COLLABORATE. INNOVATE. EDUCATE. What Smartphone Bicycle GPS Data Can Tell Us About Current Modeling Efforts Katie Kam, The University of Texas at Austin Qiqian (Angela) Yang, The Fresno Council of Governments Jennifer Duthie, Network Modeling Center, The University of Texas at Austin Presenter: Qiqian (Angela) Yang

COLLABORATE. INNOVATE. EDUCATE. Take Away From This Presentation: GPS app to track cycle routes  estimate bike trips for travel demand modeling A GPS data “cleaning” process  a sample set of data Differences in GPS data and other methods of estimation  unique bicycle demand characteristics

COLLABORATE. INNOVATE. EDUCATE. Prior Study Examples GPS-based bicycle route choice model GPS data analysis for commercial vehicle demand modeling Location-based social network study Example of GPS Bike Tracks Structure of Location-Based Social Network Example of GPS Truck Trips

COLLABORATE. INNOVATE. EDUCATE. Research Purposes Find out  GPS bicycle tracking data V.S. current modeling estimation efforts Move forward  Incorporating GPS bicycle data into the transportation demand modeling process The role GPS bicycle data has?  In the trip generation and trip distribution steps How to prepare the data?  Resulting dataset can be considered a sample dataset suitable for MPO model Questions we asked

COLLABORATE. INNOVATE. EDUCATE. Study Area The metropolitan area of Austin, Texas Urban area in Austin, Texas Why Austin?  Cycling city, bicycle friendly  4.6 miles per sq. mile  Resources Austin Bike lane Map – Downtown Area

COLLABORATE. INNOVATE. EDUCATE. Data Collection 1 CycleTrack app was developed by the San Francisco County Transportation Authority Collected by volunteers that biked (not randomly selected) in Austin, Texas.  316 volunteers  May 1st to October 31st, 2011  1,048,576 continuously collected GPS points CycleTracks Application Screens

COLLABORATE. INNOVATE. EDUCATE. Bicyclist Recruiting Process CycleTracks Austin Website Screen Shot

COLLABORATE. INNOVATE. EDUCATE. Postcards Distributed in May and September of 2011 to Area Bicyclists

COLLABORATE. INNOVATE. EDUCATE. Data Collection 2 CAMPO – Household Travel Survey. CAMPO – 2010 estimation, from 2010 Travel Demand Model. CAMPO 2010 Model - Bike Trips Origin and Destination Data Screen Shot

COLLABORATE. INNOVATE. EDUCATE. Methodology - CycleTrack GPS Data Preparation Recruit Bicyclists Record Bike Trips GPS points to bike trips Kept the weekday trips Attach TAZ and Time Period Delete Recurring Trips Peak vs Off Peak Reformat GPS Trip Tables Points to trips Trip tables finalization

COLLABORATE. INNOVATE. EDUCATE. Summery Statistic of Trip User Characteristics Gender (n = 302)Age of Participants (n = 304 )

COLLABORATE. INNOVATE. EDUCATE. Why Data Cleaning ? Additional cleaning – recurring trips Keep the O/D pattern consistent with the CAMPO HH Travel Survey method; BeforeAfter

COLLABORATE. INNOVATE. EDUCATE. Methodology - Data Cleaning 1,048,576 points Kept beginning and ending points. 650 weekday trips Joined CAMPO 2010 TAZ layer. Defined trip time periods. Defined the recurring trips. 486 non- recurring trips Finalized the final CycleTracks dataset. LayersIssues  Four time period?  Repeated trips in the same day?  No User ID?

COLLABORATE. INNOVATE. EDUCATE. Data Analysis Trip Estimations Comparison  Compare the CycleTrack trip tables with the CAMPO estimation

COLLABORATE. INNOVATE. EDUCATE. Trip Estimation Comparison Origin – Off Peak HourOrigin – Peak Hour

COLLABORATE. INNOVATE. EDUCATE. Trip Estimation Comparison Origin – Off Peak Hour Origin – Peak Hour

COLLABORATE. INNOVATE. EDUCATE. Trip Estimation Comparison Destination – Off Peak HourDestination – Peak Hour

COLLABORATE. INNOVATE. EDUCATE. Trip Estimation Comparison Destination – Off Peak Hour Destination – Peak Hour

COLLABORATE. INNOVATE. EDUCATE. Comparison of CAMPO and GPS Data Collection CycleTracks : Non-authorized travel monitoring Transportation infrastructure need analysis Air quality/GHG emission reduction study Transportation and public health modeling, etc.

COLLABORATE. INNOVATE. EDUCATE. Future Research Explore a more refined method in cleaning data (e.g., clustering method). Consider seasonal factors, and geographic differences. CycleTracks Recorded Bicycle Trips in Austin per Week May Jun Jul Aug Sep Oct

COLLABORATE. INNOVATE. EDUCATE. Future Research - Trip Purpose Comparison CycleTrack Participants’ Trip Purpose CAMPO TDM’s Trip Purpose Home Based Non Work Trip Commute Account for possible sample bias (smartphone users)  Structure approach to get the cycle participators

COLLABORATE. INNOVATE. EDUCATE. Take Away From This Presentation: CycleTracks or other similar GPS app to track cycle routes may be used to estimate bike trips for travel demand modeling Acquired GPS data requires a data “cleaning” process that converts the data into a sample set of data Seeing differences in GPS data results to other methods of estimation (e.g., household surveys) can reveal areas of town with unique bicycle demand characteristics

COLLABORATE. INNOVATE. EDUCATE. Thank you! Contact information: Angela Yang: Katie Kam: Jennifer Duthie: