Center for Urban Transportation Research | University of South Florida

Enabling Better Mobility Through

Innovations For Mobile Devices

Sean J. Barbeau, Ph.D.

OST-R Speaker Series - February 18, 2015

National Center for Transit Research

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Opportunities

• 97% of US households have mobile phones (Nov. 2014)[1]

– Avg. household owns 5.2 connected devices

• Smartphones accounted for 66% of total phone market in 2014[2]

– Estimated to increase to 9 out of 10 phones in 2018[2]

• Mobile app use up 76% in 2014[2]

• National Center for Transit Research (NCTR)

– UTC focused on public transportation, other alternatives to single occupancy vehicle travel

[1] http://www.ctia.org/[2] http://www.gartner.com/newsroom/id/2944819

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Overview

Problems• Increase riders

independence via fixed route transit

• Assess impact of real-time bus arrival information

• Collecting multimodal data to support community-based social marketing

NCTR Project

TRAC-IT

OneBusAway

Travel Assistance Device (TAD)

Funded by National Center for Transit Research (NCTR) and Florida Department of Transportation

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Problem – Exiting the bus

• One of the hardest of the 23 skills necessary for riding public transportation

– Especially for those with intellectual impairment

– Hardest skill to “travel train”

• Paratransit is expensive for transit agencies

– ~10x the cost of one-way fixed route[1]

• Paratransit limits independence and spontaneity of travel for some riders

[1] National Transit Database (2005)

National Center for Transit Research

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TADTAD

Cancel Select

Select Trip

(1) Home to Work

(2) Work to Home

(3) Home to Movie

Work to HomeWork to Home

Back #

Distance to Final Stop:

5.6 miles

18 Livingston West

TADTAD

OK

Pull the Cord Now!

(+Sound and Vibration)

Travel Assistance Device (TAD)

• TAD mobile app tells the traveler to “Get Ready” and “Pull the Cord Now!” when it is time to exit the bus.

• Prompts are visual, auditory, and tactile

Funded by NCTR, FDOT, TRB IDEA program

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Evaluation & Results

• Partnered with USF Florida Mental Health Institute & Hillsborough Area Region Transit– 3 individuals with moderate intellectual impairments– Evaluated requesting stop, getting off at correct stop– ABAB experimental design– Safeguards in place

• When no prompts were given, all subjects failed to pull the stop request cord and exit the bus at the correct location

• When TAD was used, the subjects pulled the stop request cord and exited the bus at the correct location

• One rider without TAD asked the bus driver for help– Driver gave the rider incorrect directions– Rider exited the bus at the wrong stop

Bolechala, Miltenberger, Barbeau, and Gordon. “Evaluating the Effectiveness of the Travel Assistance Device on the Bus Riding Behavior of Individuals with Disabilities,” 37th Association for Behavior Analysis International (ABAI) Annual Convention, Denver, CO, May 27-31, 2011. Paper #11396.

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Recognition

• 2 patents granted in 2012– #8,138,907 - System and method to assist

transit rider with intellectual disability

– #8,169,342 - Algorithm used to notify the rider when to exit the vehicle

Protected under U.S. patents #8138907, 8169342

“Implementing a TAD in the hands of our trainees will not only help to reduce the amount of time we must dedicate to each trainee, but it will open the door of [travel trainers’] availability to many more trainees,”

- Mark Sheppard, Travel Trainer, Hillsborough Area Regional Transit

• Endorsements by those closest to the problem

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Next Steps

• Identifying partnerships/grants to adapt TAD to help other populations (e.g., visually impaired, seniors, tourists/visitors, wheelchair bound, etc.)

• Continued collaboration with USF Florida Mental Health Institute for evaluations

• Identifying opportunities with Veterans Administration (VA) to help veterans with multiple disabilities

• USF Patents and Licensing actively pursuing licensing opportunities to commercialize TAD

National Center for Transit Research

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What is OneBusAway?

• What? Suite of tools that provides real-time bus/train tracking information

– Originally deployed in greater Seattle, WA

– Started as graduate student project at UW, grew to over 100,000 unique weekly users

• Why? Make riding public transit easier by providing good info in usable formats

– Research evaluates the impacts

• Includes apps on many platforms:

http://onebusaway.org

Android Windows Phone

iPhone Windows 8

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Problem – Lack of transit apps

• In some cities, transit apps aren’t as prevalent

• OneBusAway is open-source software

• How can we replicate success of OneBusAway in other cities?

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Multi-region architecture

• Region information is stored in centralized server directory

• Apps now find nearby regions using Regions API

• Supports multiple cities!

Barbeau, Borning, Watkins. “OneBusAway Multi-region: Rapidly expanding mobile transit apps to new cities,” Journal of Public Transportation – Vol. 17 No. 4 (2014).

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OneBusAway Tampa

• USF lead a pilot deployment of OneBusAway multi-region in Tampa

• Survey lead by Dr. Candace Brakewood and Dr. Kari Watkins at Georgia Tech*

Objective: Quantify the impacts of real-time bus information on transit rider behavior and satisfaction in pilot deployment prior to public launch

Methodology: Before and after web-based survey with a non-user (control) group

BEFORE SURVEY

of Control Group

(approx. 230 participants)

AFTER SURVEY

of Control Group

(107 Non-Users)

BEFORE SURVEY

of OneBusAway Group

(approx. 230 participants)

AFTER SURVEY

of OneBusAway Group

(110 OneBusAway Users)

* Dr. Candace Brakewood is now at City College of New York

No

OneBusAway

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OneBusAway Tampa - Results

• Significant improvements in the waiting experience[1]:– Decreases in self-reported usual wait times by ~2

min.

– Decreases in negative feelings, particularly frustration

– Increases in satisfaction with wait times

• Other research is ongoing– Affects ridership? (in NYC, yes![2])

– Issue reporting / rider feedback

[1] Brakewood, Barbeau, Watkins. “An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida”, Transportation Research Part A: Policy and Practice, Volume 69, November 2014, Pages 409-422[2] Brakewood, C., Macfarlane, G., and Watkins, K. ”The Impact of Real-Time Information on Bus Ridership in New York City.” Transportation Research Part C: Emerging Technologies, 2015.

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TRAC-IT

• Allows “high-definition” view of travel

• Frequent sampling allows us to determine:

– Path, distance traveled

– Origin-Destination pairs

– Average speeds

• Two modes:

– “Passive” - GPS only

– “Active” - GPS + input

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Battery Life Problems!

• Infrequent tracking solves energy, data problems

• BUT, doesn’t give us the data we want:– Path, distance

traveled

– Origin-Destination pairs

– Avg. speeds

15National Center for Transit Research

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What is “Stationary”?Detecting User Movement

Moving Stopped d4 second GPS sampling

5 min. GPS sampling

• What if we only sample GPS while the user is moving?

• GPS noise causes uncertainty in states• Many false transitions waste battery

energy

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4 second

GPS sampling

5 min.

GPS sampling

State

0State

1

State

n – 1

State

n

Move directly to state[0] when speed

exceeds high_speed threshold

Location

Recalculation

Interval = 4 sec.

Location

Recalculation

Interval = 8 sec.

Location

Recalculation

Interval = 64 sec.

Location

Recalculation

Interval = 128 sec.

Move gradually towards state[n] when (speed < low_speed value)

and (distance_between_fixes < distance_threshold).

Move gradually towards state[0]

when (speed < high_speed value)

and (distance_between_fixes >

distance_threshold).

GPS Auto-Sleep - Dynamically change the GPS sampling rate

Moving Stopped

U.S. Patent # 8,036,679 – Optimizing performance of location-aware applications using state machines

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0.51%

29.10%

11.60% 10.54%

15.67%

23.97%

7.37%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

Min Max Mean 50th 68th 95th STD DEV

GPS Auto-Sleep -State Error Percentage

• Approx. 88% mean accuracy in state tracking• Avg. doubling of battery life (based on TRAC-IT tests)

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Critical Point Algorithm

• Purpose – to reduce battery energy expenditures and amount of data transferred by eliminating non-essential GPS data

• Pre-filters real-time GPS data on mobile device before it is wirelessly transmitted

U.S. Patent # 8,249,807 – Method for Determining Critical Points in Location Data Generated by Location-Based Applications

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Critical Point Algorithm

• Avg. point reduction of 77% per trip

• Avg. 18.8kB saved per trip

• Average distance error percentage under 10%

• On avg., as Tx interval doubles, battery life doubles

Min Max Avg.5th

percentile

25th

percentile

50th

percentile

68th

percentile

95th

percentile

Total Critical Point Count 2 322 35 3 13 27 38 97

Total GPS Fix Count 20 3,710 193 31 74 130 188 511

% Savings 20.83% 99.40% 77.43% 47.97% 69.49% 80.00% 86.83% 95.84%

Bytes Saved* 595 403,172 18,883 2,380 6,426 12,138 17,493 54,788

Distance Critical Points (m) 0.00 1,043,805.50 7,437.09 328.14 1,162.37 2,675.00 4,049.37 22,815.61

Total Distance (m) 2.36 1,087,043.20 7,878.02 380.79 1,252.55 2,913.39 4,345.91 24,231.34

Distance Error Percentage 0.00% 100.00% 8.90% 1.94% 3.98% 6.20% 8.70% 24.11%

* Based on 119 bytes per UDP payload

U.S. Patent # 8,249,807 – Method for Determining Critical Points in Location Data Generated by Location-Based Applications

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Measuring Spatial Patterns of Activity-Travel for Carsharing

Minor Axis

Major Axis

Standard deviation ellipse (SDE)

Y

X

• USDOT Value Pricing Project

• TRAC-IT w/ 30 participants in Tampa, avg. 40 days per participant

• Daily emails so users can view data

• Results:– Carsharing users have smaller

activity space (0.5 sq mi) than non-carsharing (7.8 sq mi), using SDE

– Activity space of carsharing users contracts while using carsharing (.2 sq mi vs. 0.5 sq mi), but is directed

Concas, Barbeau, Winters, Georggi, Bond. “Using Mobile Apps to Measure Spatial Travel Behavior Changes of Carsharing Users,”Proceedings of 2013 Transportation Research Board Conference, Washington, D.C., January 13-17, 2013.

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Problem-solving approach

• Multi-disciplinary – collaboration between:– Computer Science & Engineering– Civil Engineering

• Brainstorming meetings• Target real-world problems with solutions not yet

commercially available• CUTR => contract & grant-funded• Leverage existing (or soon-to-exist) commercially-

available mobile devices• 14 U.S. patents to date

– (see http://bit.ly/CUTRPatents for full list)

National Center for Transit Research

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Software Tech Transfer

Pros Cons

Patents & Licensing • Exclusive rights to technology may lead to more investment fromprivate sector• Revenue for university

• Must identify & contract with a licensee• Slow - May take a long time from idea to patent to license (~3-6 yrs for patent alone)

Open-source software • Fast – available to the world with the click of a button• Open – can create new collaboration opportunities (and funding)

• No licensing revenue for university• Private sector may be slower to invest in technology due to lack of exclusivity

• For open-source software, Apache v2 license offers the most freedom of use (including private sector)

• Sites such as Github offer “social” code sharing, collaboration

National Center for Transit Research

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Examples of open-source tech transfer

• OneBusAway– Deployed new pilot Tampa region in 2013– In 2013-2014, several new regions have started up:

• Atlanta• York, Canada• Washington, D.C. (beta)• Bear Transit (UC Berkeley) (beta)

– 3 vendors involved with deploying/supporting technology– MTA in NYC leveraged OneBusAway for their Bus Time system

• OpenTripPlanner for Android– Developed as multi-modal trip planning tech demo– Attracted interest/funding via Google Summer of Code– Now available in 5 countries (10 cities) and 3 languages

National Center for Transit Research

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Conclusions

• Mobile devices offer many opportunities, many challenges

• Multi-disciplinary collaboration yields innovation

• Tech transfer for software

– Patents & licensing and open-source are two different paths

– Consider your goals when choosing your path

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Thanks!

Sean J. Barbeau, Ph.D.barbeau@cutr.usf.edu813.974.7208

Principal Mobile Software Architect for R&DCenter for Urban Transportation ResearchUniversity of South Floridahttp://www.linkedin.com/in/seanbarbeau

Protected under U.S. patents #8138907, 8169342

Funded by the National Center for Transit Research and Florida Department of Transportation