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New Methods and Technologies for Collecting Origin-Destination (O-D) Data Ed Hard Byron Chigoy Praprut Songchitruksa, Ph.D., P.E. Steve Farnsworth Darrell Borchardt, P.E. 15 th National Tools of the Trade Conference Charleston, South

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New Methods and Technologies for Collecting Origin-Destination (O-D) Data

15th National Tools of the Trade Conference Charleston, South Carolina

September 13, 2016

Ed Hard Byron Chigoy Praprut Songchitruksa, Ph.D., P.E. Steve Farnsworth Darrell Borchardt, P.E.

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Presentation Overview

  • Overview of cell, GPS, and Bluetooth O-D data
  • Takeaways and lessons-learned from prior

studies

  • Capabilities and limitations by technology
  • Comparisons of O-D data between technologies
  • O-D data suitability by study type

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New Technology in Travel Surveys

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Trip Tables Trip Length Freq. Corridor Studies Select Link and Zone Analyses Residency Status

Bluetooth Cellular Data GPS

Validation Tools

  • classification

counts and travel time

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Cell Data Overview

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Cell O-D Data

What it Represents

  • Estimated device movements/flows
  • Spatio-temporal analysis of device over

weeks/months

  • Algorithms used to

‒ ID activity points and trip ends based on dwell times, speed, proximity ‒ Impute/derive estimated device trips based on patterns between home, work, other activity locations

  • Each sampled device computed individually, then

expanded, then aggregated with others

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Cell O-D Data

What it Represents

  • Data from 1 cell provider and 1 carrier (currently)
  • Possibly a combination of linked and unlinked trips
  • Analysis of activity points, clusters

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Cell Data Examples

  • Mobile Bay Bridge

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Source: Airsagepopulationmovements_v9

Select Zone Analysis External Surveys

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Cell O-D Data Example

Source: Fussel R, Gresham, C. No Horsing Around, A Hole in One with Mobile Phone Data, TRB TPAC Conference, 2015.

Regional Corridor Study

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Through Trips Between Counties Surrounding Moore County

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Takeaways from Cell O-D Studies

Beginning in about 2010, at least 30+

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  • Majority of studies for trip matrices, modelling
  • Cell data acceptable, lower cost option for O-D
  • Total cell derived trips compare well to total model trips,

differences surface when disaggregated

  • Accuracy increases as size of analysis geography

increases

  • Important attributes; larges sample size, basic trip

purposes, residents, visitors, commuters

  • Inability to discern vehicle type or mode
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Takeaways from Cell O-D Studies

Beginning in about 2010, at least 30+

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  • Not well suited for small urban TAZs due to poor

accuracy

  • Uncertainty on how much to aggregate internal TAZs
  • Challenges in configuring external cell capture areas
  • Misses short trips (proportion unknown)
  • Mixed results on trip purpose (research needed)
  • Are commercial vehicles under-represented?
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SLIDE 11

GPS O-D Data Overview

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Types of GPS Data

  • Primary GPS Data

– You collect and own raw GPS data – Resource intensive – Limited sample size – Customized algorithm R&D possible

 Third Party GPS Data (focus of this presentation)

– Obtained from private data providers – Raw data are protected due to privacy – Larger sample size – More economical – Work with data providers for specific customizations

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Third Party GPS Data Providers, Sources

  • INRIX – ‘Insights Trips’ O-D product
  • HERE – ‘Trip Data’ O-D product
  • TomTom – No O-D product, but case-by-case
  • Data continuously collected, compiled; processed for O-D

and anonymity

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Providers Data Sources

  • In-vehicle navigation systems, fleet/freight, mobile

devices, mobile apps, portable navigation devices

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Caveats for Third-Party GPS Data

  • GPS traces are available only when users turn on

navigation session .

  • Non-commercial GPS users may not immediately

activate navigation session

  • GPS data sources can be biased towards specific user

groups.

  • Data providers generally apply anonymization

techniques in time and/or space to ensure privacy

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Data Products from GPS Data Providers

  • O-D table
  • Trip records
  • Trip records with waypoints

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Takeaways from GPS O-D Studies

Recent Studies with New O-D Products

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  • New O-D products in 2015/2016 a major advancement
  • Ability to provide routing between O-Ds using waypoints and/or

Traffic Message Channels (TMCs)

  • Enhanced ability to assess route choices and patterns
  • Ability to analyze travel patterns of different GPS sources/vehicle

categories

  • Low sample penetration, but improving
  • Commercial bias, but data can be segregated by sources so can

segregate

  • Driver habits of using (consumer) GPS major influence on data
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GPS O-D Data Example – Select Link Analysis

17 Source: Paul Morris, SRF Consulting Group, I-94/TH 62 Study, 1/20/16 presentation to Met Council

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Bluetooth O-D Data Overview

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Bluetooth O-D Data

What is it?

  • Wireless technology for exchanging data over

short distances

  • BT embedded in mobile phones, GPS, in-

vehicle navigation systems

  • Each BT device has a unique Media Access

Control (MAC) ID

  • Permanent or portable sensors along roads

detect MAC IDs

  • MAC ID’s matched between sensor locations

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Bluetooth Data

What it represents

  • Expanded to counts when data collected
  • Unlike cell and GPS, does not detect trip ends
  • Not feasible for E-I/I-E trips
  • Caveat – ability to change MAC address, reducing

detection rates

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  • Samples of ‘portions’ of trips

detected by roadside sensors

  • Does not detect trip ends
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Takeaways from Bluetooth O-D Studies

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  • Vetted source for travel movement data for small scale

studies (does not provide true O’s or D’s)

  • Detection rates by study area about 5-10 percent
  • Detection rates by roadway about 3-12 percent

Area Sites

  • Avg. Daily

Count

  • Avg. Daily

Reads Percent Match Tyler, TX (2014) 20 175,343 12,975 7.4% Austin, TX (2013) 14* 2,219,908 118,365 5.3% Omaha, NE (2013) 22 235,526 10,470 4.4% Corpus Christi, TX (2013) 9 226,098 20,059 8.9% Bryan/College Statiton, TX (2011) 13 92,076 7,003 7.6%

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Technology Comparisons

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Comparison of Characteristics by Technology

O-D Data Element Technology Cell GPS Bluetooth Data unit

Cell sighting based on event or network hand-over GPS ping MAC address

Positional accuracy

  • Ave. 300 meters

1-10 meters About a 100 meter range

Data saturation/ penetration

Good, but varies Relatively low Varies from about 3 percent to 10 percent

Sample frequency

Varies widely, in minutes to hours In seconds or minutes In seconds

Continuous data stream?

No, random events Sometimes, but typically pieces of trips captures Yes, but only in about 100 meters range of reader

How trips estimated and defined

Based on activity clusters Trip based on GPS data stream MAC address matches between

  • readers. Trip ends cannot be

determined.

Anonymization

Encrypted to anonymize individual device IDs IDs scrambled and time/distance offsets

  • applied. Actual trip ends

not provided Removal of digits from MAC address, aggregating data prior to O-D table creation

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Comparison of Cell and GPS Data Points

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  • Small green dots

= GPS points

  • Pink circles = cell

device on the move

  • 5 mile stretch of

SH 295 in DC

  • 16 cell data

points

Source: airsagepopulationmovements_v9 powerpoint

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Comparison of Characteristics by Technology

25 Suitability by Geographic Scale Cell Data Third-Party GPS Data Comments External Surveys E-E trips limited  GPS more comparable to Bluetooth. Limited ability to apply E-E travel time constraints with cell. E-I/I-E trips   For large regions cell may be best at this time due to good sample penetration. However, GPS may be better at urban TAZ and smaller geographies. Trip purpose   Cell estimates basic trip purposes based primarily on device’s home and work locations. Research needed here. Residency   Resident vs. non-resident splits needed for some models Commercial/ Freight   GPS can split O-D data into freight and non-freight sources (cars, apps, and freight categories). Route information   GPS can determine route between O-Ds using waypoints and TMCs. Ability to apply travel time constraint   Typically needed to develop E-E trips/matrices.

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Comparison of E-E Trips By Technology

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  • 2014 Tyler External Study Comparing Cell, GPS, and Bluetooth O-D
  • Bluetooth used to estimate E-E movements
  • Bluetooth E-E’s used as benchmark against cell and GPS E-E’s
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Comparison: Cell and GPS Data for Corridor Studies

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Suitability by Geographic Scale Cell Data Third-Party GPS Data Comments Corridor Studies

Within Urban Areas (operational)   Cell not well suited doe to data lack of positional accuracy Within urban areas (planning, select link analysis) limited  GPS has better accuracy and can provide

  • routing. GPS has ability to constrain data to

corridors in urban settings County to county   Cell sample size makes it best for total traffic, plus it can inform on residents, visitors, commuters, etc. However, GPS needed for freight. Depends on study

  • bjectives

Multi-county metro regions   Statewide  

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Comparison: Cell and GPS Data by Time Periods

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Suitability by Geographic Scale Cell Data Third-Party GPS Data Comments Time Period Options

Hourly or Peak Hour   Cell sightings not frequent enough to provide data in hourly

  • increments. GPS sample size may

be low for this short of duration. Peak Period   GPS better suited since it is collected in more frequent time increments. 15 min. bins   Sample frequency and size probably too small to provide cell

  • r GPS data in this time increment
  • Ave. weekday,

weekend, etc.   Either is fine.

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Data Collection/Capture Time Periods

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Bluetooth - 2 weeks Cell - 4 weeks GPS - 3 months

  • Varies by Technology due to sample

penetration

  • Example from Tyler, TX study (below)
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Final Thoughts and Conclusions

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  • Understanding of each O-D technology important in making

decision on what source or sources to use

  • Which to use depends on O-D study type, objectives, budget,

spatial and temporal resolution needed, attributes desired, etc.

  • Combination of technologies and providers is ‘ideal’ approach to

capture all type and categories of O-D, plus extra attributes

  • If one technology will not work, explore possibility of acquiring just

the portions or aspects of data needed from each source

  • New technology O-D data still evolving and will continue to change
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See TMIP Report FHWA-HEP-16-083 for More Details

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http://www.fhwa.dot.gov/ planning/tmip/publication s/other_reports/origin- destination/index.cfm

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Acknowledgments and Special Thanks!

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Bill Knowles Janie Temple Jordan Helwagen Vijay Sivaraman Rick Schuman Ted Trepanier Joe Guthridge Finn Swingley Terri Johnson Paul Morris Paul Czech Sarah Sun

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For more Information:

Ed Hard e-hard@tamu.edu (979)845-8539 Byron Chigoy b-chigoy@ttimail.tamu.edu (512)407-1156 Praprut Songchitruksa praprut@tamu.edu (979)862-3559 Steve Farnsworth s-farnsworth44@tamu.edu (979)862-4927

Thanks for Listening!

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