Yingling Fan, yingling@umn Qian Chen Chen-Fu Liao Frank Douma

1. UbiActive Smartphone-Based Tool for Trip Detection and Travel-Related Physical Activity Assessment Yingling Fan, yingling@umn.edu Qian Chen Chen-Fu Liao Frank Douma

2. Sensing – Survey – Assess & Report Auto Sensing Location & Speed (every 30 seconds); acceleration (1Hz) User Wear smartphone on her right hip Being Sensed by smartphone sensors Enable movement/trip detection Compile daily sensing data Report to user daily travel experience & travel-related PA After-trip survey Trip mode, activity, companionship & experience Daily Assessment % of active & happy travel; % of energy expenditures related to travel Compile daily survey data

3. Raw sensing outputs

4. How to detect a trip? • Counter A is for judging the start of a trip • Every 30seconds, if the detected movement is larger than 30 meters, counter A would automatically add one. • When counter A reaches 20 counts, indicating there is a 10-minute continuous movement, a valid trip is considered to be happening. • Counter B is for determining the end of a trip. • Every 30 seconds, if no “location change” is updated, count B will automatically add 1. • When counter B reaches 10 counts, meaning there is no significant movement for 5 consecutive minutes, the trip is considered. • Note: • Both counters A and B have default value at zero. • Counter A will be reset to zero if location change is not detected before reach 20 cts. • Counter B will be reset to zero if location change is detected before reach 10 cts.

5. After-Trip Survey

6. Evaluation and Testing • Lab testing • Network usage: data size is less than 1 KB per day • Memory storage requirement: collect around 7Mb of raw sensor data and statistics per day (150mb for 3 weeks) • Battery life: around 12-15 hours without additional voice/text/data usage • Trip Detection: almost 100% • Testing among 17 real smartphone users recruited from the University of Minnesota campus • Time: October-November, 2011 • $100 cash reward upon completion of 3 weeks of compliance. • Initial background survey, exit survey, and requirement to fill out paper version diary. • 23 Participants recruited

7. Participant summary statistics • Of the 17, 12 males, 9 White, 5 Asian, average age 23. • 8 undergraduate, 8 graduate students, 1 alumni • 7 car owners.

8. A Case Study Trip Information of a Participant on November 3, 2011 – Part I

9. Trip Information of a Participant on November 3, 2011 – Part II

10. Findings: What went well? • Phone based survey collected info on 509 trips occurred in 256 person-days with valid data. • 36% were made on foot, 1% by bike, 26% by private car, and 37% by transit • 29% were back-to-home trips, 30% school-related, 10% work-related, 11% eating-related, and 9% were shopping/errands. • 56% were made alone, 34% with friends, and the rest with family.

11. Participation Experience & Compliance • 76% participants reported “satisfied” • 88% reported increased travel behavior awareness. • 98% at least “somewhat agree” that they felt comfortable having smartphone detecting travel behavior

12. Caveats: What went wrong? • Some reported poor trip detection rates. Trip detection rates (range 0-90%) depend on • phone brands & phone newness • GPS signal strength at trip origin, destination, route. • Converting acceleration outputs to energy expenditure estimates is much more complex than expected. Hardware differences exist. • Battery consumption issue is a key challenge. • No behavioral differences between intervention and control groups. • Issues of missing data

13. Missing Data Plots HTC EVO Motorola Droid Google Nexus Samsung Galaxy

14. Next step: UbiActive → SmartTrAC • Sensing + survey → Sensing + data mining + survey • After-trip survey → end-of-the-day activity or trip survey walk walk car school home eat car home shop walk walk car school home eat car home shop

15. This project and subsequent work are supported by • the ITS Institute, and • the Center for Transportation Studies at the University of Minnesota. yingling@umn.edu