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
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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
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Prior Study Examples
• GPS-based bicycle route choice model
• GPS data analysis for commercial
vehicle demand modeling
• Location-based social network study
Structure of Location-Based Social Network
Example of GPS Bike Tracks
Example of GPS Truck Trips
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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
Questions we asked
• 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
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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
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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
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Bicyclist Recruiting Process
CycleTracks Austin Website Screen Shot
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Postcards Distributed in May and September of 2011 to Area Bicyclists
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Data Collection 2
• CAMPO – 2006-2007
Household Travel Survey.
• CAMPO – 2010 estimation,
from 2010 Travel Demand
Model.
CAMPO 2010 Model - Bike Trips Origin
and Destination Data Screen Shot
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Methodology - CycleTrack GPS Data Preparation
Recruit
Bicyclists
Attach
TAZ and
Time
Period
GPS
Kept the
Record
points to weekday
Bike Trips
bike trips
trips
Points to trips
Delete
Recurring
Trips
Peak vs
Off Peak
Reformat
GPS Trip
Tables
Trip tables finalization
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Summery Statistic of Trip User Characteristics
Gender (n = 302)
Age of Participants (n = 304)
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Why Data Cleaning ?
• Additional cleaning –
recurring trips
• Keep the O/D pattern
consistent with the CAMPO
HH Travel Survey method;
Before
After
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Methodology - Data Cleaning
Layers
1,048,576
Issues
• Kept beginning and ending points.
points
650
weekday
trips
486 nonrecurring
trips
• Joined CAMPO 2010 TAZ layer.
• Defined trip time periods.
• Defined the recurring trips.
 Four time period?
• Finalized the final CycleTracks dataset.
 No User ID?
 Repeated trips
in the same day?
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Data Analysis
• Trip Estimations Comparison
Compare the CycleTrack trip tables with the CAMPO estimation
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Trip Estimation Comparison
Origin – Off Peak Hour
Origin – Peak Hour
Legend
Legend
HWY
HWY
polylakes
polylakes
O_OP_ZeroTrip
O_PK_ZeroTrip
Differences_GPS_V.S._CAMPO
Differences_GPS_V.S._CAMPO
diff_op_or
diff_pk_or
-14 - 0 (53)
-19 - 0 (77)
0 (1,750)
1 - 0 (1,771)
1 - 20 (231)
1 - 20 (268)
21 - 40 (93)
21 - 40 (94)
41 - 100 (110)
41 - 100 (44)
101 - 275 (21)
101 - 129 (4)
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Trip Estimation Comparison
Origin – Off Peak Hour
Legend
Origin – Peak Hour
Legend
HighUsageBikeRoute
HighUsageBikeRoute
LowUsageBikeRoute
LowUsageBikeRoute
O_OP_NegativeTrip
O_PK_NegativeTrip
diff_op_or
diff_pk_or
-14 - -12 (2)
-19 - -12 (5)
-11 - -5 (3)
-11 - -5 (9)
-4 - -3 (6)
-4 - -3 (10)
-2 (13)
-2 (11)
-1 - 0 (29)
-1 - 0 (41)
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Trip Estimation Comparison
Destination – Off Peak Hour
Destination – Peak Hour
Legend
Legend
HWY
polylakes
D_OP_ZeroTrip
Differences_GPS_V.S._CAMPO
diff_op_de
-10 - 0 (16)
0 (845)
1 - 20 (1,182)
21 - 40 (145)
HWY
polylakes
D_PK_ZeroTrip
Differences_GPS_V.S._CAMPO
diff_pk_de
-22 - 0 (46)
0 (1,040)
1 - 20 (1,081)
21 - 40 (63)
41 - 100 (65)
41 - 100 (24)
101 - 133 (5)
101 - 381 (4)
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Trip Estimation Comparison
Destination – Peak Hour
Destination – Off Peak Hour
Legend
Legend
HighUsageBikeRoute
HighUsageBikeRoute
LowUsageBikeRoute
LowUsageBikeRoute
D_OP_NegativeTrip
D_PK_NegativeTrip
diff_op_de
diff_pk_de
<-13 (0)
-22 - -13 (1)
-12 - -5 (1)
-12 - -5 (4)
-4 - -3 (2)
-4 - -3 (2)
-2 (3)
-2 (9)
-1 - 0 (10)
-1 - 0 (28)
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Comparison of CAMPO and GPS Data Collection
CycleTrack
Size of valid sample
Valid bicycle trips
Sample collection
Who completed the survey
Available data unit
Data update frequency
Cost
Other applications
316
486
Convenience
Bicyclists
Trips
Anytime
Many
COMPARISON
CAMPO
CAMPO GPS equipment
1500
153
421
N/A
Random
Random
Householders
Householders
Days riding bike/per week
10 years
Total $1 million, $200/per survey
Less (policy analysis)
CycleTracks :
 Non-authorized travel monitoring
 Transportation infrastructure need analysis
 Air quality/GHG emission reduction study
 Transportation and public health modeling, etc.
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Future Research
• Explore a more refined
method in cleaning data
(e.g., clustering method).
• Consider seasonal
factors, and geographic
differences.
May
Jun
Oct
Jul
Sep
Aug
CycleTracks Recorded Bicycle Trips in Austin per Week
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Future Research - Trip Purpose Comparison
Account for possible sample bias (smartphone users)
 Structure approach to get the cycle participators
Home Based Non Work Trip
Commute
CAMPO TDM’s Trip Purpose
CycleTrack Participants’ Trip Purpose
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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
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Thank you!
Contact information:
Angela Yang: ayang@fresnocog.org
Katie Kam: katie.a.larsen@utexas.edu
Jennifer Duthie: jduthie@mail.utexas.edu
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