Transportation Planning Research Lessons Learned from Passive Mobile Data Collection

Transcription

1 Transportation Planning Research Lessons Learned from Passive Mobile Data Collection

2 Who s Here Today Mike Rodenburgh Shirley Lui Basse Clement 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 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 2

3 Today s Focus Help you understand how mobile passive measurement can be used to supplement transportation research Helping you understand: 1. Why you might consider it 2. How to maximize chances of success 3. Understand the limitations 3

4 The world is becoming increasingly MOBILE 80% Of adults will have a smartphone by 2020* Smartphone penetration is strong everywhere and even higher in desirable targets 18-35*** 3,000 2,500 2,000 1,500 1,000 1,593 1,859 2,083 2,296 2,480 2, billion SMP owners in 5 years *** Number of SMP users worldwide (in millions) - actual and estimated ** Country S. Korea Australia USA Spain UK Malaysia Chile China 58 * Worldwide World Bank, GSMA **: Statista. *** Spring 2015 Global Attitudes survey. Q70 & Q72.

5 People s REALITY has changed Attention span went from 12 seconds in 2000 to 8 seconds today Picks up their phone 221 times a day Uses their phone for 1500 mins a week 2016 Ipsos

6 Whenever and wherever people are? SMARTPHONE ARE ALWAYS IN PEOPLE S POCKETS or handbags and USED ALL THE TIME EVERYWHERE and in particular in places where other devices don t go, like in stores. 6 Source: Forrester Research 2016 Ipsos Ipsos.

7 More people are joining panels via smartphone +70% in one year More respondents are attempting to take surveys via smartphone Source: Panelists joining via mobile (Ipsos US Active Panel) And mobile registration is high today in all regions 34% on average in the US 50% in younger groups even higher in some markets (Ipsos figures) Ipsos.

8 Sample Applications Sampling Universe Trip Diary Samples Smartphone Sample Subset of sampling plan Partner with off-the-shelf product and customize to local context Future Mobility Survey App 2012/2013 Singaporean Household Interview Travel Survey 1,500 users rmoves app 2017 Puget Sound Regional Travel Study 1,500 households 2015 Madison County In the Moment Travel Study 295 Participants City Logger (Itinerum on IOS, InnoZ on Android) 2017 Greater Toronto Area City Logger (ongoing) Pilot projects (target at 1000 downloads) Westat App 2017 Translink s Trip Diary Target 2,500 households

9 App Choice Simple Functionality Moderate Functionality Complex Functionality Purpose-built GPS device or simple mobile app No ability to customize at all Can t handle other question types 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 9

10 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

11 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? 11 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 Ipsos.

12 Ipsos Purpose-Built Web-Interface for Travel Diaries 12

13 Ipsos Purpose-Built Web-Interface for Travel Diaries 13

14 Ipsos Purpose-Built Web-Interface for Travel Diaries 14

15 Ipsos Purpose-Built Web-Interface for Travel Diaries 15

16 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 16

17 Customized Mobile App with Similar Functionality 17

18 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 18

19 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? 19

20 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) 20

21 Mobile App Allows GPS Trace 21

22 Continuum for GPS Trace Accuracy - No Perfect Solution Less GPS Sensitivity of Sensing a Stop More Failure to Identify a True Stop Increased Rate of False Stops 22

23 TransLink Specific Configurations Start 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 23

24 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 24

25 RESULTS SO FAR

26 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 26

27 Architecture More Important than Originally Anticipated Survey Engine Survey Engine Logic Checks Google Maps API Logic Checks Survey Database Survey Database 27

28 Support Management Proportion of Sample Proportion of Support Inquiries 90% 10% App 77% 23% App Web Web 28

29 Support Management Web-based Inquiries Mobile App Inquiries 20 min 40 min 29

30 Support Inquiries Can I change my diary date? When can I uninstall the app? The app stopped working. What should I do? Do I have to pay for the app? I did not receive the with mobile app instructions and pin? Where do I download the app from? What is my mobile app pin? The app didn t record my trips. I m stuck on a page. How do I edit the start of the day? 30

31 Support Inquiries Mobile App 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 5% 4% 2% 2% 2% 1% 1% 1% 1% 0% 0% 24% 29% 28% 31

32 Respondent Drop-Offs Online Users Registered App Users Registered Online Started Diary Opted-in Completed Entire Diary Initialized App/PIN Started Diary Completed all 3 Days 32

33 Response Rates for Online Online Users Registered 64% Overall Response Rate Started Diary 71% 45% Completed Entire Diary 33

34 Response Rates for Mobile App App Users Registered Online Opted-in 55% Overall Response Rate Initialized App/PIN Started Diary 87% 97% 31% Completed all 3 Days 66% 34

35 Application for Planning and Modelling 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

36 Potential Implications for Transportation Modelling Smartphone Feature Better representation of trip rates and discretionary travel More accurate reporting of trip ends Records intermediate trip destinations (ie, stop for coffee) More accurate representation of trip start/end times GPS trace of trips between origin/destination Multiple days of trip reporting Better representation of younger age demographics Better ability to conduct ongoing travel surveys Modelling Implication Helpful for calibration of trip generation Less under-reporting of trips particularly for large households Helpful for calibrating trip distribution and trip lengths Ability to model trip chaining Better estimates of time of day travel patterns and peak spreading Validation of route choice in assignment stage Can expand subset of trips to corridor traffic volumes Cordon analysis of through travel (ie, town centres) Better understanding of variability in travel patterns More uniform weighting of trip records Monitoring seasonality Ability to conduct before and after studies

37 Summary of Lessons Learned 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 37

38 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? 38