collaborate. innovate. educate. what smartphone bicycle gps data can tell us about current modeling...

COLLABORATE. INNOVATE. EDUCATE.

What Smartphone Bicycle GPS Data Can Tell Us

About Current Modeling Efforts

Katie Kam, The University of Texas at AustinQiqian (Angela) Yang, The Fresno Council of Governments

Jennifer Duthie, Network Modeling Center, The University of Texas at Austin

Presenter: Qiqian (Angela) Yang


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


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


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


Study Area

• The metropolitan area of Austin, Texas

• Urban area in Austin, Texas• Why Austin?

Cycling city, bicycle friendly4.6 miles per sq. mileResources

Austin Bike lane Map – Downtown Area


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, 20111,048,576 continuously collected

GPS pointsCycleTracks Application Screens


Bicyclist Recruiting Process

CycleTracks Austin Website Screen Shot


Postcards Distributed in May and September of 2011 to Area Bicyclists


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


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


Summery Statistic of Trip User Characteristics

Gender (n = 302) Age of Participants (n = 304)


Why Data Cleaning ?

• Additional cleaning – recurring trips

• Keep the O/D pattern consistent with the CAMPO HH Travel Survey method;

Before After


Methodology - Data Cleaning

1,048,57

6 poin

ts

• 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.

Layers Issues

Four time period?

Repeated trips in the same day?

No User ID?


Data Analysis • Trip Estimations Comparison Compare the CycleTrack trip tables with the CAMPO estimation


Trip Estimation Comparison

Origin – Off Peak Hour Origin – Peak Hour

Legend

HWY

polylakes

O_OP_ZeroTrip

Differences_GPS_V.S._CAMPO

diff_op_or

-14 - 0 (53)

0 (1,750)

1 - 20 (231)

21 - 40 (93)

41 - 100 (110)

101 - 275 (21)

Legend

HWY

polylakes

O_PK_ZeroTrip


diff_pk_or

-19 - 0 (77)

1 - 0 (1,771)

1 - 20 (268)

21 - 40 (94)

41 - 100 (44)

101 - 129 (4)



Origin – Off Peak Hour Origin – Peak Hour

Legend

HighUsageBikeRoute

LowUsageBikeRoute

O_OP_NegativeTrip

diff_op_or

-14 - -12 (2)

-11 - -5 (3)

-4 - -3 (6)

-2 (13)

-1 - 0 (29)

Legend

HighUsageBikeRoute

LowUsageBikeRoute

O_PK_NegativeTrip

diff_pk_or

-19 - -12 (5)

-11 - -5 (9)

-4 - -3 (10)

-2 (11)

-1 - 0 (41)



Destination – Off Peak Hour Destination – Peak Hour

Legend

HWY

polylakes

D_OP_ZeroTrip


diff_op_de

-10 - 0 (16)

0 (845)

1 - 20 (1,182)

21 - 40 (145)

41 - 100 (65)

101 - 133 (5)

Legend

HWY

polylakes

D_PK_ZeroTrip


diff_pk_de

-22 - 0 (46)

0 (1,040)

1 - 20 (1,081)

21 - 40 (63)

41 - 100 (24)

101 - 381 (4)



Destination – Off Peak Hour Destination – Peak Hour

Legend

HighUsageBikeRoute

LowUsageBikeRoute

D_OP_NegativeTrip

diff_op_de

<-13 (0)

-12 - -5 (1)

-4 - -3 (2)

-2 (3)

-1 - 0 (10)

Legend

HighUsageBikeRoute

LowUsageBikeRoute

D_PK_NegativeTrip

diff_pk_de

-22 - -13 (1)

-12 - -5 (4)

-4 - -3 (2)

-2 (9)

-1 - 0 (28)


Comparison of CAMPO and GPS Data Collection

Cycl eTrack CAMPO CAMPO GPS equi pmentSi ze of val i d sampl e 316 1500 153Val i d bi cycl e tri ps 486 421 N/ A

Sampl e col l ecti on Conveni ence Random RandomWho compl eted the survey Bi cycl i sts Househol ders Househol ders

Avai l abl e data uni t Tri psData update frequency Anyti me

CostOther appl i cati ons Many

Days ri di ng bi ke/ per week10 years

COMPARISON

Less (pol i cy anal ysi s)Total $1 mi l l i on, $200/ per survey

CycleTracks : Non-authorized travel monitoringTransportation infrastructure need analysisAir quality/GHG emission reduction studyTransportation and public health modeling, etc.


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


Future Research - Trip Purpose Comparison

CycleTrack Participants’ Trip PurposeCAMPO TDM’s Trip Purpose

Home Based Non Work Trip

Commute

Account for possible sample bias (smartphone users) Structure approach to get the cycle participators


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


Thank you!

Contact information:Angela Yang: [email protected] Kam: [email protected] Duthie: [email protected]

mailto:[email protected]



collaborate. innovate. educate. what smartphone bicycle gps data can tell us about current modeling...

Documents

gps bicycle tracking

role gps bicycle data

smartphone bicycle gps

gps data cleaning process

gps app

data collection

gps trip tables

weekday trips