COMMUTECOMMUTEAtlantaAtlanta
A Comparison of Geocoding Methodologies for Transportation Planning Applications
Jennifer Indech Nelson Dr. Randall Guensler
Dr. Hainan Li
Georgia Institute of Technology
May 9th, 2007
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Agenda
Purpose… Background Process
– Acquisition of data– QAQC– Final data set
Analysis– Positional Accuracy– Polygon Assignment
Discussion
…Assess the accuracy of various geocoding methods to provide insight on field data collection, calibration of travel demand model inputs, and automation of travel behavior analysis
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Geocoding and How It Is Used in Transportation Planning
“Geocoding” - Generation of coordinates within a spatial geographic framework, where single points serve as proxies for places
Used to:– Prepare TAZ data from travel diary studies for Travel
Demand Model development– Better represent spatial travel patterns– Verify 4-step model components – Provide primary input to next generation behavior-
based micro-simulation Travel Demand Models
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Methods of Obtaining Geocoded Coordinate Data
GPS field surveys (active) Aerial image processing Address matching Road network address interpolation GPS tracking (passive)
Increasedautomation
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Geocoding: Address Matching Vs. Interpolation
Assign coordinates
1:1 - Check address
existence / integrity from list
inc. other attributes
Estimate position from spatial reference
(network link)
AddressInterpolation
Address Matching
Linear Address
interpolation
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GPS and GIS Data Acquisition in Transportation – Commute Atlanta
Commute Atlanta study– GPS-instrumented vehicle tracking– 3+ years, second-by-second– 487 vehicles, 268 households– 1.8 million trips
GT Server
CellularNetwork
GPS Satellite
In-vehicle Event Data Recorder
Profile Data
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Data for Comparative Analysis
Two days of parallel data in March 2004 from 137 HH’s– Travel diary self-reported locations– GPS recorded trip files
Parcel-level geographic reference– GIS shapefiles generated by MPO and individual
counties (Fulton and Gwinnett Counties)
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Example of GPS Trip Ends
All GPS Trip-Ends in 13-County Region during travel diary survey period
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Final Data Format
Each location record has three associated coordinates– GPS trip-end point– Parcel centroid– Interpolated location (street network)
Characteristics– Unique ID– Area– Land use– TAZ 40’
Centroid
GPSGeocode w/ offset
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Data Quality Issues – GPS/Diaries
Travel diaries versus GPS trip-ends– Under-reporting of visited
locations in travel diaries GPS wander
– Dependent on weather, satellite, and hardware conditions
– Primarily occurs at < 5 mph– Data point is last GPS
coordinate at engine-off
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Data Quality Issues – Reference
GIS parcel boundaries and centroids– Not all parcels have existent or
correct address data– Topology errors may lead to
inaccurate centroid calculation Road network geocoding
– Uses national database generated by NavTeq and TeleAtlas, may not have current/correct address ranges
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The Incredible Shrinking Data Set
Fulton: 195 locations, 119 unique Gwinnett: 129 locations, 75 unique
Vehicle trips taken by survey participants (post-validity check) 2292 100.0%
Vehicle trips recorded by address in diary 1622 70.8%
Vehicle trips matched (diary to GPS, manually by timestamp) 1585 69.2%
Diary/GPS locations corresponding to available county GIS references 700 100.0 %
Diary/GPS locations with ‘correct’ address data (two counties) 541 77.3 %
Diary/GPS locations geocoded using nat’l road network database 541 77.3 %
Diary/GPS locations matched to parcels 369 52.7 %
Further QAQC based on trip-end distance and standard deviation yields… (note: miscoded GPS trips, should have been screened out earlier) 324 46.2%
Data Source n % of set
Two-county subset
Metro Atlanta (13 counties +)
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Analysis – Positional Accuracy
Complete (3-source) data only: 324 points (194 unique) – 195 Fulton, 129 Gwinnett
Compare– GPS trip-end data with parcel centroids– Interpolated addresses with parcel centroids– GPS trip-end data with interpolated addresses
Further comparison according to– Land use– Parcel size (e.g. < 5 acres, >= 5 acres)
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Positional Accuracy – GPS vs Geocode
GPS significantly more accurate than geocoding
– Combined: 273’ vs 402’ (Single-family) residential
locations more accurate than non-residential parcels
Smaller parcels more likely than larger parcels to have better positional accuracy for all methods
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Positional Accuracy – Land Use / Size
GPS to centroid accuracy has some correlation to parcel size, but land use and typical parking location are probably more important
Within particular land uses, inverse relationship of accuracy to area
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Results – Polygon AssignmentParcel and Blockgroup
Match rates to potential TDM inputs– Parcels, Census Blockgroup
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Results – Polygon AssignmentLand Use and TAZ
Match rates to potential TDM inputs– Land Use, Traffic analysis zone (TAZ)
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Polygon Assigment Rate – TAZ
Non-residential locations especially prone to mis-assignment
Interpolation GPSComm < 5 acres (n = 49) 83.7% 91.8%Comm >= 5 acres (n = 38) 60.5% 97.4%Industrial (n=16) 100.0% 100.0%Office/Inst >= 5 acres (n=69) 63.6% 72.7%Office/Inst < 5 acres (n=33) 77.8% 72.2%Residential (n=148) 100.0% 98.6%Park / Recreation (n=4) 75.0% 75.0%TOTAL (n = 324) 86.4% 91.7%
COMMUTECOMMUTEAtlantaAtlantaTRB Planning Applications Conference - May 2007, Daytona Beach, FL
Discussion
Reference Data– Must be accurate and standardized!
Positional Accuracy– Method of creating geocoded data depends on degree
of accuracy needed Most to least accurate (<10 ft to >1000 away):
Address matching, GPS, interpolation– Off-site parking creates issues for passive
determination of trip purpose from GPS data
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Discussion
Polygon Assignment– TAZ “hit rate” lower than expected, particularly for
non-residential locations– Degree of zoning homogeneity and size of parcels are
directly proportional to chance of matching “correct” land use for TDM verification
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COMMUTECOMMUTEAtlantaAtlantaTRB Planning Applications Conference - May 2007, Daytona Beach, FL
Next Steps
Assess method of GPS tracking and data gathering– Quantify error associated trip-ends
Determine how to evaluate large parcels / campuses– Internal destinations, land uses
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Any Questions?
Please use the Microphone
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Appendix: Sources and Additional Figures
All figures created by Commute Atlanta researchers, except spatial interpolation picture (slide 5 from “Three Standard Geocoding Methods” – Dramowicz, 2004) and Google Earth imagery (slides10 and 21)
Right: GPS position off due to urban canyon (tall buildings in Midtown Atlanta)