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Road Safety Data Integration using FME


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

Published in: Technology, Business
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Road Safety Data Integration using FME

  1. 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. 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. 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. 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. 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 ibuted
  6. 6. The Goal: Complete Data Integration QueryCollisions AE Violations Neighborhoods Police Divisions Traffic Surveys Traffic Signals Speed Limits Road Network
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. Step 3 – Spatial Linking
  18. 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. 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. 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. 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. 22. Thank You!  Questions?  For more information:  Brandt Denham (  City of Edmonton – Office of Traffic Safety  (780)-495-9905