Traditionally Intelligent Transportation Systems (ITS) data has been collected using a variety of sensors, approaches and techniques. Some of these use simple technologies, offering a narrow view of reasonably robust data; some claim to deliver more data, through complicated technology, linked to equally as intricate processes, but delivering less robust data. If we accept that data, once processed becomes information, which once categorized becomes intelligence, we must also accept that weaknesses or omissions in any part of this chain can substantially affect the quality of the data, information and subsequent intelligence we receive. This presentation looks into different forms of data collection, such as electro-magnetic loops and side-swipe radar, and specifically how camera technologies could be used to collect an unparalleled quality of traffic data.

1. Can valuable ITS databe delivered using machine vision technologies?
www.alliedvision.com

2. •What is ITS
•ITS the past, present, and future
•How does it differ from machine vision?
•The landscape of the future
–What will impact the delivery of visioning technologies
•The availability of data from a camera
•How can that data be used
•To answer the question
Proposed Agenda

3. What is ITS?

4. What is ITS?
Intelligent Transportation Solutions

5. What is ITS? -Traffic
Anything using technology and intelligence to further improve travel, congestion and/or the traveling experience.
•Advanced Traffic Management Systems (ATMS)
•Advanced Traveler Information Systems (ATIS)
•Open Road Tolling
•Lane Control Systems
•Signaling Systems
•Red Light Running
•Speed Enforcement
•Lane Obstruction Detection
•Wrong Way Running
•Weather Detection and Reporting
•Variable Speed Controls
•Automatic Accident Detection
•Integrated Corridor Management
•Connected Vehicle
•Smart Mobility/City
Current Markets
Longer-Term Markets

6. What is ITS? –Other Stuff
Anything using technology and intelligence to further improve travel, congestion and/or the traveling experience.
•Airport and Hub movement analysis
•Ticketing Systems
•Scheduling Systems
•Visual Street Map
•Customer Information Systems
•Vehicle Load Management
•Over Height Vehicle Detection
•Vehicle Back-up Detection
•Accessibility Management
•Autonomous vehicles
•Driverless vehicles
•Drone technology
•Integrated Corridor Management
•Connected Vehicle
•Smart Mobility/City
Current Markets
Longer-Term Markets

7. ITS, the Past, Present and Future

8. The Past, Present and Future
Mesoscopic
Microscopic
Macroscopic
Modelling
Concepts
Real-Time
Predictive
Historic
ITS
Requirements
Information
Intelligence
Data
Process
Modelling
ETL
Big Data
Data Collection
Data
Understanding
1990’s
2000’s
2010’s

9. Understand Current Sensor Capabilities
Traditional Sensor Technology is not working
1.Loops –not robust only, high percentage non-operational –limited data
2.Magnetic pucks –Purported to have a 7yr MTBF
3.Radar –do not identify a particular vehicle
4.Toll tag readers –Identify vehicles –but most roads are not toll roads
Not robust
Does not identify a specific vehicle
Vehicle identification
but
limited penetration
Only available technology to identify every vehicle
5.Blue tooth sniffing –about 10% penetration –many issues with picking up signals from buses, trains traveling in parallel to roads, pedestrians and cyclists
6.GPS Floating Vehicle Data (GFVD) -Expensive –only 4% penetration – Freeways and Arterials –poor data quality during issues and incidents
7.Cellular Floating Vehicle data (CFVD) –Expensive –up to 10% penetration (during normal hours) –cannot differentiate a specific road within high network environments, such as a freeway and frontage road
8.Cameras –Can provide Speed, Occupancy, Volume, Classification and a host of other services.

10. `
•Non-Binary Data Delivery
•Occupancy
•Volume
•Can deduce flow
•Counts every vehicle
•Speed
•Not robust
•Maintenance issues
•60% functional

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GFVD –GPS Floating Vehicle Data

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GFVD –GPS Floating Vehicle Data

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•Rich data availability
•Speed
•Volume
•Can Clearly identify flow
•Can identify Prediction
•Statistically representative?
•Most accurate at peak flow
•Arterial Assessment?
GFVD –GPS Floating Vehicle Data

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CFVD –Cellular Floating Vehicle Data
•Rich data availability
•Speed
•Volume
•Can Clearly identify flow
•Can identify Prediction
•Phones don’t have to be on
•Statistically representative?
•Most accurate at peak flow
•Arterial Assessment?

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ALPR –Automatic License Plate Recognition

16. How is Machine Vision different from ITS?

17. So what does ITS Need Specifically?
•IP Platform
•Compression H.264, Mpeg4, MJPEG
•Firmware –Pixel correction, color correction etc.
•Linux and/or Windows?
•Sensor requirements –HDR, Fast Shutter, 30FPS, Low Lux, Mpixels?
•Integrated communications options
•Extended Temperature Range -40 -+ 65C
•IR lighting arrays

18. ITS Summary
ITS is a very different market, requiring very different product philosophies.
It requires
•Different hardware
•Different software
•Different applications

19. The Landscape of the Future

20. Congestion must be managed
•Peaked in 2006
•Peak hours start earlier in 2007
•Congestion now surpassed 2006 Conditions
•Currently Trend Starting Earlier and Finishing later
•Trend cannot continue

21. Understanding the Market Drivers
•MAP-21 –focusing on quantified improvement
–Travel time reliability
–Average speed by time of day
–Average volume by time of day
–Incident resolution and management
•More cameras being used in open road tolling systems than ever before
•More cameras being used in safety applications than ever before
•The World is suffering from congestion meltdown forcing organizations to invest in:
–Congesting charging
–Integrated corridor management
–HOT/HOV lanes
–Average speed
–Freight user charging
–Open road tolling
These all require MV / imaging technology to be successful

22. Drivers for Change
•Economic downturn has cancelled many capital road-building projects
•Dawning realization that additional lanes of freeways are counter productive
•Acceptance that congestion must be actively managed
•Acceptance that the entire transportation “network” must be actively managed
•ANPR becoming mainstream and license costs dropping
•Already ITS cameras used within a multi-role environments
•Congestion Mitigation Strategies
–Integrated Corridor Management
–Smart Transportation
–Smart City

23. The Road to Improvement

24. •Infrastructure Data
–Speed
–Volume
–Occupancy
•Systems Data
•Imputed Data
•Environmental Data
•Weather Data
•GIS Data
Real-time
Long-term
Historic
Short-term
Historic
Pseudo -Prediction
Providing
Actionable Intelligent
Layer
Current ITS Building Blocks

25. Current ITS Process Flow
Process
Raw Data
Systems
Information
Intelligence
•Raw Data –ITS Infrastructure
•Process –Cleanse, Verify, Validate
•Systems –Ingested ATMS
•Information –Real-time operator entered info
•Intelligence –Actionable
–Prediction
–Decision Support
–Automated Systems
•DMS Signs
•Variable Speed Controllers
•Variable Traffic Light Phasing

26. Current ITS Process Flow
Systems
Information
Intelligence
•Integrating Clever Software
–Speed
–Occupancy
–Volume
–Classification
–Wrong-Way Running
–Pedestrians on Road
–Cyclists on Road
–Debris on Road
–Over height Vehicles
–Hazardous Materials

27. In Conclusion

28. Can Valuable Data be delivered using Cameras?
•Without any doubt.
•Not only can it, but I believe it will become the defactostandard for all ITS data collection in any country enabling ANPR
•With the integration of ANPR and camera technologies there will be:
–More data
–Better quality data
–More comprehensive data
–More data availability
•Providing:
–The opportunity of a microscopic model
–The opportunity of valuable predictive services
•And Thereafter
–The opportunity to truly manage our roads efficiently
–The opportunity to better manage congestion
–The opportunity to fully identify where capital road building projects are absolutely essential

29. Jorgen Pedersen
Business Development Manager (ITS)
Allied Vision Jorgen.Pedersen@AlliedVision.com
+1 978 394 0563
Thank you all for listening
Any Questions?