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Can valuable ITS data be delivered using camera technologies?

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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.

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Can valuable ITS data be delivered using camera technologies?

  1. 1. Can valuable ITS databe delivered using machine vision technologies? www.alliedvision.com
  2. 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. 3. What is ITS?
  4. 4. What is ITS? Intelligent Transportation Solutions
  5. 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. 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. 7. ITS, the Past, Present and Future
  8. 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. 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. 10. ` •Non-Binary Data Delivery •Occupancy •Volume •Can deduce flow •Counts every vehicle •Speed •Not robust •Maintenance issues •60% functional
  11. 11. 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m 11:20A 11:30A 11:40A 11:50A 12:00P 12:10P 12:20P 12:30P 12:40P 12:50P 1:00P GFVD –GPS Floating Vehicle Data
  12. 12. 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m 11:20A 11:30A 11:40A 11:50A 12:00P 12:10P 12:20P 12:30P 12:40P 12:50P 1:00P 2.0 GFVD –GPS Floating Vehicle Data
  13. 13. 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m 11:20A 11:30A 11:40A 11:50A 12:00P 12:10P 12:20P 12:30P 12:40P 12:50P 1:00P •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
  14. 14. 0.5m 1.0m 1.5m 2.0m 2.5m 3.0m 3.5m 11:20A 11:30A 11:40A 11:50A 12:00P 12:10P 12:20P 12:30P 12:40P 12:50P 1:00P 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?
  15. 15. MVF786 MVT176 MD T176KY -TX ACESS1 -VA T1876 -VA HFT12 -VA MVF786 MVT176 MD T176KY -TX ACESS1 VA T1876 VA HFT12 POP8 -VA K16754 VA MVF786 MVT176 MD T176KY -TX ACESS1 VA T1876 -VA HFT12 POP8 -VA MVT176 MD T176KY TX ACESS1 VA T1876 -VA HFT12 POP8 -VA K16754 VA WQ786 MD T176KY -TX ACESS1 -VA T1876 -VA HFT12 POP8 K16754 -VA WQ786 -TN ACESS1 VA T1876 HFT12 POP8 K16754 -WQ786 -MD LU123Y -TN LU123Y MD T1876 -VA HFT12 -VA POP8 -VA K16754 WQ786 -MD LU123Y TN PP453Y -MD FJ897Y -CA ALPR –Automatic License Plate Recognition
  16. 16. How is Machine Vision different from ITS?
  17. 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. 18. ITS Summary ITS is a very different market, requiring very different product philosophies. It requires •Different hardware •Different software •Different applications
  19. 19. The Landscape of the Future
  20. 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. 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. 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. 23. The Road to Improvement
  24. 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. 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. 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. 27. In Conclusion
  28. 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. 29. Jorgen Pedersen Business Development Manager (ITS) Allied Vision Jorgen.Pedersen@AlliedVision.com +1 978 394 0563 Thank you all for listening Any Questions?

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