30 Toronto respondents called for quality assessment

“did people take short cuts?”, “did they experience problems?”, etc.

• 12 Toronto respondents outfitted with GPS units for several days.

• Add-on survey of accessibility constraints of low income women

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Analysis Opportunities

•Understanding the process of activity scheduling

•Understanding long and short term habits

•Informing model design

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Understanding the process of planning activities

When planned (5 categories, non-merged routine)

Impulsive Same day Days before Weeks/months/years ago Routine

Unknown/cant recall/missing Total

Night sleep & oth basic needs 18.2% 7.5% 11.6% 22.6% 16.8% 23.3% 100.0%

Meals 26.2% 14.6% 9.3% 14.1% 13.0% 22.8% 100.0% Work/School 10.2% 12.0% 17.6% 23.0% 10.6% 26.5% 100.0% Household Obligations

29.0% 16.0% 9.7% 11.4% 10.9% 23.0% 100.0%

Drop-off/Pick-up 17.7% 16.4% 16.0% 17.8% 9.6% 22.4% 100.0% Shopping 28.9% 34.4% 15.3% 3.0% 2.4% 16.0% 100.0% Services 18.2% 19.2% 23.1% 15.8% 4.8% 18.9% 100.0% Active recreation 19.5% 15.6% 18.1% 15.9% 7.2% 23.7% 100.0% Entertainment 36.9% 14.6% 9.0% 9.1% 8.2% 22.1% 100.0% Social 32.7% 19.2% 17.7% 7.7% 4.4% 18.4% 100.0%

Activity Group

Other 47.6% 24.6% 10.2% 1.1% 2.6% 13.9% 100.0% Total 24.7% 14.0% 12.3% 15.3% 11.1% 22.5% 100.0%

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Observing Habitual

Behaviour

Home Sleep

Breakfast

Home Cleaning

Meal TV

Wash/dress Sleep

Work

Bank

Post Office

Get Newspaper

6:00

15:00

15:45

16:15 17:15

16:30

Home Sleep

Breakfast

Home Cleaning

Meal TV

Wash/dress Sleep

Work

Shop

6:00

15:30

Thursday Friday

HomeSleep

BreakfastReady for work

HomePlay with kidsPrepare dinner

Eat dinner

Work

5:50

Thursday Friday

HomeKids to bed

Sleep

Coffee

WorkLoc’n 2

Work

Shop

Drop off wife VisitFriendPickup wife

10:30

10:5013:3014:30

15:00

18:1518:25

21:3021:40

HomeSleep

Ready for work

HomeClean house

Prepare dinnerEat dinner

Work

5:40

HomeKids homework

Coffee

WorkLoc’n 2

Work

Shop

9:10

9:3014:0815:00

15:50

18:15

Drop off wife

Pickup wife

18:25

HomeKids to bed

Sleep

21:45

22:00

HomeSleep

BreakfastReady for work

HomePlay with kidsPrepare dinner

Eat dinner

Work

5:50

Thursday Friday

HomeKids to bed

Sleep

Coffee

WorkLoc’n 2

Work

Shop

Drop off wife VisitFriendPickup wife

10:30

10:5013:3014:30

15:00

18:1518:25

21:3021:40

HomeSleep

Ready for work

HomeClean house

Prepare dinnerEat dinner

Work

5:40

HomeKids homework

Coffee

WorkLoc’n 2

Work

Shop

9:10

9:3014:0815:00

15:50

18:15

Drop off wife

Pickup wife

18:25

HomeKids to bed

Sleep

21:45

22:00

Wave 1

Wave 2 • Long term

vs.Short term habits

Understanding habits and routines is the first step to develop policies designed to change habits (like auto dependence)

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Using data to inform models• The current implementation of TASHA assumes “micro-

level” rules about scheduling behaviour

At – HomeWork OtherShop At-homeOtherPerson Schedule

= Activity= Travel

At-home

We would like to improve the rule base using empirical data

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Questions?