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Carlos canudas

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  • 1. ICT in Intelligent Transportation Systems:  real‐time traffic forecasting and control Carlos Canudas de Wit NeCS Joint INRIA/CNRS Team DR‐CNRS Control System Department 21 June 2010, Jouy-en-Josas GIPSA‐Lab Grenoble France Information flow: a holistic view Traffic Forecasting & Control  http://necs.inrialpes.fr Impacts & benefits carlos.canudas‐de‐wit@gipsa‐lab.inpg.fr UMR 5216
  • 2. Information: uses and abuses Collection Transport Processing Serving Real‐time Information (ICT) flow P/2
  • 3. Information collection: senses &  aggregates real‐time information   Collection Transport Processing Serving Era of new sensor Technologies is  at place: • Wireless,  • Heterogeneous,  • Richness, • Mobile  P/3
  • 4. Transporting Information; makes the  information flow from sensors to system Collection Transport Processing Serving New communication Technologies will open  opportunities: • Vehicle‐to‐Vehicle communications,  • Vehicle‐to‐Infrastructure,  • Infrastructure‐to‐Vehicles, • Information to users P/4
  • 5. Processing Information: brings add  value at the brut information Collection Transport Processing Serving Ramp meeting control (EURAMP source) Variable speed control (Mail online source) Ramp metering control: Variable velocity control: • Products already in use are not  • Under investigation,  optimal,  • Relay on “Soft” actuators (drivers), • Decentralized, • High potentially  • Room for a lot of improvements  P/5
  • 6. Information serving: services to users Collection Transport Processing Serving The results of the processed information is   transformed into user services: • Desktop applications,  • Mobile phones,  • On‐board navigation devices, • Traffic control centers P/6
  • 7. Market evolution: in Advanced Traffic  management Systems (ATMS) Total value of the European  ATMS market (in M€) A clear grown & opportunities in: • ATMS • Sensors, Signal & systems Total interurban advanced traffic  • Infrastructure & communications management market 2004‐2015.  • Services & business   Source Frost & Sullivan P/7
  • 8. GTL is a WSN data collection platform for real-time traffic modeling, prediction and control Show room Data Base Model-based control NeCS Research in model estimation & Control M2M network M2M network 4 sensors per line each 400 m Public Data DIR-CE Micro‐Simulator • A national center of traffic data collection • Multi‐purposes data exploitation (model, prediction control, statistics, etc.) • A partnership with: INRETS, DIR‐CE, CG38 Wireless magnetic sensor • Research focusing transfer to KARRUS‐ITS (start‐up) Speed and density P/8
  • 9. Micro & Macro models Macro models Micro models P/9
  • 10. Traffic Forecasting  Predicted quantities at;  (t+T) State Observers And Prediction Out‐products: Demand (t+T) • Predicted Traveling time • Time to congestion Demand Prediction • Distant to congestion • Imputation (sensors maintenance) Past demand data P / 10 P / 10 • Change in capacity
  • 11. Centralized Control Setup P / 11
  • 12. Limitation of the Decentralized Control strategies Local control: • Two possible versions • Does not handle ramps queue • Try to get maximum capacity • Limited by its preview   P / 12
  • 13. Cooperative ramp metering control Cooperative ramp metering control: • Control with Forward‐(Back) view  • Limited amount of information (decentralized implementation)   • Increases system robustness • Control also the waiting queue • Finally trades flow throughput  vs. Ramp waiting queue P / 13
  • 14. Mixed control:  variable‐speed and  ramp metering control Cooperative mixed variable speed, and ramp metering control: • Distributed actuators • More control authority  • Compensate lack of queuing space    • Relay of drivers behavior (radars will help) P / 14
  • 15. NeCS Team Agenda Agenda for Grenoble experiments in 2010: • Installation of 30/40 sensors covering 2Km (Fev.) • Calibrate a micro & macro models • First traffic congestion predictions • Model‐based Travel‐time Estimation • Evaluate improvement by using control metering • Semi‐decentralized metering control • Developing desktop applications  • Show case (HYCON2) Associated Projects/ collaborations: • HYCON2 (NoE‐FP7), VTT‐MOCOPO • DIR‐CE, CG38, INRETS, METRO,  • Start up Karrus‐ITS P / 15
  • 16. Expected impact & Benefits of using  feedback control  Expected Benefits • Decrease traveling time • Regularity  • Reduce accidents • Decreases stop‐go behavior    • Reduce emission of pollutants • Minimize fuel consumptions  P / 16 From Cambridge Systematics for the Minnesota Department of Transportation 2001
  • 17. Summary: “academic” challengers Challengers: • Bring to maturity sensor technologies with a holistic view • Massive data aggregation: noise, geo‐localization, video, radars… • Heterogeneous traffic models: peri‐urban, arterials, more on micro‐macro… • Simulations: develop associated simulators for all kinds of traffic models, • Communications: new control opportunities when using VéV & V2I information • Traffic forecasting: short terms and real‐time (adaptive) prediction • Traffic control: Hybrid systems (analysis) , collaborative ramp metering control, combined  ramp metering with variable speed control, large scale experiments and evaluation • Traffic services. Many things already there, much more to be invented. Needs & gateways: • Merging communities: mathematics, control, transportation, communications, computing • Large‐scale (city labs) control experiments. Evaluate the impact of such technologies • Holistic view of the whole information chain (sensing, communication, control & services) P / 17
  • 18. Collection Transport Processing Serving Workshop . « ICT challengers in Intelligent Transportation Systems: Information  transportation &  processing» •(15 min) Olivier Berder (CAIRN.)  “Vehicle‐to‐infrastructure  communication”, •(15 min) Michel Parent (IMARA)  « Urbain Mobility Management » •(15 min) Christian Laugier (EMOTION)  “ICT for improving Car Safety" Demos & posters • P / 18