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Traffic Light Control


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Traffic Light Control

  1. 1. Traffic Light Control <ul><ul><li>Hoàng Hải </li></ul></ul><ul><ul><li>Lưu Như Hòa </li></ul></ul><ul><ul><li>Department of Automatic Control </li></ul></ul><ul><ul><li>Hanoi University of Technology </li></ul></ul>
  2. 2. Overview <ul><li>Improving safety </li></ul><ul><li>Minimizing travel time </li></ul><ul><li>Increasing the capacity of infrastructures </li></ul>The problem of transport system is an optimal problem control Main Goals are:
  3. 3. Outline <ul><li>Section 1 : How traffic can be modeled ? </li></ul><ul><li>Section 2 : What a traffic light control system contain ? </li></ul><ul><li>Section 3 : New approaches to traffic light control !!! </li></ul>
  4. 4. Modeling and Controlling Traffic Section 1
  5. 5. Modeling and Controlling Traffic <ul><li>Macroscopic scale : Similar to models of fluid dynamics </li></ul><ul><li>PDE </li></ul><ul><li>Microscopic scale: each vehicle is considered as an individual </li></ul><ul><li>ODE </li></ul>How traffic can be modeled ?
  6. 6. Macroscopic models <ul><li>Macroscopic models based on fluid dynamics model </li></ul><ul><li>Relation between: traffic flux, traffic density and velocity. </li></ul>
  7. 7. Macroscopic models
  8. 8. Microscopic models <ul><li>Microscopic models focus on vehicles (position and velocity ) </li></ul><ul><li>Cellular automaton (CA): discrete model </li></ul><ul><ul><li>Road Δ x </li></ul></ul><ul><ul><li>Time steps Δ t </li></ul></ul><ul><ul><li>Nagel-Schreckenberg model </li></ul></ul>Stephen Wolfram Creator of CA
  9. 9. Microscopic models Road Cell Rule If next state is available Then Move forwards Else Stop current pattern new state for center cell 0 0 0 1 1 1 0 1 000 001 010 011 100 101 110 111
  10. 10. Microscopic models <ul><li>Self-caused slowdown: </li></ul><ul><li>Stable &quot;stop-waves“ </li></ul><ul><li>Two stable states </li></ul>
  11. 11. Traffic Light Control System Section 2
  12. 12. Traffic Light Control System <ul><li>No obvious optimal solution </li></ul><ul><li>In practice most traffic lights are controlled by fixed-cycle controllers </li></ul><ul><li>Fixed controllers need manual changes to adapted specific situation </li></ul>
  13. 13. Driver Detector - Camera <ul><li>Identification image </li></ul><ul><li>Expensive </li></ul><ul><li>Complex Traffic System </li></ul>
  14. 15. Driver Detector - Loop Detector <ul><li>Measure Inductive </li></ul><ul><li>Most popular </li></ul><ul><li>Cheap </li></ul>
  15. 16. Traffic Light Control System <ul><li>Distributed System </li></ul><ul><ul><li>A set of intersections </li></ul></ul><ul><ul><li>A set of connection (roads) </li></ul></ul><ul><ul><li>Traffic lights regulating </li></ul></ul><ul><ul><li>Traffic lights are co-operation </li></ul></ul>
  16. 17. Traffic Control and Command Centre In Thailand
  17. 18. Green Waves <ul><li>Offset of cycle can be adjusted to create green waves . </li></ul>
  18. 19. Control Algorithms Section 3
  19. 20. Expert Systems <ul><li>uses a set of given rules to decide upon the next action (change some of the control parameters) </li></ul><ul><li>Findler,Stapp,1992 describe a network of roads connected by traffic light-based expert systems </li></ul><ul><li>improve performance but much computation </li></ul>Can Machines Think?
  20. 21. Evolutionary Algorithms <ul><li>Taaleetal,1998 using evolutionary algorithms to evolve a traffic light controller for a single intersection </li></ul><ul><li>Result: </li></ul><ul><ul><li>Generates green times for next switching schedule. </li></ul></ul><ul><ul><li>Minimization of total delay / number of stops. </li></ul></ul><ul><ul><li>Better results (3 – 5%) / higher flexibility than with traditional controllers. </li></ul></ul><ul><ul><li>Dynamic optimization, depending on actual traffic (measured by control loops). </li></ul></ul>
  21. 22. Fuzzy Logic <ul><li>Passed through 31% more cars </li></ul><ul><li>Average waiting time shorter by 5% </li></ul><ul><li>Performance also measure 72% higher. </li></ul><ul><li>In comparison with a human expert the fuzzy controller passed through 14% more cars with 14% shorter waiting time and 36% higher performance index </li></ul>
  22. 23. Reinforcement Learning <ul><li>how an agent take actions in an environment to maximize long-term reward </li></ul><ul><li>Thorpe used it for the traffic-light problem </li></ul>
  23. 24. Intelligent Agents
  24. 25. Conclusion <ul><li>Modeling </li></ul><ul><ul><li>Macroscopic Model </li></ul></ul><ul><ul><li>Microscopic Model </li></ul></ul><ul><li>Traffic Control System </li></ul><ul><ul><li>Traffic light control in a junction </li></ul></ul><ul><ul><li>Co-operation in traffic control system </li></ul></ul><ul><li>Control Algorithms </li></ul>
  25. 26. References <ul><li> </li></ul><ul><li>[Wiering,2004], Intelligent Traffic Light Control </li></ul><ul><li>[Tan Kok Khiang,1997] Intelligent Traffic Lights Control by Fuzzy Logic </li></ul>
  26. 27. Thank you for your attention!