Presented by Brandt Denham, City of Edmonton ‐ Office of Traffic Safety

1. CONNECT. TRANSFORM. AUTOMATE.
Road Safety Data Integration
Using FME
Brandt Denham, B.Sc.
Collision Data Supervisor / Spatial Analyst
City of Edmonton – Office of Traffic Safety

2. Introduction to the OTS
• The Office of Traffic Safety was established in 2006
• Supports national and provincial traffic safety targets
to help achieve reductions in traffic collisions and
make streets safer for drivers and pedestrians
• OTS will reduce the prevalence of fatal, injury, and
property damage collisions through the 4 E‟s of traffic
safety (engineering, education, enforcement, and
evaluation) by improving data analysis and
business intelligence, speed management, urban
traffic safety engineering and road user behavior.

3. The Data
• The OTS has a vast amount of transportation and
traffic safety related data at its disposal
• This data is stored in many different places, with
many different owners in many different formats
• The ability to extract relevant, meaningful and
accurate information in a timely manner is a MAJOR
challenge

4. The Data
 Collisions
 Automated Enforcement
 Red light violations
 Speed violations
 Neighborhoods
 Police divisions
 Traffic Surveys
 Traffic volume & speeds
 Traffic Signals
 Speed Limits
 Road Network
 Road geometry
 Functional class

5. The Problem – Data Silos
 Inadequate knowledge about the
existence of various data and their
availability
 Lack of linkages with other databases
resulting in duplicate data collection,
processing and management
 No standardized method for the
specific identification of attributes
across data sources
 Lack of communication among
stakeholders of important changes to
the data
 Lack of access to other data systems
Modified from the original picture published in
http://blogs.sun.com/bblfish/entry/business_model_for_open_distr
ibuted

6. The Goal: Complete Data Integration
QueryCollisions
AE Violations
Neighborhoods
Police Divisions
Traffic Surveys
Traffic Signals
Speed Limits
Road Network

7. Data Integration
• Data integration is achieved in 3 high level steps:
Step 1 – Create common geographic base layers
Step 2 – Clean and format datasets
Step 3 – Spatial Linking

8. Step 1 – Create Base Layers
• All datasets need a common geographic link, I refer these as
„Base Layers‟
• All OTS datasets are either located at an intersection or
somewhere along a roadway segment or mid-block
• For the purpose OTS data, two base layers are needed
• Intersection base layer
• Mid-block base layer

9. Step 1 – Create Base Layers
• Unique reference points for
intersections are created
• Relatively easy using „Intersector‟
transformer
• Unique road segment lines are
created for mid-blocks
• A lot of simplification of the road
network must be done
• Each point or line has a unique ID #

10. Step 1 – Problem Example
Problem:
 Cul-De-Sac roads with the exact
same name as the main road they
branch off of
Why is it a problem?
 Two roads with the same name
 Creates an unwanted intersection
Solution?
 These Cul-De-Sac roads need to be
removed
Same Name

11. Step 1 – Problem Solution Example
Selects Cul-De-
Sacs from the
Road Network
Finds the
neighboring
streets around
each Cul-De-
Sac
Tests if any
neighboring
streets have
the same name
Snap roads
back together
after Cul-De-
Sacs removed
Re-Merge
roads with the
same name

12. Step 2 – Clean/Format Datasets
• Datasets come from various sources in various formats
• In order to integrate, all datasets must:
• Be spatially referenced
• May require geo-coding if spatial reference is missing
• Be consistently formatted

13. Step 2 – Geocoding Problem Example
• Collision data from EPS does not come with a spatial
reference, only a text location description
• Ex) “Near McDonalds on 23 Ave”
• Data entry staff translate the location into an intersection or
mid-block when entering the information into the OTS
collision database
• Spatial reference still needs to be added
• From the inception of OTS in 2006 until 2013, this was done
manually, adding points one-by-one

14. Step 2 – Geocoding Solution Example
• The base layers from Step 1 can
be used to automate the manual
geo-coding process
 This spawned another major
„Automatic Geocoding‟ FME
project that was created to do
exactly that
• Thousands of hours of time and
money are saved
• Human error is eliminated
Over 37,000 points had been created
manually. On average it takes 5 mins to
enter one point. 37,000 x 5 mins… you get
the point!

15. Step 3 – Spatial Linking
• Once you have achieved clean base layers and clean
datasets, you can link the datasets to the base layers
• By spatially linking each dataset to the base layers, each
dataset can be given the unique base layer IDs which can
then be used to link one dataset to another

16. Step 3 – Spatial Linking Example
• In this example, two datasets have varying
spatial accuracy but should be associated
with the intersection of 100 Ave & 99 St
• A „NeighborFinder‟ transformer can find the
nearest base layer intersection to each
dataset (you can also specify a max search
distance)
• They can then be moved to match the
spatial location of the base layer and
both can gain the ID# attribute of the
base layer
• After this is done, you can then link the
Traffic Survey dataset with the Traffic
Signal dataset based on the ID# from the
Base Layer without actually needing the
Base Layer
100 Ave
99St
Base Layer
Intersection
(ID# 5457)
Traffic Signal
Traffic Survey
Device

17. Step 3 – Spatial Linking

18. Utilizing the Results
• When all datasets are linked and accessible, we can turn the
data into information and the information into knowledge
• The following example shows how integrated data was used
to get a „full picture‟ of data to do a comprehensive analysis
of a particular problem location in Edmonton

19. 2nd from Curb
50%
3rd from Curb
8%
Right Curb
25%
Unknown
17%
Collisionsby Driving Lane (2012)
 2012 data has less unknown
traffic lanes so it may be a
more accurate breakdown of
the collisions by lane
 The 2nd from curb lane is lane
#3 (The right curb lane is not
a through lane)
Chng. Lanes
Impr.
18% Fld. Yield
R.O.W.
6%
Flwd. Too
Closely
72%
Ran Off Road
2%Struck Parked
Veh
2%
Collisionsby Cause (09-11)
1 2 3
Study
area
 The top 5 violators are all
rental and cab companies.
0
5
10
15
20
25
Mon Tue Wed Thur Fri Sat Sun
Collisionsby Day of Week (09-11)
 Peak collision periods:
 Nov-Dec Christmas shopping
 Fri-Sat weekend shopping
 Mid afternoon shopping
23%
57%
20%
Average Monthly
Speed Tickets Issued
Lane 3 (67)
Lane 1
(77)
Lane 2
(195)
24%
40%
36%
Average Monthly
Red Light Tickets Issued
Lane 3
(10)
Lane 1 (6)
Lane 2
(11)
41.26%
58.74%
ViolatorRegistered Owner Postal Code
Within
Edmonton
Outside of
Edmonton

20. Conclusion
 Integrated data builds a foundation for business intelligence
 We can‟t manage what we can‟t track
 FME supplies the tools to take datasets in any format and make them
consistent and linkable
 The processes created in FME are repeatable and can be used to automate
regular maintenance of integrated data
 As an evidence-based organization, integrated traffic safety related data
helps OTS and the City of Edmonton to make efficient and effective
operational and strategic decisions

21. Mission of OTS
The City of Edmonton Office of Traffic Safety will
reduce the prevalence of fatal, injury, and property damage collisions
through the 4 E’s of traffic safety (engineering, education,
enforcement, and evaluation) by improving data analysis and
business intelligence, speed management, urban traffic safety
engineering and road user behaviour
OTS Vision:
0
Injuries and Fatalities

22. Thank You!
 Questions?
 For more information:
 Brandt Denham (brandt.denham@edmonton.ca)
 City of Edmonton – Office of Traffic Safety
 (780)-495-9905