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
π π1 ( 1)π π π2 ( 2)π π ππ ( j)πππ1 π1
1 π 11 π1
1 π 11 . . . π1
1 π 11
ππ2π1
1 π 11 π1
1 π 11 . . . π1
1 π 11
...
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.ππiπ1
1 π 11 π1
1 π 11 . . . π1
1 π 11
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
πππ = ΫΫ
ΰ΅Ϋ
πππ 100ΰ΅ β ππ·πππππππ₯ππΉπΰ΅±+ π ππ 100ΰ΅ ππ πππ > 0 π ππ 100ΰ΅ ππ‘βπππ€ππ π
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!