1. BIG BIKE DATA: HOW GPS ROUTE DATA COLLECTED FROM SMARTPHONES CAN BENEFIT BICYCLE PLANNING MEYER, 2013 UNIVERSITY OF TEXAS AT AUSTIN 1

2. 2 The Pedestrian and Bicycle Information Center Texas Transportation Institute (TTI) The Federal Highway Administration

3. RESEARCH QUESTIONS How can smartphone technology aid in the collection of GPS route data? How can GPS route data collected from smartphones be used to answer specific planning questions to make better informed decisions in the planning process? What are the limitations of smartphone-based data collection methods that must be addressed before they can be implemented on a larger scale? 3

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5. TRADITIONAL DATA COLLECTION METHODS Active and passive infrared sensors Ultrasonic wave detectors Doppler radar Automated video imaging Piezometric sensors Pneumatic tubes In-pavement magnetic loops 5

6. IN A FHWA SURVEY OF STATE AGENCIES AND MPOS 49% of conducted manual counts 33% used automated fixed location equipment 1% used active data collection methods such as GPS devices or smartphones 16% did not conduct any counts at all 6 MPOS: metropolitan planning organizations

7. DATA COLLECTION METHODS User Surveys National/Regional Travel Surveys When local data collection is not possible planners often times turn to national or regional travel surveys to collect data on bicycle activity in their communities. transportation (including bicycles), trip purpose, travel time, and time of day/week for all trips taken by individuals in a household over a 24-hour period. 7

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11. SMARTPHONE-BASED DATA COLLECTION EFFORTS RouteScoutRouteScout (NuStats) AccuTrackAccuTrack (Resource Systems Group ) CycleTracks (San Francisco County Transportation Authority) 11

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13. AUSTIN CASE STUDY 13

14. APPLICATION 1: BICYCLE NETWORK PLANNING 14

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24. HOW SMARTPHONE-BASED DATA COLLECTION CAN BENEFIT BICYCLE PLANNING GPS data provides revealed behaviors, as opposed to stated preferences Smartphones allow for detailed data on user characteristics to be collected Using smartphones for data collection can lead to significant cost and time savings compared with traditional methods 24

25. POTENTIAL LIMITATIONS OF SMARTPHONE-BASED DATA COLLECTION Significant time required to post-process the data Smartphone data collection is active rather than passive in nature Sampling bias is difficult to avoid Collecting data from users’ smartphones raises privacy concerns 25

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