ieee traffic

8/8/2019 IEEE Traffic

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Evaluation of Road Traffic Congestion Using Fuzzy

TechniquesPanita Pongpaibool

1, Poj Tangamchit

2, Kanokchai Noodwong

2

1NECTEC, 112 Pahol Yothin Rd., Klong Luang, Pathumthani 12120 THAILAND2Dept. of Control System and Instrumentation Engr., King Mongkuts University of Technology Thonburi, THAILAND

Abstract-This paper presents a road-traffic evaluationsystem from image processing data using manually tuned fuzzylogic and adaptive neuro-fuzzy techniques. The system isdesigned to emulate humans expertise on specifying threelevels of traffic congestion within Bangkok Metropolitan Area.The traffic information comes from a vehicle detection andtracking software, which takes a road-traffic video signal as aninput and computes vehicle volume and velocity. We verifyaccuracy of our system by comparing outputs of the systemwith opinions of volunteers who watch the same traffic video.Results show that manually tuned fuzzy logic achieves 88.79%accuracy, while the adaptive neuro-fuzzy technique achievesonly 75.43% accuracy.

I. INTRODUCTIONReporting road traffic congestion can be a confusing task

since there is no standard way of measuring congestion.

Typical users need a concise and easy-to-understand traffic

report. One of the popular methods used is to report road

congestion by severity levels. One example is the Jam

Factor used at http://www.traffic.com, where the

continuous scale of 0-10 represents overall measure of the

traffic conditions on a roadway. Another example is the

Bangkok Metropolitan Areas Intelligent Message Signs [1],

which broadcast congestion status on major city roads. The

signs show congestion status in three levels, namely red

(traffic jam), yellow (slow moving), and green (free flow).

Some problems of reporting traffic condition by severity

levels are how many levels to use and what the exact

definition of each level is. Road congestion is a subjective

quantity because it comes from the feeling of drivers. In the

same road condition, some may feel that the road is heavily

congested, while some others may feel that the road is only

slightly congested. This is the problem of mismatching data

interpretation due to different users experience. Therefore,

we introduce a fuzzy technique to tackle this problem.

Fuzzy logic is known to be suitable for problems that arenonlinear such as human feelings.

Other studies that aim to determine levels of congestion

from traffic flow information include [2][3][4]. Reference [4]

estimates five traffic statuses from video images using

hidden Markov models. References [2] and [3] use fuzzy

logic to determine continuous and six discrete levels of

congestion respectively. Both systems use velocity and

vehicle volume as inputs into their fuzzy inference systems.

Our work also uses fuzzy inference systems, but differs

from previous studies in that we examine both fuzzy logic

and adaptive neuro-fuzzy [6] systems. The motivation

behind our interest in adaptive neuro-fuzzy is that theeffectiveness of the manually tuned fuzzy system depends

highly on the fuzzy rules created by human. There are no

systematic ways to create these rules. Therefore, we have to

adjust these rules by trial-and-error method until the results

are satisfied. The adaptive neuro-fuzzy can solve this

problem by automatically creating fuzzy rules according to

given inputs and outputs. In addition, we limit the traffic

status to only three levelsfree flow, slow moving, and

heavily congested, to facilitate a quick and easy-to-

understand report.

The outline of the rest of paper is as follows. Section II

describes our approach based on fuzzy logic and adaptive-

neuro fuzzy. We show effectiveness of our techniques

through experimental results in Section III. Finally, Section

IV concludes the paper.

II. OUR APPROACHTo evaluate congestion condition of a road segment, we

obtain real traffic information at the desired location. This

information consists of vehicle volume and average velocity

per minute. This information is fed into our fuzzy system.

The output of the system is the estimated level of congestion.

In this work, we experiment with two types of fuzzy

systems. First is the manually trained fuzzy logic system.

The second is the adaptive neuro-fuzzy inference system.Section III will compare accuracy of the two systems.

Figure 1. Fuzzy Logic with traffic volume and velocity as inputs

A. Fuzzy LogicFuzzy logic is a model that matches the relationship

between inputs and outputs based on the probability theorem.

It can handle situations where there are uncertainties

involved, such as problems that depend on human feeling

and experience. Therefore, it is suitable for reporting road

traffic where different people may feel differently in the

same congestion situation.

There are two main parts of the fuzzy logic process: 1)

input and output membership functions, whose range we can

manually define to fit with input/output logics, and 2) fuzzy

rules which are manually designed by a programmer

according to his/her expertise on solving particular problems.There are several types of membership functions, such as


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trapezoidal, triangular, Bells, Gaussian, etc. In our work,

we try these functions and find the trapezoidal function to

work best.

B. Adaptive Neuro-FuzzyThe adaptive neuro-fuzzy system is similar to the normal

fuzzy logic, except that the range of the membership

functions are automatically adjusted based on training data.

We adopt the adaptive neuro-fuzzy inference system(ANFIS) [6], which uses both gradient and least-square

techniques to create rules and adjust membership range to fit

our training data. The membership function used in ANFIS

is the Bells function. We train our ANFIS for 100 epoches.

C. Data AcquisitionWe acquire traffic video at Sukhumvit Road, a busy 3-

lane Bangkok road. The video is 220 minutes long taken in

the afternoon of August 2, 2006. We then use the vehicle

detection and tracking software, shown in Figure 1, which

detects number of vehicles and their speed from video

inputs. The software outputs the vehicle volume and average

speed every 30 seconds. These outputs become the inputs ofour fuzzy system.

Figure 2 Vehicle detection and tracking software

Another type of input besides vehicle volume and speed,

is human evaluation of congestion level. Human opinion is

used to benchmark accuracy of our approach. We obtain

opinions from ten volunteers who watch the aforementioned

traffic video and rate a congestion level (red, yellow, orgreen) every 30-second interval. The majority vote of

volunteers opinion (i.e., the mode of data set) at every

interval is taken as target benchmark.

D. Accuracy MeasurementThe performance metric we use to evaluate performance

of our fuzzy systems is accuracy, which is defined as

Accuracy = (TotalDataPoints IncorrectDataPoint).

TotalDataPoints

In addition, to measure how far off the incorrect data

points are from the volunteer opinions, we define anothermetric called average deviation.

AvgDeviation = |FuzzyScore HumanOpinionScore|

TotalDataPoints

HumanOpinionScore is the congestion level rated by

majority of volunteers, and FuzzyScore is the congestion

level rated by the fuzzy system. The scores of 1, 2, and 3 are

given to congestion levels red, yellow, and greenrespectively. Therefore, AvgDeviation will range from 0-2

levels, with 0 meaning most accurate and 2 meaning largest

error (i.e., evaluation error by 2 levels on average).

III. RESULTSWe perform six experiments of congestion level

evaluation based on the video data described previously. In

each experiment, we vary types of inputs and the evaluation

interval. The MATLAB tools Fuzzy Logic Control and

ANFIS are used to implement the fuzzy logic and the

adaptive-neuro fuzzy.

Figure 3 MATLAB program showing fuzzy rules and membership functions

A. Single-Lane, Velocity EvaluationIn the first experiment, we assume 30-second evaluation

interval. Inputs into the fuzzy logic are average velocity in

the middle lane and average velocity in the right lane. Note

that we omit traffic information in the left lane because there

are frequent bus and taxi stops. The set of fuzzy rules for

this experiment is shown in Figure 4.

We manually adjust three membership classes for eachinput, namely Slow, Medium, and Fast. We obtain following

membership ranges for right lane velocity. Slow means less

than 18 km/hr. Medium is between 11-35 km/hr. Fast is

more than 18 km/hr. The ranges are the same for middle

lane velocity, except Medium is between 11-26 km/hr. The

output has three members, namely Red, Yellow, and Green,

corresponding to the ranges of 0-1, 1-2, and 2-3,

respectively.

The membership functions above yield the accuracy of

75.86% and average deviation of 0.2414 levels.


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Rule 1: If (Velocity-Mid-Lane is Slow) and (Velocity-Right-Lane

is Slow) then (Output is Red)


is Medium) then (Output is Yellow)


is Fast) then (Output is Yellow)

Rule 4: If (Velocity-Mid-Lane is Medium) and (Velocity-Right-

Lane is Slow) then (Output is Yellow)Rule 5: If (Velocity-Mid-Lane is Medium) and (Velocity-Right-

Lane is Medium) then (Output is Yellow)

Rule 6: If (Velocity-Mid-Lane is Medium) and (Velocity-Right-

Lane is Fast) then (Output is Green)

Rule 7: If (Velocity-Mid-Lane is Fast) and (Velocity-Right-Lane is

Slow) then (Output is Yellow)


Medium) then (Output is Green)


Fast) then (Output is Green)

Figure 4 Fuzzy logic rules for experiment A

B. Single-Lane, Velocity & Volume EvaluationIn this experiment, besides average velocity, we add the

number of vehicle in each lane as another input. We perform

fuzzy logic control twice. The first set uses velocity and

vehicle volume in the middle lane, and the second set uses

velocity and vehicle volume in the right lane. The output is

taken as the average output of both sets.

We obtain following membership functions for inputs.

The membership ranges for velocity are 0-11 km/hr for

Slow, 8-26 km/hr for Medium, and more than 18 km/hr for

Fast. The volume of vehicles is also divided into three

levelsMin, Medium, and Maxwith the ranges of 0-6

cars, 2-10 cars, and more than 6 cars respectively. The set offuzzy rules for this experiment is shown in Figure 5.

The membership functions above achieve the accuracy of

88.79% and average deviation of 0.1208 levels. It seems that

the fuzzy logic performs better when we use two different

types of inputs (velocity and volume) than when using

velocity alone.

Rule 1: If (Velocity is Slow) and (Volume is Min) then (Output is

Red)

Rule 2: If (Velocity is Slow) and (Volume is Medium) then

(Output is Yellow)

Rule 3: If (Velocity is Slow) and (Volume is Max) then (Output is

Yellow)

Rule 4: If (Velocity is Medium) and (Volume is Min) then (Output

is Yellow)

Rule 5: If (Velocity is Medium) and (Volume is Medium) then

(Output is Yellow)

Rule 6: If (Velocity is Medium) and (Volume is Max) then (Output

is Green)

Rule 7: If (Velocity is Fast) then (Output is Green)

Figure 5 Fuzzy logic rules for experiment B

C. Two-Lane, Avg Velocity & Total Volume EvaluationIn this experiment, we still use both velocity and volume

as inputs, except we combine the total vehicle volume and

average the velocity over both lanes. The membership

function for velocity remains the same as those in

experiment B. However, the membership function for total

traffic volume changes to 0-8 cars for Min, 2-15 cars for

Medium, and more than 8 cars for Max. The set of fuzzy

rules for this experiment is the same as those for experiment

B (Figure 5).

The fuzzy logic in this experiment yields the accuracy of

86.20% and average deviation of 0.1466 levels.

D. Single-Lane, Velocity Evaluation, 60-SecondIn this experiment, we want to examine the effect of

evaluation interval. Experiments A-C all assume 30-second

interval. This experiment lengthens the interval to 60

seconds. All inputs, outputs, fuzzy rules, and membership

functions are the same as those in experiment A.

The accuracy of this experiment is 81.96%, and average

deviation is 0.1804 levels. This performance is better than

that of experiment A, which leads us to think that the

evaluation period may affect performance of the fuzzy logic

systems.

E. Single-Lane, Velocity & Volume Evaluation, 60-SecondTo confirm our speculation that increasing evaluation

period could improve accuracy of fuzzy logic systems, we

repeat experiment B with 60-second evaluation interval.

Again we simulate the fuzzy logic control twice for each

lane. The membership functions for velocity are the same as

experiment B. That is 0-11 km/hr for Slow, 8-26 km/hr for

Medium, and more than 18 km/hr for Fast. However, the

membership functions for traffic volume are modified to

accommodate the increased interval. The new functions

become 0-23 cars, 20-34 cars, and more than 31 cars for

Min, Medium, and Max respectively.

It turns out that the accuracy in this experiment is not

better than that of experiment B. The accuracy is 81.96%while the average deviation is 0.1804. Therefore, we cannot

conclude that evaluation period could improve accuracy.

F. Adaptive Neuro-Fuzzy EvaluationThe adaptive neuro-fuzzy technique is similar to the fuzzy

logic control system. We use average velocity and total

traffic volume of both lanes as inputs, and assume 60-

second evaluation interval. Instead of trapezoidal functions

of fuzzy logic, we assume Bells functions for input

membership. The outputs are linear equations. Input

member fuzzification is done through the MATLAB

GENFIS1 tool, which uses Grid partition technique. The

inputs and training outputs (volunteers opinions) are thenpassed through the MATLAB ANFIS tool for learning and

automatically adjusting the output equations.

After 100 training epochs, the adaptive neuro-fuzzy

system is ready to evaluate congestion status. We compare

results of the adaptive neuro-fuzzy with human opinions

(using different sets of opinions for training and for

verifying accuracy). The results show 75.43% accuracy and

0.2808 average deviation levels. Contrary to our expectation,

this accuracy is lower than all fuzzy logic systems examined

previously.

Table I summarizes the accuracy and average deviation of

all experiments.


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TABLE ISUMMARY OF EXPERIMENTAL RESULTS

Exp Input Parameters Eval

Interval

Accuracy Avg

Deviation

A Velocity of middle lane,

velocity of right lane

30 sec 75.86% 0.2414

B Velocity and volume of middle

lane, velocity and volume of

right lane

30 sec 88.79% 0.1208

C Average velocity of both lanes,

total volume of both lanes

30 sec 86.20% 0.1466

D Velocity of middle lane,

velocity of right lane

60 sec 81.96% 0.1804

E Velocity and volume of middle

lane, velocity and volume of

right lane

60 sec 81.96%

0.1804

F Adaptive Neuro-Fuzzy 60 sec 75.43% 0.2808

IV. CONCLUSIONSIn this paper, we proposed to use fuzzy logic and

adaptive-neuro fuzzy systems to evaluate levels of road

traffic congestion. Performance of our proposed systems is

evaluated by measuring accuracy of outputs against

volunteer opinions. Through experiments, we find that the

manually tuned fuzzy logic system achieves much higher

accuracy than the adaptive neuro-fuzzy system. This is

because the fuzzy logic system can capture human expertise

better through manual adjustment of membership functions.

We examine two types of fuzzy logic inputsaveragevelocity and traffic volume within an interval. Results show

that using both parameters as inputs yields better accuracy

than using just the velocity information. Moreover, since our

test road is a multi-lane street, we investigate effects of

using single-lane traffic information as opposed to two-lane

information. Results show no difference between the two

cases. This is probably because each lane of the test road

does not differ much in terms of traffic flow characteristics.

These results may differ on different roads, however.

We also observe how evaluation interval affects accuracy

of the system. The results are inconclusive whether the

longer interval can improve accuracy. We believe this effect

depend on nature of traffic flow at particular road segments,

and particular time.

In conclusion, it is possible to use fuzzy logic to evaluate

road congestion status with high accuracy and low error

margins. However, users should keep in mind following

limitations. Accuracy of fuzzy logic systems depends highly

on how rules are defined. Time-of-day and day-of-weekvariations will play an important role on how to characterize

the fuzzy logic. Moreover, manual tuning of membership

functions might be troublesome, but it is necessary for every

new road segment to be evaluated.

ACKNOWLEDGMENT

The author would like to thank Supakorn Siddhichai and

Kantip Kiratiratanapruk from NECTEC who provide image

processing software and video clips used in our experiments.

REFERENCES

[1] Genius Joint Venture Co. Ltd, Intelligent Message SignSystem, available at http:// /www.forth-its.com/ (accessedMay 1,2007)

[2] J. Lu and L. Cao, Congestion evaluation from traffic flowinformation based on fuzzy logic, in IEEE Intelligent

Transportation Systems, vol. 1, 2003, pp. 5053.

[3] B. Krause and C. von Altrock, Intelligent highway by fuzzylogic: Congestion detection and traffic control on multi-lane

roads with variable road signs, in 5th International

Conference on Fuzzy Systems, vol. 3, September 1996, pp.

18321837.

[4] F. Porikli and X. Li, Traffic congestion estimation usinghmm models without vehicle tracking, in IEEE Intelligent

Vehicles Symposium, June 2004, pp. 188193.

[5] W. Pattara-atikom, P. Pongpaibool, and S. Thajchayapong,2006 Estimating Road Traffic Congestion using Vehicle

Velocity , in Proceeding of ITST 2006, June 2006.

[6] A.P. Paplinski, Adaptive Neuro-Fuzzy Inference System(ANFIS), May 20, 2005

[7] N. Patchanee, P. Tangamchit, P. Pongpaibool, Road TrafficEstimation from a GPS-equipped Car using Fuzzy Logic, in

Proceeding of 29th Electrical Engineering Conference, Vol.2

pp.10811084, Chonburi, Thailand, November 2006.

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