PCU estimation

1. 1

2. Introduction
Need for the study
Objectives
Works done
Methodology
Table of
contents
2

3. • In developing countries like India the nature of the traffic conditions
are heterogeneous - no lane discipline is followed and no
segregation of different classes of vehicles.
• In order to study the traffic characteristics, the different vehicular
categories should be converted to one common standard vehicular
unit.
• It is the common practice to consider the passenger car as the
standard vehicle unit to convert the other vehicle classes and this
unit is called the Passenger Car Unit or PCU.
• The PCU value varies based on the traffic characteristics, hence
dynamic PCU values have to be adopted.
INTRODUCTION
3

4. NEED FOR THE STUDY
• The Highway Capacity Manual (HCM) has stated PCU values for the different vehicle
categories and for the different road sections.
• The stated values are static in nature but many researches has found out that the PCU values
are dynamic in nature – it varies depends upon the nature of the vehicular dimensions and
the traffic characteristics.
• Many researches has found out many methods for the estimation of the dynamic PCU’s for
different kind of vehicle categories but the PCU values found out using those methods varies
from one another.
• A slight variation in the PCU values may lead to the inappropriate effects in calculating the
capacity of the roads.
4

5. • The present study is an attempt to discuss the suitability of the different methods for
different circumstances so that one can make the right choice while adopting a PCU
estimation method.
• This study considers all the factors which may lead to the variations in the PCU values
and comes up with a new methodology for the estimation of the PCU, so that the
variations may be less when compared to the other estimation methods.
Contd…..
5

6. OBJECTIVES
Following are objective of this study,
 To Identify the various factors which affects the PCU values in the urban arterial roads.
 To study the suitability of different PCU estimation methods for the urban arterial roads.
 To come up with a new methodology for estimating the PCU based on heterogeneous traffic
conditions.
6

7. LITERATURE REVIEW
• The study of Literature Review is carried in relation to the objective of this study and identified
the different methodologies and concepts adopted in estimating the PCU of a selected section of
the road.
• The following are the different methods adopted to find out the PCU values
 PCU based on speed and area ratio
 PCU based on Homogeneous Co-efficient Method
 PCU based on Lagging headway Method
 PCU based on Platoon Formation Method
7

8. INFERENCE FROM LITERATURE REVIEW
 The lane width and the number of the lanes have the significant effect on the speed of the vehicles.
 The provision of a service lane has higher benefit cost ratio as compared to adding an extra lane on the main
carriageway without a service lane.
 The physical size of a vehicle is an indicator of the pavement occupancy, which is crucial in operational
characteristics of traffic stream.
 The PCU for a vehicle type increases linearly with the carriageway width.
 The influence of the roadway and traffic characteristics on vehicular movement could be easily studied from
the model which simulates the traffic flow characteristics
8

9.  When volume of the traffic increases, the physical dimensions and low maneuverability of heavy vehicles
become dominating and therefore heavy vehicles become more detrimental to the traffic stream as
compared to all other vehicle types.
 The PCE of the vehicle type varies with the traffic volume and its composition. It increases with an increase
in compositional share of respective vehicle types in the traffic stream.
 The parameters used in the estimation of PCU for homogeneous and mixed traffic are different for almost
all the facility types.
 Dynamic PCU values might better define the influence of a vehicle type in a traffic stream over different
traffic flow conditions.
Contd…..
9

10. Problem Identification
Formulation of objective
Review of literature
Selection of study stretch
Data collection
Geometric data:
Carriageway width
Roadway type
Presence of shoulders
IR method:
Classified volume count
Speed
Headway
Data extraction
PCU Estimation by the selected methods
Result and Conclusion
Methodology
10

11. Study location
For this study, a 4-lane divided urban road and a 6- lane divided urban road are selected as study locations in
Chennai.
6 lane divided roads
Rajiv Gandhi Road or IT Corridor is a major road in suburban Chennai, India, beginning at
the Madhya Kailash temple in Adyar in South Chennai and continuing south till Mahabalipuram, ultimately
merging with the East Coast Road. This road is State highway-49A
4 lane divided roads
Sardar Patel road which connects Guindy and Adyar. It is a Four Lane Divided Carriageway.
11

12. Study Stretch – IT corridor, Madhya
Kailash
12

13. Study Stretch – Sardar Patel Road
13

14. PARAMETERS INFLUENCING PCU VALUES
Speed
Headway
Delay
Density
Travel time
Queue Discharge
flow
For homogeneous traffic conditions several factors
are considered, some of them are listed below
Besides these for mixed traffic some other parameters
are also considered which are as follows
Area
occupancy
Platoon
Formation
Time
occupancy
Vehicle
hours
Influence area
V/C ratio
Note : Road width, Presence of shoulders, roughness of road, traffic composition, Land use and type of facility
are also some of the factors which influences the PCU values
14

15. Sardar patel
road
Characteristics of study area
SH – 49 A
Towards Perungudi
Six lane divided
3.1 m
No Shoulder
Location
Direction
Type
Lane width
Shoulder type
Study area
IT-Corridor
SH – 49
Towards Guindy
Four lane divided
3.5 m
Paved Shoulder
15

16. Vehicle Composition – IT Corridor
2 - Wheeler
57 %
Auto
12 %
Car
23 %
LCV
4 %
MCV
3%
16

17. Vehicle Composition – Sardar Patel Road
2 - Wheeler
53 %
Auto
10 %
MCV
3 %
Car
30 %
LCV
4 % 17

18. Lane usage – IT Corridor
L1 L2 L1 – L2 L2 – L3
• Lane usage are detected by the instrument used for
data collection known as TIRTL.
• Based on the time interval between the infrared
beams from transmitter to the receiver, the lane
usage are detected.
• The collected data shows that the 28% of the total
vehicle used L1, 40% of the total vehicle used the
lane L2, 22% of the total vehicle used L3, 6% of the
total vehicle doesn’t follows the lane discipline and
travels in between lane 1 and lane 2 and 9% of the
total vehicle doesn’t follows the lane discipline and
travels in between lane 2 and lane 3
L3
18

19. Lane usage – Sardar Patel road
L1 L2 L1 – L2 SL
• Lane usage are detected by the instrument used for data
collection known as TIRTL.
• Based on the time interval between the infrared beams from
transmitter to the receiver, the lane usage are detected.
• The collected data shows that the 56% of the total vehicle
used L1, 33% of the total vehicle used the lane L2, 11% of
the total vehicle travelled in the intermediate distance, 0% of
the total vehicle used shoulder for maneuvering
19

20. Speed and area
ratio
01
02
Platoon Formation
03
Based on
Homogeneous
Co-efficient method
Lagging headway
04
PCU estimation methods
20

21. Speed and area ratio method
• The PCU of different categories of vehicles are estimated based on the
speed of the individual vehicles and the area occupied by them. PCU could
be estimated from the relation shown below
PCUi = (Vc /Vi ) / (Ac/Ai )
PCUi = Passenger Car Unit of vehicle type i.
Vc, Vi = Average speed of small car and vehicle type i, respectively
Ac, Ai = Projected area of small car and vehicle type i, respectively.
21

22. Speed and area ratio method
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.2 0.25 0.36
Auto 0.87 1.1 1.61
Car - 1 -
LCV 1.65 2.2 3.79
MCV 5.11 6.51 10.29
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.14 0.24 0.44
Auto 0.64 1.02 2.66
Car - 1 -
LCV 1.17 2.04 4.16
MCV 4.23 6.11 9.74
Sardar Patel Road
IT Corridor
22

23. PCU based on Homogeneous Co-efficient Method
Permanent International Association of Road Congress (PIARC) proposed a model to
determine Homogeneous Coefficient (or PCU) of a vehicle category present in a mixed
traffic stream. The speed, as well as the length of a vehicle, were considered to
formulate the Homogeneous Coefficient (HCi) as given below.
PCUi = (Li/ Vi) / (Lc / Vc )
Where Li is the length of the subject vehicle (m)
Lc is the length of the standard car
Vi is the speed of the subject vehicle (Km/hr)
Vc is the speed of the standard car (Km/hr) 23

24. PCU based on Homogeneous Coefficient Method
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.38 0.46 0.65
Auto 0.75 0.96 1.42
Car - 1 -
LCV 0.82 1.18 2.43
MCV 1.66 2.4 3.67
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.29 0.44 1.05
Auto 0.56 0.9 2.72
Car - 1 -
LCV 0.65 1.15 2.25
MCV 1.13 5.25 9.37
Sardar Patel Road
IT Corridor
24

25. PCU based on lagging headway
Here, PCEs is defined as the ratio of the mean lagging headway of a subject vehicle
divided by the mean lagging headway of the basic passenger car. Lagging headway
is defined as the time or space from the rear of the leading vehicle to the rear of the
vehicle of interest; it is composed of the length of the subject vehicle and the
intravehicular gap.
PCUi = (Hij) / (Hpcj )
Lagging headway of various categories of vehicles was calculated from the
aggregated data and the PCUs of various categories of vehicles are calculated.
25

26. PCU based on lagging headway
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.23 0.78 3.27
Auto 0.10 0.87 4.36
Car - 1 -
LCV 0.14 1.04 3.91
MCV 0.13 1.2 4.6
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.23 0.89 3.27
Auto 0.10 0.85 4.36
Car - 1 -
LCV 0.14 1.31 3.91
MCV 0.13 1.15 4.6
Sardar Patel Road
IT Corridor
26

27. Platoon Formation Method
The proper value of critical headway is used to decide whether vehicles included in platoons or not.
The value of critical headway can be determined based on the mean relative speed method.
RSPi = Si – Si-1
RSPi is the relative speed between vehicles i and i-1 in km/h
Si is the speed of vehicles i in km/h.
In the present study, to estimate the PCU values of car, two-wheeler, LCV, MCV and trucks, the
Huber’s concept was used. In Huber method two streams, one containing only Passenger car (base
stream) and the other containing Passenger cars and vehicle type for which PCU values is going to
be estimated
.
27

28. Sardar Patel Road
IT Corridor
Platoon Formation Method
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.53 0.55 0.55
Auto 0.55 0.71 0.75
Car - 1 -
LCV 1.07 1.15 1.22
MCV 2.47 2.66 3.35
Vehicle Category Minimum Value PCU Maximum Value
2-Wheeler 0.44 0.52 0.55
Auto 0.52 0.7 0.78
Car - 1 -
LCV 1.16 1.18 1.23
MCV 2.37 2.47 2.52 28

29. CAPACITY ESTIMATION - Greenshield model
0
10
20
30
40
50
60
70
0 1000 2000 3000 4000 5000
Speed
Flow
0
10
20
30
40
50
60
70
0 5000 10000 15000
Speed
Flow
0
10
20
30
40
50
60
70
0 5000 10000 15000
Speed
Flow
y = -0.1033x + 51.737
R² = 0.4764
0
10
20
30
40
50
60
70
0 100 200 300 400
Speed
Density
y = -0.1598x + 53.733
R² = 0.6351
0
10
20
30
40
50
60
70
0 50 100 150
Speed
Density
y = -0.1622x + 53.845
R² = 0.6449
0
10
20
30
40
50
60
70
0 50 100 150
Speed
Density
Speed and area ratio method Homogeneous Coefficient method Lagging Headway method Platoon formation method
Sardar Patel Road
29

30. CAPACITY ESTIMATION - Greenshield model
PCU estimation
Methods
Capacity (PCU/hr) R2 value Equation
Speed and area ratio
method.
4469 0.6449 Y= -0.1622x + 53.845
Homogeneous
coefficient method
4517 0.6351 Y= -0.1598x + 53.733
Lagging headway
method
6478 0.4764 Y= -0.1033x+ 51.737
Platoon formation
method
3008 0.1948 Y= -0.3261x +53.866
Sardar Patel Road
30

31. CAPACITY ESTIMATION - Greenshield model
Speed and area ratio method Homogeneous Coefficient method Lagging Headway method Platoon formation method
IT Corridor
y = -0.1455x + 52.824
R² = 0.3219
0
10
20
30
40
50
60
0 20 40 60 80
Speed
Density
0
10
20
30
40
50
60
0 2000 4000 6000
Speed
Flow
y = -0.1372x + 52.905
R² = 0.3595
0
10
20
30
40
50
60
0 20 40 60 80 100
Speed
Density
0
10
20
30
40
50
60
0 2000 4000 6000
Speed
Flow
y = -0.0778x + 50.778
R² = 0.3335
0
10
20
30
40
50
60
0 50 100 150
Speed
Density
0
10
20
30
40
50
60
0 5000 10000
Speed
Flow
31

32. CAPACITY ESTIMATION - Greenshield model
IT Corridor
PCU estimation
Methods
Capacity (PCU/hr) R2 value Equation
Speed and area ratio
method.
4295 0.3219 Y= -0.1455x + 52.824
Homogeneous
coefficient method
5100 0.3595 Y= -0.1372x + 52.905
Lagging headway
method
8285 0.3335 Y= -0.0778x + 50.778
Platoon formation
method
7140 0.1462 Y= -0.0467x + 49.145
32

33. PCU Comparison
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
2 Wheeler Auto Bicycle Car HCV
0.25
1.10 1.00
2.20
6.51
0.46
0.96 1
1.18
2.4
0.78 0.87
1 1.04
1.2
0.52
0.7
1
1.18
2.47
IT Corridor
Speed area ratio
method
Homogeneous
Coefficient Method
Lagging headway
method
Platoon PCU
33

34. PCU Comparison
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
2 Wheeler Auto Bicycle Car HCV
0.24
1.02 1.00
2.04
6.11
0.44
0.9 1
1.15
2.15
0.89 0.85
1
1.31
1.15
0.55
0.71
1
1.15
2.66
PCU
Vehicle class
Sardar Patel Road
speed and area
ratio
Homogeneous
coefficient method
Lagging Headway
Platoon PCU
34

35. CONCLUSION
 Based on the analysis of the observed data it was found out that the Speed and area ratio method is
the best suited for estimating the PCUs of the different vehicle categories in the Urban roads in which
there is no lane discipline is being followed.
 The platoon formation methods could also be used to identify the PCU values in a situation in
which the Speed and area ratio method is found to be unreliable.
Capacity (PCUs/hr)
Sathish Chandra
method
Homogeneous
Coefficient method
Lagging headway
method
Platoon formation
method
Sardar Patel Road 4469 4517 6479 3008
IT Corridor 4795 5100 8285 7140
35

36. LIMITATIONS OF THIS STUDY
 Because the data was collected on urban roads, large vehicles such as HCVs, MAVs, and tractors
have a very minor role in the traffic flow. As a result, the PCU values for these vehicles could not be
calculated in this study.
 If the nature of the traffic flow changes, the PCU values found out during this study may alter.
 Since the PCU values found out using the Platoon formation method are based on the vehicular
combinations, if there are more combinations available then the PCU values may alter.
36

37. REFERENCE
1. Anand. S., Sekhar. S. V. C., & Karim. M. R. (1999). Development of passenger car unit values for Malaysia.
Journal of the Eastern Asia Society for Transportation Studies, Vol.3, No.3, September, 73-80.
2. Ashish Dhamaniya and Satish Chandra (2013), Concept of Stream Equivalency Factor for Heterogeneous Traffic
on Urban Arterial Roads, Journal of Transportation Engineering, Vol. 139, No. 11, November 1, 2013.
3. Chandra, S., and Sikdar, P. K. (2000). “Factors affecting PCE in mixed traffic situations on urban roads.” Road
Transp. Res., 9(3), 40–50.
4. Debasis Basu, Swati Roy Maitra and Bhargab Maitra (2006), Modelling Passenger Car Equivalency at an urban
midblock using stream speed as the measure of equivalence, European Transport Trasporti Europei n. 34 (2006):
75-87.
5. Geetam Tiwari, Joseph Fazio, Sri Pavitravas (2007), ‘Passenger car units for heterogenous traffic using modified
density method’, National transportation library, US department of Transportation, Document 8612, pp. 246.
6. Pooja Raj, Kalaanidhi Sivagnanasundaram, Gowri Asaithambi and Ayyalasomayajula Udaya Ravi Shankar
(2019), Review of Methods for Estimation of Passenger Car Unit Values of Vehicles, Journal of Transportation
Engineering, Part A: Systems.
37

38. 7. Rahman and Nakamura (2005), Measuring Passenger Car Equivalents for nonmotorized vehicle
(rickshaws) at mid-block sections, Journal of the Eastern Asia Society for Transportation Studies, Vol. 6,
pp. 119 - 126, 2005.
8. Sabayasachi Biswas, Ms. Somya Singh, Mr. Nihal Malik, Mr. Atul V.Bisen (2018), Estimation of Passenger Car
Unit by Multi-objective optimization technique.
9. Satish Chandra and U. Kumar (2003), “Effect of Lane Width on Capacity under Mixed Traffic Conditions in
India.” Journal of Transportation Engineering, ASCE 129 (2): 155–160. doi:10.1061/(ASCE)0733-
947X(2003)129:2(155).
10. Subhadip Biswas, Satish Chandra & Indrajit Ghosh (2018): An advanced approach for estimation of PCU values
on undivided urban roads under heterogeneous traffic conditions, Transportation Letters, DOI:
10.1080/19427867.2018.1563268
11. V. Thamizh Arasan and Reebu Zachariah Koshy (2005), Methodology for Modeling Highly Heterogeneous
Traffic Flow, the Journal of Transportation Engineering, Vol. 131, No. 7, July 1, 2005.
12. V. Thamizh Arasan and Shriniwas S. Arkatkar (2010), Microsimulation Study of Effect of Volume and Road
Width on PCU of Vehicles under Heterogeneous Traffic, the Journal of Transportation Engineering, Vol. 136, No.
12, December 1, 2010 38

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