Stone Soup Data Collection w/ CycleTracks
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Transcript of Stone Soup Data Collection w/ CycleTracks
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Building the Technology Potfor the Stone Soup Method
of Data Collection: Facilitating Cooperation in the Face of
Scarcity
Elizabeth A. Sall
Transportation Research Board Annual Meeting in Washington, D.C.
Tuesday January 15th, 2013
DATA.But travel models need…
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I AM A TRAVEL MODELER.
Caveat: I am not a data collection or surveying expert
So what am I going to talk about?
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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
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Cost per
Record
Cost per Respond
ent
Respondent LOE
Data Precisio
n
Data Qualit
y
RP or SP
Web-based stated preference
$ $ High SP
CATI Route recall
$$$ $$$ High Low Low RP
Personal GPS
$ $$ Med High Med RP
Bicycle GPS
$ $$ Med High High RP
Smart Phone
$ $ Low Med Med RP
Publicity! Advertising! Stickers!
CycleTracks: from coder to cyclist
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iTunes Store
Android
Market
Amazon EC/2 Server running Apache
CycleTracks Data: from cyclist to analyst
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mySQL
JSON
PHP PHP
Bay Area Participants (if they noted their home ZIP)
CycleTracksN-366
BATSN=153
Age Mean 34 33
Gender Female 20% 36%
Cycling Frequency Daily Several Times/Week Several Times/Month Less than once a month
48%36%13%3% N/A
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Data Quality: some good, some bad
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Urban Canyon Effect
Downtown
Haight Ashbury
vs
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GPS Signal at Beginning of Trip
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Not on a Bike
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Post Processing Warranted
Gaussian smoothing
Activity & mode detection
Map matching
5,178 traces497 users
3,034 bike stages
366 usersh
(Schüssler & Axhausen 2009)
~60% of submitted datauseful
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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
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Where do people use CycleTracks?
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*based on optional homeZIP field, NOT TRIP LOCATION
Agencies using Cycletracks:1. San Francisco2. Monterey Bay, CA3. Austin, TX4. Seattle, WA5. Fort Collins, CO6. Twin Cities, MN7. Raleigh, NC8. 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
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*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?
• New user registrations directly correlates with publicity
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020406080
100120140160180200
Cycletracks New User's First Trip Submissions
San Francisco Monterey
AustinSeattle
Twin Cities
Fort Collins
Many just try it out , but half use it for a while
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One Day Day - Week Week - Month 1 - 3 Months 3 - 6 Months 6 - 9 Months 9 - 12 Months Over a Year0
100
200
300
400
500
600
700
800
900
1000
41%
11%
16%15%
8%
3% 3% 3%
Users by Duration of Use
Broad Spectrum of Users
• Half of users submit > 5 trips• 10% of users submitted > 20 trips• 40 users submitted >100 trips (Max =
685)
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1 2-5 6-10 11-15 16-20 21-7000
100
200
300
400
500
600
700
80031%
20%
8%
5%3%
10%
Users by Trips Submitted
Capturing Infrequent Cyclists
• 20% (500+) users infrequent cyclists (10% of trips)
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Less than once a month Several times per month Several times per week Daily
187 411 852 853577
2,036
9,122
13,506
Trips and Users by Cycling Frequency
Users Trips
All Open Source
• GPL3 License• Code on GitHub• Fork us!
www.github.com/sfcta
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Bonus Benefit: Transferability+ 750 users +8,500 trips
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AggieTracks~35 users
Cville Bike mApper~120 users/1500 trips
Cycle Atlanta~ 400 users/4500 trips NuStats PaceLogger
Rolling their own from our code:
Cycle Lane~ 200 users/2500 trips
Combined Reach: ~44,000 trips
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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?
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Issues - Bias
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NHTS Sample Rate Smartphone Ownership Effective Probable Response Rate0%
20%
40%
60%
80%
100%
120%
140%
Race/Ethnicity
White Black Hispanic
Sources: 2009 NHTS (NHTS Sample/1,000 population) ; Pew 2011 Smartphone Survey; Census
People who answersurveys
People who have Smartphones
People who answerSurveys over theirSmartphones
x =
Does…
…if so, this looks pretty good.
?
Issues - Bias
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Sources: 2009 NHTS (NHTS Sample/1,000 population) ; Pew 2011 Smartphone Survey; Census
…and this looks even better.
NHTS Sample Rate Smartphone Ownership Effective Probable Response Rate0%
50%
100%
150%
200%
250%
Age Group
18-24 24-34 35-44 45-54 55-64 65+
Issues - Bias
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Sources: 2009 NHTS (NHTS Sample/1,000 population) ; Pew 2011 Smartphone Survey; Census
…but looks like income exacerbates the divide.
NHTS Sample Rate Smartphone Ownership Effective Probable Response Rate0%
50%
100%
150%
200%
250%
300%
Total Household Income
<$10k $10k-<$20k $20k-<$30k $30k-<$40k $40k-<$50k $50k-<$75k $75k-<$100k $100k+
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
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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
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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 communityDevelop App under your Apple Developer ID
Changing is painfulUse an API interface rather than have the
app hard-coded to a database More flexible in case others want to
contribute data
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Thanks!
Credits: Lisa Zorn, Billy Charlton, Matt Paul
elizabeth [at] sfcta [dot] orgwww.sfcta.org/modeling
www.sfcta.org/cycletrackshttp://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
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CountDracula
https://github.com/sfcta/CountDracula