Business Intelligence System for Traffic Data Integration ...onlinepubs.trb.org/onlinepubs/conferences/2014/NATMEC/Tjandra.pdf · Business Intelligence System for Traffic Data Integration:

NATMEC: Improving Traffic Data Collection, Analysis, and Use

June 29– July 2, 2014, Chicago, Illinois

Business Intelligence System for Traffic Data Integration: Linking Roadway, Collision and Traffic Flow Data

to Improve Traffic Safety

Stevanus A. Tjandra, Ph.D. City of Edmonton Office of Traffic Safety



Outline

City of Edmonton Office of

Traffic Safety

Performance Measurement

and Traffic Safety

Problem

Business Intelligence (BI) System for Traffic

Data Integration

Integrated Data

Analysis

BI, KPI and Predictive Analytics



City of Edmonton Office of Traffic Safety (OTS)

Established in 2006 as a result of Mayor Traffic Safety Task Force

World urbanization - UN report 2009:1

Year Urban Population Rural Population

2009 3.4 billion 3.4 billion

2050 6.3 billion 2.9 billion

The 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

urban traffic safety engineering

road user behaviour

speed management and

data, business intelligence and analytics

http://www.edmonton.ca/transportation/traffic-safety.aspx

1World Urbanization Prospects The 2009 Revision http://esa.un.org/unpd/wup/Documents/WUP2009_Highlights_Final.pdf




http://esa.un.org/unpd/wup/Documents/WUP2009_Highlights_Final.pdf



Measuring Performances

Key Result and Performance Indicators (KRIs and KPIs) Strategic Statement

+

Data

Analytics

“You Can’t Manage What You Don’t Measure” “What Gets Measured Gets Done” (Peter Drucker)

KPI Level I and II

Level I

Level II

Traffic Safety: A Multidimensional Problem

Traffic

safety

Road User

Behaviour Regulation

Roadway

Engineering

Nature Vehicle Enforcement

Driving

decision

Speeding

Seat belt

Driving skill/training

Alertness:

alcohol/drug,

fatigue, health

Attention: cell phone, music

Age

Speed limit Geometric

design:

curves,

slope, lane

width, etc.

Lighting

Traffic signs

Non-reflective

paint

Sun glare

Ice,

Rain,

Fog

Animal

crossing

Enforcement

camera:

intersection,

road segment

Community

Program to

promote traffic

safety

Visibility

of

windows

Brake

malfunction

Side-view

mirrors

Road connection that

creates high traffic

variability

Speed bump Gender

Presence of

enforcement

officers

Vehicle safety

performance:

crashworthiness,

aggressivity.

Presence of

passengers

Road physical conditions

Demand vs. capacity: road

congestion, required # road

connections to meet the traffic

growth

Socio-

economic level

Visibility

Index

Distracted

Driving

Law

Seat belt

Education

Speed

display

Check Stop

Dynamic

Message Sign

(DMS)

Weather Impacts on

Traffic Safety:

Trends and Patterns



Repeat Violators



Violations and Collisions

0%

5%

10%

15%

20%

25%

30%

1 2 3 4 5 6 7 8 9 10 11 12+

Number of AE violations % V

eh

icle

s I

nvo

lve

d i

n a

t L

east

1 C

ollis

ion

Edmonton Automated Enforcement and Collision Data: January 1, 2010- December 31, 2011

Topinka, Neil. Project Mercury: Automated Enforcement Data and Driver Behaviour in the Edmonton Capital Region. International

Forum on Traffic Records And Highway Safety Information Systems - Special Session, May 2, 2013. Edmonton, Canada.



Urban Traffic Safety Engineering Road Design Improvement

Yellowhead Trail WB RAMP – Victoria Trail NB



Urban Traffic Safety Engineering Video Analytics

Before After Yellowhead Trail WB RAMP – Victoria Trail NB

The project cost: $437,240

The collision reduction resulted in an annual cost saving of $887,685



Data, Information, and Knowledge 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, and

Lack of access to other data systems

Crime

Modified from the original picture published in

http://blogs.sun.com/bblfish/entry/business_model_for_open_distributed





Traffic Data Coordination Committee (TDCC)

Task Force

Traffic Data

Oversight Committee

Task Force Task Force Task Force

Policy

direction,

strategic

needs

Program

oversight

Recommended operational

needs and actions (e.g.,

standardized format, critical

data, data collection and

analysis procedures)

Recommended

Integrated System and

Five-Year Plan

Policy

direction,

strategic and

tactical needs

Program

oversight

Traffic Data Coordination Committee

University of Alberta



Data Analysis and Business Intelligence – Traffic Data Integration

• Enforcement durations

• Traffic counts • Offence statistics • Issued tickets

statistics • Locations

Spatial

Business

Intelligence

Framework

Environment Canada

Federal Census

Traffic Complaints

System

Edmonton Police

Service

Transit Safety & Security Transit Transportation

Operations

Speed & traffic complaints

• Manned enforcement

• Impaired driving • Crime statistics • Shift schedules

Accidents/incidents

• Bus Stops & routes • Passenger counts

• Road conditions • Roadway maintenance

Weather conditions

Demographic statistics

Spatial Land Inventory

Management (SLIM)

Roadway inventory

Traffic Count Management

(TCM)

• Traffic volume • Turning movement

counts • Speed surveys

Automated Enforcement (Intersection

Safety Cameras and Photo

Radar Cameras)

Motor Vehicle Collision

Information System (MVCIS)

Roadway collisions

Initial Release 2011 WINNER of IBM SMARTER CITY,

one of 24 cities worldwide



Spatial Business Intelligence

Spatial Business Intelligence (SBI) supports traditional and spatial data through merging Geographic Information Systems (GIS) and Business Intelligence (BI) technologies

Reporting

Mapping

Analytics

EFFICIENT

&

EFFECTIVE

DECISION

MAKING

Integration

&Collaboration

Business Intelligence of Traffic Data Integration

Use BI Launchpad in BusinessObjects 4.0 as an Enterprise Report Portal for sharing business intelligence reports

Business Intelligence of Traffic Data Integration

The last traffic

survey: 2011



Benefits of Business Intelligence

(Eckerson, The Data Warehousing Institute, 2003)

Integrated Data Analysis: Inner Ring Road

Empirical Bayes analysis led us to identify

170 St - North of 95 Ave as the worst section

for traffic collisions

From 2009 to 2011 there were:

55% more collisions than other similar

mid-block locations

23 more collisions than expected

West

Edmonton

Mall

*Brandt Denham, City of Edmonton Office of Traffic Safety, 2013

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

Implementation

Integrated Data Analysis: Inner Ring Road



BI, KPI and Predictive Analytics

Predictive Analytics

Short-Term Weather and Collision Prediction



City of Edmonton Weather, Collision and Traffic Flow Prediction

Calendar Data

Month, Day of Week, Holiday

Weather Data

Set-up for Edmonton

Traffic Flow Data

VDS Sites: Speed, Volume

0

2

4

6

8

10

12

14

16

12

-Feb

13

-Feb

14

-Feb

15

-Feb

16

-Feb

17

-Feb

18

-Feb

19

-Feb

20

-Feb

21

-Feb

22

-Feb

23

-Feb

24

-Feb

25

-Feb

26

-Feb

27

-Feb

28

-Feb

1-M

ar

2-M

ar

3-M

ar

4-M

ar

5-M

ar

6-M

ar

7-M

ar

8-M

ar

9-M

ar

10

-Mar

Sno

w F

all A

mo

un

t (cm

)

1-day-earlier Forecast Snow (cm)

Actual Amount Snow (cm)

7-day-earlier Forecast Snow (cm)

Past Future

Weather

Prediction 0

1

2

3

4

5

6

7

8

0

20

40

60

80

100

120

140

1-A

pr

2-A

pr

3-A

pr

4-A

pr

5-A

pr

6-A

pr

7-A

pr

8-A

pr

9-A

pr

Am

ou

nt o

f Pre

cip

itat

ion

(MM

)

Pre

dic

ted

To

tal &

FTC

Co

llisi

on

sPredictedTotal PredictedFTC AmountPrecipitation

60

65

70

75

80

85

90

11

/20

/20

12

11

/22

/20

12

11

/24

/20

12

11

/26

/20

12

11

/28

/20

12

11

/30

/20

12

12

/02

/20

12

12

/04

/20

12

12

/06

/20

12

12

/08

/20

12

12

/10

/20

12

12

/12

/20

12

12

/14

/20

12

12

/16

/20

12

12

/18

/20

12

12

/20

/20

12

12

/22

/20

12

12

/24

/20

12

12

/26

/20

12

12

/28

/20

12

12

/30

/20

12

01

/01

/20

13

01

/03

/20

13

01

/05

/20

13

01

/07

/20

13

01

/09

/20

13

Day

Traffic Speed (km/h, Location: SW VDS Site - WMD & 156 St)

High Speed Alert Warranted

Volume & Speed

Prediction

Collision

Prediction

Future Work

5,665 5,951 6,3276,817 7,151

7,658

6,3815,564 5,873 6,092

5,5134,758

3,991 3,792 3,504 3,389 3,246

17,648

19,128 19,082

20,992 21,000

23,542

22,137

20,606

22,784

26,066

28,52029,072 28,832 28,480

23,442 23,243

24,803

28.2

30.129.4

31.9 31.5

34.8

31.7

29.1

32.0

35.2

38.5 38.6

36.835.9

28.928.4

29.7

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Collisions in Edmonton 1997-2013

Total Collisions

Injury and Fatal Collisions

Collisions per 1,000 Population

The Growing Challenge

Decrease 14,451 injury & fatal collisions (20,795 injuries and fatalities) Save $ 999.8 M



Zero fatal and

serious injury collisions Our Vision:

Zero Fatal and Injury Collisions



Thank You Contact: [email protected]