Traffic Light Control
Transcript of Traffic Light Control
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Traffic Light Control
Hoàng Hải
Lưu Như Hòa
Department of Automatic Control
Hanoi University of Technology
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Overview
Improving safety Minimizing travel time Increasing the capacity
of infrastructures
The problem of transport system is an optimal problem control Main Goals are:
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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 !!!
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Modeling and Controlling Traffic
Section 1
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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 ?
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Macroscopic models
Macroscopic models based on fluid dynamics model
Relation between: traffic flux, traffic density and velocity.
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Macroscopic models
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Microscopic models
Microscopic models focus on vehicles (position and velocity )
Cellular automaton (CA): discrete model Road Δx Time steps Δt Nagel-Schreckenberg
model
Stephen WolframCreator of CA
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Microscopic models
Road Cell
111 110 101 100 011 010 001 000
1 0 1 1 1 0 0 0
Rule 10 2184 10111000If next state is available Then Move forwardsElse Stop
current pattern
new state for center cell
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Microscopic models
Self-caused slowdown:
Stable "stop-waves“
Two stable states
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Traffic Light Control System
Section 2
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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
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Driver Detector - Camera
Identification image Expensive Complex Traffic System
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Driver Detector - Loop Detector
•Measure Inductive•Most popular•Cheap
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Traffic Light Control System
Distributed System A set of intersections A set of connection
(roads) Traffic lights regulating Traffic lights are co-
operation
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Traffic Control and Command Centre
In Thailand
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Green Waves
Offset of cycle can be adjusted to create green waves.
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Control Algorithms
Section 3
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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?
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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).
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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
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Reinforcement Learning
how an agent take actions in an environment to maximize long-term reward
Thorpe used it for the traffic-light problem
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Intelligent Agents
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Conclusion
Modeling Macroscopic Model Microscopic Model
Traffic Control System Traffic light control in a junction Co-operation in traffic control system
Control Algorithms
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References
http://en.wikipedia.org [Wiering,2004],Intelligent Traffic Light Control [Tan Kok Khiang,1997] Intelligent Traffic
Lights Control by Fuzzy Logic
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Thank you for your attention!