COMMUTE Atlanta A Comparison of Geocoding Methodologies for Transportation Planning Applications Jennifer Indech Nelson Dr. Randall Guensler Dr. Hainan

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

COMMUTECOMMUTEAtlantaAtlantaTRB Planning Applications Conference - May 2007, Daytona Beach, FL2

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


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


Methods of Obtaining Geocoded Coordinate Data

GPS field surveys (active) Aerial image processing Address matching Road network address interpolation GPS tracking (passive)

Increasedautomation


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

George

Not sure if this slide is necessary, since the original point was how the term "geocoding" was sometimes used interchangeably with "address matching" in literature.


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


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)

hl63

1. Two-day travel diaries -> reported destination daddressed from *** households during March 2004.2. GPS travel data collectors -> *** trips ends collected from the same households during the same time period.3. Tax parcel data -> parcel centroids of 3 counties


Example of GPS Trip Ends

All GPS Trip-Ends in 13-County Region during travel diary survey period

George

Double check "n"... This number corresponds to all trips isolated by Joonhoo for survey period for participating households. Did everybody in the households participate? This number is much greater than ones shown in half-finished presentation


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


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


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


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 +)


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)


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


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


Results – Polygon AssignmentParcel and Blockgroup

Match rates to potential TDM inputs– Parcels, Census Blockgroup


Results – Polygon AssignmentLand Use and TAZ

Match rates to potential TDM inputs– Land Use, Traffic analysis zone (TAZ)


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

19


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

20


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

21


Any Questions?

Please use the Microphone


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)

Documents

COMMUTE Atlanta A Comparison of Geocoding Methodologies for Transportation Planning Applications Jennifer Indech Nelson Dr. Randall Guensler Dr. Hainan