It is of interest to explore the spatial correlation between traffic collisions and other types of available data, for example demographic and economic information, land-use, license premises and weather. To effectively prepare the data for further analysis, the City of Edmonton Office of Traffic Safety used a series of extract-transform and load (ETL) tools provided by FME. This presentation will introduce three key ETL processes performed with FME: 1) manipulate roadway GIS data for network-based spatial analysis; 2) build spatial connection between various sources of data; 3) effectively prepare the proper format of spatial data to establish advanced spatial statistics.

1. Using FME to Manipulate Various
Sources of Data for Traffic Collision
Spatio-temporal Correlation Analysis
Ying (Ariel) Luo, M.Sc., E.I.T.
Traffic Safety Spatial Analyst
City of Edmonton, Office of Traffic Safety

3. City of Edmonton Office of Traffic Safety
• Established in 2006, first municipal office of traffic safety
in North America
• Five “E”s traffic safety strategy: Engineering, Enforcement,
Education, Evaluation and Engagement
• Focusing on traffic safety engineering, automated
enforcement and speed management, data analytics and
road user behavior studies

4. OTS Ultimate Goal:OTS Ultimate Goal:
00
Fatal and Injury Collisions in EdmontonFatal and Injury Collisions in Edmonton

5. Outline
 Project background
 Network-based traffic safety spatial analysis
 Objectives
 Challenges
 Spatial Data Extract-Transform and Load (ETL)
using FME

6. Project background
 Ran-off-road is the most severe collision cause in
the City of Edmonton

7. Project background

8. Project background

9. Network Kernel Density Estimation
 Identify hot spots along the roadway network
Density of seg. s
(#collisions/km)
Summation of segments
within search bandwidth
Search
bandwidth
distance between collision i to
end node of segment s

10. Network-based spatial analysis
Collisions are network
constrained events!
City of Edmonton ROR Collisions Hot Spot Map (Grid-based)
Any distance
related analysis
will need network
distance, instead
of Euclidean
distance

11. Network Kernel Density Estimation
 Identify hot spots along the roadway network
Density of seg. s
(#collisions/km)
Summation of segments
within search bandwidth
Search
bandwidth
distance between collision i to
end node of segment s

12. Step 1: Build Network Topology
 Segmentation: custom transformers
 Build network connectivity: some of the polylines
are drawn in opposite direction/ two-way
segments are drawn in single line

13. Step 2: snap collision points to
unit segments
 Challenge- Snap to the
correct direction

14. Step 3: Estimate density for
each unit segment
 For each unit segment, find
collisions within search bandwidth;
 Calculate K function for individual
collision to the analysis segment;
 Get segment kernel density.
Over 14,000Over 14,000
Over 6,000Over 6,000

15. City of Edmonton ROR Collisions Hot
Spot Map (2009-2014,network-based)
(Grid-based hot spot map)

16. Location specific treatment

17. Next step- spatial correlation
analysis
 Spatial components
 Alcohol outlet locations
 Land use pattern
 Infrastructure information (e.g. traffic control)
 Enforcement activities
 Socio-ecomonic factors
 Non-spatial components
 Weather, geometry, traffic volume etc.

18. Spatial Correlation Example
(qualitative visualization)
Weekend Late-night
ROR Density vs.
Alcohol Outlets
Density

19. Thank You!
 Questions?
 For more information:
 Ying (Ariel) Luo: ariel.luo@edmonton.ca
 City of Edmonton, Office of Traffic Safety
 http://www.edmonton.ca/transportation/traffic-
safety.aspx