An Equation is Generated with the help of Previous Traffic Data of NH-06.(Nagpur - Amravati) for Forecasting of Traffic at NH-06. A Simulation Model is Prepared using Microscopic Simulation tool VISSIM 7.0 which shows the behaviour of heterogeneous Traffic at Indian Highway. Diffrent Forms of Graph are Obtained for Estimation of Capacity of highway and Relation between Volume and Stream Speed. From the Graph a polynomial equation having second degree is obtain which shows the relationship between Widh of Carriageway and Capacity of Highway. This Equation is used for Estimation of Capacity at Indian Highway.

1. FINAL SEMINAR
ON
“FORECASTING OF TRAFFIC SIMULATION MODEL
UNDER HETEROGENEOUS TRAFFIC CONDITION”.
Guided By:-
Dr. B.V.Khode
Professor, Department of Civil Engg,
Nagpur
Presented By:
Pratik Raj
MTech(Transportation Engg.)
Roll No- 08.

2. OUTLINE
• Introduction
• Literature survey
• Need of research
• Objective
• Methodology
• Field Data
• Simulation Model Development
• Forecasting of Traffic
• Validation of data
• Conclusion.
• Publish paper.
• References.

3. INTRODUCTION
• Traffic flow behavior is a complex phenomenon.
• Analytical and mathematical concepts have been successful in providing
the basic understanding of traffic flow, but with limitations
• Limitations are the practical difficulties, non availability of required field
conditions etc.,
• Solution: Computer simulation which replicates the complex traffic
systems and allows experimentation
• One such simulation model is VISSIM.
• Traffic forecasting is the process of estimating the no of people or vehicle
that will use a specific transportation facility in the future.

4. LITERATURE SURVEY
 Siddhartha S MP et. al , Elsevier (2013)
Siddhartha et al, purpose a method of automatic calibration of VISSIM by
using data from an intersection in Chennai. He used ANOVA and
elementary effect method for sensitivity analysis.
 Manjra Singh Bains et.al, Elsevier (2012)
Manjra Singh Bains et al, develop a model for traffic flow on Indian
express way by evaluating passenger car unit (PCU) of different vehicle at
different volume level in a level terrain using the microscopic simulation
model VISSIM. He also evaluate capacity of express way.
 Errampalli Madhu et al, IJSTER (2012)
Errampalli et.al, developed a microscopic simulation model by
considering eight-lane divided urban expressway. He developed a speed-
flow equation for different vehicle type by using PARAMICS simulation
software.

5. Contd..
 Pan Liu et.al, ASCE (2012)
Pan Liu et.al, purposed a procedure for model U- turn movement at
unsignalized intersection with traffic simulation program VISSIM. They
introduce genetic algorithm to calibrate and validate the VISSIM simulation
model.
 V. Thamizh Arasan et.al, ASCE (2010)
V. Thamizh Arasan et.al, studied an estimation of PCU values of vehicle
under different condition by using microscopic simulation. They also develop a
model using computer programming.
 Satish Chandra et.al, ASCE(2008)
Satish Chandra et.al, developed a computer program to simulate the traffic
flow on two-lane highway. Their model is used to determine capacity of two-
lane road and to study the effect of traffic mix on capacity and speed.

6. Contd..
 P. Vedagiri et.al ,(2006)
P. Vedagiri et.al purposed a procedure for saturation flow rate under
heterogeneous traffic condition (measured in PCU per meter width) by using
Computer simulation software HETEROSIM.
And he found that under heterogeneous traffic conditions, there is a significant
increase in the saturation flow rate (measured in PCU per meter width) with
increase in the width of approach road.
 Shriniwas S Arkatkar et.al, Elsevier (2012)
Shriniwas S Arkatkar et.al, develop a model for traffic flow on Indian
express way by evaluating passenger car unit (PCU) of different vehicle at
different volume level in a level terrain using the microscopic simulation model
VISSIM. He also evaluate capacity of express way.
And he found that For all categories of vehicles, the PCU of a given vehicle
category decreases with increase in volume capacity ratio. This is due to the
decreasing speed difference as volume increases from free flow to that at
capacity

7. Contd..
 Akhilesh Kumar Mauryac et.al, Elsevier (2013)
Akhilesh Kumar Mauryac et.al develop a traffic simulation model with
heterogeneous traffic and No lane discipline together by using Computer
simulation software develop in "C" language. And mechanism used by
CUTSiM model developed by Mauryac (2007).
 Satish Chandra et.al. ASCE, (2008)
Satish Chandra et.al. studies the effect of traffic mix on capacity and
speed. By using Computer simulation software, it is coded in Visual Basic
language. and he found capacity of a two-lane road under all cars situation
is estimated as 2,860 PCU/h, which is lower than that suggested in HCM
TRB 2000.
Lower capacity in the present case may be due to the lower operating
speeds of vehicles on Indian highways as compared to the United States.
However, this is comparable to the capacity value of 2,400–3,000 PCU/h
given in IRC 1990 .

8. Contd..
 Satish Chandra et.al. ASCE,2003
Satish Chandra et.al. he develop a new concept to estimate the passenger
car unit (PCU) of different types of vehicles under mixed traffic by using the
formula
 Tom V. Mathew et.al. ASCE,2014
Tom V. Mathew et.al. development of a simulation framework that can be
used to represent both lane-based homogeneous traffic as well as non-lane-
based mixed vehicle type heterogeneous traffic.
By using simulation framework, SiMTraM (Simulation of Mixed Traffic
Mobility), And computer simulation software SUMO.
 S. Gangopadhyay et.al. Central Road Research Institute. 2013
S. Gangopadhyay et.al. examine the travel time reliability under the influence
of various demand and supply side factors of the transportation system by
using computer simulation software VISSIM.

9. NEED OF RESEARCH
• Extensive research work was carried to estimate the capacity the of
highway, this study will update the capacity.
• Knowledge of future traffic flow is an essential input in the planning
implementation and development of transportation system. It is the
essential input to start the planning or development phase of any major
transportation project.
• In case of highway, the geometry and structural design are based on
forecast traffic volume.
• Sometimes forecast even helps us to know whether a project is needed at
all.
• Traffic simulation is the mathematical modeling of transport system
through the application of computer software to better help plan, design,
and operates transportation system.

10. OBJECTIVE
• To Analyze the capacity of selected four lane divided highway based on the
field data.
• To Examine the performance of selected highway with the help of
microscopic simulation model.
• To develop speed-flow relationship for National Highway.
• To Forecasting the Traffic by analyzing the past data and present
simulation of heterogeneous traffic.

11. METHODLOGY
The methodology for present research work are summarized
below:-
 Collection of field data by using videography method
 Examine the performance of selected highway with the help of simulation
model.
 Development of speed-flow relation using field data.
 Simulation model development using VISSIM.
 Comparison of theoretical and simulated speed-flow curve
 Forecasting is carried out by analysing the past traffic data at NH-06 and
present situations of heterogeneous traffic.

12. SELECTED STRETCH (NH-06, NAGPUR-AMRAVATI)

13. FIELD DATA
• On National Highway-06 Nagpur-
Amravati near wadi (Indian oil petrol
pump)
• A Trap length of 50 m is marked on
the carriage way to collect the spot
speed.
• Video graphic method is used to
collect traffic data.
• Data is then extracted to find the spot
speed of different vehicles types,
traffic volume for 5 minute interval
and composition
• All the vehicles are classified into
seven categories namely Car, Two
wheeler(2W), Three wheeler
(3W),Bus, LCV, Truck, MAV,

14. FIELD DATA ANALYSIS
Table 1.0 Shows the Physical Dimension
of Different Vehicles in Traffic Stream
Vehicle
Type
Length
(m)
Width
(m)
Projected
Area
(m2)
CAR 3.5 1.5 5.5
2-
WHEELE
R
1.8 0.6 1.2
3-
WHEELE
R
3.2 1.4 4.4
BUS 11.4 2.5 28.5
LCV 6 1.9 11.4
MAV 13.7 2.5 34.25
TRUCK 13.6 2.42 32.91
Fig. 1 Shows the Vehicle Composition at
NH-06 During Peak Hour (7.30-9.30)
32%
8%
3%
9%
13%
16%
19%
Vehicle Composition at NH-06
(Peak Hour 7.30 -9.30 AM)
CAR
2-WHEELER
3-WHEELER
BUS
LCV
MAV
TRUCK

15. DETAIL OF PHYSICAL DATA AT DIFFERENT SECTION
SECTION HIGHWAY
WIDTH OF
THE
CARRIAGE
WAY(m)
PAVED
SHOULDER
(m)
EARTHEN
SHOULDER
(m)
1 NH-06 7.2 0.5 1.5
2 NH-07 7 0.5 1.5
3 MSH-260 7.8 0.5 1.2
4 MSH-255 6.5 0.5 1.2

16. PASSENGER CAR UNIT FOR DIFFERENT TYPES OF
VEHICLES AT DIFFERENT SECTIONS
SECTION
CARRIAGE
WAY WIDTH
(m)
BUS TRUCK LCV
2-
WHEEL
ER
3-
WHEE
LER
HEAVY
VEHICLES
CAR
1 7.8 5.7 6.8 3.6 0.7 2.2 8.2 1
2 7.2 5.3 6.5 3.4 0.5 2.1 8.1 1
3 7 5.1 6.2 3.1 0.3 0.8 7.8 1
4 6.8 4.6 5.2 2.6 0.2 0.5 7.1 1

17. SPEED ANALYSIS
Vehicle
types
Speed Parameter (km/h)
Percentile
speeds(km/h)
Maximum Minimum Average
Standard
Deviation 15th 85th
CAR 112.2 30.2 72.8 31.61 41.63 94.076
2-
WHEELER 119.6 29.2 74.5 29.1 43.849 94.602
3-
WHEELER 76.5 34.6 55.55 14.45 36.097 67.917
BUS 93.2 28.9 60.75 22.57 37.486 82.286
LCV 79.1 32.3 53.2 15.66 37.908 62.456
MAV 72.3 23.16 47.73 15.26 22.439 66.663
TRUCK 84.7 24.6 54.65 19.17 30.45 72.769

18. SPEED-FLOW RELATIONSHIP FROM FIELD DATA
• The extracted traffic flow
was in terms of number of
vehicles/hour.
• Dynamic PCU concept given
by Satish Chandra and
kumar (2003) was used.
• A polynomial model was
tried to fit.
• Capacity is 3688 pcu/hr/dir.
• The capacity of two lane
road is 2800 Pcu/h
( HCM1994) and 3200 Pcu/h
(HCM 2000)
Fig. 1.0 shows the Speed-Flow Relation obtain
from field data.
y = -7E-07x2 - 0.009x + 86.71
R² = 0.955
0
10
20
30
40
50
60
70
80
0 1000 2000 3000 4000
STREAM
SPEED
(KM/H)
VOLUME (PCU/H/DIR)
THEORITICAL
CAPACITY
Stream spot
speed

19. SIMULATION MODEL DEVELOPMENT
• A link of 1km stretch and two lanes of 3.5m width was created in Vissim.
• A travel time section of 50m was marked at a distance of about 650m from
the vehicle input.
• Field inputs were assigned to the link.
• The lateral and overtaking behaviour was modified so as to replicate the
field behaviour.

20. DEVELOPMENT OF SIMULATION MODEL IN VISSIM
7.0
Fig.(2a). Shows the Snap Short of Simulation Model of Four-lane divided
Highway Created in VISSIM 7.0.

21. Fig.(2.b). Shows the snap short of 3D view of simulation Model.

22. SIMULATED SPEED-FLOW CURVE.
Fig.1.1 Shows the Simulated Speed-Flow Curve obtain from Vissim. And
Simulated Capacity is 3733 pcu/hr/dir.
y = 8E-07x2 - 0.016x + 95.13
R² = 0.906
0
10
20
30
40
50
60
70
80
0 1000 2000 3000 4000
STREAM
SPOT
SPEED(KM/H)
Volume(PCU/H/DIR)
SIMULATED CAPACITY.
Stream spot speed

23. COMPARISON OF THEORETICAL CAPACITY
AND SIMULATED CAPACITY.
Fig. 1.2 Shows the Comparison of Theoretical Capacity and Simulated
Capacity. And The Simulated Capacity is 3733 pcu/hr/dir against the
Field Capacity of 3688 pcu/hr/dir.
y = -7E-07x2 - 0.0092x + 86.718
R² = 0.9559
y = 8E-07x2 - 0.0161x + 95.134
R² = 0.9065
0
10
20
30
40
50
60
70
80
0 1000 2000 3000 4000
STREAM
SPOT
SPEED(KM/H)
VOLUME(PCU/HR/DIR)
THEORITICAL CAPACITY(Spot
speed)
SIMULATED CAPACITY(Spot
speed)

24. COMPARISON OF AVERAGE SPEED
Fig.1.3 Shows the Comparison of Theoretical and Simulated Average
Speed.
0
10
20
30
40
50
60
70
80
CAR 2-WHEELER 3-WHEELER BUS LCV MAV TRUCK
Average
Speed(km/h)
Vehicle Types
Theoritical Average
Speed
Simulated Average Speed

25. COMPARISON OF THEORETICAL CAPACITY WITH
ARPAN MEHAR AND SATISH CHANDRA
Fig. 1.4 Shows the Comparison of Theoretical Capacity with Arpan Mehar
and Satish Chandra’s Capacity
y = 6E-07x2 - 0.011x + 81.287
R² = 0.9425
y = -7E-07x2 - 0.0092x + 86.718
R² = 0.9559
0
10
20
30
40
50
60
70
80
0 1000 2000 3000 4000 5000 6000
Stream
Spot
Speed(km/h)
Volume(PCU/Hr/dir)
Comparison of Theoretical Capacity with the other researcher’s
THEORITICAL CAPACITY(Stream
Spot speed,Satish Chandra,2007)
THEORITICAL CAPACITY(Stream
Spot speed,Pratik Raj,2016)

26. COMPARISON OF SIMULATED CAPACITY WITH
ARPAN MEHAR AND SATISH CHANDRA
Fig. 1.5 Shows the Comparison of Simulated Capacity with the Arpan
Mehar and Satish Chandra's Capacity.
y = 3E-07x2 - 0.0114x + 95.327
R² = 0.9455
y = 9E-07x2 - 0.017x + 96.438
R² = 0.9081
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000
Stream
speed(km/hr)
Volume(pcu/hr/dir)
Comparison of simulated capacity with the other Researchers
SIMULATED CAPACITY(Stream Spot
speed,Satish Chandra,2007)
SIMULATED CAPACITY(Stream Spot
speed,Pratik Raj,2016)

27. VARIATION OF THEORETICAL CAPACITY AND
SIMULATED CAPACITY
Fig1.6 Shows the Variation of Theoretical Capacity and Simulated
Capacity are between +5% and -5%.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 700 1400 2100 2800 3500 4200
Simulated
Capacity
Theoretical Capacity
VOLUME(PCU/hr/ln)
-5%
+5%

28. VARIATION OF THEORETICAL STREAM SPOT
SPEED AND SIMULATED STREAM SPOT SPEED
Fig.1.7 Shows the Variation of Theoretical Stream Spot Speed and
Simulated Stream Spot Speed are between +5% and -5%.
0
10
20
30
40
50
60
70
80
90
0 20 40 60 80 100
Simulated
Stream
Speed
(km/hr)
Theoritical Stream Speed (km/hr)
(Stream spot
speed)
-5%
+5%

29. RELATION BETWEEN CAPACITY AND WIDTH OF
CARRIAGE WAY
Fig.1.8 Shows the Relation obtain Between Width of Carriageway and
Capacity of Road.
C = -843.4W2 + 15263W - 61926
R² = 0.996
0
1000
2000
3000
4000
5000
6000
7000
6.6 6.8 7 7.2 7.4 7.6 7.8 8
Capacity(Pcu/h)
Width of Carriage Way(m)
Capacity
(Pcu/h)

30. FORECASTING OF DIFFRENT CLASS OF VEHICLES AT
NH-06
Fig.1.9 Shows the Variation of Cars at National Highway -06 during 2011-
2015.
No of Cars = 31099Year2 - 1E+08Year + 1E+11
R² = 0.943
0
100000
200000
300000
400000
500000
600000
700000
800000
2010 2011 2012 2013 2014 2015 2016
NO
OF
CARS
YEARS
Variation of Cars at National Highway-06 during year 2011-2015
Number of CARS

31. Total Number of Cars After Forecast (2016-2030) at NH-6
Fig. 2.0 Shows the Total number of cars at NH-06 after forecast (2016-
2030)
0
2000000
4000000
6000000
8000000
10000000
12000000
2014 2016 2018 2020 2022 2024 2026 2028 2030 2032
No
of
Cars
YEARS
Number of CARS

32. VARIATION OF LCV (LIGHT COMMERCIAL
VEHICLES) AT NH-06
Fig. 2.1 Variation of LCV(Light Commercial Vehicles) at NH-06 During
Year 2011-2015
No of LCV = -4897.Year3 + 3E+07Year2 -
6E+10Year + 4E+13
R² = 0.902
0
50000
100000
150000
200000
250000
2010 2011 2012 2013 2014 2015 2016
NO
OF
LCV
YEARS
Variation of LCV(Light Commercial Vehicles) at NH-06 During
Year 2011-2015
Number of LCV(Light
commercial vehicles)

33. Total Number of LCV After Forecast (2016-2030) at NH-06
Fig. 2.2 Shows the Total number of LCV after Forecasting (2016-2030) at
National Highway.
200000
250000
300000
350000
400000
450000
500000
550000
2013 2016 2019 2022 2025 2028 2031
No
of
LCV
Years
Number of LCV(Light
commercial vehicles)

34. VARIATION OF BUSES AT NH-06
Fig. 2.3 Shows the Variation of Buses at National Highway during Year
2011-2015.
No of Buses = 3536.Year2 - 1E+07Year + 1E+10
R² = 0.921
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
2010 2011 2012 2013 2014 2015 2016
NO
OF
BUSES
YEARS
Variation of Buses at NH-06 During Year 2011-2015
NO OF BUSES

35. Total Number of Buses After Forecast (2016-2030) at NH-06
Fig. 2.4 Shows the Total Number of Buses at National Highway After
Forecasting (2016-2030).
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
2013 2016 2019 2022 2025 2028 2031
No
of
Buses
Years
NO OF BUSES

36. Variation of Trucks at NH-06
Fig. 2.5 Shows the Variation of Truck during Year (2011-2015) at
National Highway
No of Trucks = 23089Year2 - 9E+07Year + 9E+10
R² = 0.983
0
50000
100000
150000
200000
250000
300000
350000
2010 2011 2012 2013 2014 2015 2016
NO
OF
TRUCKS
YEARS
Variation of Trucks at National Highway During Year 2011-2015
NO OF
TRUCKS

37. Total Number of Trucks After Forecast (2016-2030) at NH-06
Fig. 2.6 Shows the Total Number of Truck after Forecasting (2016-2030)
at National Highway.
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
2014 2016 2018 2020 2022 2024 2026 2028 2030 2032
No
of
Truck
Years
NO OF TRUCKS

38. VARIATION OF MAV(MULTI AXCEL VEHICLES) AT
NH-06
Fig. 2.7 Shows the Variation of Multi Axcel Vehicles During Year 2011-
2015 at National Highway
NO OF MAV= 11091Year3 - 7E+07Year2 + 1E+11Year - 9E+13
R² = 0.892
1000000
1020000
1040000
1060000
1080000
1100000
1120000
2010 2011 2012 2013 2014 2015 2016
NO
OF
MAV
YEARS
Variation of MAV(Multi axel vehicles) at NH-06 During Year 2011-
2015
NO OF
MAV(Mu
lti axel
vehicles

39. Total Number of MAV(Multi Axcel Vehicles) After Forecast (2016-2030) at
NH-06
Fig. 2.8 Shows the Total Number of Multi Axcel Vehicles after Forecasting
(2016-2030) at National Highway.
500000
1000000
1500000
2000000
2500000
3000000
2014 2016 2018 2020 2022 2024 2026 2028 2030 2032
No
of
MAV
Years
NO OF MAV(Multi
axel vehicles

40. Variation of Total Traffic at NH-06 During Year (2011-2015)
Fig. 2.9 Shows the Variation of Total Traffic at National Highway During
Year (2011-2015).
No of Vehicles = 64260Year2 - 3E+08Year +
3E+11
R² = 0.911
1000000
1500000
2000000
2500000
2010 2011 2012 2013 2014 2015 2016
NO.
OF
VEHICLES
YEARS
Variation of Total Traffic at NH-06 During Year 2011-0215
NUMBER OF VEHICLES

41. Variation of Total Traffic at NH-06 During Year (2011-2030)
Fig. 3.0 Shows the Variation of Total Number of Vehicles at National Highway
During Year (2015-2030). And Growth rate for traffic on highway should not be
exceed 6%. ( IRC : 108-1996 Guidelines For Traffic Prediction On Highways)
No of Vehicles= 65271Year2 - 3E+08Year + 3E+11
R² = 0.999
0
5000000
10000000
15000000
20000000
25000000
2010 2013 2016 2019 2022 2025 2028 2031
NO
OF
VEHICLES
YEARS
NUMBER OF VEHICLES

42. SIMULATED CAPACITY AT NH-06 ON 2030.
• After forecasting of traffic at National Highway the simulated capacity is 6237.15
PCU/hr.
y = -7E-07x2 - 0.0039x + 95.323
R² = 0.9212
0
10
20
30
40
50
60
70
80
0 2000 4000 6000 8000
STREAM
SPOT
SPEED(KM/H)
Volume(PCU/h/dir)
Simulated Capacity at NH-06 on 2030.
SIMULATED CAPACITY(Spot
speed)

43. CONCLUSION
• Present Capacity on National Highway-06 is 3688 pcu/hr/dir or 1844
pcu/hr/lane.
• Simulated Capacity on National Highway is 3733 pcu/hr/dir against the
Field Capacity 3688 pcu/hr/dir.
• After Forecasting the simulated capacity is 6237.15 PCU/hr/dir.
• It is found that the PCU for a vehicle type increases with increasing lane
• width.
• A general equation is establish between relation of capacity of highway and
width of carriageway.
• Capacity can be reduce at National Highway by increasing the public
transportation capacity.
• Strict lane for heavy vehicles.

44. PAPER PUBLISHED IN INTERNATIONAL JOURNAL
1. “ Comparative Study of Traffic Simulation Model for Mix Traffic Condition”
IJSTE - International Journal of Science Technology & Engineering |
Volume 2 | Issue 08 | February 2016 ISSN (online): 2349-784X
2. “Forecasting of Traffic Simulation Model Under Heterogeneous Traffic
Condition”
“International research journal of Engineering and Technology” Volume
3, Issue 4, April 2016.

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48. THANK YOU