CONNECT. TRANSFORM. AUTOMATE.

Road Safety Data Integration Using FMEBrandt Denham, B.Sc.Collision Data Supervisor / Spatial AnalystCity of Edmonton – Office of Traffic Safety

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.

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

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

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

Collisions

Traffic Signals

Roadway

Modified from the original picture published in http://blogs.sun.com/bblfish/entry/business_model_for_open_distributed

Traffic Surveys

The Goal: Complete Data Integration

QueryQueryCollisionsCollisions

AE ViolationsAE Violations

NeighborhoodsNeighborhoods

Police DivisionsPolice Divisions

Traffic SurveysTraffic Surveys

Traffic SignalsTraffic Signals

Speed LimitsSpeed Limits

Road NetworkRoad Network

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

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

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 #

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

172 Ave

172

Ave

Same Name

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

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

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

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!

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

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

99

St

Base Layer Intersection(ID# 5457)

Traffic Signal

Traffic Survey Device

Step 3 – Spatial Linking

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

2nd from Curb50%

3rd from Curb8%

Right Curb25%

Unknown17%

Collisions by 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 Road2%Struck Parked

Veh2%

Collisions by 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

Collisions by 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%

Violator Registered Owner Postal Code

Within Edmonton

Outside of Edmonton

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

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:

0Injuries and Fatalities

Thank You!

Questions?

For more information: Brandt Denham (brandt.denham@edmonton.ca) City of Edmonton – Office of Traffic Safety (780)-495-9905