Intelligent traffic control decision support system

INTELLIGENT TRAFFIC CONTROL DECISION SUPPORT SYSTEMMade by:Apoorva Aggarwal & Shubham GulatiJaypee Institute of Information Technology

INTELLIGENT TRAFFIC CONTROL DECISION SUPPORT SYSTEMMade by:Apoorva Aggarwal & Shubham GulatiJaypee Institute of Information Technology

WHAT IS ITC DSS?

INTELLIGENT TRAFFIC CONTROL DECISION SUPPORT

SYSTEM

WHAT DOES ITC DSS DO?

ITC DSS GIVES SUPPORT TO THE

HUMAN OPERATOR AT TRAFFIC CONTROL CENTER

WHAT DOES ITC DSS DO?

IT HELPS HUMAN TRAFFIC

OPERATOR TO ACT IN MORE

ORGANIZED MANNER

WHAT DOES ITC DSS DO?

ITC-DSS TAKES CURRENT TRAFFIC STATE VARIABLES AS

INPUT

WHAT DOES ITC DSS DO?

SUCH AS

AVERAGE TRAFFIC DENSITY

WHAT DOES ITC DSS DO?

SUCH AS

AVERAGE TRAFFIC DEMAND

WHAT DOES ITC DSS DO?

WHAT IS THE RESULT?

WHAT DOES ITC DSS DO?

RESULT IS A RANKED LIST OF CONTROL MEASURESWHICH ARE BEST SUITED TO

CONTROL A GIVEN TRAFFIC STATE

WHAT DOES ITC DSS DO?

HOW DOES THIS RANKED LIST

HELP THE HUMAN OPERATOR IN TRAFFIC CONTROL?

WHAT DOES ITC DSS DO?

HUMAN OPERATOR CAN SELECT THE

BEST CONTROL MEASURESTARTING FROM THE FIRST IN THE LIST

WITHOUT THINKING

WHAT IS THE PURPOSE?

PURPOSE OF MAKINGITC-DSS

WHAT IS THE PURPOSE?

IN THE CASE OF NON RECURRENT

TRAFFIC CONGESTION STATE OF

TRAFFIC NETWORK IS VERY CRITICAL

WHAT IS THE PURPOSE?

HUMAN OPERATOR OF TRAFFIC

CONTROL CENTRE HAS TO SELECT MOST

APPROPRIATE TRAFFIC CONTROL

MEASURE

WHAT IS THE PURPOSE?

OR A COMBINATION OF

CONTROL MEASURES IN A

SHORT TIME TO MANAGE TRAFFIC NETWORK

WHAT IS THE PURPOSE?THIS COMPLEX TASK REQUIRES

EXPERT KNOWLEDGE

WHAT IS THE PURPOSE?

THIS COMPLEX TASK REQUIRES

EXPERIENCE

WHAT IS THE PURPOSE?

THIS COMPLEX TASK REQUIRES

FAST REACTION

WHAT IS THE PURPOSE?

IDENTIFICATION OF SUITABLE

CONTROL MEASURES CAN BE TOUGH EVEN FOR EXPERIENCED OPERATORS

WHAT IS THE PURPOSE?

SIMULATION MODELS ARE USED IN MANY CASES

WHAT IS THE PURPOSE?

BUT

WHAT IS THE PURPOSE?

SIMULATING DIFFERENT TRAFFIC SCENARIONS FOR NUMBER OF CONTROL MEASURES

IN COMPLICATED SITUATION IS TIME CONSUMING

WHAT IS THE PURPOSE?

WHY DID WE CHOOSE

INTELLIGENT TECHNIQUES?

APPROACH OF ITC-DSS

WHAT IS THE APPROACH USED IN ITC-DSS?

APPROACH OF ITC-DSS

ITC-DSS COMBINESTHREE

SOFT-COMPUTING APPROACHES

APPROACH OF ITC-DSS

FUZZY LOGICNEURAL NETWORK

GENETIC ALGORITHM

APPROACH OF ITC-DSSFUZZY LOGIC

NEURAL NETWORKGENETIC ALGORITHM

APPROACH OF ITC-DSS

FUZZY LOGICNEURAL NETWORK

GENETIC ALGORITHM

APPROACH OF ITC-DSS

COMBINATION OF THE THREE

APPROACHES FORMS

FNN Tool

ARCHITECTURE

HERE IS THE OVERALL

ARCHITECTURE OF ITC-DSS

ARCHITECTURE

WHAT IS FNN TOOL?

WHAT IS?

FNN Tool

WHAT IS FNN TOOL?

Fuzzy Neural NetworkTool

HOW DOES FNN TOOL WORK?

HOW DOES FNN-TOOL WORK?

HOW DOES FNN TOOL WORK?

FUZZY NEURAL NETWORK TOOL

USES THREE STAGELEARNING APPROACH

HOW DOES FNN TOOL WORK?

FIRST STAGESECOND STAGETHIRD STAGE

HOW DOES FNN TOOL WORK?

FIRST STAGE INITIALIZES MEMBERSHIP FUNCTIONS USING

EXPECTATION-MAXIMIZATIONALGORITHM

HOW DOES FNN TOOL WORK?

WHAT IS A

EXPECTATION MAXIMIZATION

ALGORITHM

HOW DOES FNN TOOL WORK?

EXPECTATION MAXIMIZATION

ALGORITHMUSES CLUSTERING ON A MIXTURE OF GAUSSIAN

MODELS

HOW DOES FNN TOOL WORK?

EM ALGORITHMIT COMPUTES PROBABILITY OF EACH DATA

POINT BELONGING TO A PARTICULAR CLUSTER

HOW DOES FNN TOOL WORK?

PROCEDURERANDOMLY INITIALIZE THE GAUSSIAN PARAMETERSREPEAT UNTIL CONVERGE

1. COMPUTE PROBABILITY FOR ALL DATA POINTS BELONGING TO EACH CLUSTERS (THIS IS CALLED E-STEP) AS IT COMPUTES THE EXPECTED VALUES OF THE CLUSTER MEMBERSHIPS FOR EACH DATA POINT

HOW DOES FNN TOOL WORK?

PROCEDURE2. RECOMPUTE THE PARAMETERS OF EACH GAUSSIANTHIS IS CALLED M-STEP AS IT PERFORMS MAXIMUM LIKELIHOOD

ESTIMATION OF PARAMERTERS

HOW DOES FNN TOOL WORK?

FIRST STAGE

SECOND STAGETHIRD STAGE

HOW DOES FNN TOOL WORK?

SECOND STAGE IDENTIFIES FUZZY RULES USING GENETIC

ALGORITHM BASED LEARNING METHOD

HOW DOES FNN TOOL WORK?

FIRST STAGESECOND STAGE

THIRD STAGE

HOW DOES FNN TOOL WORK?

THIRD STAGE EMPLOYS BACK PROPAGATION NEURAL NETWORK

ALGORITHM FOR FINE TUNING THE SYSTEM PARAMETERS

MODEL VERIFICATION

HOW HAVE WE VERIFIED THE

CORRECTNESS OF OUR MODEL?

MODEL VERIFICATION

USING A SIMULATOR AVAILABLE FROM TECHNICAL UNIVERSITY OF CRETE,

DYNAMIC SYSTEMS AND SIMULATION LABORATORY,

DR. ING. A. MESSMER.FOR RESEARCH BASED PROJECTS

MODEL VERIFICATION

METANETIS THE TRAFFIC SIMULATOR

MODEL VERIFICATION

WHAT DOES METANET DO?

MODEL VERIFICATION

METANET TAKES INPUT OF CURRENT TRAFFIC STATE IN FORM OF VARIABLES SUCH AS

SPEED, DENSITY AND FLOW

MODEL VERIFICATION

METANET TAKES INPUT OF CURRENT

TRAFFIC STATE IN FORM OF INCIDENTS WITH THEIR TIME STAMPS

MODEL VERIFICATION

METANET COMPILES THESE INPUTS AND

OUTPUTS THE VALUES OF TOTAL TRAVELED TIME AND TOTAL TRAVELED DISTANCE FOR CONTROL MEASURES

MODEL VERIFICATIONON SIMULATING DIFFERENT CONTROL

MEASURES USING METANET WE FOUND THAT

THE VALUES OF TTT AND TDT FROM

ITC-DSS AND FROM METANET

WERE A CLOSE MATCH

VERIFICATION RESULTS  Metanet Model   ITC DSS  

  TTT TDT TTT TDT

C1 2258.14 189700.2 2205.16 189139.22

C2 2570.91 189756.9 2528.738 189139.22

C3 2627.16 189645.1 2528.738 189139.22

C4 2234.64 193770.8 2528.738 192480.96

C5 2704.92 192721 2528.738 192480.96

ISSUES AND LIMITATIONS

PREDEFINED CONTROL MEASURES FOR EVERY POTENTIAL SITE ON GEOGRAPHIC

AREA ARE NEEDED AS INPUT BY FNN-TOOL

ISSUES AND LIMITATIONS

FNN-TOOL TAKES A SMALL SET OF

PREDEFINED VARIABLES IN TRAFFIC DATA INPUT SUCH AS AVERAGE TRAFFIC

DEMAND AND AVERAGE TRAFFIC DENSITY

ISSUES AND LIMITATIONS

WHAT HAPPENES IF INPUT VARIABLES ARE

INCREASED?

ISSUES AND LIMITATIONS

TOO MANY INPUT VARIABLES WIIL CAUSE THE

SYSTEM TO OVERLOAD.

ISSUES AND LIMITATIONS

WHAT HAPPENES IF INPUT VARIABLES ARE

NOT DEFINED?

ISSUES AND LIMITATIONS

LAYERS OF FNN-TOOL NEEDS TO BE

MODIFIED AND NEW TRAINING DATA IS NEEDED TO

TRAIN THE NEURAL NETWORK

ISSUES AND LIMITATIONS

WHAT HAPPENES IF INPUT VARIABLES ARE

TOO MANY?

ISSUES AND LIMITATIONS

INITIAL POPULATION OF

CHROMOSOMES WILL GROW VERY LARGE

ISSUES AND LIMITATIONS

AND GENETIC ALGORITHM WILL TAKE

INFINITE TIME TO FIND BEST FIT CHROMOSOME

ISSUES AND LIMITATIONS

WHAT HAPPENES IF INPUT VARIABLES ARE

NUMERICALLY TOO LARGE?

ISSUES AND LIMITATIONS

FNN-TOOL WILL THROW ERRORAND NUMERICAL VALUES OF INPUT VARIABLES

WILL NEED A SCALE DOWN

TESTING THE SYSTEM

WE HAVE PERFORMED VARIOUS TYPES OF TESTING WHILE DEVELOPING THE SYSTEM IN ORDER TO

MAKE SURE IT WORKS CORRECTLY WHEN DEPLOYED

TESTING THE SYSTEMWE HAVE DONE

UNIT TESTING FORFNN STRUCTURE, FUZZY SETS IN CONDITION LAYER,

CORRECT OUTPUT OF EACH NEURON, RULE BASE USING POP

USING WHITE BOX TESTING

TESTING THE SYSTEMWE HAVE DONE

INTEGRATION TESTINGFOR CHECKING THE OUTPUT OF NEURON IN FUZZY LAYER,

CORRECT MEMBERSHIP VALUES FOR TRAINING DATA VALUES, OPTIMAL CHROMOSOME OF FNN USING GENETIC

ALGORITHM

USING BLACK AND WHITE BOX TESTING

TESTING THE SYSTEMWE HAVE DONE

REQUIREMENTS TESTINGFOR RANKED LIST OF CONTROL MEASURES AND

CORRECT OUTPUT VALUES (TTT AND TDT) OF FNN

TESTING THE SYSTEM

WE HAVE DONE

PERFORMANCE TESTING

TESTING THE SYSTEM

WE HAVE DONE

STRESS TESTINGFOR PERFORMANCE OF GA AND ROBUSTNESS OF FNN

ON INCREASING INPUT VARIABLES

USING BLACK AND WHITE BOX TESTING

TESTING THE SYSTEM

WE HAVE DONE

LOAD TESTINGBY INCREASING THE NUMERICAL INPUT VALUES UP

TO THE BREAKING POINT

USING BLACK BOX TESTING

TESTING THE SYSTEM

WE HAVE DONE

VOLUME TESTING

TESTING THE SYSTEM

NO HARDWARE ITEMS WERE NEEDED TO TEST THE SYSTEM

TESTING THE SYSTEM

SOFTWARE ITEMS WERE NEEDED TO TEST THE SYSTEM

1. NETBEANS WITH JDK 1.7 OR ABOVE2. METANET3. LINUX BASED OPERATING SYSTEM

MULTI AGENT SYSTEM

CENTRALIZED TRAFFIC CONTROL SYSTEM

FLOW OF CONTROL

CONTROL ACTION TABLETraffic 

Control Action

Affected sub-networks

𝑠𝑛1 ( 1)𝑔 𝑠𝑛2 ( 2)𝑔 𝑠𝑛𝑗 ( j)𝑔𝑐𝑎1  𝑌1

1 𝑅11 𝑌1

1 𝑅11 . . . 𝑌1

1 𝑅11

𝑐𝑎2𝑌1

1 𝑅11 𝑌1

1 𝑅11 . . . 𝑌1

1 𝑅11

 ...

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.𝑐𝑎i𝑌1

1 𝑅11 𝑌1

1 𝑅11 . . . 𝑌1

1 𝑅11

FITNESS FUNCTION

FITNESS OF EACH CHROMOSOME IS CALCULATED USING THE FORMULA

MEAN OF EACH FUZZY SET

MEAN OF EACH FUZZY SET IS CALCULATED USING THE FORMULA

VARIANCE OF EACH FUZZY SET

VARIANCE OF EACH FUZZY SET IS CALCULATED USING THE FORMULA

PREDICTED TRAFFIC DEMAND

P_DEM IS CALCULATED BY EACH AFFECTED AGENT USING THE FORMULA

GLOBAL PERFORMANCE

GLOBAL PERFORMANCE OF EACH CONTROL MEASURE IS CALCULATED BY COORDINATOR USING THE

FORMULA

IMPACT OF CONTROL ACTION

IMPACT OF EACH CONTROL MEASURE ON AFFECTED AGENT IS CALCULATED BY COORDINATOR USING THE FORMULA

𝜇𝑗𝑖 = ە۔

൭ۓ

𝑌𝑗𝑖 100൘ ∗ 𝑃𝐷𝑒𝑚𝑗𝑖𝑀𝑎𝑥𝑂𝐹𝑗൱+ 𝑅𝑗𝑖 100൘ 𝑖𝑓 𝑌𝑗𝑖 > 0 𝑅𝑗𝑖 100൘ 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

FUTURE WORK

OUTLIER DETECTION

FUTURE WORK

USING CLUSTERING ALGORITHMS TO REDUCE WORK DONE IN IDENTIFICATION OF

FUZZY RULES

FUTURE WORK

USING LIVE VIDEO AND IMAGES TO CALCULATE NUMERICAL VALUES OF INPUT

VARIABLES

FUTURE WORK

ONSITE INTERFACE FOR ITC-DSS

THANK YOU!