LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Dohyung Kim
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Transcript of LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Dohyung Kim
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What if Crash Data Does Not Mean for Mapping: Lesson Learned from Crash
Mapping for Riverside County
Do Kim, Ph.D.Assistant ProfessorDepartment of Urban and Regional PlanningCalifornia State Polytechnic University - Pomona
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Project Background
• Improvement of bicyclists and pedestrians safety in Riverside County– Finding physical environment factors to bicyclists
and pedestrian crashes– Funded by Leonard Transportation Center
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Crash Data
• Crash data is important data for measuring safety on highways, but local governments does not often utilize this data.
• The main reason for the under-usage is the difficulty and inefficiency of the current crash mapping system.
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Crash Data Flow
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Crash Mapping• Converting test or tabular data to spatial data
that locates crashes on a roadway map
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Riverside County Crash Data Analysis• Collected from California Statewide Integrated
Traffic Records System (SWITRS)• 5 year of pedestrian and bicycle crashes (2004
– 2008)• Total 4,769 crashes were reported during the
period (2,230 bicycle and 2,539 pedestrian crashes)
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Automatic Mapping Using Geocoding
• ArcGIS Geocoding engine is the most well-known address matching function.
• However, it only matched 1,107 out of 4,769 (23%) crashes after intensive data cleaning and pre-processing.
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Main Issue with Geocoding
• Geocoding engine identifies the locations of property addresses and intersections.
• However, the large portion of location information of crash data is certain distance and direction from intersections
W 500
E 300
S 1000
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Matching with Customized Application• The application moves crash records from
intersections by given distance and direction.
Crash Record = 500 ft South from University Ave. & 1st St.
500 ft.
Unive
rsity
Ave
.
1 st St.
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Results with Customized Application• Matched 2,094 records more (44%)
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Manual Matching• Most time consuming and labor intensive works• Need to review the location information of each
individual record one by one using the customized application
• Systemic conflicts + Human errors
Systemic ConflictsHuman Errors
Manual Matching
State road name vs. Local name
Multiple Candidate
Total 1,568
(100%) 629
(40%)159
(10%) 780
(50%)
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State Road Names vs. Local Names
• Police officers collect state road numbers, but the street names of roadway network are local names.
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State Road vs. Local Name Resolution
• A street alias table can resolve this issue.• 629 records (13%) belong to this category.
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Multiple Candidate Issue
• Multiple possibilities of a matching point• ArcGIS Geocoding use zip codes for zonal
details, crash records does not have the codes
Crash Record = ORANGE ST & 10TH ST
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Multiple Candidate Resolution
• Screening with city boundaries• 159 (3%) crashes
ORANGE ST & 10TH ST at city of Riverside
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Human Errors on Data Collection
• Incomplete information– University Ave & 1st (St) – (W) Palo BLVD & Main St
• Redundant Information– Chicago Ave & 1981 Chicago Ave
• Others– Misspelled street names– Using place names instead of street names (e.g.
Gateway Plaza)– And so on…
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Human Error Resolution• Review each individual record one by one and
correct if mistakes are identified• 587 records (12%) matched
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Unmatchable Crashes
• Irresolvable humane errors
Crash Record = CYPRESS AVE & PHILBIN AVE
CYPRESS AVE
PHILBIN AVE
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Impacts of The Errors
Crash Record = GRAND AVE & 4TH ST
W. G
RAN
D AV
EE. G
RAND AVE
E. 4th STW. 4th ST
• Possibly change the crash hotspots by excluding crashes at particular locations from mapping
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Incremental Resolutions
• Reduce human errors by educating police officers and data entry persons
• Construct better quality of roadway network data
• Develop street alias tables• Adopt crash mapping software
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MN DOT Case
• Minnesota Crash Mapping Analysis Tool (MnCMAT)– Crash mapping and analysis software covering entire
state
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FL DOT Case
• Web-based State Crash Record System– Police officers pinpoint
crash locations on a map that displays an aerial photograph of the area pulled up directly from the sever, much like systems such as Google Maps or Yahoo Maps.
X X