This is a lecture from a graduate GPS class I taught at the University of South Florida in 2013 on the use of GPS in mobile phones.

1. Cell Phones and GPS
Sean J. Barbeau, Ph.D.
Protected under U.S. Patents #8036679, #8045954, #8140256, #8145183, #8169342, Other Patents Pending USF 2012,. © 2014 Sean J. Barbeau

2. Mobile Computing Opportunities
 Proliferation of cell phones & apps
 5.9B mobile subscriptions worldwide, approx. 87% of global
population (Sept. 11)[1]
 102.4% U.S. mobile subscriber rate (322.9M) (Jun. 11) [2]
 26.6% of U.S. Households are Wireless–Only (April 11) [3]
 29B apps downloaded in 2011, up from 9B in 2010 [4]
 Evolution of positioning technologies
 U.S. F.C.C. e-911 mandate for locating cell phones ~2001
 79.9% of cell phones shipped in Q4 2011 (318.3M) had
integrated GPS [5]
[1] International Telecommunications Union, “ITC Facts and Figures – The World in 2011” International Telecommunications Union, Sept 2011.
[2] CTIA. “Wireless Quick Facts,” http://www.ctia.or g /a dvocacy /res earch /inde x.cfm/a id /10323
[3] National Center for Health Statistics. “Wireless Substitution: State -level Estimates from the National Health Interview Survey” , National Health Statistics Reports, Number 39, April 20, 2011.
[4] ABIresearch. “Android Overtakes Apple with 44% Worldwide Share of Mobile Apps Downloads,” October 24, 2011.
[5] Rebello, Jagdish. “Four Out of Five Cell Phones to Integrate GPS by End of 2011,” Integrate -GPS-by-End-of-2011.aspx 2
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3. Google Maps/Inrix/Foursquare/Facebook/Latitude
 Maps and Navigation
 Real-time traffic
 Allows users to “check-in”
to locations to earn
points/rewards/discounts
 Alerts you to friend check-ins
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3
Sugar

4. Location technology available to cellular network providers
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4

5. Positioning Technologies
 Global Positioning System
(GPS)
 Uses satellite signals to
determine its current location
 Can be accurate within 3
meters
 Small enough to manufacture
as a “chip” inside phone
 Assisted GPS uses data
provided by the network to
reduce time-to-first-fix
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6. Positioning Technologies
 Advanced Forward-Link
Trilateration (AFLT)
 Phone takes measurements
of network signals and
reports time/distance back
to network server to do
position calculation
 Used in CDMA networks
 Accurate from 50-250m
 Typically not exposed to
apps due to network
overhead
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7. Positioning Technologies
 Uplink Time Difference of
Arrival (U-TDOA)
 Towers take measurements
of device signals and
position is calculated by
network servers
 Used in GSM networks
 Accurate from 50-250m
 Typically not exposed to
apps due to network
overhead
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8. Positioning Technologies
 Cell Sector
 Cellular network
identifies the section of
the cell that the phone is
in
 Accuracy depends on
size of cell (100m-20km)
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9. Positioning Technologies
 Cell-ID
 Cellular network
identifies the cell tower
that the phone is
communicating with
 Accuracy depends on
size of cell (100m-
20km)
 Least-accurate
positioning method
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10. Network vs. Handset-Initiated
Handset-initiated – app (or device)
made location request
Network-initiated – web
application, or cellular carrier, made
location request
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11. MS-assisted vs. MS-based
 Mobile Station (MS) = mobile device
 MS-assisted = Device provides additional info, but
network server calculates the final position
 E.g., A-FLT, U-TDOA
 Typically only used for e911 due to burden on carriers
 MS-based = Network provides additional info, but the
device calculates the final position
 E.g., traditional assisted GPS
 Used by mobile apps
 Note that you can have MS-assisted GPS
 Device collects GPS signal info, but sends to server, where
pseudoranges are calculated
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11

12. MS-based Assisted GPS
 Assistance information implemented within the cell network
communication protocol itself
 Device must talk to Positioning Determination Entity (PDE)
server every few hours
 Scales poorly (i.e., significant cost to add more devices)
 Maintained solely by cell carrier
 Autonomous mode may not be possible
 Depends on device
Operating System
(e.g., Linux)
Link Layer
(e.g., CDMA IS-95)
Custom Network Server
Operating System
(e.g., Linux)
(e.g., Intel CPU/Motherboard)
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Hardware
(e.g., Qualcomm chipset)
Device Cell Network
Hardware
Carrier Servers

13. gpsOneXTRA
 Predictive ephemeris model – good for 7 days
 Accuracy degrades over time – typically refreshed every 48 hrs
 Packet data over internet (application layer) scales well
 Server can be maintained by third-party (e.g., Google, Apple)
Application Layer
(e.g., HTTP, FTP, VOIP)
Tranport Layer
(e.g., TCP, UDP)
Network Layer
(e.g., IP)
Operating System
(e.g., Linux)
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Application
Server
Operating System
13
Link Layer
(e.g., CDMA IS-95)
Cell Network
(e.g., Linux)
Hardware
(e.g., Intel CPU/
Motherboard)
Server
Hardware
(e.g., Qualcomm chipset)
Device

14. gpsOneXTRA (Cont)
 Autonomous GPS supported
 On Android, applications given limited control over refreshing
gpsOneXTRA info
 Example file (~39kB) - http://xtra3.gpsonextra.net/xtra.bin
 Drawbacks:
 May not be as accurate as traditional MS-based Assisted GPS
 Requires internet connection
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15. GPS Data Characteristics Can Vary
Two major influences
1. Mobile Device Hardware & Software
> GPS hardware sensitivity
> Antenna quality and device integration
> Assisted vs. Unassisted GPS
> MS-based vs. gpsOneXTRA
> Firmware/software filters
2. Environment
> Indoor / Outdoor
> “Urban Canyons”
> Building materials
> Shielding by enclosure (e.g., purse, car)
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16. Benchmarking GPS accuracy is useful
95th percentile: 550m
68th percentile: 398m
50th percentile: 335m
Samsung Moment HTC EVO 4G 172 meters
Environment: Indoor, 2nd floor, on desk near window, Tampa, Fl
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172 meters
Data from GPS Benchmark (www.gpsbenchmark.com)

17. HTC Hero 29.7 meters HTC EVO 4G
Environment: Indoor, 2nd floor, on desk near window
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29.7 meters

18. Location technology used by mobile apps
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18

19. Smartphone Network Location
 Used when you can’t get a GPS fix
 Typically very quick TTFF (sub-second)
 Based on hybrid of WiFi and cell network
location info, depending on availability
 WiFi position depends on “geostamped” SSID
network identifier
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20. Samsung Galaxy S3 (Sprint)
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Cell network based location Wi-Fi based location
Android 4.1.2

21. Indoor Location – Soon!
 More precise Wi-Fi
location
 Dead-reckoning -
based on
accelerometers,
barometers, and other
sensors
 Requires extensive
“ground-truthing”
21 Google’s Floor Plan Maker App
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22. USF Library
 1st gen. indoor location is
available at USF library
 Floor plan has been
uploaded by USF
 Some measurements:
 https://docs.google.com
/presentation/d/1AK3K
DU8kqpT6hullzIrU6EVy
_CHx58LEKe7AremGEB
c/edit?usp=sharing
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23. Protected under U.S. Patents #8036679, #8045954, #8140256, #8145183, #8169342, Other Patents Pending USF 2012,. © 2014 Sean J. Barbeau
23

24. Known Arch. Limitations
 Frequent GPS sampling (4 s) and transmissions to
server cost significant battery energy
1. Fixed-interval
Location
updates
Java ME / Android
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24
Location-Aware Application
(Device-side)
Server
Legend
Location Data
Device Platform Software
Location API I/O API
Virtual Machine
2. Send data to server
SOAP
/ SIP
Mobile Device

25. Impact of GPS on Battery Life
14
8.04
16
14
12
10
8
6
4
2
0
4 sec. sampling interval
Battery Life (hours)
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Requirement
Sanyo Pro 200
25
Sprint CDMA
EV-DO Rev. A
network

26. Impact of Wireless Tx on Battery Life
14
7.02
16
14
12
10
8
6
4
2
0
4 sec. Tx interval
Battery Life (hours)
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Requirement
Motorola i580
26
Nextel iDEN
Network
JAX-RPC

27. Impact of GPS & Wireless Tx
on Battery Life
14
4.21
16
14
12
10
8
6
4
2
0
4 sec. sampling interval
Battery Life (hours)
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Requirement
Sanyo Pro 200
27
Sprint CDMA
EV-DO Rev. A
Network
UDP

28. Cellular Data Transfer Limitations
 Location tracking once per second
equals 86,400 records (~10.3MB) for
one user on one day
 Most cellular carriers only offer
limited data plans
 e.g., Verizon = $20 per month for 1GB
 ~10.3MB per day = 319.3MB per
month
 Almost 1/3 of user’s plan would be
location data
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28

29. U.S. Patent # 8,036,679 – Optimizing performance of location-aware applications
using state machines.
IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011. © 2011 IEEE
Proceedings of IEEE UBICOMM 2008 – The Second International Conference on
Mobile Ubiquitous Computing, Systems, Services, and Technologies, Valencia, Spain,
September 29 – October 4, 2008. © 2008 IEEE
The Journal of Navigation, volume 64, issue 03, pp. 381-399. July 2011. © 2011
The Royal Institute of Navigation.
Protected under U.S. Patents #8036679, #8045954, #8140256, #8145183, #8169342, Other Patents Pending USF 2012,. © 2014 Sean J. Barbeau
29

30. GPS Tracking
 “High-definition” view of
travel
 Frequent sampling allows
us to determine:
 Path, distance traveled
 Origin-Destination pairs
 Avg. speeds
 Enables high-accuracy real-time,
historical LBS
 Challenges:
 Battery life
 Amount of data
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30

31. GPS Tracking
 Infrequent tracking
solves energy, data
problems
 BUT, doesn’t give us the
data we want:
 Path, distance traveled
 Origin-Destination pairs
 Avg. speeds
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31

32. What is “Stationary”?
Detecting User Movement
4 second GPS
sampling
5 minute GPS
sampling
Moving Stopped d
• GPS noise causes uncertainty
in states
• Many false transitions waste
battery energy
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33. GPS-Auto Sleep
State
[0]
Gradually move towards state[0] when
(low_speed_threshold < current_speed <
high_speed_threshold) OR
(distance_between_fixes >
moved_distance_threshold).
State
[1]
State
[n – 1]
AWAKE ASLEEP
 Dynamically adjust the GPS sampling interval based on user movement
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State
[n]
Move directly to state[0] when current_speed >
high_speed_threshold.
GPS Sampling
Interval = 4 sec.
GPS Sampling
Interval = 8 sec.
GPS Sampling
Interval = 128 sec.
GPS Sampling
Interval = 256 sec.
After leaving state[0], gradually move towards state[n] when ((current_speed <
low_speed value) AND (distance_between_fixes < moved_distance_threshold))
OR if a GPS fix can’t be acquired.
33
Moving Stopped

34. 45
40
35
30
25
20
15
10
5
Impact of Interval Between GPS Fixes on
Battery Life
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34
8.04
10.71
13.01
14.20
15.68
18.77
41.94
0
4 8 15 30 60 150 300
Battery Life (hours)
Interval Between GPS Fixes (s)
Sanyo
Pro 200
Sprint CDMA
EV-DO Rev. A
network

35. 300
250
200
150
100
50
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35
0
1
23
45
67
89
111
133
155
177
199
221
243
265
287
309
331
353
375
397
419
441
463
485
507
529
551
573
595
617
639
661
683
705
727
749
771
793
815
837
859
881
903
925
947
969
991
1013
1035
Interval Between GPS Fixes (seconds)
GPS Auto-Sleep Transitions - “Awake” to “Asleep”
Sanyo Pro 200
Sprint CDMA
EV-DO Rev. A
network
“Asleep”
“Awake”
State errors
No GPS signal

36. 35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
GPS Auto-Sleep -
State Error Percentage
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36
0.51%
29.10%
11.60% 10.54%
15.67%
23.97%
n = 30
7.37%
0.00%
Min Max Mean 50th 68th 95th STD DEV
 Approx. 88% mean accuracy in state tracking
 Avg. doubling of battery life (based on TRAC-IT tests)

37. IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011,
doi:10.1109/MPRV.2010.48 © 2011 IEEE
The Journal of Navigation, volume 64, issue 03, pp. 381-399. July 2011. ©
2011 The Royal Institute of Navigation.
Proceedings of IEEE UBICOMM 2008 – The Second International
Conference on Mobile Ubiquitous Computing, Systems, Services, and
Technologies, Valencia, Spain, September 29 – October 4, 2008. © 2008
IEEE
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37

38.  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
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38
Critical Point Algorithm
38

39. Critical Point Algorithm
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39
changeInDirection() = |Angle2 – Angle1|
NORTH
Last Critical Point
Current Point
Last Trigger Point
(Under Evaluation)
Angle1
Angle2
= Mobile Device Path
= Location Points

40. Speed >
max_walk_speed
(Optional)
Conditional Evaluations = TRUE?
(for Real-time Applications)
NO
lastTriggerPoint=
currentLocation
(changeInDirection() >
angle_threshold) AND
(currentSpeed >
min_speed_threshold)?
Critical Point
Algorithm
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40
START
(Input = currentLocation)
TransportationMode=
WALKING
TransportationMode=
VEHICLE
First Point?
lastCriticalPoint=
currentLocation
lastCriticalPoint=lastTriggerPoint
YES
NO
YES
NO
YES
NO
(Since currentLocation is first point in
sequence, it is saved as both the
lastCriticalPoint and LastValidPoint)
(lastTriggerPoint is a CriticalPoint, and
is stored as lastCriticalPoint for future
executions of CP algorithm and
returned to application)
(No Critical Points were found)
YES
Return currentLocation
lastTriggerPoint=currentLocation
(Optional) Reset Conditional
Evaluation Variables
(for Real-time Applications)
Return Return null lastCriticaPoint
 changeInDirection()
 Uses angle threshold
 Changed per speed
 min_speed()
 If currentSpeed >
min_speed, device is moving
 Real-time Conditional
Evaluations (Optional)
 timerExpired()?
 distanceCounterExceeded?
 receivedServerProbe?

41. Protected under U.S. Patents #8036679, #8045954, #8140256, #8145183, #8169342, Other Patents Pending USF 2012,. © 2014 Sean J. Barbeau
Sanyo 7050
Sprint CDMA
1xRTT Network
UDP
41
Effect of Wireless Transmission Interval on Battery Life

42. • Angle 1
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43. • Angle 2
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44. • Angle 3
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45. • Angle 4
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46. • Angle 5
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47. • Angle 6
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48. • Angle 7
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49. • Angle 8
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50. • Angle 10
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51. • Angle 11
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52. • Angle 15
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53. Introduction
Known LBS
Architectures
• Angle 18
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions
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54. Accuracy Evaluation using Distance
Full GPS Path
a
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Sampled GPS position
Critical Point path
Critical Point
b c d
e
f
g
x
y
Distancefull_GPS_path = a + b + c + d + e + f + g
Distancecritical_point_path = x + y
퐷푖푠푡푎푛푐푒 푒푟푟표푟 푝푒푟푐푒푛푡푎푔푒 =
퐷푖푠푡푎푛푐푒푓푢푙푙 _퐺푃푆 _푝푎푡 ℎ − 퐷푖푠푡푎푛푐푒푐푟푖푡푖푐푎푙 _푝표푖푛푡 _푝푎푡 ℎ
퐷푖푠푡푎푛푐푒푓푢푙푙 _퐺푃푆_푝푎푡 ℎ
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions

55. # Critical Points vs. Distance Error Percentage
60
50
40
30
20
10
Walk
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20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Distance Error Percentage
Number of Criticdal Points
Angle Threshold (Degrees)
Number of Critical Points Total Number of Points Distance Error Percentage
55
Chosen Walk
Angle Threshold
= 4.5 degrees
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions

56. # Critical Points vs. Distance Error Percentage
400
350
300
250
200
150
100
50
Car
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20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Distance Error Percentage
Number of Criticdal Points
Angle Threshold (Degrees)
Number of Critical Points Total Number of Points Distance Error Percentage
56
Chosen Car
Angle Threshold
= 3 degrees
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions

57. Critical Point Algorithm
 Avg. GPS 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
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57
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions
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

58. Conclusions
 Many different positioning methods for mobile
devices with a cellular connection
 All devices are different
 Hybrid technologies will dominate next 5-10 years
 Indoor location is “next big thing”
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58

59. Extra slides
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59

60. U.S. Patent # 8,036,679 – Optimizing performance of location-aware
applications using state machines.
IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011. © 2011
IEEE
Proceedings of IEEE UBICOMM 2008 – The Second International Conference on
Mobile Ubiquitous Computing, Systems, Services, and Technologies, Valencia,
Spain, September 29 – October 4, 2008. © 2008 IEEE
The Journal of Navigation, volume 64, issue 03, pp. 381-399. July 2011. © 2011
The Royal Institute of Navigation.
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60

61. GPS Speed Observations When Stationary Indoors
0.00
3.25
(n = 165, recorded over 5.5 hours)
0.36
0.25
3.50
3.00
2.50
2.00
1.50
1.00
0.50
 stopped_speed_threshold = 1 m/s
 95th percentile of horizontal error
 high_speed_threshold = 1.5 m/s
0.5
1
 98th percentile of horizontal error 61
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1.435
1.5925
0.00
Min Max Avg 50th
percent.
68th
percent.
95th
percent.
98th
percent.
99th
percent.
Speed (m/s)
Speed Error

62. GPS Auto-Sleep
 moved_distance_threshold = 100 m
 Based on max. observed
horizontal error of 90.69 m
Horizontal Error Statistics (meters)
True Location
Motorola i580
Sanyo 7050
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62
Device GPS Type
Sample
Size
Min Max Avg 50th 68th 95th RMSE
Motorola i580 Assisted 478 0.74 90.69 15.16 9.78 15.15 47.9 21.64
Sanyo 7050 Assisted 1513 0.16 32.04 8.78 6.23 9.33 24.44 11.33

63. GPS Auto-Sleep
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63
 high_horizontal_accuracy_threshold = 80 m
 Based on max. observed hor. acc. of 58 m

64. IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011,
doi:10.1109/MPRV.2010.48 © 2011 IEEE
The Journal of Navigation, volume 64, issue 03, pp. 381-399. July 2011. ©
2011 The Royal Institute of Navigation.
Proceedings of IEEE UBICOMM 2008 – The Second International
Conference on Mobile Ubiquitous Computing, Systems, Services, and
Technologies, Valencia, Spain, September 29 – October 4, 2008. © 2008
IEEE
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64
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions

65. Critical Point Algorithm
Last Critical Point
[ ] [ ] [ ]
Last Trigger Point (Under Evaluation)
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65
Critical Point Evaluation Sliding Window
Non-critical Point (discarded)
Current Point

66. Critical Point Algorithm
Iteration X Iteration X+1 Iteration X+2
Last Critical Point
[ ] [ ] [ ]
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66
Critical Point Sliding Window Memory Requirements
Non-critical Point (discarded)
Current Point
[ ] [ ] [ ]
[ ] [ ] [ ]
Last Trigger Point (Under Evaluation)

67. Critical Point Algorithm
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
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67
0.0
Min Max Avg 20th
percent
25th
percent
50th
percent
68th
percent
95th
percent
Std dev
Speed (meters per second)
Outdoor Walking GPS Speed
n = 53
 min_speed_threshold = 0.1 m/s
 Based on walk speed 25th percentile of 0.2 m/s, 20th percent. of 0 m/s

68.  When comparing a) all points to b) critical points using a min_speed_threshold of
0.1 meters per second, the general walking path of the user is preserved, with some
filtering at the beginning of the trip (bottom left of each image)
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68
a)
b)

69.  Over 97% of the GPS drift shown here at an indoor stationary location can be filtered
out by the Critical Point Algorithm when using a 0.1 meters per second
min_speed_threshold
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69
Min
Speed
Number of
Critical Points
Total Number
of Points % Savings
Bytes
Saved*
Walking 0 50 53 5.66% 357
0.1 39 53 26.42% 1,666
Min
Speed
Number of
Critical Points
Total Number
of Points % Savings
Bytes
Saved*
Stationary 0 904 3519 74.31% 311,185
0.1 91 3519 97.41% 407,932
* Based on 119 bytes per UDP payload

70. Critical Point Algorithm
Possible true path
Observed Path
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70
Possible true position when sampled
Sampled GPS position
Estimated horizontal accuracy (68th percentile by Java ME specification)

71. Funded by:
• National Center for Transit Research
• US Department of Transportation
• Florida Department of Transportation
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71
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions

72. TRAC-IT
 Created for bus, bike, walk,
car travel data collection
 Passive and Active modes
 Simultaneous location-based
services as incentive
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Purpose of Trip:
72
TRAC--IIT
(1) Work Related
(2) Shopping
(3) Pickup
Someone
(4) Go Home
etc. ...
<- Back Select

73. TRAC-IT Deployment
 2011 USDOT Value Pricing project in Tampa, FL
 30 participants, 40 days avg. per participant
 4,023,917 GPS fixes, 1,633 processed trips
TRAC-IT Data Collection for USDOT-funded project
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73
Date Range 2/10/2011 to 4/29/2011
Total Number of Users 30
Total Number of Sessions 1,857
Avg. Session Length (hrs) 15.44
Total Survey Time (days) 1,194.80
Avg. Survey Time per User (days) 39.83
Total Number of GPS fixes Received 4,023,917
Avg. Number of GPS fixes per Session 2,166.89
Avg. Number of GPS fixes per User 134,130.57

74. TRAC-IT Deployment Analysis
 95% of 899 sessions had less than 3.95% of lost UDP
packets
UDP and Location Data Buffering - Packets Lost
 Average session length was 15.44 hrs
 (Avg. battery life was at least this long)
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74
# Lost Per Session % Lost Per Session
Min 0 0.00%
Max 290 66.15%
Avg 15.67 1.19%
50th percentile 8 0.48%
68th percentile 13 0.88%
95th percentile 59.15 3.95%

75. Funded by:
• National Center for Transit Research
• US Department of Transportation
• Florida Department of Transportation
• Transportation Research Board (TRB) IDEA program
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75
Introduction
Known LBS
Architectures
Limitations
Proposed LAISYC
Architecture
Evaluation Conclusions

76. Travel Assistance Device
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76
 Transit navigation app - Assists transit riders with intellectual
disabilities by telling them when to exit the bus in real-time

77. Travel Trainers Plan trips via TAD website
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77

78. TAD Mobile App Interface
Select Trip
(1) Home to Work
(2) Work to Home
(3) Home to Movie
18 Livingston West
Distance to Final Stop:
5.6 miles
Pull the Cord Now!
(+Sound and Vibration)
 TAD cell phone app tells the traveler to “Get Ready” and “Pull
the Cord Now!” when it is time for them to exit the bus.
 Prompts are visual, auditory, and tactile.
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78
TTAD
Cancel Select
Woorrkk ttoo Hoomee
Back #
TAD
OK

79. TAD Bus Stop Detection Algorithm
Legend
 Multiple iterations of algorithm after field tests
 2 U.S. patents issued on final version, TAD system
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79
Second-to-Last
Stop
Transit Vehicle
Direction
“Pull the Cord Now” Alert Location
Destination Stop
Second-to-Last
Stop
Transit Vehicle
Direction
Zone2 Departure Check:
If ((Zone1Arrival = true ||
Zone1Departure = true) &&
(Device in Zone 2) )Then
Trigger “Pull Cord Now
Get Ready Check:
If (Device within W meters of 2nd
to Last Stop) Then
Trigger “Get Ready”
Destination Stop
Y
X

80. TAD Analysis
 Collaboration with Florida Mental Health Institute
 33 trials with 3 individuals with moderate mental
retardation (TAD = 100% accuracy for alerts)
 Riders only requested stop and exited bus at correct
location when TAD alerted was used
2
1
Baseline With TAD Baseline With TAD
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80
0
1 2 3 4 5 6 7 8 9 10 11
Steps Completed
Trials

81. LAISYC Publications (A)
Session Management
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
 IEEE Communications Magazine, Vol. 44, No. 11, pp. 156-163, November 2006.
 IEEE Network Magazine, Vol.24 No.4, July 2010.
GPS Auto-Sleep
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
 The Journal of Navigation, volume 64, issue 03, pp. 381-399. July 2011. (C) 2011 The Royal Institute of Navigation.
 Proceedings of IEEE UBICOMM 2008 – The Second International Conference on Mobile Ubiquitous Computing, Systems,
Services, and Technologies, Valencia, Spain, September 29 – October 4, 2008.
 U.S. Patent # 8,036,679 – Optimizing performance of location-aware applications using state machines.
Critical Point Algorithm
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
 The Journal of Navigation, volume 64, issue 03, pp. 381-399. July 2011. (C) 2011 The Royal Institute of Navigation.
 Proceedings of IEEE UBICOMM 2008 – The Second International Conference on Mobile Ubiquitous Computing, Systems,
Services, and Technologies, Valencia, Spain, September 29 – October 4, 2008.
Adaptive Location Data Buffering
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
Location Data Encryption
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
Location Data Signing
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
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81

82. LAISYC Publications (B)
TRAC-IT
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
 15thWorld Congress on Intelligent Transportation Systems, New York, New York, November 16-20, 2008. Paper # 30153.
 Proceedings of the National Academy of Sciences’ Transportation Research Board 88th Annual Meeting, Paper #09-3175.
January, 2009.
 15thWorld Congress on Intelligent Transportation Systems, New York, New York, November 16-20, 2008. Paper # 30413.
Travel Assistance Device (TAD)
 IEEE Pervasive Computing, vol. 10, no. 3, pp. 58-67, July-Sept. 2011, doi:10.1109/MPRV.2010.48
 15thWorld Congress on Intelligent Transportation Systems, New York, New York, November 16-20, 2008. Paper # 30429.
 Proceedings of the National Academy of Sciences’ Transportation Research Board 89th Annual Meeting, Paper # 10-2571.
Washington, D.C., January 12th, 2010.
 Transportation Research Record: Journal of the Transportation Research Board, Transit 2010 Vol 1, No. 2143, pp. 168-176,
October 2010.
 Institution of Engineering and Technology (IET) Intelligent Transportation Systems, 2010, Vol. 4, Iss. 1, pp. 37–49. doi:
10.1049/iet-its.2009.0029. © The Institution of Engineering and Technology 2010.
 Proceedings of the 2011 ITS World Congress, Orlando, FL, October 18, 2011.
 Proceedings of the National Academy of Sciences’ Transportation Research Board 90th Annual Meeting, Paper #11-2254. January
24, 2011. Paper #11-2254.
 Institution of Engineering and Technology (IET) Intelligent Transportation Systems, 2010, Vol. 4, Iss. 1, pp. 12–23. doi:
10.1049/iet-its.2009.0028. © The Institution of Engineering and Technology 2010.
 Proceedings of the National Academy of Sciences’ Transportation Research Board 90th Annual Meeting, Paper #11-2213. January
24, 2011.
 U.S. Patent # 8,138,907 – Travel Assistant Device.
 U.S. Patent # 8,169,342 - Method of Providing a Destination Alert to a Transit System Rider
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82