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