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!