IT HELPS TO STUDY THE TRAFFIC FLOW PATTERN AND USAGE OF LANE WIDTH

1. CAPACITY ESTIMATION OF TWO LANE
UNDIVIDED HIGHWAY
Presented By
Manjunath Borakanavar
Assistant Professor Sanjivani College of Engineering
Kopargaon

2. INTRODUCTION
 Heterogeneous traffic condition is characterized by lack of lane discipline and wide variation in
static and dynamic characteristics of vehicles sharing common road space. Hence it is very difficult to
study the traffic flow behavior in heterogeneous traffic conditions.
 Very little empirical research has been conducted regarding the mix traffic characteristics of two
lane undivided carriageway.
 Present study is concerned with the macroscopic traffic characteristics observed on 2-lane undivided
carriageway of Bardoli and Olpad city in Gujarat. Classified traffic volume survey for 16 hour duration
on a typical weekday has been carried to identify peak and off peak hours.
 Data has been processed in M S-Excel to develop relationships between speed (V)-density (K), flow
(Q)-density (K) and speed (V) - flow (Q). Important parameters of the models at capacity and at jam
conditions are derived.
 Estimated parameters are compared with the parameters obtained from Greenshield’s model and
Chandra’s method.

3. OBJECTIVES
 To analyse the traffic flow data collected on three sections of two-lane undivided
highway.
 To find dynamic PCU values at each 5-minute interval for the study sections using
Chandra’s method.
 To develop speed-flow-density relationship on two-lane undivided highway for all
three sections under consideration.
 To study the effect of varying lane width on capacity of two-lane undivided
highway.

4. LITERATURE REVIEW
 Hoban et al. (1987)
 S.Chandra and Upendrakumar (2002)
 S. Chandra (2004)
 Dey et al. (2008)
 Rinkal T. Patel, Shree Chetan. R. Patel &
Dr. G. J. Joshi (2015)
 Summarized that a linear speed-volume relationship can be
used in most of the cases to represent speed-volume
relationships.
 Reported data were collected at ten sections of two lane
roads in different parts of India and PCU’s were estimated.
 The effect of influencing parameters like gradient, lane
width, shoulder width, traffic composition, directional split,
slow moving vehicles and pavement surface conditions.
 Used a simulation technique to estimate the capacity of a
two lane road by developing a speed-volume relationship.
 Studied the macroscopic traffic stream parameters for
undivided collector streets in India and summarized the Q-
K-V models.

5. STUDY METHODOLOGY

6. STUDY SITE
Figure 1: Location of Bardoli 1st Section Figure 3: Location of Olpar SectionFigure 2: Location of Bardoli 2nd Section

7. DATA COLLECTION
Serial
no
Study area
Identification
Right of
way width
Remarks Trap
length(m)
1 Bardoli 1st section 14 Both side
movement
60
2 Bardoli 2nd section 9 Both side
movement
60
3 Oldpad road 7 Both side
movement
50
Serial
no
Study area
Identification
Carriage
way width
(m)
Paved shoulder
width
(m)
Earthen
shoulder width
(m)
1 Bardoli 1st section 7 3 4
2 Bardoli 2nd section 7 3 2.4
3 Olpad road 7 3 2
 The data were collected by video filming technique at sections given in Table. A longitudinal trap of
50-60 m was made on the road and recording was done for 8 hours during morning/evening hours of
a typical weekday for each section.
 These sections were marked in white color. Movement of all categories of vehicles was observed and
position of left wheels of a vehicle passing though the section was recorded.
 In order not to influence the driver, the recording was done by an observer standing at an obscure
place such as the shadow of a roadside tree at a little distance away from the pavement. For the same
reason of not disturbing the general pattern of traffic, observations were restricted to a typical
weekday and clear sky.

8. SNAPSHOT OF STUDY SITE

9. DATA EXTRACTION
ENTRY STAMP EXIT STAMP
GRAPHIC USER INTERFERENCE OF SCREEN MARKER

10. DATA PROCESSING
 The data collected is used in the calculation on travel time, speed, flow and density for
an each section for an interval of 5min.
 The trend lines are developed using average speed, flow and density.
 The flow values are projected for the change in density values.

11. VEHICLE COMPOSITION
Vehicle Composition Identification
number
Small car 1
Big car 2
Two-wheeler 3
LCV 4
Bus 5
Single axle truck 6
Multi axle truck 7
Auto 8

12. TRAFFIC COMPOSITION AT STUDY SECTION
26%
4%
39%
8%
2%
7%
5% 9%
Vehicle composition of bardoli 1st section
small car big car two wheeler
lcv bus single axle vehicles
multi-axle vehicles auto
26%
7%
41%
5%
3%
8% 7%3%
Vehicle composition of bardoli 2nd section
small car big car two wheeler
lcv bus single axle vehicles
multi-axle vehicles auto
29%
5%
48%
3%2%4%2%7%
Vehicle composition of Olpad road
small car big car 2w lcv
bus single axle truck multi axle truck auto

13. HOURLY VOLUME VARIATION FOR BARDOLI 1ST SECTION

14. HOURLY VOLUME VARIATION FOR BARDOLI 2ND SECTION

15. HOURLY VOLUME VARIATION FOR OLPAD ROAD

16. MACROSCOPIC FUNDAMENTAL RELATIONSHIPS
 Empirical relationships among three basic variables speed, density and flow can
be explained on the basis of fundamental diagrams of traffic flow.
 It is a macroscopic theoretical model which is an eminent indicator of the
operational capability of road facility.
 On the basis of the data collected from the field for this study, three primary
relationships have been established considering Greenshield’s assumption of
flow-density relationship.

17. Q-K-V MODELS
 Extracted data of speed, flow and density are compiled for each direction and
speed–density, speed-flow and flow- density plots are drawn and models are
developed by curve fitting technique.
 In order to fully understand the flow behavior across the range of volume, it is
necessary to generate missing speed–flow–density data.
 For generating missing data flow-density curve is considered.
 By differentiating the equation of flow-density curve with respect to density, the
capacity and optimum density values are obtained.

18. DERIVED EQUATIONS WITH R2 VALUE
Study area
identification
Derived Equation R2
Bardoli 1st section y = -0.1475x2 + 55.292x 0.9061
Bardoli 2nd section y = -0.3781x2 + 58.634x 0.92
Oldpad road y = -0.2091x2 + 49.798x 0.8553
Where y= Flow (veh/hr)
x= Density (veh/km)

19. FLOW VS DENSITY CURVE FOR DIFFERENT STUDY SECTIONS

20. SPEED VS FLOW CURVE FOR DIFFERENT STUDY SECTIONS

21. SPEED VS DENSITY CURVE FOR DIFFERENT STUDY SECTIONS

22. SUMMARY OF Q-K-V MODEL PARAMETERS
Study area
Identification
Qmax
(Veh/Hr)
Kopt
(Veh/Km)
Kjam
(Veh/Km)
Vopt
(Kmph)
Bardoli 1st section 5200 190 380 25
Bardoli 2nd section 2300 70 240 29
Olpad road 3000 120 150 23
Where Qmax (Veh/Hr) – Maximum flow rate in Veh/Hr
Kopt (Veh/Km) – Optimum Density in Veh/Km
Kjam (Veh/Km) – Jam Density in Veh/Km
Vopt (Kmph) – Speed at capacity in Kmph

23. PCU ESTIMATION USING CHANDRA’S METHOD
 The main problem in developing the analytical speed-flow relationship is heterogeneity of traffic.
 Simply adding the number of vehicles does not give the authentic speed flow relationship.
 For this reason, the vehicles are normally presented in terms of standard type of vehicle using
certain conversion factors.
 Generally, passenger car is adopted as standard vehicle and therefore the factor is known as
passenger car unit (PCU).
 The basic concept used to estimate the PCU is that it is directly proportional to the ratio of clearing
speed, and inversely proportional to the space occupancy ratio with respect to the standard design
vehicle, a car, i.e.
PCUi=
Speed ratio of the car to the ith vehicle
Space ratio of the car to the ith vehicle

24. RECTANGULAR AREA OF DIFFERENT CATEGORY OF VEHICLES
Vehicle type Rectangular area of vehicle
(m2)
Small car 5.36
Big car 8.11
Two wheeler 1.2
LCV 12.81
Bus 24.54
Single axle truck 16.28
Multi axle truck 17.63
Auto 4.48

25. DERIVED EQUATIONS WITH R2 VALUE
Study area
Identification
Derived formula R2
Bardoli 1st section y = -0.1481x2 + 57.31x 0.9507
Bardoli 2nd section y = -0.2131x2 + 56.62x 0.9374
Olpad road y = -0.537x2 + 80.632x 0.8624
Where y= Flow (veh/hr)
x= Density (veh/km)

26. FLOW VS DENSITY CURVE FOR DIFFERENT STUDY SECTIONS

27. SPEED VS FLOW CURVE FOR DIFFERENT STUDY SECTIONS

28. SPEED VS DENSITY CURVE FOR DIFFERENT STUDY SECTIONS

29. SUMMARY OF Q-K-V MODEL PARAMETERS
Study area
Identification
Qmax
(PCU/HR)
Kopt
(VEH/KM)
Kjam
(VEH/KM)
Vopt
(Kmph)
Bardoli 1st section 5500 190 390 29
Bardoli 2nd section 3700 140 280 29
Olpad road 3100 80 150 40
Where Qmax (Veh/Hr) – Maximum flow rate in Veh/Hr
Kopt (Veh/Km) – Optimum Density in Veh/Km
Kjam (Veh/Km) – Jam Density in Veh/Km
Vopt (Kmph) – Optimum Speed at capacity in Kmph

30. MEAN PCU’S OF EACH VEHICLE FOR DIFFERENT SECTIONS
Study area Identification Right
of
way
width
PCU’s for
Small
car
Big car Two-
wheeler
LCV Bus Single
axle
truck
Multi
axle
truck
Auto
Bardoli 1st section 14 1 1.47043
5
0.2416
5
2.5330
93
4.8102
66
3.4894
99
3.9461
28
1.0715
5
Bardoli 2nd section 9 1 1.42581
9
0.2593
79
2.8052
91
4.9145
55
3.7138
38
4.3184
36
1.2918
59
Oldpad road 7 1 1.48506
4
0.2964
79
3.4582
94
5.6274
13
3.8558
48
4.6703
31
1.2694
52

31. EFFECT OF LANE WIDTH
Study area
Identification
Observed
Qmax
(PCU/HR)
Right of
way width
width (m)
Carriage way
width(m)
Qmax (PCU/HR) equivalent
to 7m
Bardoli 1st
section
5500 14 7 2750
Bardoli 2nd
section
3700 9 7 2670
Olpad road 3100 7 7 3100

32. EFFECT OF LANE WIDTH WITH CAPACITY
Serial no Study area
Identification
Right of
way width
Capacity(PCU/Hr)
1 Bardoli 1st section 14 5500
2 Bardoli 2nd section 9 3700
3 Oldpad road 7 3100
0
1000
2000
3000
4000
5000
6000
0 2 4 6 8 10 12 14 16
Capacity(PCU/hr)
Right of way width (m)
Capcity vs Right of Way width

33. CONCLUSION
 For efficient design of traffic facility understanding of traffic flow characteristics and their inter
relationship is necessary. In the present study, macroscopic traffic flow characteristics has been
adopted for developing speed–flow–density equations for selected undivided two lane road.
 Flow–density–speed models are developed in the study are able to explain the behaviour of traffic
stream precisely with a significant coefficient of determination R2 under heterogeneous traffic
environment.
 From the present study vehicle composition in major proportion is observed to be for two wheeler
with 39% for Bardoli section one, section two it is 40% and for Olpad road it is 48%.
 The composition of heavy vehicle category is estimated to be 12% for Bardoli section one, 16%
for section two and olpad road is 6%. The capacity was found out by Greenshield model and
Chandra’s method for the study undertaken.

34.  The capacity of the Bardoli road on section one is estimated as 5500 PCU/Hr, section two is 3700
PCU/hr and olpad road is 3100 PCU/hr.
 The capacity of the Bardoli road on section one for 7m carriage way width is estimated as 2750
PCU/Hr, section two is 2877 PCU/hr and olpad road is 2285 PCU/hr.
 Percentage increase in capacity for section 2 and section 1 from section 3 by considering right of
way width are 19.35% and 77.41 % respectively. These results may be useful considering the
importance of right of way and its effect on capacity.
 Percentage decrease in capacity for section 2 and section1 from section 3 by considering carriage
way width 7m are 11.29 % and 7.19 % respectively. These results may be useful considering the
importance of right of way and its effect on capacity.
CONTINUED . . .

35. REFERENCES
1. Chandra, S. and Kumar U., (2003), “Effect of lane width on capacity under mixed traffic
conditions in India”, Journal of Transportation Engineering, ASCE, 129, 155-160.
2. Dey, P.P., Chandra S. and Gangopadhyay S., (2008), “Simulation of mixed traffic flow on two-
lane roads”, Journal of Transportation Engineering, 134, ASCE, 361-369.
3. Ramanayya, T.V. (1988), “Highway capacity under mixed traffic conditions”, Traffic Engineering
and Control, 29, 284 – 287.
4. Sahoo, P.K., Rao S.K., and V.M. Kumar (1996), A study of traffic characteristics on two stretches
of National Highway No.-5. Indian Highways, Indian Roads Congress, 24, 11 – 17.
5. Bang, K.L., Carlsson, A. and Palgunadi (1995), “Development of speed-flow relationship for
Indonesia rural roads using empirical data and simulation”, Transportation Research Record,
1484, Transportation Research Board, Washington, D.C., 24-32.
6. Rinkal T. Patel, Shree Chetan. R. Patel, Dr. G. J. Joshi(2015), “A study of Macroscopic Traffic
Stream Parameters for Undivided Collector Streets in India” National Conference on Recent
Research in Engineering and Technology (NCRRET -2015) International Journal of Advance
Engineering and Research Development (IJAERD) e-ISSN: 2348 – 4470
7. Chandra, S. (2004), “Capacity estimation procedure for two lane roads under mixed traffic
conditions”, Indian Roads Congress Journal, 165, New Delhi, 139-170.
8. Zhou, M. and F. L. Hall (1999), “Investigation of speed-flow relationship under congested
conditions on a freeway”, CD ROM Proceedings of the 78th Annual Meeting of Transportation
Research Board, Washington, D.C.
9. Sarana A.C., P. K. Jain and G. Chandra (1989), “Capacity of urban roads-a case study of Delhi
and Bombay”, Highway Research Bulletin, 48, Indian Roads Congress, 1-38.

36. THANK YOU