H4 David Verma Beca

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Putting the Intelligence into Intelligent Transportation Systems

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H4 David Verma Beca

  1. 1. Putting the Intelligence into Intelligent Tra nsporta tion Systems David Verma, Senior Transportation Specialist (Intelligent Transportation Systems) Beca Infrastructure Ltd (Auckland)
  2. 2. WHERE THE REAL INTELLIGENCE RESIDES IN AN INTELLIGENT TRANSPORTATION SYSTEM A system is a combination of people/process and technology. Most Intelligent Transportation Systems are components in a wider decision support framework. Intelligence is typically vested in Operators who use outputs from ITS based technologies in order to make better decisions. Decision Support is therefore the real aim of ATMS systems and therefore the area to invest more Smarts in.
  3. 3. WHERE THE REAL INTELLIGENCE RESIDES IN AN INTELLIGENT TRANSPORTATION SYSTEM ATMS View Graphical User Interface Operators Typical ATMS Structure Application Layer - response rules Provide the Intelligence Database And Algorithm Layer Proprietary Device Drivers & Protocols Communications Layer Road Side ITS Devices
  4. 4. WHERE THE REAL INTELLIGENCE RESIDES IN AN INTELLIGENT TRANSPORTATION SYSTEM Decision Support View Graphical User Interface Operators Act on Predictive Reactive Intelligence Business Device Situation Situation Logic Control Manager Manager Common Communications Layer + Protocols Data Repositories Incident Processing Logic Fault Management Logic Motorway Management SNMP Open MIBS Physical Communications Layer Road Side ITS Devices
  5. 5. THE ROLE OF BUSINESS INTELLIGENCE IN DEFINING ROLES AND FUNCTIONS Business analysis is required to obtain the right approach to developing an ITS functional architecture. Business analysis for operational ITS systems should focus on the role of the Operators and the inter-related sets of functions that the Roading Authority requires of them. Non functional as well as functional requirements must be captured.
  6. 6. THE ROLE OF BUSINESS INTELLIGENCE IN DEFINING ROLES AND FUNCTIONS
  7. 7. THE ROLE OF NEURAL SYSTEMS INTERCONNECTIVITY PRINCIPLES TO SUPPORT DECISION MAKING SYSTEMS DESIGN Learning systems require the ability to make dynamic interconnections between relevant underlying components and then enact relevant actions. Intelligent decision support systems must be able to learn from previous events to ensure that procedures are enacted automatically and faster the next time they are required. Software components need to be modular and designed to expose connectors that allow them to establish many to many relationships and execute dynamic responses and actions.
  8. 8. The use of Neura l Processing in a Decision Support System Graphical User Interface Operators Act on Predictive Reactive Intelligence Business Device Situation Situation Logic Control Manager Manager Common Communications Layer + Protocols Data Repositories Incident Processing Logic Fault Management Logic Motorway Management SNMP Open MIBS Physical Communications Layer Road Side ITS Devices
  9. 9. CONCLUSIONS The future for more Intelligent Transportation Systems lies in enabling technologies that will allow operators to focus on priority issues whilst business as usual activities become increasingly fully automated. Decision making from a network management point of view is also likely to become nearly fully automated. Decision support systems and ATMS software should be based around commonly available off the shelf operating systems.

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