Stone Soup Data Collection w/ CycleTracks

Building the Technology Pot
for the Stone Soup Method
of Data Collection:
Facilitating Cooperation in the Face of Scarcity

Elizabeth A. Sall

SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Transportation Research Board Annual Meeting in Washington, D.C.
Tuesday January 15th, 2013

Caveat: I am not a data collection or surveying expert

I AM A TRAVEL MODELER.

But travel models need…

DATA.

SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 2

So what am I going to talk about?

Story : We needed data for something we
had never seen collected before. And we
didn’t have much money or time.
…so we built this app called CycleTracks


Route Choice Data Collection
Choices Considered

Cost per Cost per Respondent Data Data RP or SP
Record Respondent LOE Precision Quality
Web-based $ $ High SP
stated
preference
CATI Route $$$ $$$ High Low Low RP
recall
Personal GPS $ $$ Med High Med RP
Bicycle GPS $ $$ Med High High RP
Smart Phone $ $ Low Med Med RP


CycleTracks: from coder to cyclist

Publicity! Advertising! Stickers!

iTunes
Store

Android
Market


CycleTracks Data: from cyclist to analyst

Amazon EC/2 Server running Apache

JSON
PHP mySQL PHP


Bay Area Participants
(if they noted their home ZIP)

CycleTracks BATS
N-366 N=153
Age
Mean 34 33

Gender
Female 20% 36%
Cycling Frequency
Daily 48%
Several Times/Week 36%
Several Times/Month 13%
Less than once a month 3% N/A


Data Quality: some
good, some bad


Urban Canyon Effect

Haight Ashbury

vs

Downtown


GPS Signal at Beginning
of Trip


Not on a Bike


Post Processing Warranted

5,178 traces
Gaussian
497 users smoothing

Activity & mode
detection

~60% of
3,034 bike
submitted Map stages
h
data
useful
matching 366 users

(Schüssler & Axhausen 2009)


Unintended Benefit: Scalability

 It works anywhere you can get
a satellite signal
 Database and cloud server
highly scalable
 Web interface for data
minimizes human resources
 Data cleaning open-source
 Cost for data:
Keeping server on
Promotion


Where do people use CycleTracks?

Agencies using Cycletracks:
1. San Francisco
2. Monterey Bay, CA
3. Austin, TX
4. Seattle, WA
*based on optional homeZIP field, NOT TRIP LOCATION 5. Fort Collins, CO
6. Twin Cities, MN
7. Raleigh, NC
8. Salt Lake City, UT


Where?
Where it’s advertised the most.

Place Users* Trips*
San Francisco 665** 11,458
Austin 276 2,950
Fort Collins 126 1,560
Seattle 108 1,175
Minneapolis 67 1,326
Oakland 26 127
Saint Paul 23 449
San Jose 22 70
Santa Cruz 17 254
Berkeley 14 127
*based on optional homeZIP field, NOT TRIP LOCATION
** compared to 153 cyclists in the 2000 HH Travel Survey

When did new users submit first trip?

Cycletracks New User's First Trip Submissions
200
180
160 San Francisco Monterey
140
120 Fort Collins
100
80 Austin Twin Cities
Seattle
60
40
20
0

• New user registrations directly correlates with publicity


Many just try it out , but half use it for a while

Users by Duration of Use
1000

900 41%

800

700

600

500

400

300 16%
15%

200 11%
8%
100

3% 3% 3%
0
One Day Day - Week Week - Month 1 - 3 Months 3 - 6 Months 6 - 9 Months 9 - 12 Months Over a Year


Broad Spectrum of Users

• Half of users submit > 5 trips
• 10% of users submitted > 20 trips
• 40 users submitted >100 trips (Max = 685)
Users by Trips Submitted
800
31%
700

600

500 20%

400

300 10%
8%
200
5%
3%
100

0
1 2-5 6-10 11-15 16-20 21-700


Capturing Infrequent Cyclists

• 20% (500+) users infrequent cyclists (10% of trips)

Trips and Users by Cycling Frequency
Users Trips
13,506

9,122

2,036
577 852 853
187 411

Less than once a month Several times per month Several times per week Daily


All Open Source

• GPL3 License
• Code on GitHub
• Fork us!

www.github.com/sfcta


Bonus Benefit: Transferability
+ 750 users +8,500 trips
Rolling their own from our code:

AggieTracks
~35 users

Cville Bike mApper
~120 users/1500 trips

Cycle Atlanta
NuStats PaceLogger
~ 400 users/4500 trips

Cycle Lane
~ 200 users/2500 trips


Combined Reach: ~44,000 trips

http://goo.gl/maps/DuqGh 22

Issues - Bias

 Tradeoff between bias and quantity
But bias can be dealt with if quantity is high enough.
 Which biases are acceptable and when?
i.e. does income affect how adverse to biking up hills
you are (vs. biking around them) ?
 What biases can we undo with technology?


Issues - Bias

Does…
People who answer People who have People who answer
x = Surveys over their ?
surveys Smartphones
Smartphones
Race/Ethnicity
140%

120%

100%

80%

60%

40%

20%

0%
NHTS Sample Rate Smartphone Ownership Effective Probable Response Rate

White Black Hispanic
…if so, this looks pretty good.
Sources: 2009 NHTS (NHTS Sample/1,000
population) ; Pew 2011 Smartphone Survey; Census


Issues - Bias

…and this looks even better.

Age Group
250%

200%

150%

100%

50%

0%

18-24 24-34 35-44 45-54 55-64 65+

Sources: 2009 NHTS (NHTS Sample/1,000 population) ;
Pew 2011 Smartphone Survey; Census


Issues - Bias

…but looks like income exacerbates the divide.
Total Household Income
300%

250%

200%

150%

100%

50%

0%

<$10k $10k-<$20k $20k-<$30k $30k-<$40k $40k-<$50k $50k-<$75k $75k-<$100k $100k+

Sources: 2009 NHTS (NHTS Sample/1,000
population) ; Pew 2011 Smartphone Survey; Census


Issues - Recruitment

 Recruitment can be difficult
Small publicity campaigns --> Small datasets
Areas most successful in recruiting users had large
publicity campaigns
App needs to have value itself:
Monetary value
Feel like ‘they are helping’ something they care about
Fun (at least not painful) to use


Cliffs Notes

 All we did was build a little phone app:
Very tiny investment (<$20,000 total ) for CycleTracks
Yielded 35,000+ records
Open source policy has afforded 8,500 more and
counting


Lessons Learned

 Think about ways to get data to come to you.
 Reach far with small levels of investment.
 Be open. Open-source works!
 Set aside real money to:
Maintain and grow the app and associated scripts
Advertise what we have done with it/develop a community
 Develop App under your Apple Developer ID
Changing is painful
 Use an API interface rather than have the app hard-coded
to a database
More flexible in case others want to contribute data


Thanks!

Credits: Lisa Zorn, Billy Charlton, Matt Paul

elizabeth [at] sfcta [dot] org
www.sfcta.org/modeling
www.sfcta.org/cycletracks
http://github.com/sfcta


Another story: We had a lot of unorganized data
collected by a zillion
projects, agencies, consultants…and wanted to
make sense of it.
…so we built this app called CountDracula


CountDracula

https://github.com/sfcta/CountDracula


Stone Soup Data Collection w/ CycleTracks

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