Mobile phone data from carriers like Sprint and Verizon can be used to derive 24/7 operational origin-destination (OD) matrices showing travel patterns. A pilot study in Sacramento used encrypted Sprint data to identify over 280,000 trips which were mapped to traffic analysis zones to generate hourly OD matrices. These matrices were refined using traffic assignment and counts with results having R-squared values over 0.85. Further research opportunities exist to analyze trip modes, activity chains, and travel behavior changes over time using continuous mobile phone data observations.

1. Deriving 24/7 Operational OD Matrices
From AirSage Mobile Phone Data
Sacramento Pilot Study and Beyond
October 2011
Jingtao Ma, PhD, PE,
Mygistics, Inc.

2. Agenda
‣ Brief overview of OD derivation methodology and techniques
‣ AirSage data processing
‣ MobileOD pilot for Sacramento, CA
‣ Pre-processing: sample trips
‣ Projection based on CTPP survey data
‣ Hourly Vehicular OD (path flow) refinement based on static traffic
assignment
‣ Vehicular path flow estimation based on observed path choice
‣ Path matching
‣ Path flow aggregation
‣ OD estimation (TFlowFuzzy) from path flows
2

3. Traditional Methods for Operational OD Derivation
‣ Travel demand model:
‣ Calculated, not observed and thus only as good as the model itself
‣ Only a fixed point snapshot of the mobility pattern
‣ Active probing: Automated number plate recognition (ANPR) or Bluetooth
MAC matching
‣ Potentially more accurate, but usually case by case on a small scale
‣ Relatively slow turnaround
‣ Very expensive
‣ Passive probing: GPS based navigation devices
‣ Small samples
‣ “Biased towards fleets and are thus not representative of a community’s
travel patterns”
3

4. OD Derivation Methods: Why Mobile OD?
‣ Mobile OD: travel pattern inference from mobile phone traces
‣ also a passive probing method
‣ In general:
Sprint
‣ High device penetration: >85% conservatively estimated
(285M devices/308M population in US)
‣ Wide overage
‣ Ubiquitous usage
‣ Travel patterns could be
Verizon
‣ Weekday versus weekend
‣ Seasonal variation, special events
‣ Work trips/non work trips
‣ Continuous OD at fine grain spatial/temporal resolutions
‣ What is offered to clients
‣ Off-the-shelf 24/7 operational OD
‣ Add-on survey tool for household surveys as alternative to traditional
GPS tracking
‣ Long-distance, inter-regional, external-external travel data 4

5. How AirSage Technology Works
AirSage patented WiSETM platform transforms normal operational signaling data
from wireless carriers into real-time and historical location and movement data.
CDMA network techonology: Sprint & Verizon
Currently 35 million Sprint devices in US; 90 million Verizon devices to be added

6. Operational 24/7 MobileOD Workflow
AirSage Public NAVTEQ Various Sources
Mobile Sightings Socio-economics Navigation Net Traffic Detectors
Trips Block groups Model Traffic
Paths Travel survey network counts
Projected
Mobile based OD
Path flow
Mygistics/PTV Operational
proprietary 24/7 MobileOD

7. Sacramento Pilot: Project Background
‣ Customer Fehr & Peers Associates
‣ I-80/CA-65 Interchange improvement
project
‣ Study period: 6-10AM, and 3-7PM
‣ A lengthy process was originally
proposed for demand estimation
‣ Initial discussion at TRB 2011
7

8. Sacramento Pilot: Mobile Phone Data
‣ Encrypted Sprint subscribers data
from one mobile switch coverage
area for October 2010
‣ Total mobile sightings: 256 million
(255,828,842)
‣ Filtered and analyzed: 98 million
‣ Subscribers: more than128 thousand
‣ 400,000 sightings from 600 randomly
selected subscribers
8

9. “Snowball” Trip Identification and Analysis System
(STIAS)
‣ An Expert System
‣ Rule-based knowledge base
‣ Inference engine
‣ 20+ rules, one inference engine
‣ Mygistics proprietary

10. Trip Identification: The Mygistics Difference
‣ 14 randomly selected subscribers from the
Sacramento dataset Regression Analysis: Eyes vs Myg-alg
0.4.1
‣ Trips from three methods 80
70
60
Eyes Myg-alg0.4.1 AirSage
22 18 7 50
16 8 15
Eyes
40
68 54 6
Predicted Y
8 6 7 30
22 20 10 20
13 10 2
25
41
22
46
17
26
10
0
R2 = 0.89
9 9 3 0 20 40 60
21 25 5 Mygi-Alg 0.4.1
6 4 2
10 13 1 Regression analysis: Eyes vs AirSage
28 18 7 80
38 27 5
70
327 280 113
100% 85.6% 34.6% 60
80 Improvement 50
Eyes
60 factor of 2.5 40
30
Eyes
40 20
Alg0.4.1
20 10 R2 = 0.11
AirSage 0
0 0 5 10 15 20 25 30
AirSage
1 3 5 7 9 11 13
10

11. STIAS: Benchmark & Validation
‣ Do these numbers apply to the entire dataset?
‣ For these samples: 280 versus 113 (MYG alg 0.4.1 vs. AirSage Known Trips)
‣ Factor of 2.47
‣ For the entire Sacramento dataset: 2.20 million vs. 1.04 million
‣ Factor of 2.12
‣ The sample benchmarking favored Myg-alg 0.4.1 a little, but not too much
‣ Mygistics currently working on version 0.5, hopefully to get to the point of 90+% of
trips identifiable by human eyes
‣ Which will bring to the same level of factor 2.5
11

12. OD Matrices from STIAS
‣ Identified trips mapped to TAZs
‣ Hourly aggregate over all weekdays
of October 2010
‣ 288 thousand (non-zero) active O-D
pairs
‣ 1070 active TAZ
‣ 1.14 million OD pairs
12

13. Path Matching (Trajectories)
‣ Path search & enumeration from VISUM
‣ For Sacramento, 65 million paths
stored for query
‣ GIS functions in PostGIS assisted in path
matching
‣ Shortest distance from via points to
candidate paths
‣ Selected the most likely one(s)
‣ Using observed paths for OD refinement
improves accuracy and requires fewer
counts
13

14. Sacramento Pilot: Results
‣ Sample OD from identified trips mapped to TAZs
‣ OD projection based on CTPP survey to generate better seed matrix
‣ TFlowFuzzy (OD refinement in VISUM) (8x1h)
‣ Traffic assignment and matrix verification
R^2 RMSE(%)
6AM 0.92 42
7AM 0.94 26
8AM 0.91 26
9AM 0.91 28
3PM 0.87 30
4PM 0.86 30
5PM 0.86 29
6PM 0.86 30
(Link/turn counts vs. model volume after matrix refinement) 14

15. Market Response to Date
Ongoing projects, proposals, request for information…
‣ Positive feedback for the
Sacramento pilot project
‣ Active discussion on social media
(LinkedIn groups, ITS America,
etc.)
‣ Inquiries for new proposals and
projects
‣ Interest from researchers, …
consultants and government
agencies
15

16. The beginning of the more research and applications
Ongoing projects, proposals, request for information…
‣ 24/7 hourly OD
matrices
…
16

17. The beginning of the more research and applications
Ongoing projects, proposals, request for information…
‣ 24/7 hourly OD
matrices
…
17

18. OD Matrices Analysis
‣ Identified trips mapped to TAZs
‣ Hourly aggregate over all
weekdays of October 2010
‣ 597,529 for Mobile OD (block group
level for two months data)
‣ (non-zero) active O-D pairs
‣ 308,988 for weekdays
‣ 102,571 for weekends
‣ 158,617 for event days
Active OD Pairs Sample Size Internal + Paths/Active OD
External=Num of Pair (Internal/
Paths External)
Weekdays 289,059+1992 51.7% 41 days 270,661+245,851=5 1.95 (0.93/12.3)
9=308,988 16,512
Weekends 82,642+19,929 17.2% 16 days 27,771+84,075=111 1.85 (0.34/4.2)
=102,571 ,846
Event Days 138,688+19,92 26.5% 4 days 21,222+80,795=102 1.92 (0.15/4.1)
9=158,617 ,017
18

19. The beginning of the more research and applications
Ongoing projects, proposals, request for information…
‣ Trip mode
inference
‣ Activity chain
and tour
imputation
…
19

20. The beginning of the more research and applications
Ongoing projects, proposals, request for information…
‣ Travel behavior
change from
continuous
observations
‣ … and more yet
to explore
…
20

21. Mygistics MobileOD™
‣ Full OD trip tables, not OD samples
‣ 24 hourly matrices for 7 days a week
‣ Census block group resolution (custom zone structure
possible)
‣ Internal, external/internal and external/external trips
‣ Survey add-on tools (on-board survey, household survey)
21

22. Contact
‣ Jingtao Ma
‣ jma@mygistics.com
‣ 503-575-2191 ext 2802
22