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ADVANCED TRAFFIC CONTROL TECHNIQUES FOR FREEWAY SYSTEMS (AND URBAN NETWORKS). by Prof. Markos Papageorgiou Dynamic Systems and Simulation Laboratory, Technical University of Crete, Chania, Greece. OUTLINE. Introduction Motorway (Freeway) Traffic Control Ramp Metering

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ADVANCED TRAFFIC CONTROL TECHNIQUES FOR FREEWAY SYSTEMS (AND URBAN NETWORKS)


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    1. ADVANCED TRAFFIC CONTROL TECHNIQUES FOR FREEWAY SYSTEMS (AND URBAN NETWORKS) by Prof. Markos Papageorgiou Dynamic Systems and Simulation Laboratory, Technical University of Crete, Chania, Greece

    2. OUTLINE • Introduction • Motorway (Freeway) Traffic Control • Ramp Metering • Route Information and Guidance • Variable Speed Limits • Urban Signal Control • Integrated Urban-Freeway Traffic Control • Motorway Traffic Surveillance • Other Control Tools

    3. 1. INTRODUCTION Who are we? Dynamic Systems and Simulation Laboratory (DSSL) (~ 15 Professors, Researchers, PhD/MSc Students) at the Technical University of Crete also acting occasionally as professional engineers. What is our Background? • Automatic Control • Electrical Engineering • Industrial Engineering • Civil Engineering

    4. Experience • 30 years of high-quality research in traffic flow modelling and control • Numerous successful implementations • High number of research or implementation contracts: national, EC, companies, authorities worldwide. • October 2008: IEEE Outstanding ITS Research Award

    5. What We Do Concepts  Algorithms  Software for traffic flow modelling, surveillance, route guidance, traffic control (freeway + urban) What we Don’t Do Implementation hardware, System integration Our Usual Partners • Road authorities • Local Consultants/University groups • Local System Integrators

    6. Our Principles • Theoretical soundness: There is nothing more practical than a good theory • Practical Usefulness. • General Applicability, Interoperability • Highest Efficiency: deep understanding • Practicability: As simple as possible, as complex as necessary.

    7. Automatic Control Loop

    8. Example: Urban traffic control system

    9. Minimization of Total Time Spent  Maximization of (Early) Exit Rates

    10. Simple Queuing Systems • Demand > Capacity  Queuing • Capacity ≠ f (Queuing) • Delay depends on D−C only! Water Systems More inflow  Higher Pressure  Higher Outflow

    11. Traffic Networks • Congestion degrades the infrastructure (capacity) Local link demand exceeds local capacity  Local congestion degrades local capacity Accelerated increase of congestion Further capacity degradation ... until generalized network congestion although Demand << Nominal network capacity

    12. Conclusion: Generalized traffic congestion is not only due to high demand. Congested Motorway Networks: Expensive infrastructure capacity not fully available at the only time it is actually needed, i.e. the peak periods! Goal: Operate motorway and urban networks optimally (as controllable systems)

    13. 2. MOTORWAY (FREEWAY) TRAFFIC CONTROL Motorways were originally conceived to provide virtually unlimited mobility to road users, but …

    14. Man has reached to the moon but … … even ants were taught by evolution to address their transportation problems more efficiently, see I.D. Couzin and N.R. Franks: “Self-organized lane formation and optimized traffic flow in army ants”, Proc. R. Soc. Lond. B (2003) 270, 139–146

    15. Available Motorway Control Measures • Ramp metering (RM): valuable; limited storage space • Variable speed limits (VSL): improved safety; no system improved efficiency • Route guidance (RG): best under incident-caused congestion • Vehicle-infrastructure integration (VII): promising; emerging

    16. Prerequisite for efficient traffic control: Understanding the reasons for infrastructure degradation! • (Latent) Motorway bottleneck location: Capacity upstream > Capacity downstream • Bottlenecks are candidates for congestion appearance: • on-ramp merge • geometry (lane drop, grade, curvature, tunnel, …) • weaving • speed limits • over-spilling off-ramps

    17. Two Main Reasons for Motorway Infrastructure Degradation 1. Capacity Drop (CD) CD not well-understood but is deemed to occur due to vehicle acceleration

    18. 2. Blocking of Off-Ramps (BOR) off-ramp flow reduced: vehicles bound for the off-ramp contribute to accelerated congestion increase!

    19. 3. RAMP METERING

    20. Why Ramp Metering 1st Answer e.g. qcon = 0.95 qcap ; qin+d = 1.2 qcap  (veh/h)

    21. 2nd Answer Note: On-ramp queue should not interfere with surface street traffic.

    22. Further reasons • Influence driver route choice • Utilisation of reserve capacity on parallel arterials • Increased traffic safety (less congestion, safer merging)

    23. When is ramp metering less helpful? Exit flow problems

    24. Local Control Issues Note: ocr less sensitive than qcap (e.g. under adverse weather conditions)

    25. Sample from Glasgow Implementation of ALINEA

    26. Many additional tools • Real-time estimation of ocr • Real-time estimation of ramp queue • Ramp queue management • Various traffic-light policies • Switch on/off logic . . .

    27. Why Coordinated Ramp Metering? No ramp queue constraints • Little efficiency improvement via coordination • Equity ? • Realistic ? • Diversion

    28. Limited ramp storage capacity: • Full ramp Congestion “merely” retarded • Equity: bad significant improvement of both efficiency and equity via appropriate coordination

    29. HERO: Ramp Metering coordination algorithm • Rule-based central control • employs (modified) ALINEA at each on-ramp • Master/Slave ramps for increased storage space • High efficiency (depending on available ramp storage space)

    30. ALINEA Field Implementations >100 ramps in Europe (even w/o our involvement) • Boulevard Périphérique, Paris, France • A6, Ile-de-France • A10, Amsterdam, Netherlands • Glasgow, Scotland • A94, Munich, Germany • UK Highways Agency ramp metering roll-out • Tel Aviv, Israel . . . Status: Mature tool, ready for immediate implementation

    31. HERO/ALINEA Field Implementations • A6, Ile-de-France (simplified) • Ile-de-France network (~ 80 ramps) • A10, Amsterdam, Netherlands (~ 40 ramps) • Monash Freeway, Melbourne, Australia (665 ramps) Status: Mature tool, ready for immediate implementation

    32. 4. ROUTE INFORMATION AND GUIDANCE • Multi-origin, multi-destination, multi-route per O-D pair. • Fixed direction signs: shortest path in absence of congestion • Rush hours • Changing demands, weather conditions, exceptional events, incidents  underutilisation of infrastructure  congestion, delays, reduced safety, increased fuel consumption, environmental pollution

    33. VMS (Variable Message Signs) or two-way communication with equiped vehicles • Real-time information: – Drivers’ knowledge – Message length – Decision efficiency – System controllability – Travel time or queue length: drivers’ stress (e.g. BP in Paris) but also basis for route choice – Instantaneous (estimation) or predicted information • Route guidance – Control strategy

    34. Our Concept: Simple feedback-based route guidance

    35. Automatic Control of VMS in Aalborg, Denmark • Main goal: efficient crossing (northbound or southbound) of Limfjorden via BRIDGE (urban) or TUNNEL (motorway) • Particularly in presence of incidents, road works etc. • 116 loop detectors; 14 VMS in front of important bifurcations • Sample time: 1min

    36. Two display modes (no real-time switch) – Delay information – Route Recommendation • Operational constraints: – Information is the most accurate available – Route recommendation according to user optimum principle – No incompatible VMS displays – Police report input

    37. Aalborg network with VMS positions indicated. Bold black lines represent detector equipped segments.

    38. VMS control modes: Delay information (a) and route guidance (b).

    39. Automatic Control of VMS in the Interurban Scottish Highway Network • Motorway/Expressway Network • Geographically extended ( predictions necessary) but less complex in topology. • VMS: Combination of Information and Route Recommendation

    40. VMS Plans pre-approved (some 200) • Predictive/Feedback Strategy (Smith-Predictor type) • Off-line simulations • Implementation • Dummy Control • Evaluation/Comparison

    41. The Scottish interurban network

    42. Glasgow integrated traffic control • Also involved: Boulevard Périphérique travel time information system Status: Mature for implementation

    43. 5. VARIABLE SPEED LIMITS

    44. 5. VARIABLE SPEED LIMITS • Increasingly popular control measure with many applications worldwide. • Several evaluations indicate substantial safety improvements (– 30 % accidents)… • … but no efficiency improvements (e.g. no reduction of travel times) • Why?

    45. 1) Very simple threshold-based control strategies An example of VSL switching logic.