Husky Lines Mobile AppADAPTING TRANSPORTATION STUDIES TO OUR CHANGING TECHNOLOGIESElisabeth Leafelisabethleaf@gmail.comleafe@uw.edu

Masters of Science Geospatial Technologies

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University of WashingtonTacoma Campus

Husky Line Research Project

Green Seed Fund grant recipient

Started with the UWT Chancellor’s Advisory Committee for Sustainability

Team of 5 researchers: Dr. Britta Ricker, Dr. Jim Gawel, Alexa Brockamp, Elisabeth Leaf, Greg Lund

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Husky Line Research Project

Researching: 1) Current and future transit options for UW Tacoma Campus 2) Student perceptions of transit and barriers to transit use 3) Ways to incorporate technology and GIS into transit studies

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Retrieved from http://blog.nskinc.com/it-services-boston/small-business-challenges-and-mobile-solutions

Retrieved from http://charlestonup.com/strategies/transportation-mobility/

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Underlying Problem:Transportation planners often use self-reported data via travel

surveys and travel diaries to gather information on

transportation. They use this information to create travel demand

models and estimate changes in transportation activity over time.

However, self-reported data can only measure perceptions of

transportation usage, not actual habits.6

Underlying Question:

How can we use mobile technology to improve the way we conduct

transportation studies?

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How can we use mobile technology to improve the way we conduct

transportation studies?

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sensors

How can we use mobile sensors to improve the way we conduct transportation studies

by estimating a user’s mode of transportation?

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Husky Lines Mobile App

Spring Quarter: Choose target device(s) Create paper prototype Conduct literature review

Summer Quarter: Develop app Write findings

iPhone and iPad Storyboard designed in XCode Literature review found many examples

of research exploring similar use of sensors and their accuracy

The Vision:

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1) Standard main menu2) Consent form and demographic

survey on the first start-up3) Daily transportation diary that is

pushed to the device nightly with notification via text or email

4) Record location when in study participant is in motion

5) Record accelerometer data when in motion

6) Upload all data nightly to a remote server

7) Provide the participant with a visualization of their data

Reality:

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1) Standard main menu2) Consent form and

demographic survey on the first start-up

3) Daily transportation diary that is pushed to the device nightly with notification via text or email

4) Record location when study participant is in motion

5) Record accelerometer data when in motion

6) Upload all data nightly to a remote server

7) Provide the participant with a visualization of their data

ResearchKit Framework

ResearchKit Framework

Tested separately but not included in app

Access through Core Location Access through

Core Motion

Not possible to finish within time constraints

Mobile App Main Menu

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Consent Form PDF

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Mobile App Main Menu

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Survey

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Survey

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Accelerometer History

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Record Accelerometer

Record Location

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Accelerometer

Get Accelerometer History

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Record Accelerometer Live

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Record Location

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What are the challenges to building a mobile application for research?

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• Core Motion requires testing outside of the Xcode simulator

• Apple Developer License has some hurdles in set-up

Working with new technology

• Lack of documentation

• Limited examples

• Subtle differences in syntax between versions

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Apple Developer License

Working with Swift & Objective-C

• Apple uses both Swift and Objective-C

• Syntax can be quite different and is not interchangeable

Findings: challenges in creating the app

Findings: ResearchKit has many possibilities

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Findings: Filters used in CMMotionActivityManager (accelerometer history) can, and should, be improved

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Next Steps

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● Build out the survey modules, set up notifications, and improve user interface

● Research the filters used in CMMotionActivityManager to see how they could be improved

● Set up server and automated data uploading

● With both live location and accelerometer data, limit the data collection to ‘when in motion’

● Work with “raw” accelerometer data to predict the mode of transportation

Any Questions?

Elisabeth Leafelisabethleaf@gmail.comleafe@uw.eduwww.linkedin.com/in/elisabethleaftwitter.com/ellieoftheworld