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Transportation Planning ResearchLessons Learned from Passive Mobile Data Collection
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Who’s Here Today
20 yrs experience in transportation planning and modelling
10 yrs at TransLink, 10 yrs in consulting Focus on evidence-based planning and
transportation demand modelling Application for project evaluation and
business casing
• Executive VP for Ipsos Western Canada
• Innovative thinker in all areas of research
• 20+ yrs in the industry
• Senior Account Manager for Ipsos
• Significant transportation research expertise
• 10+ yrs in the industry
Mike Rodenburgh Shirley Lui Basse Clement
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Today’s Focus
Helping you understand:
1. Why you might consider it
2. How to maximize chances of success
3. Understand the limitations
Help you understand how mobile passive measurement can be used to supplement transportation research…
***1,5931,859
2,0832,296
2,4802,659
0
500
1,000
1,500
2,000
2,500
3,000
2014 2015 2016 2017 2018 2019
**: Statista.
Number of SMP users worldwide (in millions) - actual and estimated **
Smartphone penetration is strong everywhere and even higher in desirable targets – 18-35***
88
77
72 7168
65 65
58
100
9592
91 9188
86 85
40
50
60
70
80
90
100
S. Korea Australia USA Spain UK Malaysia Chile China
Country 18 - 35
+ 1 billion SMP owners in 5 years
80% Of adults will have a smartphone by 2020*
* Worldwide – World Bank, GSMA
The world is becoming increasingly MOBILE
*** Spring 2015 Global Attitudes survey. Q70 & Q72.
© 2016 Ipsos© 2016 Ipsos
Picks up their phone
221 times a day
Uses their phone for
1500mins a week
Attention span went from 12seconds in 2000 to 8
seconds today
People’s REALITY has changed
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6 Ip
sos.
Whenever andwherever people are?SMARTPHONE ARE ALWAYS IN PEOPLE’S POCKETS or handbags and USED ALL THE TIME EVERYWHEREand in particular in places where other devices don’t go, like in stores.
Source: Forrester Research
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More respondents are attempting to take surveys via smartphone• 34% on average in the US
• 50% in younger groups – even higher in some markets (Ipsos figures)
More people are joining panels via smartphone
Source: Panelists joining via mobile (Ipsos US Active Panel)
+70%in one year
And mobile registration is high today in all regions
Sample Applications • 2012/2013 Singaporean Household Interview Travel Survey• 1,500 users
Future Mobility Survey App
• 2017 Puget Sound Regional Travel Study• 1,500 households
• 2015 Madison County “In the Moment” Travel Study• 295 Participants
rMoves app
• 2017 Greater Toronto Area City Logger (ongoing)• Pilot projects (target at 1000 downloads)
City Logger (Itinerum on IOS, InnoZ on Android)
• 2017 Translink’s Trip Diary• Target 2,500 households
Westat App
• Subset of sampling plan• Partner with off-the-shelf
product and customize to local context
Smartphone Sample
Trip Diary Samples
Sampling Universe
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App Choice
Complex Functionality
Moderate Functionality
• Good user interface design• Open-source design• $$• More challenging to build in
logic checks, limited customization available
• Complex user interface design• Proprietary ownership• $$$$$$• Allows for more complex question
types• Can handle logic checks and other
customizations
• Purpose-built GPS device or simple mobile app
• No ability to customize at all• Can’t handle other question
types
Simple Functionality
Lessons Learned from Other Studies
• More accurate and detailed data
– Short activities are under-reported in online surveys
– Travel times are over-estimated in online surveys
– Capture day-to-day variation with multi-day surveys
• Increased post-processing efforts
– Missing legitimate trips with short stop-overs
– Cutting multi-modal trips as multiple trips
– Require mode input from user for pedestrian, cyclist or car driver in congestion
• Built-in sensor performance varies as smartphone evolving over generations
• App with small file size encourages downloads
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2017 Metro Vancouver Regional Travel Survey
SIMILAR TO 2011…• Household weekday diary of everyone age 5+ in the household• Recruiting conducted by:
- Letter invitation to all Metro Vancouver residents
- Telephone follow-up recruitment call • 27,000 participating households
WHAT’S NEW?• One-day trip diary online (90% of sample)
• Three-day trip diary on mobile app (aim 10% of sample) • Planned for $5 incentives plus random prizes for participation• Mobile app respondents receive an additional $5/each plus 3 additional entries for prize draw
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Ipsos’ Purpose-Built Web-Interface for Travel Diaries
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Ipsos’ Purpose-Built Web-Interface for Travel Diaries
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Ipsos’ Purpose-Built Web-Interface for Travel Diaries
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Ipsos’ Purpose-Built Web-Interface for Travel Diaries
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Logic-Checks & Features Built into the System
On-screen messages to alert respondents to possible inaccuracies or inconsistencies in their responses:• Logic checks based on demographic information:
- Driver needs to have a valid driver’s license;- Trip purpose vs. employment status
• Time logic checks:- Departure time vs. arrival time- Departure time against arrival time of the next trip- Travel time against distance- Travel time against mode(s) used
• Mode checks:- Travel distance against mode(s) used- Reasonable transportation mode(s) used based on OD points
LOCATION LOGIC CHECKS:- Generic location check (minimum: intersections)- Origin/destination- Elimination of trips made entirely outside of Metro Vancouver
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Customized Mobile App with Similar Functionality
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Things to Consider when Customizing your App
• iTunes minimum turnaround for approvals is 3 to 5 days, but two weeks is recommended• Google Play Store (Android) less (2 to 3 business days)• If updates are pushed during field, no guarantees that respondents will update in time• Customize the questions & user interface• Build LOTS of time in for testing customizations for all iterations:
- By type of phone- OS version- Types of respondents- Etc.
• Customize privacy rules specific to your needs• Above all, test it in-house - carry out a pilot before rolling out a full launch
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Methodological Considerations for Using an App
The Observation Effect, also known as the Hawthorne Effect or the Observers Paradox
Occurs when individuals subconsciously modify an aspect of their behavior in response to the awareness of being observed.
Open question is whether this applies to mobile data – will people modify their behaviours as a result of the observation?
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Impact on Battery Life
• Driven primarily by the location services functionality required to track the device (hybrid of GSM, GPS, WiFi and Bluetooth detection)
• However, we found that battery drain on phones is nominal
• There are options available to minimize battery drain (e.g. make extensive use of smartphone’s geofence capabilities to determine when GPS trace data logic should resume, encouraging participants to turn on WiFi)
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Mobile App Allows GPS Trace
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Continuum for GPS Trace Accuracy - No Perfect Solution
GPS Sensitivity of Sensing a Stop MoreLess
Increased Rate of False
Stops
Failure to Identify a True
Stop
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TransLink Specific ConfigurationsStart Travel- Move out of a 75 m geofence
Stop Travel- Move less than 75 m over a 5-minute interval or 45 min without any logged points.
Delete Auto-Captured Travel- Minimum straight distance of 150 m from previous destination- At least one point is 150 m away from the last logged point
Snap Auto-Captured Travel to Location- Search named locations that are within Max(375, 0.5 * distance_to_last_destinationn) meters of last logged point- If no named locations are found any saved location within 75 m are used or a new one is created
Show Trip Removal Choices- Straight distance from last destination < 25 meters
- Activity duration < 45 minutes
- Travel time < 2.5 minutes
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• Everyday lingo, not transportation speak• Think like a respondent• Daily confirmations were confusing• Map size on the screen was too small• For confirmation screens, show the “to” and
“from” for each trip• Consider screen size and complexity of the
question type• Mobile app more intuitive for those <55
years of age
RESULTS SO FAR
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Age & Gender Distribution – Total vs. Smartphone Participation
• Current run rate of households skews 55+
• Mobile app participation isn’t addressing the skew to older households entirely
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Architecture – More Important than Originally Anticipated
Survey Database
Survey Engine
Google Maps API
Logic Checks
Survey Database
Survey Engine
Logic Checks
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Support Management
10%
90%
23%77%
Proportion of Sample Proportion of Support Inquiries
App
App
WebWeb
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Support Management
Web-based Inquiries Mobile App Inquiries
20 min 40 min
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Support Inquiries
Do I have to pay for the app?
The app stopped working. What should I
do?
I’m stuck on a page.
Can I change my diary date?
When can I uninstall the app?
The app didn’t record my trips.
How do I edit the start of the day?
I did not receive the email with mobile app instructions and pin?
What is my mobile app pin?
Where do I download the app from?
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Support Inquiries – Mobile App
29%
28%
24%
5%
4%
2%
2%
2%
1%
1%
1%
1%
0%
0%
App issues/glitches
Mobile app pin
Switch to online
Download issues
Opt-in issues
Link expired
Mobile app (general)
Uninstall instructions
Phone compatibility
App day confusion
Incorrect App data
Mobile app link
Phone capability
Download issue
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Respondent Drop-Offs
App Users Registered Online
Opted-in
Initialized App/PIN
Started Diary
Completed all 3 Days
Online Users Registered
Started Diary
Completed Entire Diary
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Response Rates for Online
Online Users Registered
Started Diary
Completed Entire Diary
64%
71% 45%
Overall Response Rate
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Response Rates for Mobile App
App Users Registered Online
Opted-in
Initialized App/PIN
Started Diary
Completed all 3 Days
55%
87%
97%
66%
31%
Overall Response Rate
• Consistency with web-based data – Demographic information– Origin, destination, modes, purpose, cost
• Certain route choices, which informs Traffic Management Plans and Transit Service Plans
• Be able to recognize travel demand market for Corridor Studies
• Accurate travel distance & duration, help monitoring VKT targets
• Rich driver behaviour dataset for Micro-Simulation Models
Application for Planning and Modelling
Potential Implications for Transportation Modelling
Smartphone Feature Modelling Implication
Better representation of trip rates and discretionary travel Helpful for calibration of trip generationLess under-reporting of trips particularly for large households
More accurate reporting of trip ends Helpful for calibrating trip distribution and trip lengths
Records intermediate trip destinations (ie, stop for coffee) Ability to model trip chaining
More accurate representation of trip start/end times Better estimates of time of day travel patterns and peak spreading
GPS trace of trips between origin/destination Validation of route choice in assignment stageCan expand subset of trips to corridor traffic volumesCordon analysis of through travel (ie, town centres)
Multiple days of trip reporting Better understanding of variability in travel patterns
Better representation of younger age demographics More uniform weighting of trip records
Better ability to conduct ongoing travel surveys Monitoring seasonalityAbility to conduct before and after studies
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1. Addition of mobile app isn’t a perfect solution for more representative data
2. Battery life is not a significant limiting factor for participation
3. Be prepared for greater drop-offs with app-based studies
4. Be prepared for greater support on mobile app
5. User interface for the mobile app is critical
6. Architecture of mobile app + online web interface should be tied together more closely
7. Make sure you have weekend support for inquiries on app respondents
Summary of Lessons Learned
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Open Unanswered Questions
1. Does mobile app provide more accurate representation of all trips?
2. Can the mobile app better pick-up multi-mode trips?
3. Is the more detailed GPS trace a useful addition to the data?
