ICT in Intelligent Transportation Systems: real time traffic forecasting and control

Transcription

1 ICT in Intelligent Transportation Systems: real time traffic forecasting and control 21 June 2010, Jouy-en-Josas Information flow: a holistic view Traffic Forecasting & Control Impacts & benefits Carlos Canudas de Wit NeCS Joint INRIA/CNRS Team DR CNRS Control System Department GIPSA Lab Grenoble France 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: Products already in use are not optimal, Decentralized, Room for a lot of improvements Variable velocity control: Under investigation, Relay on Soft actuators (drivers), High potentially 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: Total interurban advanced traffic management market Source Frost & Sullivan ATMS Sensors, Signal & systems Infrastructure & communications Services & business P / 7

8 GTL is a WSN data collection platform for real-time traffic modeling, prediction and control Data Base Show room Model-based control NeCS Research in model estimation & Control M2M M2M network network 4 sensors per line each 400 m Public Data Micro Simulator DIR-CE Wireless magnetic sensor Speed and density A national center of traffic data collection Multi purposes data exploitation (model, prediction control, statistics, etc.) A partnership with: INRETS, DIR CE, CG38 Research focusing transfer to KARRUS ITS (start up) P / 8

9 Micro & Macro models Macro models Micro models P / 9

10 Traffic Forecasting Predicted quantities at; (t+t) State Observers And Prediction Demand (t+t) Demand Prediction Past demand data Out products: Predicted Traveling time Time to congestion Distant to congestion Imputation (sensors maintenance) Change in capacity P / 10

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 From Cambridge Systematics for the Minnesota Department of Transportation 2001 P / 16

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