Traffic Light Control


Published on

Published in: Technology, Business
  • ple send downloading link for traffic control project report
    Are you sure you want to  Yes  No
    Your message goes here
  • plz send downloading link...i wish to download.
    Are you sure you want to  Yes  No
    Your message goes here
  • heyy..thats a very good ppt...i hope u dont mind us downloading cn u pls enable d download link??
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

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, predicted and controlled ? </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. Macroscopic models <ul><li>Basic Statements </li></ul><ul><li>The more vehicles are on a road, the slower their velocity will be </li></ul><ul><li>The number of vehicles entering the control zone has to be smaller or equal to the number of vehicles leaving the zone in the same time </li></ul><ul><li>At a critical traffic density and a corresponding critical velocity the state of flow will change from stable to unstable. </li></ul><ul><li>If one of the vehicles brakes in unstable flow regime the flow will collapse </li></ul><ul><li>Road Capability, Speed Limit </li></ul><ul><li>not simulate directly certain driver behaviors </li></ul>
  9. 9. Microscopic models <ul><li>In contrast to macroscopic models, 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
  10. 10. 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
  11. 11. Microscopic models <ul><li>Self-caused slowdown: </li></ul><ul><li>Stable &quot;stop-waves“ </li></ul><ul><li>Two stable states </li></ul>
  12. 12. Microscopic models <ul><li>Cognitive Multi-Agent Systems (CMAS): Agents interact and communicate with each other and the infrastructure </li></ul><ul><ul><li>receives information from the environment using its sensors </li></ul></ul><ul><ul><li>believes certain things about its environment </li></ul></ul><ul><ul><li>uses these beliefs and inputs to select an action </li></ul></ul><ul><ul><li>using learning capabilities to optimize agent </li></ul></ul>
  13. 13. Microscopic models
  14. 14. Microscopic models <ul><li>Dia (2002) use CMAS in traffic problem. </li></ul><ul><li>But no result were presented!!! </li></ul>
  15. 15. Predicting Traffic <ul><li>Measuring traffic over a certain time, assuming that conditions will be the same for the next period </li></ul><ul><li>Ledoux(1996) used neural networks perform long-term prediction of the queue length at a traffic light </li></ul>
  16. 16. Vehicle Control <ul><li>Get information through dynamic road signs, radio, or even on-board navigation systems </li></ul><ul><li>Traffic flow increase if all drivers drive at the same (maximum) speed. (But …) </li></ul><ul><li>Learned strategies better than hand-crafted controllers </li></ul>
  17. 17. Traffic Light Control System Section 2
  18. 18. 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 controlled independently </li></ul></ul>
  19. 19. Traffic Control and Command Centre In Thailand
  20. 20. 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>
  21. 21. Green Waves <ul><li>Offset of cycle can be adjusted to create green waves . </li></ul>
  22. 22. Driver Detector - Sonar Sensor <ul><li>Few drivers </li></ul><ul><li>Unusual </li></ul>
  23. 23. Driver Detector - Camera <ul><li>Identification image </li></ul><ul><li>Expensive </li></ul><ul><li>Complex Traffic System </li></ul>
  24. 25. Driver Detector - Loop Detector <ul><li>Measure Inductive </li></ul><ul><li>Most popular </li></ul><ul><li>Cheap </li></ul>
  25. 26. Traffic Light Control System What does it do ?
  26. 27. N S W Let’s See!
  27. 28. N S W   No turning
  28. 29. N S W Binary traffic lights
  29. 30. N S W Safety Property This should not happen
  30. 31. N S W Safety Property This should not happen
  31. 32. N S W Liveness Property Thank God! Traffic in each direction must be served
  32. 33. Finite State Machine <ul><li>The Problem is Synchronous </li></ul>
  33. 34. Finite State Machine
  34. 35. Control Algorithms Section 3
  35. 36. 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?
  36. 37. 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><li>Generates green times for next switching schedule. </li></ul><ul><li>Minimization of total delay / number of stops. </li></ul><ul><li>Better results (3 – 5%) / higher flexibility than with traditional controllers. </li></ul><ul><li>Dynamic optimization, depending on actual traffic (measured by control loops). </li></ul>
  37. 38. 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>
  38. 39. Reinforcement Learning <ul><li>Reinforcement learning is a branch of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward </li></ul><ul><li>Thorpe used a neural network for the traffic-light based value function which predicts the waiting time for all cars standing at the junction </li></ul>
  39. 40. Intelligent Agents
  40. 41. References <ul><li> </li></ul><ul><li>[Wiering] Intelligent Traffic Light Control </li></ul><ul><li>[Tan Kok Khiang] Intelligent Traffic Lights Control by Fuzzy Logic </li></ul>