Fatih Gündoğan Martin Fellendorf

ITS World Congress, Orlando, USA20.10.2011 1

Fatih GündoğanMartin Fellendorf Graz University of Technology, AustriaInstitute for Highway Engineering and Transport Planningwww.isv.tugraz.at

Real-time signal control using artificial neural networks

for developing megacities

ITS World CongressOrlando, October 16th – 20th, 2011

http://www.isv.tugraz.at/


Contents

Introduction - Problems in developing megacities - Need - Objective of this study

Proposed Methodology - System Architecture - Traffic Pattern Recognition - Artificial Neural Network

Results of Simulation Study Conclusion


High and increasing population Increasing in number of vehicles Inadequate Infrastructure

Problems in developing megacities

Driving behavior/Lane Discipline Engineering Experience Cost and Budget balance


Objective of the study

Development of a simplified low-cost traffic dependent signal control system using pattern recognition and

artificial neural networks

Step 1: Conduct a questionnaire with megacities Step 2: Development of simplified control strategyStep 3: Reduction number of detectors and placement optimizationStep 4: General Evaluation of the system


Real Time Signal Control (Rule-Based)

Collect Data

Check Traffic Pattern

Or Threshold

Check Timing Plan

Send new timing plans


Proposed system architecture

Data Collection

(VISSIM 5.0)

Neural Network Controller

Traffic State Estimation

Set of Signal Plans

Network Signal Control Optimization

(TRANSYT 13)

Pattern Recognition

Method

Counts and Occupancy

Data

Outputs:Travel Time

DelayNumber of Stops

(Planning Mode)

PC1


What is pattern?Pattern: A regularly repeated arrangement (especially of lines, shapes,

etc. on a surface or of sounds, words, etc.) (in Longman Dictionary)

What is pattern recognition?pattern recognition is the assignment of some sort of output value to a

given input value , according to some specific algorithm. It contains classification, clustering, regression etc.

Where can be used?Speech, Face, Finger Prints or Traffic sign recognition

Pattern Selection / Pattern Recognition


Recognition of Fish Species using features…

Source: Duda et all. Pattern Classification

Measured Features:- Width- Lightness

Classification of Fish Species

Pre-ProcessingFeature Extraction

- Salmon- Sea

bass


Measured Features:- Volume- Occupancy

Classification of Traffic States

Pre-ProcessingFeature Extraction

• Morning Peak• Afternoon Peak• Off-Peak• Weekend

Traffic pattern recognition


What are (artificial) neural networks?

Natural neurons receive signals through synapses located on the dendrites or membrane of the neuron. When the signals are received strong enough (surpass a certain threshold), the neuron is activated and emits a signal though the axon. This signal might be sent to another synapse, and might activate other neurons. Axon

Terminal Branches of Axon

Dendrites

Axon

Terminal Branches of Axon

Dendrites

S

x1

x2w1

w2

wnxn

x3 w3

The complexity of real neurons is highly abstracted when modelling artificial

neurons. These basically consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron. Another function (which may be the identity) computes the output of the artificial neuron (sometimes in dependance of a certain threshold). ANNs combine artificial neurons in order to process information


Simple (single layer) Perceptron

net w xj ji ii

m

.

1)(1

1)( Netfj eRxf

kk OTE Learning using Gradient Descent Method

X1

X2

X3

X4

F

1

1

0Features:

W1

W2

W3

W4

(Sigmoidal Function)


Backpropagation Calculate new weights for Hidden - Output Layer

Calculate new weights for Input - Hidden Layer (not in this simple example)

Calculate Error:

)..( hohoho oww ))(1(* ooooo otoo

)..( ihihih oww o

hohhhh woo )()1(*

p

otRMSE

oop

n2

1)(

X1

X2

X3

X4

F


Network architecture and system parameters

8 Input Neurons 12 Output Neurons 15 Hidden Neurons 1 Hidden layer

Im

H1

On

input layer

hidden layer

output layer

volume &

occupancy

traffic state

O4

O3

O2

O1

I4

I3

I2

I1

Hk

H2

H3

H4

H5

wHOwIH

Im Number of Input Neurons

Number of Hidden Neurons

Number of Output Neurons

Hk

On

Learning Rate: 0.3 Momentum

1.0 Starting weights (random) between 0 and 1 Number of Iteration: 20000


RMSE during the training

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

0.011

0.012

0.013

0.014

0.015

Num ber of Iterations

RM

SE

4000 8000 12000

Number of Iterations

RM

SE


System application using microscopic traffic flow simulation

1

7

System detector

Number of Lanes

and Direction

2

6 5

3

4 8

230m 270m

321

12 Scenarios / 12 Timing plans (AM-peak, PM-peak, night hours, weekend etc…) Optimization of Timing plans using TRANSYT 13 Training and Test in neural network


VISSIM-Neural Network Interface using VBA

Microscopic traffic flow simulation: VISSIM MLP Controller in VBA

COM Interface(Update interval:5 min)

signal control data

volume & occupancy

data


Simulation Test

Traffic Pattern 1

Timing Plan 1

Traffic Pattern 2

Volume (veh/h)

7:00 11:00 13:00 17:00 Time (h)

Volume (veh/h)

7:00 11:00 13:00 17:00 Time (h)

Timing Plan 1

CASE 0

CASE 1

Timing Plan 2

CASE 2

Case 0: optimized pre-timed (signal plans are optimized using TRANSYT 13

Case 1: optimized pre-timed (change the situation to am-peak for a short-time period

Case 2: Real-time signal plan selection using proposed controller)

Timing Plan 2

Timing Plan 2


Results

Average Delay Time

0

5

10

15

20

25

30

35

40

45

50

Base Case Case 1 Case 2

aver

age

dela

y tim

e (s

ec.)

Average Speed

0

10

20

30

40

50

60


aver

age

spee

d (k

m/h

)

Average Number of Stops

0

1

2

3

4

5

6

7


aver

age

num

ber o

f sto

ps (-

)


Pattern Recognition with Neural Network can be an alternative for Rule-Based Systems and reduce delays about 15% on arterial streets

The system works with less number of detectors, can be reduced further

Based on Simulation study, but each scenario could be tested.

Comparison between optimized signal plans, but in reality it is unusual.

Thank you for your attention!

Conclusions

Fatih Gündoğan Martin Fellendorf

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