Flc Traffic

Intelligent Traffic Lights Control By Fuzzy Logic Ranganath Muthu Professor, EEE SSN College of Engineering

19 December 2008

FLC Traffic

1

Fuzzy Logic Controller •

•

•

•

FLCs allow for a simpler human like approach to control system design. They do not need the mathematical model of the process. For non-linear systems, controlling with conventional controllers is difficult. FLCs provide reasonable and effective alternatives to classical controllers.

19 December 2008

FLC Traffic

2

Conventional Control System

19 December 2008

FLC Traffic

3

Fuzzy Logic Control System

19 December 2008

FLC Traffic

4

Traffic Lights n

n

19 December 2008

Have you ever thought that a green light should have lasted a couple seconds longer? Or been stuck at a red light when there was no traffic coming the other way?

FLC Traffic

5

n

“Grrrrrr !!!”

If only traffic lights were more intelligent…

19 December 2008

FLC Traffic

6

n

Fuzzy logic technology allows the implementation of reallife rules similar to the way humans would think. (Khiang)

19 December 2008

FLC Traffic

7

Conventional Traffic Lights Control n

Preset cycle time n

n

A busy street may need a regularly timed cycle of green lights.

Preset cycle time and proximity sensors n

n

A less traveled street only needs a green light when cars are present Cycle time and placement of sensors should be customized for the particular road

19 December 2008

FLC Traffic

8

Fuzzy Traffic Lights Control System n

Uses sensors that count cars instead of proximity sensors, which only indicate the presence of cars n

n

Provides the controller with traffic densities in the lanes Allows a better assessment of changing traffic patterns

19 December 2008

FLC Traffic

9

General Structure of Fuzzy Traffic Lights Control System

19 December 2008

FLC Traffic

10

Design Criteria and Constraints n

n

n n

n n

Four-way junction with traffic coming from the north, west, south, and east When traffic from the north and south moves, traffic from the east and west stops, and vice-versa No left and right turns are considered The fuzzy logic controller will observe north/south traffic as one side and west/east traffic as another side The west/east lane is the main approach Minimum green light time is 20 seconds and maximum green light time is 2 minutes 19 December 2008

FLC Traffic

11

Design of FLC

Fuzzy Logic Control System 19 December 2008

FLC Traffic

12

Fuzzy Input Variables n

Arrival n n

n

The quantity of traffic on the arrival side Note: if the north/south side is green, then this would be the arrival side

Queue n n

The quantity of traffic on the queuing side Note: if the north/south side is not green, then this would be the queuing side

19 December 2008

FLC Traffic

13

Fuzzy Output Variable n

Extension n

The extension time needed for the green light on the arrival side

The value of this fuzzy output variable will result in either extending or not extending the current green light time 19 December 2008

FLC Traffic

14

Arrival Membership Functions n n n n

19 December 2008

FLC Traffic

AN = almost no F = few MY = many TMY = too many

15

Queue Membership Functions n n n n

19 December 2008

FLC Traffic

VS = very small S = small M = medium L = large

16

Extension Membership Functions n n n n

19 December 2008

FLC Traffic

Z = zero S = short M = medium L = longer

17

Fuzzy Inference n

AN

F

MY TMY

VS

Z

S

M

L

S

Z

S

M

M

M

Z

Z

S

M

L

Z

Z

Z

S

19 December 2008

n

n

FLC Traffic

IF Arrival is TMY AND Queue is VS THEN Extension is L IF Arrival is F AND Queue is VS THEN Extension is S IF Arrival is AN AND Queue is VS THEN Extension is Z 18

Defuzzification n

n

The final output membership function for each rule is the fuzzy set assigned to that output by clipping the degree of truth values of the membership functions of the associated antecedents (Arrival and Queue). Once the membership degree of each output fuzzy variable is determined, all of the rules that are being fired are then combined and the actual crisp output is obtained using the center of gravity technique. 19 December 2008

FLC Traffic

19

Now You Try It! Suppose three cars have arrived and five cars are queued. a)

Write all fuzzy implication rules whose antecedents have nonzero membership values If F and M, then Z If F and L, then Z If MY and M, then S If MY and L, then Z

19 December 2008

FLC Traffic

20

Now You Try It! b)

Find the fuzzy output membership values for the rules in (a) using the min operator. If If If If

F = 0.5 and M = 0.5, then Z = 0.5 F = 0.5 and L = 0.5, then Z = 0.5 MY = 0.5 and M = 0.5, then S = 0.5 MY = 0.5 and L = 0.5, then Z = 0.5

19 December 2008

FLC Traffic

21

Now You Try It! c)

Clip the final output membership function using the values in (b).

19 December 2008

FLC Traffic

22

Now You Try It! c)

Clip the final output membership function using the values in (b).

19 December 2008

FLC Traffic

23

Now You Try It! d)

Estimate the extension time by approximating the center of gravity.

19 December 2008

FLC Traffic

24

Now You Try It! d)

Estimate the extension time by approximating the center of gravity.

Approximately 1.0 seconds. 19 December 2008

FLC Traffic

25

Conclusion n

n

The fuzzy logic control system reacts the way a traffic policeman would at a typical intersection The fuzzy logic control system provides better performance in terms of total waiting time as well as total moving time, reducing fuel consumption, air pollution, and noise pollution 19 December 2008

FLC Traffic

26

References

§ Driankov D., Hellendoorn H. & Reinfrank

M., An Introduction to Fuzzy Control, Narosa Publishing House New Delhi, India, 1996. § Muthu R. & Ghosh S., Fuzzy Logic based Traffic Lights Control System, Safety on Roads International Conference, Bahrain, 21-23 October 2002. 19 December 2008

FLC Traffic

27

Thank You

19 December 2008

FLC Traffic

28