Submission Number:223
Capacity Estimation of 4-lane divided Inter Urban Hill Road
Using Videography Technique.
Shrey Pahuja
Transport Planning Department, School of Planning and Architecture, New Delhi
Key Words: highway transportation; hill road; highway geometry; horizontal curve; traditional model;
simulation model
Abstract
Traffic conditions on Indian highways are highly heterogeneous in nature due to variety of vehicles with
different static and dynamic characteristics. Very few attempts have been made in India to find out the
capacity of multilane highways under mixed traffic conditions. Most of the study on capacity analysis is from
US and other developed nations where homogeneity of traffic prevails. In hill roads there are many factors
which need to be studied for accurate capacity estimation. Various geometric factors like Horizontal curvature
(curve radius), Vertical Curvature, gradient etc. are the parameters that affect the capacity values. Also
capacity estimation and capacity loss due to adverse weather conditions like rainfall, hailstorm is of great
importance as most of the research is being done considering normal weather condition.
The aim of present study is to estimate the Capacity of 4-lane Hill road. Study section was taken on Himalayan
Expressway, NH-5, formerly named as NH-22.
Effect of grade/ramp: upgrade and down grade (2.5%), super elevation (±5%) has been considered on the
capacity and capacity loss for curve (150m radius) and straight section under normal and rainfall (15mm) with
hailstorm conditions. Field data was collected through Videography technique on one curve section and one
straight section and data was analyzed to determine the basic parameters of traffic flow. Capacity estimation is
done through various traditional models like Greenshields, Greenberg, Underwood and Modified Greenberg.
Microscopic simulation model (VISSIM) is also used to find out the capacity and result is being compared with
traditional models. Capacity reduction due to horizontal curvature, gradient and adverse weather condition
has also been estimated.
1.
Introduction
The estimation and knowledge of roadway capacity are essential in the planning, designing and
operation of transportation facilities. Capacity is greatly influenced by roadway, traffic and driver
conditions. Roadway conditions may consist of various geometric parameters that describe roadways
such as lane width, shoulder width, horizontal and vertical geometry. Multilane highways need more
attention for studying traffic flow behavior and their capacity. There is a need of a study to be done on
a multi-lane hill road. Most of the studies reported in literature on a capacity of multi-lane highways
are from abroad with very few attempts made in India. IRC guidelines for rural roads (IRC 64:1990)
were framed in 1990. Capacity estimation and capacity loss due to adverse weather conditions like
rainfall, hailstorm is of great importance as most of the research is being done on Normal weather
condition. There is no generalized model. Most of the researchers have used Greensheild model for
capacity estimation, but in this paper capacity has been estimated using other models also like
Underwood and modified Greenberg model. Capacity analysis is fundamental to the planning design
and operation of roads and the basis of determining the carriageway width to be provided at any point
in a road network with respect to traffic volume and composition of traffic. It is a valuable tool for
evaluation of the investment needed for future road construction and improvement and for working out
priorities between the competing projects.
2.
Background studies
In this section previous studies that dealt with the impact of roadway characteristics on capacity and
capacity loss were reviewed.
HASHIM & ABDEL-WAHED (2012) Effect of Highway Geometric Characteristics on Capacity
Loss. Paper aims at assessing the influence of highway geometric characteristics on capacity at
tangents and horizontal curves as well as on capacity loss at the change from tangent to curve. Traffic
and geometry data obtained from twelve rural, two-lane road sites in Minoufiya Governorate, Egypt,
were used.
Capacity is estimated utilizing extrapolation from a fundamental diagram which represents the
relationship between traffic flow and density for heterogeneous traffic.
Regression analysis was used to investigate the relationships between geometric characteristics and
capacity.
For tangents, the significant independent variables are lane width, shoulder width, and tangent length.
In the case of curves, the significant variables are curve radius and lane width.
SATISH CHANDRA (2004) CAPACITY ESTIMATION PROCEDURE FOR TWO-LANE
ROADS UNDER MIXED TRAFFIC CONDITIONS. In this paper the effect of influencing
parameters like gradient, lane width, shoulder width, traffic composition, directional split, slow moving
vehicles and pavement surface conditions, on capacity of two-lane roads under mixed traffic conditions
has been evaluated and adjustment factors for each of these conditions are proposed. Capacity of a twolane road based on these adjustment factors under mixed traffic conditions is determined.
VELMURUGAN (2010) et.al. Critical Evaluation Of Roadway Capacity Of Multi-lane High
Speed Corridors Under Heterogeneous Traffic Conditions Through Traditional And Microscopic
Simulation Models.
The Paper explicitly studies the speed - flow characteristics on varying types of multi-lane highways
encompassing four-lane, six-lane and eight-lane divided carriageways in plain terrain. In this study, the
traffic flow data was analysed by typically dividing the traffic volume into two segments corresponding
to congested and uncongested traffic conditions.
Hashim mohammad (2012) Extent of Highway Capacity Loss due to Rainfall. Traffic flow in
adverse weather conditions have been investigated in this study for general traffic, week day and week
end traffic. This revealed a 4.90%, 6.60% and 11.32% reduction in speed for light rain, moderate rain
and heavy rain conditions respectively.
Empirical Highway capacity estimation method has been used.
Thamizh Arasan and Reebu Zachariah Koshy(2005) Methodology for Modeling Highly
Heterogeneous Traffic Flow.
It simulates the flow of heterogeneous traffic with vehicles of wide ranging static and dynamic
characteristics. It also considers absence of lane discipline.
As per the proposed simulation technique, the entire road space is considered to be a single unit
without specific traffic lanes. It points out that some models have been developed, e.g.(Ramanayya
1988); (Isaac 1995); (Marwah and Singh 2000) to simulate mixed traffic flow, they cannot be used for
comprehensive study of mixed traffic flow due to inherent limitations.
The model can satisfactorily replicate heterogeneous traffic flow on roads where vehicles move
without lane discipline.
3.
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Site selection
At the time of Selection of Sections following points was taken into consideration
Selected section must be a inter urban highway (rural highway)
Availability of vintage point for mounting the camera so that it can cover the trap length completely.
Select the section which satisfy the requirement for base condition, for example conditions are (Good
surface, Good shoulders flushed with pavement edge, standard carriageway width etc.)
On the basis of above points site was selected on NH-5 (Himalayan Expressway) formerly NH-22 near
pinjore at Ch. 61+100 of NH-22, with a speed limit of 50kmph. Two sections were selected one curved
section (Ch. 61+100) and one straight section (Ch. 61+250).
Geometric details of curved section:
Radius of curve section : 150m
Speed (KMPH) = 50
Super elevation = 5%
Gradient = 2.5
4.
5.
6.
Data collection
Data collection was carried out with the use of Videography Technique and free flow speed has been
determined using speed gun during free flow conditions. Following points were taken into
consideration while doing the data collection
A longitudinal trap length of 50 m was made using measuring wheel. Alternatively, white paint is used
to mark the entry and exist point of trap length.
For video recording camera was placed at a height of minimum 15 ft from the ground surface.
Recording of traffic data was done on two locations for two days.
 On rainy day data was collected for 5 hours
 On normal day data was collected for 7 hours.
Videography is done for both the directions covering the entire trap length.
Preliminary analysis of traffic surveys
5-5min interval classified volume count was done with the record of the number of vehicles in each
Lane with each category passing through the first line of the trap. Determination of the time taken by
vehicles in each category to cover the trap length was done with a minimum accuracy of 0.1 second.
Minimum of the vehicles taken in a particular category was more than 30 for any particular vehicle
type (during the 5 min count), alternate 5 minutes interval data was consider for decoding. Based upon
the time taken by vehicles in each category to cover the trap length of 60 m speed is being calculated in
(km/hr) by using the relationship V=L/T. For the calculation of Free-Flow speed data is calculated for
the vehicles in each category (which occurs when density and flow are zero, no congestion or other
adverse conditions. Based upon data decoded, hourly Traffic volume was calculated. As per the
Recommendation of PCU by IRC (64:1990) Traffic Volume in PCU/hr is also calculated. Some of
observations from preliminary traffic surveys are following:
 Speed reduction of around 6% from downgrade to upgrade (2.5%)
 Speed reduction from 11 to 14% from straight to curved section for radius of 150 m and
superelevation of 5%.
 Speed reduction of average 14% on a rainy day in comparison to a normal day.
Capacity estimation using traditional approach
Capacity estimation methodology can be divided into two categories: the direct empirical methods,
based on observed traffic flow characteristics; and indirect empirical methods, based on simulation
models. In this paper both methods has been used. Various traditional models like Greenshield Model,
Modified Greenberg Model, Underwood model are used to find out the capacity and capacity from
traditional models was compared with simulation model.
The traffic data in decoded to obtain the 5-5 minute speed data for each vehicles category wise and 5-5
min. flow data. The speed data obtained for 5-5 minutes of interval was then converted in to average
speed for each vehicle category in stream. Stream speed also been calculated from speed data at 5-5
minutes of interval. In order to develop speed- flow equations and estimate roadway capacity, it is
necessary to convert these observed traffic volume into a common unit which is termed as Passenger
Car Unit (PCU), static PCU are used given in IRC:64 (1990).
Capacity estimation was done for eight sections. Capacity estimation for straight section under normal
condition at downgrade (-2.5%) is shown in the Fig. i to iv.

Straight section under normal condition at downgrade (-2.5%)
70
CAPACITY = 2126
PCU/HR/DIRECTION
y = -0.0114x + 67.996
R² = 0.7455
60
80
50
60
CALCULATED
DATA
40
30
CAPACITY = 3090
PCU/HR/DIRECTION
20
10
0
0
1000
2000
3000
4000
Speed (Km/hr)
Speed (Km/hr)
40
FEILD DATA
20
0
0
1000
2000
3000
Flow Rate (PCU/hr)
Flow Rate (PCU/hr)
Modified Greenberg Model
Greenshield Linear Model
Fig.i
Fig.ii
CAPACITY = 1757
PCU/HR/DIRECTION
80
CAPACITY 2498
PCU/HR/DIRECTION
60
60
CALCULATED
POINTS
40
FIELD DATA
20
0
0
1000
2000
CALCULATED
POINTS
40
Speed (Km/hr)
Speed (Km/hr)
80
FIELD DATA
20
0
0
1000
2000
3000
Flow Rate (PCU/hr)
Flow Rate (PCU/hr)
Greenshield Parabolic Model
Fig.iii
Underwood Model
Fig.iv
Greenshield Linear model is giving the maximum capacity and greenshield parabolic model is giving the least
capacity. Field data is matching best with Modified Greenberg model. Standard error of estimation is least in
case of Modified Greenberg Model.
Similarly capacity estimation was done for other 7 sections by various traditional models comparison is shown
in the figure (v) below:
2896
Greensheild
Parabola
Underwood
1655
1441
1424
1647
954
1745
1773
1527
1524
1864
1586
1622
1951
Modified Greenberg
1049
1500
1086
1229
2011
1714
1766
2108
1811
1621
1901
2357
1366
2411
1461
2122
1809
1951
2338
1992
2224
Greensheild Linear
1669
1757
2000
2126
2500
2368
2498
3000
3090
3500
1000
500
0
Fig.v
Modified Greenberg model is giving the best fit. Field data is matching best with the Modified Greenberg
model.
Capacity obtained from traditional models is compared with Simulation model.
7.
Capacity estimation using Microscopic Simulation Model
Capacity was also estimated using Microscopic Simulation Model Vissim. Speed flow graphs are
plotted from microscopic simulation after calibrating and validating the model. Results of VISSIM are
matching perfectly with modified Greenberg model.
Comparision of Speed Flow graphs obtained from Vissim and Modified Greenberg Model for both
curved section and straight section is shown in the figure vi to vii.
70
60
50
CURVE VISSIM
40
Speed (Km/hr)
STRAIGHT VISSIM
STRAIGHT CALCULATED
30
CURVE CALCULATED
20
10
0
0
500
1000
1500
2000
2500
Flow Rate (PCU/hr)
Fig. vi
Figure (vi) above is showing points obtained from Microscopic Simulation model Vissim.
80
60
CALCULATED CURVE
FIELD CURVE
40
Speed (Km/hr)
CALCULATED STRAIGHT
FIELD STRAIGHT
20
0
0
1000
Flow Rate (PCU/hr)
2000
3000
Fig.vii
Figure (vii) above is showing points obtained from field data.
70
Speed (Km/hr)
60
VISSIM STRAIGHT
50
VISSIM CURVE
40
FIELD STRAIGHT
30
FIELD CURVE
20
CALCULATED M. GREENBERG STRAIGHT
CALCULATED M. GREENBERG CURVE
10
0
0
500
1000
1500
2000
2500
Flow Rate (PCU/hr)
Fig.viii
Figure (viii) above is showing points obtained from Vissim and points obtained from field data for both straight and curved section plotted
in the same graph.
As it is clear from all the plots Modified Greenberg model is fitting best with field data as well as Vissim data.
Modified Greenberg gives the best results in capacity estimation of Himalayan Expressway. Data obtained from
Vissim is also best fitted with Modified Greenberg Model.
Capacity for straight section comes out to be 2126 PCU/hr/direction from Modified Greenberg model and
from curve portion it is 1809 PCU/hr/direction.
8.
Capacity Reduction
Capacity reduction was calculated on three situations one from downgrade to upgrade (2.5 % grade),
2nd from straight portion to curved portion (150m radius) and 3 rd from normal day to rainy day.
There is a reduction of around 14 to 15 % in Capacity if we move from straight portion to curve
portion. Curve portion is of 150 m radius and 5% superelevation.
Reduction of 6 % to 10 % in capacity is witnessed moving from downgrade to upgrade. Survey is
done on 2.5 % gradient.
Reduction of around 20 % has been observed in capacity on rainy day in comparison to normal
day.\
9.

Conclusions and major findindgs
On the basis of literature survey it is easy to conclude that very less work has been done for capacity
estimation of multi-lane hill roads around the globe in general and particularly in India.




In this paper capacity estimation of Four-lane divided carriageway on Hill terrain under heterogeneous
traffic conditions for normal and adverse weather conditions has been done.
Effect of grade/ramp: upgrade and down grade (2.5%), super elevation (±5%) has been considered on
the capacity and capacity loss for curve (150m radius) and straight section under normal and
rainfall(15mm) with hailstorm conditions is also calculated. The capacity loss of around 10% is has
been observed from downgrade to upgrade which is matching with S.Chandra work, capacity loss of
around 15% from straight to curved portion and capacity loss of around 20% from normal weather
conditions to adverse weather conditions.
For the capacity estimation, four traditional models: Greenshield, Greenberg, Modified Greenberg and
Underwood have been employed and modified Greenberg gives the best fit with the observed one.
Modified Greenberg model also gives best match with Vissim results.
Capacity for straight section is coming out to be 2126 PCU/hr/direction and for curve section it is 1809
PCU/hr/direction from Modified Greenberg Model. For combined curve and straight section it is
around 1960 PCU/hr/direction.
10. Future Scope
 It is proposed that experimental data has to be collected at large number of sections for different radii,
gradients and super elevations for more accurate and reliable values of capacity.
 Also effect of lane indiscipline, freight (High Capacity Vehicles, multi axel vehicles) may be
incorporated to see the effect of these on the capacity and corresponding capacity loss.
 Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Artificial Neural Networks (ANN)
etc. are the recent techniques that need to be used as these can deal with complexity with ease and can
give improved and accurate results.
References
[1] Transportation Research Board (TRB). Highway Capacity Manual (HCM), 4 th edition, TRB,
National Research Council, Washington, D.C.,2000.
[2] Nakamura M. Research and application of highway capacity in Japan. 2 nd International Symposium
on Highway Capacity, 1994, Sydney, Australia, 103-112.
[3] Gibreel G, El-Dimeery I A, Hassan Y, et al. Impact of highway consistency on capacity utilization
of two lane rural highways. Canadian Journal of Civil Engineering, 1999, 26(6): 789-798.
[4] HASHIM & ABDEL-WAHED (2012) Effect of Highway Geometric Characteristics on Capacity
Loss. Journal of Transportation systems Engineering and Information Technology, 2012, 12(5), 65-75.
[5] SATISH CHANDRA (2004) CAPACITY ESTIMATION PROCEDURE FOR TWO-LANE
ROADS UNDER MIXED TRAFFIC CONDITIONS.
[6] VELMURUGAN (2010) et.al. Critical Evaluation Of Roadway Capacity Of Multi-lane High Speed
Corridors Under Heterogeneous Traffic Conditions Through Traditional And Microscopic Simulation
Models.
[7] Hashim mohammad (2012) Extent of Highway Capacity Loss due to Rainfall.
[8] Thamizh Arasan and Reebu Zachariah Koshy(2005) Methodology for Modeling Highly
Heterogeneous Traffic Flow.
[9] Ahmed M. semeida . Impact of highway geometry and posted speed on operating speed at multilane highways in Egypt.
[10] Chandra S. effect of road roughness on capacity of two-lane roads. Journal of Transportation
Engineering, ASCE, 2004, 130(3): 360-364.
[11] Kim J, Elefteriadou L. Estimation of capacity of two-lane two-way highways using simulation
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