ict in intelligent transportation systems - inria · ict in intelligent transportation systems: ......
TRANSCRIPT
Carlos Canudas de WitNeCS Joint INRIA/CNRS Team
DR‐CNRSControl System Department
GIPSA‐LabGrenoble France
Information flow: a holistic view
Traffic Forecasting & Control
Impacts & benefitshttp://necs.inrialpes.frcarlos.canudas‐de‐wit@gipsa‐lab.inpg.fr
21 June 2010, Jouy-en-Josas
UMR 5216
ICT in Intelligent Transportation Systems: real‐time traffic forecasting and control
P / 2
Information: uses and abuses
Collection Transport Processing Serving
Real‐time Information (ICT) flow
P / 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 / 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 / 5
Processing Information: brings add value at the brut information
Ramp meeting control (EURAMP source)Variable speed control (Mail online source)
Collection Transport Processing Serving
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 / 6
Information serving: services to users
The results of the processed information is transformed into user services:
• Desktop applications, • Mobile phones, • On‐board navigation devices,• Traffic control centers
Collection Transport Processing Serving
P / 7
Market evolution: in Advanced Traffic management Systems (ATMS)
Total value of the European ATMS market (in M€)
Total interurban advanced traffic management market 2004‐2015.
Source Frost & Sullivan
A clear grown & opportunities in:
• ATMS• Sensors, Signal & systems• Infrastructure & communications• Services & business
A clear grown & opportunities in:
• ATMS• Sensors, Signal & systems• Infrastructure & communications• Services & business
P / 8Wireless magnetic sensorSpeed and density
Model-basedcontrol
M2M networkM2M network4 sensors per line each 400 m Public Data
DIR-CE
GTL is a WSN data collection platform for real-time traffic modeling, prediction and control
NeCS Research in
model estimation & Control
Show room
• A national center of traffic data collection• Multi‐purposes data exploitation (model, predictioncontrol, statistics, etc.)• A partnership with: INRETS, DIR‐CE, CG38• Research focusing transfer to KARRUS‐ITS (start‐up)
Micro‐Simulator
Data Base
P / 9
Micro & Macro models
Macro models
Micro models
P / 10P / 10
Traffic Forecasting
Out‐products:
• Predicted Traveling time
• Time to congestion
• Distant to congestion
• Imputation (sensors maintenance)
• Change in capacity
State Observers
And Prediction
Demand Prediction
Past demand data
Demand (t+T)
Predicted quantities at; (t+T)
P / 11
Centralized Control Setup
P / 12P / 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 / 13P / 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 / 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 / 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 / 16
Expected impact & Benefits of using feedback control
From Cambridge Systematics for the Minnesota Department of Transportation 2001
Expected Benefits
• Decrease traveling time• Regularity • Reduce accidents• Decreases stop‐go behavior • Reduce emission of pollutants• Minimize fuel consumptions
Expected Benefits
• Decrease traveling time• Regularity • Reduce accidents• Decreases stop‐go behavior • Reduce emission of pollutants• Minimize fuel consumptions
P / 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 / 18
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•
Collection Transport Processing Serving