1. INTELLIGENT TRAFFIC CONTROL
DECISION SUPPORT SYSTEM
Made by:
Apoorva Aggarwal & Shubham Gulati
Jaypee Institute of Information Technology
INTELLIGENT TRAFFIC CONTROL
DECISION SUPPORT SYSTEM
Made by:
Apoorva Aggarwal & Shubham Gulati
Jaypee Institute of Information Technology

2. WHAT IS ITC DSS?
INTELLIGENTTRAFFIC
CONTROL DECISION SUPPORT
SYSTEM

3. WHAT DOES ITC DSS DO?
ITC DSS GIVES SUPPORTTO THE
HUMAN OPERATOR AT
TRAFFIC CONTROL CENTER

4. WHAT DOES ITC DSS DO?
IT HELPS HUMAN TRAFFIC
OPERATOR TO ACTIN MORE
ORGANIZEDMANNER

5. WHAT DOES ITC DSS DO?
ITC-DSS TAKES CURRENT
TRAFFIC STATE VARIABLES AS
INPUT

6. WHAT DOES ITC DSS DO?
SUCH AS
AVERAGE TRAFFIC
DENSITY

7. WHAT DOES ITC DSS DO?
SUCH AS
AVERAGE TRAFFIC
DEMAND

8. WHAT DOES ITC DSS DO?
WHAT IS THE RESULT?

9. WHAT DOES ITC DSS DO?
RESULT IS A RANKED LIST OF
CONTROL MEASURES
WHICH ARE BEST SUITED TO
CONTROL A GIVEN TRAFFIC STATE

10. WHAT DOES ITC DSS DO?
HOWDOES THIS RANKED LIST
HELPTHE HUMAN OPERATOR IN
TRAFFIC CONTROL?

11. WHAT DOES ITC DSS DO?
HUMAN OPERATOR CAN SELECT THE
BEST CONTROL MEASURE
STARTING FROM THE FIRST IN THE LIST
WITHOUT THINKING

12. WHAT IS THE PURPOSE?
PURPOSE OF MAKING
ITC-DSS

13. WHAT IS THE PURPOSE?
IN THE CASE OF NON RECURRENT
TRAFFIC CONGESTION STATE OF
TRAFFIC NETWORK IS VERY CRITICAL

14. WHAT IS THE PURPOSE?
HUMAN OPERATOR OF TRAFFIC
CONTROL CENTRE HAS TO SELECT MOST
APPROPRIATE TRAFFIC CONTROL
MEASURE

15. WHAT IS THE PURPOSE?
ORA COMBINATION OF
CONTROL MEASURES IN A
SHORT TIME TO MANAGE TRAFFIC
NETWORK

16. WHAT IS THE PURPOSE?
THIS COMPLEX TASK REQUIRES
EXPERT
KNOWLEDGE

17. WHAT IS THE PURPOSE?
THIS COMPLEX TASK REQUIRES
EXPERIENCE

18. WHAT IS THE PURPOSE?
THIS COMPLEX TASK REQUIRES
FAST REACTION

19. WHAT IS THE PURPOSE?
IDENTIFICATIONOF SUITABLE
CONTROL MEASURES CAN BE TOUGH
EVEN FOR EXPERIENCED OPERATORS

20. WHAT IS THE PURPOSE?
SIMULATION MODELS ARE USED
IN MANY CASES

21. WHAT IS THE PURPOSE?

22. WHAT IS THE PURPOSE?
SIMULATINGDIFFERENT TRAFFIC
SCENARIONS FOR NUMBER OF CONTROL MEASURES
IN COMPLICATED SITUATION IS TIME
CONSUMING

23. WHAT IS THE PURPOSE?
WHY DID WE CHOOSE
INTELLIGENT TECHNIQUES?

24. APPROACH OF ITC-DSS
WHAT IS THE APPROACHUSED
IN ITC-DSS?

25. APPROACH OF ITC-DSS
ITC-DSS COMBINES
THREE
SOFT-COMPUTING APPROACHES

26. APPROACH OF ITC-DSS
FUZZY LOGIC
NEURAL NETWORK
GENETIC ALGORITHM

27. APPROACH OF ITC-DSS
FUZZY LOGIC
NEURAL NETWORK
GENETIC ALGORITHM

28. APPROACH OF ITC-DSS
FUZZY LOGIC
NEURAL NETWORK
GENETIC ALGORITHM

29. APPROACH OF ITC-DSS
COMBINATIONOF THE THREE
APPROACHES FORMS
FNN Tool

30. ARCHITECTURE
HERE IS THE OVERALL
ARCHITECTURE OF ITC-DSS

31. ARCHITECTURE

32. WHAT IS FNN TOOL?
WHAT IS?
FNN Tool

33. WHAT IS FNN TOOL?
Fuzzy Neural Network
Tool

34. HOW DOES FNN TOOL WORK?
HOW DOES FNN-TOOL WORK?

35. HOW DOES FNN TOOL WORK?
FUZZY NEURAL NETWORK TOOL
USES THREE STAGE
LEARNING APPROACH

36. HOW DOES FNN TOOL WORK?
FIRST STAGE
SECOND STAGE
THIRD STAGE

37. HOW DOES FNN TOOL WORK?
FIRST STAGE INITIALIZES
MEMBERSHIP FUNCTIONS USING
EXPECTATION-MAXIMIZATION
ALGORITHM

38. HOW DOES FNN TOOL WORK?
WHAT IS A
EXPECTATION
MAXIMIZATION
ALGORITHM

39. HOW DOES FNN TOOL WORK?
EXPECTATION
MAXIMIZATION
ALGORITHM
USES CLUSTERING ON A MIXTURE OF GAUSSIAN
MODELS

40. HOW DOES FNN TOOL WORK?
EM ALGORITHM
IT COMPUTES PROBABILITY OF EACH DATA
POINT BELONGING TO A PARTICULAR CLUSTER

41. HOW DOES FNN TOOL WORK?
PROCEDURE
RANDOMLY INITIALIZE THE GAUSSIAN PARAMETERS
REPEAT 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

42. HOW DOES FNN TOOL WORK?
PROCEDURE
2. RECOMPUTE THE PARAMETERS OF EACH GAUSSIAN
THIS IS CALLED M-STEP AS IT PERFORMS MAXIMUM LIKELIHOOD
ESTIMATION OF PARAMERTERS

43. HOW DOES FNN TOOL WORK?
FIRST STAGE
SECOND STAGE
THIRD STAGE

44. HOW DOES FNN TOOL WORK?
SECOND STAGE IDENTIFIES FUZZY
RULES USING GENETIC
ALGORITHM BASED LEARNING
METHOD

45. HOW DOES FNN TOOL WORK?
FIRST STAGE
SECOND STAGE
THIRD STAGE

46. HOW DOES FNN TOOL WORK?
THIRD STAGE EMPLOYS BACK
PROPAGATION NEURAL NETWORK
ALGORITHM FOR FINE TUNING THE
SYSTEM PARAMETERS

47. MODEL VERIFICATION
HOW HAVE WE VERIFIED THE
CORRECTNESS OF OUR MODEL?

48. MODEL VERIFICATION
USING A SIMULATORAVAILABLE
FROM TECHNICAL UNIVERSITY OF CRETE,
DYNAMIC SYSTEMS AND SIMULATION
LABORATORY,
DR. ING. A. MESSMER.
FOR RESEARCH BASED PROJECTS

49. MODEL VERIFICATION
METANET
IS THE TRAFFIC SIMULATOR

50. MODEL VERIFICATION
WHAT DOES METANETDO?

51. MODEL VERIFICATION
METANET TAKES INPUTOF CURRENT
TRAFFIC STATE IN FORM OF VARIABLES SUCH AS
SPEED, DENSITY AND FLOW

52. MODEL VERIFICATION
METANET TAKES INPUTOF CURRENT
TRAFFIC STATE IN FORM OF INCIDENTS
WITH THEIR TIME STAMPS

53. MODEL VERIFICATION
METANET COMPILES THESE INPUTS AND
OUTPUTSTHE VALUES OF TOTAL
TRAVELED TIME AND TOTAL TRAVELED
DISTANCE FOR CONTROL MEASURES

54. MODEL VERIFICATION
ON SIMULATING DIFFERENT CONTROL
MEASURES USING METANET WE FOUND THAT
THE VALUESOF TTT AND TDT FROM
ITC-DSSAND FROM METANET
WERE A CLOSE MATCH

55. 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

56. ISSUES AND LIMITATIONS
PREDEFINEDCONTROL MEASURES
FOR EVERY POTENTIAL SITE ON GEOGRAPHIC
AREA ARE NEEDED AS INPUTBY FNN-TOOL

57. 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

58. ISSUES AND LIMITATIONS
WHAT HAPPENES IF INPUT VARIABLES ARE
INCREASED?

59. ISSUES AND LIMITATIONS
TOO MANY INPUT VARIABLES WIIL CAUSE THE
SYSTEM TO OVERLOAD.

60. ISSUES AND LIMITATIONS
WHAT HAPPENES IF INPUT VARIABLES ARE
NOT DEFINED?

61. ISSUES AND LIMITATIONS
LAYERSOF FNN-TOOL NEEDS TO BE
MODIFIEDAND NEW
TRAINING DATA IS NEEDED TO
TRAIN THE NEURAL NETWORK

62. ISSUES AND LIMITATIONS
WHAT HAPPENES IF INPUT VARIABLES ARE
TOO MANY?

63. ISSUES AND LIMITATIONS
INITIAL POPULATION OF
CHROMOSOMES WILL GROW VERY
LARGE

64. ISSUES AND LIMITATIONS
AND GENETICALGORITHM WILL TAKE
INFINITE TIME TO FIND BEST FIT
CHROMOSOME

65. ISSUES AND LIMITATIONS
WHAT HAPPENES IF INPUT VARIABLES ARE
NUMERICALLY TOO LARGE?

66. ISSUES AND LIMITATIONS
FNN-TOOL WILL THROW ERROR
AND NUMERICAL VALUES OF INPUT VARIABLES
WILL NEED A SCALE DOWN

67. TESTING THE SYSTEM
WE HAVE PERFORMED VARIOUS TYPES OF TESTING
WHILE DEVELOPING THE SYSTEM IN ORDER TO
MAKE SURE IT WORKS
CORRECTLY WHEN DEPLOYED

68. TESTING THE SYSTEM
WE HAVE DONE
UNIT TESTING FOR
FNN STRUCTURE, FUZZY SETS IN CONDITION LAYER,
CORRECT OUTPUT OF EACH NEURON, RULE BASE
USING POP
USING WHITE BOX TESTING

69. TESTING THE SYSTEM
WE HAVE DONE
INTEGRATION TESTING
FOR 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

70. TESTING THE SYSTEM
WE HAVE DONE
REQUIREMENTSTESTING
FOR RANKED LIST OF CONTROL MEASURES AND
CORRECT OUTPUT VALUES (TTT AND TDT) OF FNN

71. TESTING THE SYSTEM
WE HAVE DONE
PERFORMANCE TESTING

72. TESTING THE SYSTEM
WE HAVE DONE
STRESS TESTING
FOR PERFORMANCE OF GA AND ROBUSTNESS OF FNN
ON INCREASING INPUT VARIABLES
USING BLACK AND WHITE BOX TESTING

73. TESTING THE SYSTEM
WE HAVE DONE
LOAD TESTING
BY INCREASING THE NUMERICAL INPUT VALUES UP
TO THE BREAKING POINT
USING BLACKBOX TESTING

74. TESTING THE SYSTEM
WE HAVE DONE
VOLUME TESTING

75. TESTING THE SYSTEM
NO HARDWARE ITEMS WERE
NEEDED TO TEST THE SYSTEM

76. TESTING THE SYSTEM
SOFTWARE ITEMS WERE NEEDED TO
TEST THE SYSTEM
1. NETBEANS WITH JDK 1.7 OR ABOVE
2. METANET
3. LINUX BASED OPERATING SYSTEM

77. MULTI AGENT
SYSTEM

78. CENTRALIZED TRAFFIC CONTROL SYSTEM

79. FLOW OF CONTROL

80. CONTROL ACTION TABLE
Traffic
Control
Action
Affected sub-networks
𝑠𝑛1 (𝑔1) 𝑠𝑛2 (𝑔2) 𝑠𝑛𝑗 (𝑔j)
𝑐𝑎1 𝑌1
1 𝑅1
1 𝑌1
1 𝑅1
1
. . . 𝑌1
1 𝑅1
1
𝑐𝑎2
𝑌1
1 𝑅1
1 𝑌1
1 𝑅1
1
. . . 𝑌1
1 𝑅1
1
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
𝑐𝑎i
𝑌1
1 𝑅1
1 𝑌1
1 𝑅1
1
. . . 𝑌1
1 𝑅1
1

81. FITNESS FUNCTION
FITNESS OF EACH CHROMOSOME IS
CALCULATED USING THE FORMULA
𝐹𝐼𝑇 = 1 − (
1
𝑛 𝑑
𝑖=1
𝑛 𝑑
(𝑦𝑖 − 𝑦𝑖)2
)

82. MEAN OF EACH FUZZY SET
MEAN OF EACH FUZZY SET IS CALCULATED USING THE
FORMULA
𝜇𝑖
= 𝑡=1
𝑇 𝑝(𝑖|𝑥 𝑡, ⋋)𝑥 𝑡
𝑡=1
𝑇 𝑝(𝑖|𝑥 𝑡, ⋋)

83. VARIANCE OF EACH FUZZY SET
VARIANCE OF EACH FUZZY SET IS CALCULATED
USING THE FORMULA
𝜎𝑖
2
=
𝑡=1
𝑇
𝑝(𝑖|𝑥𝑡, ⋋)𝑥𝑡
2
𝑡=1
𝑇
𝑝(𝑖|𝑥𝑡, ⋋)
− 𝜇𝑖
2

84. PREDICTED TRAFFIC DEMAND
P_DEM IS CALCULATED BY EACH AFFECTED AGENT
USING THE FORMULA
𝑃_𝐷𝑒𝑚 𝐵
𝑖
= 𝐶_𝐷𝑒𝑚 𝐵 + (𝐶_𝐷𝑒𝑚 𝐵 ∗
𝑌𝐵
𝑖
100)

85. GLOBAL PERFORMANCE
GLOBAL PERFORMANCE OF EACH CONTROL
MEASURE IS CALCULATED BY COORDINATOR USING THE
FORMULA
𝑝 𝑔
𝑖 =
𝑝𝑙
𝑖
+ 𝑗=1
𝑁
(𝐹𝑗
𝑖
∗ 𝜔𝑗 ∗ 𝜇 𝑗
𝑖
)
1 + 𝑗=1
𝑁
(𝜔𝑗 ∗ 𝜇 𝑗
𝑖)

86. IMPACT OF CONTROL ACTION
IMPACT OF EACH CONTROL MEASURE ON AFFECTED
AGENT IS CALCULATED BY COORDINATOR USING THE FORMULA
𝜇𝑗
𝑖
=
𝑌𝑗
𝑖
100 ∗
𝑃𝐷𝑒𝑚 𝑗
𝑖
𝑀𝑎𝑥 𝑂𝐹 𝑗
+
𝑅𝑗
𝑖
100 𝑖𝑓 𝑌𝑗
𝑖
> 0
𝑅𝑗
𝑖
100 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

87. FUTURE WORK
OUTLIER DETECTION

88. FUTURE WORK
USING CLUSTERING ALGORITHMS TO
REDUCE WORK DONE IN IDENTIFICATION OF
FUZZY RULES

89. FUTURE WORK
USING LIVE VIDEO AND IMAGES TO
CALCULATE NUMERICAL VALUES OF INPUT
VARIABLES

90. FUTURE WORK
ONSITE INTERFACEFOR ITC-DSS

91. THANK YOU!