TRAFFIC IMPROVEMENTS FOR SMOOTH MOVEMENT OF TRAFFIC FLOW

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1. INTRODUCTION
Now a day’s transportation is one of the most burning issues in every territory of the
world. Every country is approaching differently according to their needs and solving their
transportations problems within their capabilities.
In designing buildings we need to determine loads coming to the structure to
calculate reinforcement to be provided for safe functioning of the structure. Here in
transportation volume serves the same purpose. For planning, designing and operation of
transportation system the first and foremost requirement is volume. Volume is simply the
number of vehicles passing a section of a roadway. Expressing traffic volume as number
of vehicles passing a given section of road or traffic lane per unit time will be
inappropriate when several types of vehicles with widely varying static and dynamic
characteristics are comprised in the traffic.
Due to lack of proper implementation of transport planning and effective
management, streets of Narsapur ‘X’ Road to Shobana Theatre, have become over
numbered with vehicles and remain motionless for hours in both peak and off-peak
periods. Identification of inherent weakness of interrupted traffic flow like total number
of interruptions during a vehicle trip is prerequisite to confirm the smooth flow of vehicle
and minimize the undesirable time killing of road users.
In civil engineering, traffic flow is the study of interactions between travelers
(including pedestrians, cyclists, drivers and their vehicles) and infrastructure (including
highways, signage, and traffic control devices), with the aim of understanding and
developing an optimal transport network with efficient movement of traffic and
minimal traffic congestion problems. Traffic behaves in a complex and nonlinear way,
depending on the interactions of a large number of vehicles. Due to the individual
reactions of human drivers, vehicles do not interact simply following the laws of
mechanics, but rather display cluster formation and shock wave propagation both forward
and backward, depending on vehicle density. Some mathematical models of traffic flow
use a vertical queue assumption, in which the vehicles along a congested link do not spill
back along the length of the link.

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In a free-flowing network, traffic flow theory refers to the traffic stream variables
of speed, flow, and concentration. These relationships are mainly concerned with
uninterrupted traffic flow, primarily found on freeways or expressways. Flow conditions
are considered "free" when less than 12 vehicles per mile are on a road. "Stable" is
sometimes described as 12–30 vehicles per mile per lane. As the density reaches the
maximum flow rate (or flux) and exceeds the optimum density (above 30 vehicles per
mile), traffic flow becomes unstable, and even a minor incident can result in
persistent stop-and-go driving conditions. A "breakdown" condition occurs when traffic
becomes unstable and exceeds 67 vehicles per mile. "Jam density" refers to extreme
traffic density when traffic flow stops completely, usually in the range of 185–250
vehicles per mile per lane.
However, calculations about congested networks are more complex and rely more
on empirical studies and extrapolations from actual road counts. Because these are often
urban or suburban in nature, other factors (such as road-user safety and environmental
considerations) also influence the optimum conditions. There are common spatiotemporal
empirical features of traffic congestion that are qualitatively the same for different
highways in different countries, measured during years of traffic observations. Some of
these common features of traffic congestion define synchronized flow and wide moving
jam traffic phases of congested traffic in Kerner’s three-phase traffic theory of traffic
flow. There are three main variables to visualize a traffic stream: speed (v), density (k),
and flow.
Traffic flow is generally constrained along a one-dimensional pathway (e.g. a
travel lane). A time-space diagram shows graphically the flow of vehicles along a
pathway over time. Time is displayed along the horizontal axis, and distance is shown
along the vertical axis. Traffic flow in a time-space diagram is represented by the
individual trajectory lines of individual vehicles. Vehicles following each other along a
given travel lane will have parallel trajectories, and trajectories will cross when one
vehicle passes another. Time-space diagrams are useful tools for displaying and
analyzing the traffic flow characteristics of a given roadway segment over time (e.g.
analyzing traffic flow congestion).

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1.1. WHAT IS TRAFFIC VOLUME STUDY?
The problem of measuring volume of such heterogeneous traffic has been addressed by
converting the different types of vehicles into equivalent passenger cars and expressing
the volume in terms of Passenger Car Unit (PCU) per hour. Traffic volume studies are
conducted to determine the volume of traffic moving on the roads and classifications of
roadway vehicles at a particular section during a particular time. Volumes of a day or an
hour can vary greatly, depending on the different day of the week or different time period
of a day. Traffic Volume survey is the determination of the number, movement and
classifications of roadway vehicles at a given location.
1.1.1. USES OF TRAFFIC VOLUME STUDY
The traffic volume count study is carried out to get following useful information:
Magnitudes, classifications and the time and directional split of vehicular flows.
Magnitude is represented by volume of traffic. Vehicles are classified into some
predefined classes based on vehicle size and capacity. In a two-way road, vehicles
moving towards two directions are counted separately to get the proportion. Time and
directional split is useful to identify tidal flow. It also indicates the choice of road users.
Hourly, daily, yearly and seasonal variation of vehicular flows. These variations are
needed to establish expansion factors for future use. Using expansion factors, AADT can
be calculated from short count. Flow fluctuation on different approaches at a junction or
different parts of a road network system.
1.2. WHAT IS CAPACITY OF ROTARY
The capacity of rotary is determined by the capacity of each weaving section. Rotary
intersections or round about are special form of at-grade intersections laid out for the
movement of traffic in one direction around a central traffic island. Essentially all the
major conflicts at an intersection namely the collision between through and right-turn
movements are converted into milder conflicts namely merging and diverging. The
vehicles entering the rotary are gently forced to move in a clockwise direction in orderly
fashion. They then weave out of the rotary to the desired direction.

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1.2.1. USES OF ROTARY
Crossing maneuoure is converted into weaving or merging and diverging operations.
1. All traffic including those turning right or going straight across the rotary have
equal opportunity as those turning left.
2. The variable cost of operation of automobile is less in a traffic rotary than in a
signalized intersection where the vehicles have to stop and proceed.
3. There is no necessity of traffic police or signal to control the traffic as the traffic
rotary could function by itself as a traffic controlled intersection and is the
simplest of all controls.
4. The possible number of accidents and the severity of accidents are quite low
because of low relative speed.
5. Rotaries can be constructed with advantage when the number of intersecting roads
is between four and seven.
6. The capacity of the rotary intersection is the highest of all other intersections at
grade.

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2. LITURATURE REVIEW
Traffic congestion is a temporal condition on networks that occurs as utility increases,
and is characterized by slower speeds, longer trip times, and increased queuing. When
volume of traffic is high and so heterogeneous that the interaction between vehicles slows
down the speed of traffic, traffic congestion is the result. As demand approaches the
capacity of a road (or of the intersections along the road), traffic congestion sets in. When
vehicles are fully stopped for the period of time, this is colloquially known as a traffic
jam.
2.1 EXISTING STUDIES ON RISK PARAMETERS
A simple model was developed by Jack Mallinckrodt, 2009 on regional average
congestion delay, in a closed-form, differentiable function of regional transportation
system with volume and capacity data. This model can be used to reduce the risk
generated due to congestion.
Different views were studied by Robert A. Johnston, Jay R. Lund, Paul P. Craig,
1995 on congestion generation and degeneration. Their study revealed that, it is unlikely
that roadway construction or vehicle automation will be able to alleviate most major
urban congestion in the near future i.e. for another 5 –15 years. Traffic congestion occurs
when a volume of traffic or modal split generates demand for space greater than the
available road capacity. There are a number of specific circumstances which cause or
aggravate congestion; most of them reduce the effective capacity of a road at a given
point or over a certain length, or increase the number of vehicles required for a given
volume of people or goods. Capacity allocation studies reveal that approaches like
laissez-faire allocation, allocation by passenger load, ramp metering, road and parking
pricing, allocation by trip purpose, rationing, and mixed strategies can be used for
reducing congestion. Quantification of congestion can be done by incorporating the
volume and operational characteristics of traffic movement. Bhargab Maitra, P.K.Sikdar
and S.L.Dhingra, 1999 conducted a study on quantification of congestion. Quantified
congestion level can be used as a logical and improved measure of effectiveness to
account for the conceptual definition of level of service in a quantitative manner.

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Modeling congestion has provided a quantitative basis for understanding the
contribution of different vehicle types in overall congestion, and it is useful for evolving
the policy for congestion mitigation. The principles of duration modeling can be used to
find the extent of congestion. An approach is found for estimating the duration of
congestion on a given road section and the probability that, given its onset, congestion
will end during the indentified time period.
It was studied by Anthony Stathopoulos and Mattew G. Karlaftis, 2001.
Evaluating the efficacy of Intelligent Transportation Systems (ITS) technologies in
reducing accidents that affect research development of models (such as incident delay
and congestion models) that can accurately predict incident duration along with the
magnitude of nonrecurring congestion, have been reported by A.Garib, A.E.Radwan &
H.Al-Deek,1997.
An analysis of freeway traffic flows under congestion was conducted by
Do.H.Nam and Donald R.Drew, 1998, based on the principle of traffic dynamics, using
the example of recurring congestion. Traditional incident-detection algorithms were
developed by Chien-Hua Hsiao, Ching-Teng Lin, Michael Cassidy,2006 to distinguish
between congested and uncongested operation by comparing measured traffic-stream
parameters with predefined threshold values. Risk management is a key issue in project
management. The first step of risk management is risk identification. It includes the
recognition of potential risk causative factors and the clarification of risk. It was studied
in detail by Ming-The Wang, Hui-yu Chou, 2006.
Intelligent Transportation Systems are undergoing a transition from demonstration
projects to becoming part of the mainstream set of options available to transportation
planners. Hence, evaluation of it is one of the most critical and important steps to be
taken before any ITS technique can be deployed. Safety has been recently emerging as an
area of increased concerns, attention and awareness within transportation engineering.
Even though recent studies shed some light on driving speed factors as well as on the
direction of the effects, knowledge is still insufficient to allow for specific
quantifications. It was studied by Kai-ran Zhang, Guo-fang Li, 2007.

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Congestion leads to risk and finally may lead to accidents where urban accidents
have the highest percentage impact (75%) over the entirety of accidents; therefore they
represent a crucial event which potentially may lead to disastrous consequences.
Artificial Intelligence may be helpful for providing more powerful techniques to
understand the main causes of accidents and congestion. Accident prediction models
were developed by Rahim F. Benekohal, Asma M. Hashmi, 1992.
Accident prediction models or the before-and-after study approach is commonly
used to estimate the reduction in number of accidents resulting from highway
improvements. Bin Yuan and Wen-Hua Song, 2007 conducted a study on exploring and
accomplishing road traffic safety and rescue system based on 3S technology, which will
provide effective instruction platform for traffic instruction department. Traditional
black-spot programs were developed by Tarek Sayed and Walid Abdelwahab, 1997.
They aim at improving highway safety and locations, they identified as accident prone
based on the total number of accidents. Incidents, defined as unplanned events that
temporarily reduce roadway capacity, contribute significantly to urban freeway
congestion. Transportation agencies have developed incident management programs in
order to support the effective identification and response to incidents. It is expressed by
Brian L. Smith, Ling Qin, Ramkumar Venkat Anarayana, 2003.
Traffic research still cannot fully predict under which conditions a "traffic jam"
(as opposed to heavy, but smoothly flowing traffic) may suddenly occur. Traffic
congestion is a universal constant. Some cities have managed to break free of their
dependence of the automobile. Many more haven’t, and have lost themselves to
congestion. The approach each city takes to the problem of urban congestion and
transport is an insight into their priorities and a gauge of how successful their efforts will
be. The urban growth and future trends in urban development should be major factors in
any urban congestion and transport decision.
Now-a-days urban transport planning is not taking into account the increasing
number of motor vehicles, the growth of the city or the environment. Given the enormous
benefit to the health of every citizen, in terms of cost and in terms the benefits arising

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from innercity accessibility one would think that network functionality and successive
planning of public transport would be one of the provincial government’s primary areas
of interest. The provincial government is interested, and committees are being set up and
meetings are held.
But these seek to “solve” congestion when, in reality, it can only be dealt with and
this approach to the issue, glaring oversights are common. For the present study, risk
analysis has been proposed to be achieved through Principal component analysis
followed by causal techniques to identify the factors contributing to risk generation and
the major links which are leading to congestion.
2.1.2 GEOGRAPHIC INFORMATION SYSTEM (GIS)
GIS is a computer-based tool for mapping and analyzing things that exist and events that
happen on earth. GIS technology integrates common database operations such as query
and statistical analysis with the unique visualization and geographic analysis benefits
offered by maps. These abilities distinguish GIS from other information systems and
make it valuable to a wide range of public and private enterprises for explaining events,
predicting outcomes, and planning strategies (Moses Santhakumar, 1998). GIS store
information about the world as collection of thematic layers which can be linked together
by geography.
This is a simple but extremely powerful and versatile concept has proven
invaluable for solving many real-world problems from tracking delivery vehicles, to
recording details of planning applications, to modeling global atmospheric circulation.
GIS allows us to bring all types of data together based on the geographic and
location component of the data. But unlike a static paper map, GIS can display many
layers of information that is useful to us. Using this, one will be able to integrate,
visualize, manage, solve, and present the information in a new way. Relationships
between the data will become more apparent and the data will become more valuable.
GIS gives us the power to create maps, integrate information, visualize scenarios, solve
complicated problems, present powerful ideas, and develop effective solutions like never
before.

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2.1.3 STUDY OF RISK CRETERIONS
As discussed in the previous chapters, to identify the exact criterions and the links which
lead to congestion and to study which are the real factors of risk generation, certain
criterions have been identified. The broad criterions are categorized in four groups –
Geometric characteristics, Traffic characteristics, Land use or road side characteristics
and Utility characteristics. The attributes considered under each criterion are as follows.
2.1.4 GEOMETRIC CHARACTERISTICS
Geometric characteristics represent the geometric features of the roadway affecting the
Level of service of the link. These are the static characteristics of the road infrastructure.
1) Roadway width in meters (RW)
2) Carriageway width in meters (CW)
3) Stopping sight distance in meters (SSD)
4) Number of curves on the link (NC)
5) Pavement Condition index (PCI).
It is determine from the rating of the pavement based on the pavement condition
and riding comfort experienced by the user on a scale of 1 to 5 , 5 being excellent
pavement and 0 being an impassable pavement (PCI).
Pavement condition index is quality and comfort promoted on a pavement with
defined mobility speed of 20 kmph. The pavement condition is assessed with cumulative
bumps occurred in the travel with bump integrator machine.
If the bump depth in a one km length is between 0 to 50 mm it is ranked as 5 and
beyond 250mm per km it is ranked as 0. The transitional ranking of 4,3,2,1 is given in
proportion in the range of bump depth 50 and 250mm. The geometrics of highway should
be designed to provide optimum efficiency in traffic operations with maximum safety at
reasonable cost. The overall design of geometrics of a highway is a function of the design
speed. Geometric attributes are studied from static data. They are collected from satellite
data by using GPS and GIS as supportive tools and field survey data.

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2.1.5 TRAFFIC CHARACTERISTICS
Traffic characteristics are the dynamic characteristics of the road that influence the level
of service of the link. They are
1. Headway in seconds (H)
2. V/C Ratio (VCR)
3. Intensity of Parking (PBE), business activities and road side activities
encroachments in a point scale.
4. Speed in kmph (V)
5. Delay in seconds (D)
The traffic characteristics are quite complex with various types of road users in
the roads moving with different motives. Study of vehicular characteristics is an essential
part. Apart from these, the various studies to be carried on the actual traffic include
traffic flow characteristics.
2.1.6 TRAFFIC STUDIES
Traffic studies or surveys are carried out to analyze the traffic characteristics. These
studies help in deciding the geometric design features and traffic control for safe and
efficient traffic movements.
The traffic surveys for collecting traffic data are also called traffic census. The
various traffic studies generally carried out are-
1. Traffic volume study
2. Speed Studies
3. Traffic flow characteristics
4. Traffic capacity study
5. Parking study
6. Head way study

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2.1.6.1 TRAFFIC VOLUME STUDY
One of the fundamental measures of traffic on a road system is the volume of traffic
using the road in a given interval of time. It is also termed as flow and it is expressed in
vehicles per hour or vehicles per day. When the traffic is composed of a number of types
of vehicles, it is the normal practice to convert the flow in to equivalent passenger car
unit (PCUs) by using certain equivalency factors. Some of the advantages of manual
methods and situations where these are to be preferred are-
1. Details such as vehicle classification and number of occupants can be easily
obtained.
2. The data can be collected giving the breakdown of traffic in each direction of
travel.
3. It is more desirable to record the traffic in both the directions of travel separately
and post separate observations for each direction.
4. For all day counts, work in three shifts of 8 hours each could be organized. A
separate observer is needed if the occupancy count is to be made.
2.1.6.2 PARKING USAGE SURVEY
The purpose of usage survey is to obtain data on the extent of usage of parking spaces.
The survey will include counts of parked vehicles at regular intervals through a period,
covering both morning and evening peak period, and the parking accumulation and turn
over.
2.1.6.3 SPEED SURVEY
Speed is one of the most important characteristics of traffic and its measurements are a
frequent necessity. All vehicles do not travel at the same speed at a location along a road.
The amount of speed dispersion or the spread from the average speed affects both
capacity and safety. Actual speed of a vehicle over a particular route may be fluctuating
widely depending on several factors such as geometric features, traffic conditions, time,
place, environment and driver. Speed studies carried out occasionally give the general
trend in speeds.

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Speed is the distance covered per unit time. One cannot track the speed of every
vehicle; so, in practice, average speed is measured by sampling vehicles in a given area
over a period of time. Two definitions of average speed are identified: "time mean speed"
and "space mean speed".
1. "Time mean speed" is measured at a reference point on the roadway over a period
of time. In practice, it is measured by the use of loop detectors. Loop detectors,
when spread over a reference area, can identify each vehicle and can track its
speed. However, average speed measurements obtained from this method are not
accurate because instantaneous speeds averaged over several vehicles do not
account for the difference in travel time for the vehicles that are traveling at
different speeds over the same distance.
,
where m represents the number of vehicles passing the fixed point and vi is the speed of
the ith vehicle.
2. "Space mean speed" is measured over the whole roadway segment. Consecutive
pictures or video of a roadway segment track the speed of individual vehicles, and
then the average speed is calculated. It is considered more accurate than the time
mean speed. The data for space calculating space mean speed may be taken from
satellite pictures, a camera, or both.
,
where n represents the number of vehicles passing the roadway segment.
The "space mean speed" is thus the harmonic mean of the speeds.
The time mean speed is never less than space mean speed:

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, is the variance of the space mean
speed.
In a time-space diagram, the instantaneous velocity, v = dx/dt, of a vehicle is
equal to the slope along the vehicle’s trajectory. The average velocity of a vehicle is
equal to the slope of the line connecting the trajectory endpoints where a vehicle enters
and leaves the roadway segment. The vertical separation (distance) between parallel
trajectories is the vehicle spacing (s) between a leading and following vehicle. Similarly,
the horizontal separation (time) represents the vehicle headway (h). A time-space
diagram is useful for relating headway and spacing to traffic flow and density,
respectively.
2.1.6.4 SPEED AND DELAY STUDY
The speed and delay studies give the running speeds, overall speeds, fluctuations in
speeds and the delay between two stations of a road spaced far apart. It also give the
information such as the amount, location, duration frequency and causes of the delay in
the traffic stream.
The results of the speed and delay studies are useful in determining the spots of
congestion, the cause and in arriving at a remedial measure.
2.1.6.5 DELAY STUDIES
Delay studies along routes are best done by the moving observer method described
earlier. The delays occurring due to stopping can be conveniently recorded by separate
stop watch. Special watches which can accumulate the delay time as the observer
operates buttons will be found convenient for this purpose.
The delays that can be measured thus are stopped delays or fixed delays which
occur at intersections, railway crossing and stop signs.

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2.1.6.6 LAND USE OR ROAD SIDE CHARACTERISTICS
These are also the static elements of the link which influence the operational efficiency of
the link. They are:
1. Number of access points on the link (NA)
2. Commercial area along the road side of the link in sq .km (CA)
3. Residential area along the road side of the link in sq.km (RA)
4. Semi Residential area along the road side of the link in sq .km(SRA)
5. Industrial area along the road side of the link in sq .km (IA)
6. These are static elements of the link which influence the operational efficiency of
the link. The data is collected from satellite, field and from municipality
authorities.
2.1.6.7 UTILITY CHARACTERISTICS
Utility characteristics are the characteristics of the link indicating the degree of utility of
the link with reference to the static analysis and dynamic analysis. Utility characteristics
of the link indicate the degree of utility of the link with reference to the static and
dynamic analysis. Utility characteristics are collected from field and OD survey like-
1. Overlap size of the link from static analysis (OS)
2. Trip intensity on the link (TI) in trips / day
2.1.6.8 ORIGIN AND DESTINATION STUDIES
The origin and destination (O & D) study is carried out mainly to plan the road network
and other facilities for vehicular traffic and plan the schedule of different modes of
transportation for the trip demand of commuters. The present study is proposed to adopt
Road side interview method for collecting the origin and destination data. Road side
interview survey is one of the methods of carrying out a screen – line or cordon survey.
The road side interview survey can be done by directly interviewing drivers of the
vehicles at selected survey points. For dual carriageway or roads with very little traffic,
the traffic in both the directions is dealt with simultaneously. In other cases the traffic in
two directions will be monitored at different times.

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It is impractical to stop and interview all the vehicles. Sampling is therefore
necessary. The number of samples depends on the number of interviewers and the traffic
using the road. The analysis of the data by computers will be easy. Since the interview is
done by sampling basis, expansion factors are needed to calculate the total number of
trips.
These expansion factors will be calculated separately for each class of vehicle and
for different time periods (half – hour etc). Road side interview is an economic method of
survey and yields accurate and reliable data.
2.1.7 DEFINITIONS IN TRAFFIC ENGINEERING
2.1.7.1. SPEED
It is defined as rate of motion of individual vehicles of a traffic stream. It is measured in
meters per second, or more generally as kilometers per hour. Two types of speed
measurement are commonly used in traffic flow analysis; viz.
(a)Time mean speed and
(b) Space mean speed.
For the purpose of these guidelines, the speed measure is “space mean speed”.
TIME MEAN SPEED: It is defined as mean speed of vehicles observed at a point on the
road over a period of time. It is the mean spot speed.
SPACE MEAN SPEED: It is the mean speed of vehicles in a traffic stream at any
instant of time over a certain length (space) of road.
In other words, this is average speed based on the average travel time of vehicles
to traverse a known segment of road way. It is slightly less in value than the time mean
speed.

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Fig.2.1.7.1 Space Mean- and Time Mean speeds
2.1.7.2. VOLUME
It is defined as the number of vehicles at a given point on the road during a designated
time interval. Since roads have a certain width and a number of lanes are accommodated
in that width, flow is expressed in relation to the given width (i.e., per lane or per two
lanes etc.,). The time unit selected is an hour or a day.
1. ADT is the average daily traffic when measurements are taken for a few days.
2. AADT is the annual average daily traffic when measurements are taken for 365
days of the year and averaged out.
2.1.7.3. DENSITY
It is the number of vehicles occupying a unit length of road at an instant of time. The unit
length is generally 1km density is expressed in relation to the width of road (i.e., per lane
or per two lanes etc,). When vehicles are in jammed condition, the density is maximum.
It is then termed as the jamming density. Density (k) is defined as the number of vehicles
per unit length of the roadway. In traffic flow, the two most important densities are the

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critical density (kc) and jam density (kj). The maximum density achievable under free
flow is kc, while kj is the maximum density achieved under congestion. In general, jam
density is seven times the critical density. Inverse of density is spacing (s), which is the
center-to-center distance between two vehicles.
,
Where the density (k) within a length of roadway (L) at a given time (t1) is equal to the
inverse of the average spacing of the n vehicles.
,
In a time-space diagram, the density may be evaluated in the region A.
,
where tt is the total travel time in A.
Fig 2.1.7.3 Flow Density relationship

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2.1.7.4. CAPACITY
It is defined as the maximum hourly volume vehicles per hour at which vehicles can
reasonably be expected to traverse point during given time period.
2.1.7.5. DESIGN SERVICE VOLUME
It is defined as the maximum hourly volume vehicles can reasonably be expected to
traverse a point or uniform section of a lane or roadway during a given time period under
the prevailing roadway, traffic control conditions while maintaining a designated level of
service.
2.1.7.6. PEAK HOUR FACTOR
It is defined as the traffic volume during peak hour expressed as a percentage of the
AADT. The peak hour volume in this case is taken as the highest hourly volume based on
actual traffic counts.
2.1.7.7. PASSENGER CAR UNITS:
Urban roads are characterized by mixed traffic conditions, resulting in complex
intersections between various kinds of vehicles.
VEHICLE TYPE PCU VALUES (IRC SP 41)
CAR 1
AUTO RICKSHAW 0.5
MOTOR CYCLE 1
TAMPO 1
TRUCK 4.5
LCV 1.5
BUS 3
Table- 2.1.7.7 PCU values for different vehicles as per IRC SP 41

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2.1.7.8. FLOW
Flow (q) is the number of vehicles passing a reference point per unit of time, vehicles per
hour. The inverse of flow is headway (h), which is the time that elapses between the ith
vehicle passing a reference point in space and the (i + 1)th vehicle. In
congestion, h remains constant. As a traffic jam forms, h approaches infinity.
,
Where the flow (q) passing a fixed point (x1) during an interval (T) is equal to the inverse
of the average headway of the m vehicles.
In a time-space diagram, the flow may be evaluated in the region B.
,
where td is the total distance traveled in B.
Fig.2.1.7.8 Diagram for relationship between flow (q), density (k), and speed (v)

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2.1.7.9. GENERALIZED DENSITY AND FLOW IN TIME-SPACE DIAGRAM
A more general definition of the flow and density in a time-space diagram is illustrated
by region C:
,
where:
Fig. 2.1.7.9(a) Time Space flow

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Fig. 2.1.7.9 (b) Graphical representation of Time Space flow
2.1.7.10 CONGESTION SHOCKWAVE
In addition to providing information on the speed, flow, and density of traffic streams,
time-space diagrams may illustrate the propagation of congestion upstream from a traffic
bottleneck (shockwave).
Fig.2.1.7.10. Congestion Shockwave

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A new traffic flow model for congested arterial networks, named shockwave
profile model (SPM), is presented. Taking advantage of the fact that traffic states within a
congested link can be simplified as free-flow, saturated, and jammed conditions, SPM
simulates traffic dynamics by analytically deriving the trajectories of four major
shockwaves: queuing, discharge, departure, and compression waves.
Unlike conventional macroscopic models, in which space is often discredited into
small cells for numerical solutions, SPM treats each homogeneous road segment with
constant capacity as a section; and the queuing dynamics within each section are
described by tracing the shockwave fronts.
SPM is particularly suitable for simulating traffic flow on congested signalized
arterials especially with queue spillover problems, where the steady-state periodic pattern
of queue build-up and dissipation process may break down. Depending on when and
where spillover occurs along a signalized arterial, a large number of queuing patterns
may be possible.
Therefore it becomes difficult to apply the conventional approach directly to track
shockwave fronts. To overcome this difficulty, a novel approach is proposed as part of
the SPM, in which queue spillover is treated as either extending a red phase or creating
new smaller cycles, so that the analytical solutions for tracing the shockwave fronts can
be easily applied.
Since only the essential features of arterial traffic flow, i.e., queue build-up and
dissipation, are considered, SPM significantly reduces the computational load and
improves the numerical efficiency. It is further validated SPM using real-world traffic
signal data collected from a major arterial in the Twin Cities.
The results clearly demonstrate the effectiveness and accuracy of the model. We
expect that in the future this model can be applied in a number of real-time applications
such as arterial performance prediction and signal optimization.

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2.1.7.11. STATIONARY TRAFFIC
Traffic on a stretch of road is said to be stationary if an observer does not detect
movement in an arbitrary area of the time-space diagram. Traffic is stationary if all the
vehicle trajectories are parallel and equidistant. It is also stationary if it is a superposition
of families of trajectories with these properties (e.g. fast and slow drivers). By using a
very small hole in the template one could sometimes view an empty region of the
diagram and other times not, so that even in these cases, one could say that traffic was not
stationary.
Clearly, for such fine level of observation, stationary traffic does not exist. A
microscopic level of observation must be excluded from the definition if traffic appears
to be similar through larger windows. In fact, we relax the definition even further by only
requiring that the quantities t(A) and d(A) be approximately the same, regardless of where
the "large" window (A) is placed.
2.2 STUDY AREA
SHOBANA THEATRE- NARSAPUR ’X’ ROAD, connecting
KPHB – BALNAGAR - OLD AIRPORT ROAD - MEDAK HYDERABAD ROAD.
Fig.2.2 Location of project work

26
Fig.2.2.1 Traffic flow in Narsapur “X” Road
2.3. TRAFFIC COUNTS
Fig.2.3 Counting Traffic volume survey, Shobana Theatre to Narsapur ‘X’ Road.

27
2.3.1. TYPES OF COUNTS
Different types of traffic counts are carried out, depending on the anticipated use of the
data to be collected. They are:
1.Intersection Counts
2.Pedestrain Counts
3.Vehicle Classification Counts
4.ADT and AADT Counts
2.3.1.1. INTERSECTION COUNTS
Intersection counts are used for timing traffic signals, designing channelization, planning
turn prohibitions, computing capacity, analyzing high crash intersections, and evaluation
congestion (Hamburger et al. 1996). The manual count method is usually used to conduct
an intersection count. A single observer can complete an intersection count only in very
light traffic conditions. The intersection count classification scheme must be understood
by all observers before the count can begin.
Fig.2.3.1.1 Intersection Movements

28
Each intersection has 12 possible movements. The intersection movements are
through, left turn, and right turn. The observer records the intersection movement for
each vehicle that enters the intersection.
2.3.1.2. PEDESTRIAN COUNTS
Pedestrian count data are used frequently in planning applications. Pedestrian counts are
used to evaluate sidewalk and crosswalk needs, to justify pedestrian signals, and to time
traffic signals. Pedestrian counts may be taken at intersection crosswalks, midblock
crossings, or along sidewalks. When pedestrians are tallied, those 12 years or older are
customarily classified as adults (Robertson 1994). Persons of grade school age or
younger are classified as children. The observer records the direction of each pedestrian
crossing the roadway.
2.3.1.3. VEHICLE CLASSIFICATION COUNTS
Vehicle classification counts are used in establishing structural and geometric design
criteria, computing expected highway user revenue, and computing capacity. If a high
percentage of heavy trucks exist or if the vehicle mix at the crash site is suspected as
contributing to the crash problem, then classification counts should be conducted.
Typically cars, station wagons, pickup and panel trucks, and motorcycles are classified as
passenger cars. Other trucks and buses are classified as trucks. School buses and farm
equipment may be recorded separately. The observer records the classification of the
vehicles and the vehicles’ direction of travel at the intersection.
2.3.1.4. AVERAGE DAILY TRAFFIC, ANNUAL AVERAGE DAILY TRAFFIC
COUNTS
Average daily traffic (ADT) counts represent a 24-hour count at any specified location.
These counts are obtained by placing an automatic counter at the analysis location for a
24-hour period. Accuracy of the ADT data depends on the count being performed during
typical roadway, weather, and traffic demand conditions. Local levels of government will
typically conduct this type of count.

29
Annual average daily traffic (AADT) counts represent the average 24-hour traffic
volume at a given location averaged over a full 365-day year. AADT volume counts have
the following uses:
1. Measuring or evaluating the present demand for service by the roadway or
facility.
2. Developing the major or arterial roadway system.
3. Locating areas where new facilities or improvements to existing facilities
are needed.
4. Programming capital improvements.
2.3.2 METHODS OF TRAFFIC VOLUME COUNTS
Two methods are available for conducting traffic volume counts:
(1) Manual Counting
(2) Automatic Counting
Manual counts are typically used to gather data for determination of vehicle
classification, turning movements, direction of travel, pedestrian movements, or vehicle
occupancy. Automatic counts are typically used to gather data for determination of
vehicle hourly patterns, daily or seasonal variations and growth trends, or annual traffic
estimates.
2.3.2.1 MANUAL COUNT METHOD
Most applications of manual counts require small samples of data at any given location.
Manual counts are sometimes used when the effort and expense of automated equipment
are not justified. Manual counts are necessary when automatic equipment is not available.
Manual counts are typically used for periods of less than a day. Normal intervals for a
manual count are 5, 10, or 15 minutes. Traffic counts during a Monday morning rush
hour and a Friday evening rush hour may show exceptionally high volumes and are not
normally used in analysis; therefore, counts are usually conducted on a Tuesday,
Wednesday, or Thursday.

30
2.3.2.2 MANUAL COUNT RECORDING METHODS
Manual counts are recorded using one of three methods: tally sheets, mechanical
counting boards, or electronic counting boards.
2.3.2.3 TALLY SHEETS
Recording data onto tally sheets is the simplest means of conducting manual counts. The
data can be recorded with a tick mark on a pre-prepared field form. A watch or stopwatch
is necessary to measure the desired count interval. A blank traffic volume count
intersection tally sheet is provided in Appendix B.
2.3.2.4 MECHANICAL COUNTING BOARDS
Mechanical count boards consist of counters mounted on a board that record each
direction of travel. Common counts include pedestrian, bicycle, vehicle classification,
and traffic volume counts. Typical counters are push button devices with three to five
registers.
Fig.2.3.2.4 Mechanical Counting Board

31
Each button represents a different stratification of type of vehicle or pedestrian
being counted. The limited number of buttons on the counter can restrict the number of
classifications that can be counted on a given board. A watch or a stopwatch is also
necessary with this method to measure the desired count interval. See Figure 3.1 for an
example mechanical counting board.
2.3.2.5 ELECTRONIC COUNTING BOARDS
Electronic counting boards are battery-operated, hand-held devices used in collecting
traffic count data. They are similar to mechanical counting boards, but with some
important differences. Electronic counting boards are lighter, more compact, and easier to
handle.
Fig.2.3.2.5. Electronic Counting Board
They have an internal clock that automatically separates the data by time interval.
Special functions include automatic data reduction and summary. The data can also be
downloaded to a computer, which saves time. See Figure 3.2 for an example electronic
counting board.

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2.3.2.6 AUTOMATIC COUNT METHOD
The automatic count method provides a means for gathering large amounts of traffic data.
Automatic counts are usually taken in 1-hour intervals for each 24-hour period. The
counts may extend for a week, month, or year. When the counts are recorded for each 24-
hour time period, the peak flow period can be identified.
2.3.2.7 AUTOMATIC COUNT RECORDING METHODS
Automatic counts are recorded using one of three methods: portable counters, permanent
counters, and videotape.
2.3.2.8 PORTABLE COUNTERS
Portable counting is a form of manual observation. Portable counters serve the same
purpose as manual counts but with automatic counting equipment. The period of data
collection using this method is usually longer than when using manual counts. The
portable counter method is mainly used for 24-hour counts. Pneumatic road tubes are
used to conduct this method of automatic counts (see Figure 2.3.2.8.). Specific
information pertaining to pneumatic road tubes can be found in the users’ manual.
Fig:2.3.2.8. Pneumatic Road Tube and Recorder

33
2.3.2.9 PERMANENT COUNTERS
Permanent counters are used when long-term counts are to be conducted. The counts
could be performed every day for a year or more. The data collected may be used to
monitor and evaluate traffic volumes and trends over a long period of time. Permanent
counters are not a cost-effective option in most situations. Few jurisdictions have access
to this equipment.
2.4. TRAFFIC CHARACTERISTICS
1. Road user Characteristics
2. Vehicular Characteristics
2.4.1 ROAD USER CHARECTERISTICS
(A) Table for Road user characteristics:
Permanent Temporary
Physical Psychological Mental Alcohol
(1)Vision Attentiveness Knowledge Drug
-Actuite Fear Skill Work load
-Peripheral Anger Intelligence Illness
-Eye movement Impatience Experience Fatigue
-Glare vision Attitude Literacy Anger
and recovery
-Perception to Maturity
depth and distance
Motivation
(2)Hearing Emotion
(3)Strength Responding time
(4)Reaction time (PIEV time)

34
Total reaction or the Perception Intellection Emotion and Volition (PIEV) time
of the drivers vary considerably from driver to driver and situation.
The total reaction time of average driver may vary from 0.5 sec for simple
situations such ads 3 to 4 seconds or even more in complex condition. Generally, for
design purpose it is taken as 2 sec.
2.4.1.1 VEHICULAR CHARECTERISTICS TABLE:
STATIC DYNAMIC
- Dimensions – length, width, height Speed
- Weight - Gross vehicular weight, Acceleration
axle load Braking
- Max. turning angle/radius Engine horse power
- Height of vehicle
- Height of driver seat
- Height of head light
- Clearance below the chassis
2.4.2 TRAFFIC CONTROL DEVICES
1. The various devices used to control, regulate and guide traffic is called TCD.
2. ROAD Marking,
3. Islands
a) Divisional
b) Channelizing
c) Pedestrian loading
d) Rotary

35
2.4.2.1 ROAD MARKINGS
Road marking are made of lines, patterns, words, symbols or reflectors on the pavement,
kerbed, sides of islands or on fixed object within or near the roadway to control, warn,
guide, or regulate the traffic. The marking are made by using white, black, yellow, color
paints.
Fig:2.4.2.1 Road Markings
Longitudinal lines are 10 cm thick and traverse lines should be made in such a
way that they are visible at sufficient distance in advance. Yellow color marking are used
to indicate parking restrictions, continuous centre line and barrier line markings.
Longitudinal solid lines are used as guiding and regulating lines and are not meant
to be crossed by the driver. White color stop lines are meant for vehicles to stop near the
signalized section and pedestrian crossing.

36
2.4.2.2 CENTER LINES
Fig:2.4.2.2 Center Line
1. On undivided two-way roads, the centre line separates the opposing streams of
traffic and facilitates their movements.
2. The centre line can be a single broken line, a single continuous solid line (barrier
line), a double solid line or a combination of solid line and broken line.
2.4.2.3 MARK LINES
Fig:2.4.2.3 Mark Line
1. The Divided Lines visible in the pictures above are called Lane Lines.
2. Single and double solid lines, whether white or yellow, must not be crossed or
even straddled.
3. They should be treated as a wall on the road .
4. The Divided Lines visible in the pictures above are called Lane Lines.
even straddled.

37
2.4.2.4 DOUBLE LINES
Fig:2.4.2.4 Double Line
even straddled.
2. Double Continuous lines are also used where visibility is restricted in both
directions.
3. Neither stream of traffic is allowed to cross the lines.
4. The Double Continuous Lines can be both in WHITE or YELLOW.
2.4.2.5 COMBINATION LINES
Fig:2.4.2.5 Combination line
1. On a road with two centre lines, of which one is solid and the other broken, the
solid line has significance only it it is on the left side of the combination as
viewed by the driver.
2. In such a case, the driver must be careful not to cross or straddle the centre line.
3. If the line on your side is broken, you may cross or straddle it.
Overtake - but only if it is safe to do so.
4. If the line on your side is continuous you must not cross or straddle it.

38
2.4.2.6 STOP LINE
Fig:2.4.2.6 Stop Line
1. A stop line is a single solid transverse line painted before the intersecting edge of
the road junction/ intersection.
2. This line indicates where you are required to stop when directed by traffic officer,
traffic light of stop sign.
3. Where a pedestrian crossing is provided, the stop line is marked before the
pedestrian crossing.
2.4.2.7. GIVE WAY LINE
Fig:2.4.2.7 Give way line
1. The give way line is usually a double dotted line marked transversely at junctions.
2. These lines are generally supplemented by a reverse triangle give way sign
painted on the road surface before the dotted lines or by a road sign installed
beside the marking.
3. Give way to traffic on the main approaching road.

39
2.4.2.8 BORDER EDGE LINE
Fig. 2.4.2.8 Border Edge Line
These are continuous lines at the edge of the carriageway and mark the limits of
the main carriageway up to which a driver can safely venture.
2.4.2.9 PARKING PROHIBITED LINES
Fig:2.4.2.9 Parking Prohibited Lines
A solid continuous yellow line painted on the kerb or edge of the carriageway along with
a "No-parking" sign indicates the extent of no-parking area.

40
2.4.3 YELLOW BOX JUNCTION
Fig.2.4.3. Yellow Box Junction
1. These are yellow crossed diagonal lines within the box.
2. The vehicles should cross it only if they have a clear space available ahead of the
yellow box.
3. In this marked area vehicles must not stop even briefly.
2.4.3.1 PEDESTRIAN CROSSING
Fig:2.4.3.1. Pedestrian Crossing
1. These are alternate black and white stripes painted parallel to the road generally
known as zebra crossing.
2. Pedestrians must cross only at the point where these lines are provided and when
the signal is in their favour at controlled crossings.
3. You must stop and give way to pedestrians at these crossings.
4. Pedestrian crossings are marked to facilitate and give the right of way to
pedestrians.

41
2.5. TRAFFIC ISLANDS
Are raised areas constructed within the roadway to establish physical channels through
which the vehicular traffic may be guided. Types of Islands are:
1. Divisional Islands: Divisional islands are dividing the highway in two one way
roadways so that head on collision are eliminated and accidents are reduced.
2. Channelizing Islands: Channelizing islands guide the traffic into proper channel
through the intersection area.
3. Pedestrian loading Islands: Pedestrian loading islands are provided at regular
bus stops and similar places for the protection of passengers.
4. Rotary Islands : Rotary islands is the large central island of a rotary intersection.
Needs of traffic islands-
1. Islands provide self controlled traffic. No need of traffic police to manage the
traffic.
2. Islands reduce conflicts points hence chances of collision and accident also
reduce.
Fig.2.5 Traffic Island

42
2.6. INTERSECTIONS
Definition: Intersection is the location of roadway where two or more approaches from
different directions are meeting.
Fig.2.6(a) Intersections
Fig.2.6.(b) Types of manocuvres

43
2.6.1 CHANNELIZED INTERSECTION
Is achieved by introducing islands into the intersection area to reduce conflicts. The
intersection area is paved and there is absolutely no restriction to vehicles to use any part
of inter section area.
Fig. 2.6.1 Channelized Intersection
When there is intolerable congestion and accidents at the intersection of two
highways carrying very heavy traffic grade separate d intersection are provided which are
known as interchange.

44
2.6.2 ROTARY INTERSECTIONS
A rotary intersection is an enlarged road intersection where all converging vehicles are
forced to move round a large control island in one direction (clockwise) before they can
weave out of traffic flow into their respective directions radiating from the control island.
Fig.2.6.2 Types of rotary intersection

46
3. METHODOLOGY
3.1 METHODS FOR VOLUME SURVEY
There are two major methods of counting vehicle for volume survey. They are-
1. Manual Counting Method and
2. Automatic counting method.
3.1.2 MANUAL COUNTING METHOD
In this method, vehicles are counted manually. There are two methods of manual
counting:
1. Direct Method and
2. Indirect Method.
Direct Method: Data is counted by using hand tally and manual counters/enumerators.
Advantages: By this method traffic volume as well as vehicle classification and
turning proportions can be obtained. Data can be used immediately after collection.
Disadvantages: This method is not practicable for long duration count and when
flow is high. Error is common especially when volume is high.
Count cannot be cross checked. Count cannot be done in bad weather.
Indirect Method: In this method, data is collected using video camera. Video is captured
for long time and data is collected later by rewinding.
Advantages: Besides traffic volume, several traffic parameters can be obtained
from recorded film. Data can be cross checked and quality can be ensured. This method
is applicable when volume is high. It is suitable for non-lane based traffic operation.
Disadvantages: A suitable elevated place is required for filming operation. Data
cannot be used immediately after collection. Data must be manually transcript of
recorded film. This process is time consuming and tedious. Because of limitation of

47
capacity of film, it is not suitable for long duration counts. Quality of video recorded on
film is dependent on intensity of light and this method is not suitable in overcast days.
3.1.3 AUTOMATIC COUNTING METHOD
In this method, vehicles are counted automatically without any human involvement.
There are two techniques of automatic counting:
1. Contact system based on pneumatic, mechanical, magnetic or piezo-electric
method and
2. Contactless system based on electrical/optical, ultrasound/infrared radar,
micro wave, CCTV/video image processing method etc. Advantages: This
method is suitable for long duration or continuous count. It is used as
permanent counting station. It does not need manpower and is free from
human error. Data is obtained in usable format. It is less expensive as
manpower is not needed. Count is not affected by bad weather condition.
Disadvantages: It requires strict lane discipline. Non motorized vehicles are
hard to detect by this method. Detailed classification of vehicle is not
possible. Accuracy is less than manual method. Installation cost is high.
3.2. ROTARY ISLAND
A traffic circle is a type of intersection that directs both turning and through traffic onto a
one-way circular roadway, usually built for the purposes of traffic calming or aesthetics.
The key advantages of a rotary intersection are listed below:
1. Traffic how is regulated to only one direction of movement, thus eliminating
severe conflicts between crossing movements.
2. All the vehicles entering the rotary are gently forced to reduce the speed and
continue to move at slower speed. Thus, none of the vehicles need to be
stopped ,unlike in a signalized intersection.

48
3. Because of lower speed of negotiation and elimination of severe convicts,
accidents and their severity are much less in rotaries.
4. Rotaries are self governing and do not need practically any control by police
or traffic signals.
5. They are ideally suited for moderate traffic, especially with irregular
geometry, or intersections with more than three or four approaches.
Although, there are few specific limitations for rotaries which are listed below:
1. All the vehicles are forced to slow down and negotiate the intersection.
Therefore, the cumulative delay will be much higher than channelized
intersection.
2. Even when there is relatively low traffic, the vehicles are forced to reduce
their speed.
3. Rotaries require large area of relatively at land making them costly at urban
areas.
4. The vehicles do not usually stop at a rotary. They accelerate and exit the
rotary at relatively high speed.
5. Therefore, they are not suitable when there is high pedestrian movements.
Fig – 3.2 Rotary Island

49
3.2.1 GUIDELINES FOR THE SELECTION OF ROTARIES
Because of the above limitation, rotaries are not suitable for every location. There are few
guidelines that help in deciding the suitability of a rotary. They are listed below:
1. Rotaries are suitable when the traffic entering from all the four approaches
are relatively equal.
2. A total volume of about 3000 vehicles per hour can be considered as the
upper limiting case and a volume of 500 vehicles per hour is the lower limit.
3. A rotary is very beneficial when the proportion of the right-turn trace is
very high; typically if it is more than 30 percent.
4. Rotaries are suitable when there are more than four approaches or if there is
no separate lanes available for right-turn traffic. Rotaries are ideally suited
if the intersection geometry is complex.
3.2.2 TRAFFIC OPERATIONS IN A ROTARY
As noted earlier, the traffic operations at a rotary are three; diverging, merging and
weaving. All the other conflicts are converted into these three less severe conflicts.
1. Diverging: It is a traffic operation when the vehicles moving in one
direction is separated into different streams according to their destinations.
2. Merging: Merging is the opposite of diverging. Merging is referred to as the
process of joining the traffic coming from different approaches and going to a
common destination into a single stream.
3. Weaving: Weaving is the combined movement of both merging and
diverging movements in the same direction.
3.2.2.1 DESIGN ELEMENTS
The design elements include design speed, radius at entry, exit and the central
island, weaving length and width, entry and exit widths.
In addition the capacity of the rotary can also be determined by using some
empirical formula. A typical rotary and the important design elements.

50
3.2.2.2 DESIGN SPEED
All the vehicles are required to reduce their speed at a rotary. Therefore, the
design speed of a rotary will be much lower than the roads leading to it.
Although it is possible to design roundabout without much speed reduction, the
geometry may lead to very large size incurring huge cost of construction.
The normal practice is to keep the design speed as 30 and 40 km ph for urban and
rural areas respectively.
Fig.3.2.2.2 Traffic Operation in a rotary

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3.2.2.3 ENTRY, EXIT AND ISLAND RADIUS
The radius at the entry depends on various factors like design speed, super-elevation, and
coefficient of friction.
3. The entry to the rotary is not straight, but a small curvature is introduced. This
will force the driver to reduce the speed. The entry radius of about 20 and 25
meters is ideal for an urban and rural design respectively.
4. The exit radius should be higher than the entry radius and the radius of the rotary
island so that the vehicles will discharge from the rotary at a higher rate. A
general practice is to keep the exit radius as 1.5 to 2 times the entry radius.
However, if pedestrian movement is higher at the exit approach, then the exit
radius could be set as same as that of the entry radius.
5. The radius of the central island is governed by the design speed, and the radius of
the entry curve.
6. The radius of the central island, in practice, is given a slightly higher radius so
that the movement of the traffic already in the rotary will have priority.
7. The radius of the central island which is about 1.3 times that of the entry curve is
adequate for all practical purposes.
Fig.3.2.2.3 Various radius design in a rotary

52
It is important that the geometric design evolved for the rotary should be able to
deal with the traffic flow at the end of the design period on the rotary. The practical
capacity of a rotary is really synonymous with the capacity of the weaving section which
can accommodate the least traffic.
Capacity of the individual weaving sections depends on factors such as
(i) width of the weaving section
(ii) average width of entry into the rotary
(iii) the weaving length and
(iv) proportion of weaving traffic and could be calculated from the following
formula.
Where,
w = width of weaving section in meters(within the range of (6—18 m).
e = average entry width in meters(.i.e. average of ‘e1’ and
‘e2’, e = ), e/w to be within the range of 0.4 to 1.00.
l = length in meters of the weaving section b/w ends of
channelizing islands ( w/l to be within the range of 0.12 and 0.4) The weaving length
available at the intersection is in between 18 and 90 m.
p = proportion of weaving traffic, i.e. ratio of sum of crossing streams to
the total traffic on weaving section.
, range of ‘p’ being 0.4 to 1.0.

53
Fig.3.2.2.4 Central Island
3.3. TRAFFIC AND TRANSPORTATION THEORY
In a critical review, Kerner explained that generally accepted classical fundamentals and
methodologies of traffic and transportation theory are inconsistent with the set of
fundamental empirical features of traffic breakdown at a highway bottleneck.
Fig. 3.3 a. and b. - Fundamental diagram of traffic and transportation theory.

54
3.3.1 SET OF FUNDAMENTAL EMPIRICAL FEATURES OF TRAFFIC
BREAKDOWN AT HIGHWAY BOTTLENECKS ARE:
The set of fundamental empirical features of traffic breakdown at a highway bottleneck is
as follows:
1. Traffic breakdown at a highway bottleneck is a local phase transition from free
flow (F) to congested traffic whose downstream front is usually fixed at the
bottleneck location. Such congested traffic is called synchronized flow (S).
Within the downstream front of synchronized flow, vehicles accelerate from
synchronized flow upstream of the bottleneck to free flow downstream of the
bottleneck.
2. At the same bottleneck, traffic breakdown can be either spontaneous or induced.
3. The probability of traffic breakdown is an increasing flow rate function.
4. There is a well-known hysteresis phenomenon associated with traffic breakdown:
When the breakdown has occurred at some flow rates with resulting congested
pattern formation upstream of the bottleneck, then a return transition to free flow
at the bottleneck is usually observed at considerably smaller flow rates.
A spontaneous traffic breakdown occurs, where there are free flows both
upstream and downstream of the bottleneck before the breakdown has occurred. In
contrast, an induced traffic breakdown is caused by a propagation of a congested pattern
that has earlier emerged for example at another downstream bottleneck.
Fig.3.3.1 Free flows both upstream and downstream of the bottleneck

55
Empirical data that illustrates the set of fundamental empirical features of traffic
breakdown at highway bottlenecks as well as explanations of the empirical data can be
found in Kerner’s breakdown minimization principle and in review.
3.4. CLASSICAL TRAFFIC FLOW THEORIES
The generally accepted classical fundamentals and methodologies of traffic and
transportation theory are as follows:
1. The Lighthill-Whitham-Richards (LWR) model introduced in 1955–
56. Daganzo introduced a cell-transmission model (CTM) that is consistent
with the LWR model.
2. A traffic flow instability that causes a growing wave of a local reduction of
the vehicle speed. This classical traffic flow instability was introduced in
1959–61 in the General Motors (GM) car-following model by Herman, Gazis,
Montroll, Potts, and Rothery.
3. The classical traffic flow instability of the GM model has been incorporated in
a huge number of traffic flow models like Gipps's model, Payne's model,
Newell's optimal velocity (OV) model, Wiedemann's model, Whitham's
model, the Nagel-Schreckenberg (NaSch) cellular automaton (CA) model,
Bando et al.
4. OV model, Treiber's IDM, Krauß model, the Aw-Rascle model and many
other well-known microscopic and macroscopic traffic-flow models, which
are the basis of traffic simulation tools widely used by traffic engineers and
researchers.
5. The understanding of highway capacity as a particular value. This
understanding of road capacity was probably introduced in 1920–35 .
Currently, it is assumed that highway capacity of free flow at a highway
bottleneck is a stochastic value.
6. Wardrop's user equilibrium (UE) and system optimum (SO) principles for
traffic and transportation network optimization and control.

56
3.5. FAILURE OF CLASSICAL TRAFFIC FLOW THEORIES
Kerner explains the failure of the generally accepted classical traffic flow theories as
follows:
1. The LWR-theory fails because this theory cannot show empirical induced
traffic breakdown observed in real traffic. Correspondingly, all applications of
LWR-theory to the description of traffic breakdown at highway bottlenecks
(like related applications of Daganzo’s cell-transmission model, cumulative
vehicle count curves (N-curves), bottleneck model, highway capacity models
as well as associated applications of kinematic wave theory) are also
inconsistent with the set of fundamental empirical features of traffic
breakdown.
2. Two-phase traffic flow models of the GM model class fail because traffic
breakdown in the models of the GM class is a phase transition from free flow
(F) to a moving jam (J) (called F → J transition): In a traffic flow model
belonging to the GM model class due to traffic breakdown, a moving jam(s)
appears spontaneously in an initially free flow at a highway bottleneck. In
contrast with this model result, real traffic breakdown is a phase transition
from free flow (F) to synchronized flow (S) (called F → S transition): Rather
than a moving jam(s), due to traffic breakdown in real traffic, synchronized
flow occurs whose downstream front is fixed at the bottleneck.
3. The understanding of highway capacity as a particular value fails because this
assumption about the nature of highway capacity contradicts the empirical
evidence that traffic breakdown can be induced at a highway bottleneck.
4. Dynamic traffic assignment or/and any kind of traffic optimization and control
based on Wardrop's SO or UE principles fail because of possible random
transitions between the free flow and synchronized flow at highway
bottlenecks. Due to such random transitions, the minimization of travel cost in
a traffic network is not possible.

57
According to Kerner, the inconsistence the generally accepted classical
fundamentals and methodologies of traffic and transportation theory with the set of
fundamental empirical features of traffic breakdown at a highway bottleneck can explain
why network optimization and control approaches based on these fundamentals and
methodologies have failed by their applications in the real world. Even several decades of
a very intensive effort to improve and validate network optimization models have no
success. Indeed, there can be found no examples where on-line implementations of the
network optimization models based on these fundamentals and methodologies could
reduce congestion in real traffic and transportation networks.
Fig.3.5. Classical Traffic Flow Theory
This is due to the fact that the fundamental empirical features of traffic
breakdown at highway bottlenecks have been understood only during last 20 years. In
contrast, the generally accepted fundamentals and methodologies of traffic and
transportation theory have been introduced in the 50s-60s. Thus the scientists whose
ideas led to these classical fundamentals and methodologies of traffic and transportation
theory could not know the set of empirical features of real traffic breakdown.

58
CHAPTER-4
RESULT AND DESIGN ANALYSIS

59
4. RESULT AND DESIGN ANALYSIS
4.1 TRAFFIC VOLUME STUDY
In this we have done the traffic volume study. For doing traffic volume study we have
selected the timings (8:00 AM – 8:00 PM) of a day. We have selected the manual
counting method. In this method we have selected the in-direct method.
Fig.4.1 Classified Traffic Volume Count Survey, Shobana Theatre, Balnagar.
4.2. CAPACITY OF ROTARY:
Table 4.2 – Peak hour calculation of traffic flow- From Shobana Theatre, Balnagar.
TIME BUS CAR 3W 2W L.C.V
8:00 - 12:00 630 1893 3025 9700 402
14:00 - 18:00 1054 8172 8397 11,724 722
TOTAL 1684 10,065 11,422 21,424 1124
IRC SP-41
PCE VALUES
25 39 21 47 14
PCU VALUES 2652 4171 2422 4512 1239

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In this we found the capacity of rotary. For doing the capacity of rotary we have
taken the peak hour volume and taken the measurements .And used them in the capacity
formula. Thus we have found the capacity of the rotary.
4.2.1 TRAFFIC FLOW FROM SHOBANA THEATRE, BALNAGAR:
Table - 4.2.1. Calculation of traffic flow from Shobana Theatre, Balnagar.
8:00-9:00 650 2900 1000 4700 388
9:00-10:00 630 2736 970 4446 376
10:00-11:00 610 2577 956 4270 359
11:00-12:00 500 2068 876 3758 332
2:00-3:00 397 1437 675 3169 286
3:00-4:00 420 1705 737 3893 343
4:00-5:00 573 3145 897 4576 378
5:00-6:00 726 3198 938 5226 414

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4.2.2 PEAK HOUR CALCULATION OF TRAFFIC FLOW FROM NARSAPUR
‘X’ ROAD:
Fig.4.2.2 Classified Traffic Volume Count Survey, Narsapur ‘X’ Road, Balnagar
Table - 4.2.2 Peak hour calculation of Traffic flow from Narsapur X Road
8:00 - 12:00 930 2737 4075 10472 674
14:00 - 18:00 1013 9116 9255 12,116 905
TOTAL 1943 11,853 13,330 22,588 1579
IRC SP-41
PCE VALUES
38 54 20 48 9
PCU VALUES 4029 5853 2230 4794 389

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4.2.3 TRAFFIC FLOW FROM NARSAPUR ‘X’ ROAD, BALNAGAR:
Eight hours of continue traffic flow is calculated as mentioned below. For the classifying
of traffic volume survey, we got the results for every one hour of flow from morning
8’am to 6’pm, while the tally sheet is mentioned for every one hour of flow.
Table - 4.2.3 Calculation of traffic flow from Narsapur ‘X’ Road
8:00-9:00 707 3203 1033 5300 436
9:00-10:00 817 3122 1056 4761 411
10:00-11:00 756 2810 981 4715 398
11:00-12:00 615 2189 921 4162 381
2:00-3:00 497 1751 719 3961 309
3:00-4:00 539 1902 778 4112 401
4:00-5:00 774 3974 921 4991 462
5:00-6:00 752 3843 1031 5756 614

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4.3. ROTARY FORMULAE:
Where, e1= 6m , e2 =23m
W = 18 m
ó e = => 14.5 m
ó l = 72 m
ó e/w = 0.80 m
ó w/l = 0.24 m
ó P = 0.90 m

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4.3.1 TAKE THE PEAK HOUR TRAFFIC VOLUME:
Table - 4.3.1 Traffic flow in intersection from Narsapur ‘X’ Road
SHOBANA THEATRE:
1 = 3276
2 = 6144
3 = 540
NARSAPUR ‘X’ ROAD:
4 = 492
5 = 6210
6 = 4200
VEHICLE TYPE STRAIGHT RIGHT LEFT
BUS 336 126 30
CAR 1500 780 138
3W 978 552 66
2W 3060 1626 246
L.C.V 270 192 60
TOTAL 6144 3276 540

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Fig.4.3 Inter-sectioning point at Narsapur ‘X’ Road.
Fig.4.3.1 Rotary at Narsapur ‘X’ Road.

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4.3.2 CALCULATIONS:
P – ES = 6210+4200+5220+3276 = 0.90
492+6210+4200+5220+3276+1596
P – SW = 4992+2058+1596+6210 = 0.74
912+4992+2058+1596+6210+4200
P – WN = 6144+3276+4992+4200 = 0.87
540+6144+3276+4992+4200+2058
P – NE = 5220+1596+2058+6144 = 0.77
1134+5220+1596+2058+6144+3276
Qw = 280(18)(1+0.80)(1-0.3) = 5121 veh/hr.
1+0.24
But the limit is = 3000 veh/hr

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4.4. METHOD OF ANALYSIS
Analysts approach the problem in three main ways, corresponding to the three main
scales of observation in physics:
1. Microscopic scale: At the most basic level, every vehicle is considered as an
individual. An equation can be written for each, usually an ordinary differential
equation (ODE). Cellular automation models can also be used, where the road is
divided into cells, each of which contains a moving car, or is empty. The Nagel–
Schreckenberg model is a simple example of such a model. As the cars interact it
can model collective phenomena such as traffic jams.
2. Macroscopic scale: Similar to models of fluid dynamics, it is considered useful
to employ a system of partial differential equations, which balance laws for some
gross quantities of interest; e.g., the density of vehicles or their mean velocity.
3. Mesoscopic (kinetic) scale: A third, intermediate possibility, is to define a
function f(t, x, v) which expresses the probability of having a vehicle at time t in
position x which runs with velocity V . This function, following methods
of statistical mechanics, can be computed using an integro-differential equation
such as the Boltzmann equation.
The engineering approach to analysis of highway traffic flow problems is
primarily based on empirical analysis (i.e., observation and mathematical curve fitting).
One major reference used by American planners is the Highway Capacity Manual,
published by the Transportation Research Board, which is part of the United States
National Academy of Sciences. This recommends modeling traffic flows using the whole
travel time across a link using a delay/flow function, including the effects of queuing.
This technique is used in many US traffic models and in the SATURN model in India.
In many parts of India, a hybrid empirical approach to traffic design is used,
combining macro-, micro-, and mesoscopic features. Rather than simulating a steady
state of flow for a journey, transient "demand peaks" of congestion are simulated. These

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are modeled by using small "time slices" across the network throughout the working day
or weekend. Typically, the origins and destinations for trips are first estimated and a
traffic model is generated before being calibrated by comparing the mathematical model
with observed counts of actual traffic flows, classified by type of vehicle. "Matrix
estimation" is then applied to the model to achieve a better match to observed link counts
before any changes, and the revised model is used to generate a more realistic traffic
forecast for any proposed scheme.
The model would be run several times (including a current baseline, an "average
day" forecast based on a range of economic parameters and supported by sensitivity
analysis) in order to understand the implications of temporary blockages or incidents
around the network. From the models, it is possible to total the time taken for all drivers
of different types of vehicle on the network and thus deduce average fuel consumption
and emissions.
Much of UK as well as in India, Scandinavian, and Dutch authority practice is to
use the modeling program CONTRAM for large schemes, which has been developed
over several decades under the auspices of the UK's Transport Research Laboratory, and
more recently with the support of the Swedish Road Administration. By modeling
forecasts of the road network for several decades into the future, the economic benefits of
changes to the road network can be calculated, using estimates for value of time and other
parameters. The output of these models can then be fed into a cost-benefit analysis
program.
4.5 CUMULATIVE VEHICLE COUNT CURVES (N- CURVES)
A cumulative vehicle count curve, the N-curve, shows the cumulative number of
vehicles that pass a certain location x by time t, measured from the passage of some
reference vehicle. This curve can be plotted if the arrival times are known for individual
vehicles approaching a location x, and the departure times are also known as they leave
location x. Obtaining these arrival and departure times could involve data collection: for
example, one could set two point sensors at locations X1 and X2, and count the number of
vehicles that pass this segment while also recording the time each vehicle arrives

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at X1 and departs from X2. The resulting plot is a pair of cumulative curves where the
vertical axis (N) represents the cumulative number of vehicles that pass the two
points: X1 and X2, and the horizontal axis (t) represents the elapsed time from X1 and X2.
If vehicles experience no delay as they travel from X1 to X2, then the arrivals of
vehicles at location X1 is represented by curve N1 and the arrivals of the vehicles at
location X2 is represented by N2 in figure below. More commonly, curve N1 is known as
the arrival curve of vehicles at location X1 and curve N2 is known as the arrival curve of
vehicles at location X2.
Using a one-lane signalized approach to an intersection as an example,
where X1 is the location of the stop bar at the approach and X2 is an arbitrary line on the
receiving lane just across of the intersection, when the traffic signal is green, vehicles can
travel through both points with no delay and the time it takes to travel that distance is
equal to the free-flow travel time. Graphically, this is shown as the two separate curves in
figure 4.5.1.
Fig.4.5.1 Simple cumulative curves

70
However, when the traffic signal is red, vehicles arrive at the stop bar (X1) and are
delayed by the red light before crossing X2 some time after the signal turns green. As a
result, a queue builds at the stop bar as more vehicles are arriving at the intersection
while the traffic signal is still red.
Therefore, for as long as vehicles arriving at the intersection are still hindered by
the queue, the curve N2 no longer represents the vehicles’ arrival at location X2; it now
represents the vehicles’ virtual arrival at location X2, or in other words, it represents the
vehicles' arrival at X2 if they did not experience any delay.
The vehicles' arrival at location X2, taking into account the delay from the traffic
signal, is now represented by the curve N′2 in the figure 4.5.2.
Fig.4.5.2 Arrival, virtual arrival, and departure curves
The concept of the virtual arrival curve is flawed. This curve does not correctly
show the queue length resulting from the interruption in traffic (i.e. red signal). It

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assumes that all vehicles are still reaching the stop bar before being delayed by the red
light. In other words, the virtual arrival curve portrays the stacking of vehicles vertically
at the stop bar.
When the traffic signal turns green, these vehicles are served in a first-in-first-out
(FIFO) order. For a multi-lane approach, however, the service order is not necessarily
FIFO.
Nonetheless, the interpretation is still useful because of the concern with average
total delay instead of total delays for individual vehicles.
4.6. STEP FUNCTION VS. SMOOTH FUNCTION
Fig.4.6 Step function
The traffic light example depicts N-curves as smooth functions. Theoretically,
however, plotting N-curves from collected data should result in a step-function (figure
4.6).

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Each step represents the arrival or departure of one vehicle at that point in
time. When the N-curve is drawn on larger scale reflecting a period of time that covers
several cycles, then the steps for individual vehicles can be ignored, and the curve will
then look like a smooth function (figure 4.5.1).
4.7. N-CURVE: TRAFFIC FLOW CHARACTERISTICS
The N-curve can be used in a number of different traffic analyses, including
freeway bottlenecks and dynamic traffic assignment.
This is due to the fact that a number of traffic flow characteristics can be derived
from the plot of cumulative vehicle count curves. Illustrated (figure 4.7) are the different
traffic flow characteristics that can be derived from the N-curves.
Fig.4.7 Traffic flow characteristics from two N-curves

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These are- in the form of table for different traffic flow characteristics from figure 4.7:
Symbol Definition
N1 the cumulative number of vehicles arriving at location X1
N2
the virtual cumulative number of vehicles arriving at location X2, or the
cumulative number of vehicles that would have liked to cross X2 by time t
N′2 the actual cumulative number of vehicles arriving at location X2
TTFF
the time it takes to travel from location X1 to location X2 at free-flow
conditions
w(i) the delay experienced by vehicle i as it travels from X1 to X2
TT(i) the total time it takes to travel from X1 to X2 including delays (TTFF + w(i))
Q(t) the queue at any time t, or the number of vehicles being delayed at time t
n total number of vehicles in the system
m total number of delayed vehicles
TD total delay experienced by m vehicles (area between N2 and N′2)
t1 time at which congestion begins
t2 time at which congestion ends

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From these variables, the average delay experienced by each vehicle and the
average queue length at any time t can be calculated, using the following formulas:
4.8. APPLICATIONS
4.8.1 THE BOTTLENECK MODEL
One application of the N-curve is the bottleneck model, where the cumulative vehicle
count is known at a point before the bottleneck (i.e. this is location X1). However, the
cumulative vehicle count is not known at a point after the bottleneck (i.e. this is
location X2), but rather only the capacity of the bottleneck, or the discharge rate, μ, is
known. The bottleneck model can be applied to real-world bottleneck situations such as
those resulting from a roadway design problem or a traffic incident.
Fig.4.8.1 Roadway section experiencing a bottleneck

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Take a roadway section where a bottleneck exists such as in figure 12. At some
location X1 before the bottleneck, the arrivals of vehicles follow a regular N-curve. If the
bottleneck is absent, the departure rate of vehicles at location X2 is essentially the same as
the arrival rate at X1 at some later time (i.e. at time TTFF – free-flow travel time).
However, due to the bottleneck, the system at location X2 is now only able to have
a departure rate of μ. When graphing this scenario, essentially we have the same situation
as in figure 4.5.2, where the arrival curve of vehicles is N1, the departure curve of
vehicles absent the bottleneck is N2, and the limited departure curve of vehicles given the
bottleneck is N′2. The discharge rate μ is the slope of curve N′2, and all the same traffic
flow characteristics as in figure 4.7 can be determined from this diagram. The maximum
delay and maximum queue length can be found at a point M in figure 4.8.1.1, where the
slope of N2 is the same as the slope of N′2; i.e. when the virtual arrival rate is equal to the
discharge / departure rate μ.
Fig.4.8.1.1 Maximum queue length and delay
The N-curve in the bottleneck model may also be used to calculate the benefits in
removing the bottleneck, whether in terms of a capacity improvement or removing an
incident to the side of the roadway.

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4.8.2 TANDEM QUEUES
As introduced in the section above, the N-curve is an applicable model to estimate traffic
delay during time by setting arrival and departure cumulative counting curve. Since the
curve can represent various traffic characteristics and roadway conditions, the delay and
queue situations under these conditions will be able to be recognized and modeled using
N-curves.
Tandem queues occur when multiple bottlenecks exist between the arrival and
departure locations. Figure 4.8.2, shows a qualitative layout of a tandem-queue roadway
segment with a certain initial arrival. The bottlenecks along the stream have their own
capacity, 'μi [veh/time], and the departure is defined at the downstream end of the entire
segment.
Fig.4.8.2 Tandem Queues
To determine the ultimate departure, D(t), it can be an available method to
research on the individual departures, Di(t). As shown in the Figure 15, if the free-flow
travel-time is neglected, the departure of BNi-1 will be the virtual arrival of BNi, which
can also be presented as Di-1(t)=Ai(t).
Thus, the N-curve of a roadway with 2 bottlenecks (minimum number of BNs
along a tandem-queue roadway) can be developed as Figure 4.8.2.1 with μ1<μ2. In this
case, D2(t) will be the ultimate departure of this 2-BN tandem-queue roadway.

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Regarding of a tandem-queue roadway having 3 BNs with μ1<μ2, if μ1<μ2<μ3,
similarly as the 2-BN case, D3(t) will be the ultimate departure of this 3-BN tandem-
queue roadway. If, however, μ1<μ3<μ2, D2(t) will then still be the ultimate departure of
the 3-BN tandem-queue roadway.
Fig.4.8.2.1 N-Curve of Tandem Queues with Two BNs
Fig.4.8.2.2 N-Curve of Tandem Queues with n BNs

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Thus, it can be summarized that, the departure of the bottleneck with the
minimum capacity will be the ultimate departure of the entire system, regardless of the
other capacities and the number of bottlenecks. Figure 4.8.2.2 shows a general case with
n BNs.
The N-curve model describing above represents a significant characteristic of the
tandem-queue systems, which is that the ultimate departure only depends on the
bottleneck with the minimum capacity.
In a practical perspective, when the resources (economy, effort, etc.) of the
investment on tandem-queue systems are limited, the investment can mainly focus on the
bottleneck with the worst condition.
4.8.3 TRAFFIC LIGHT
A signalized intersection will have special departure behaviors. With simplified speaking,
a constant releasing free-flow capacity, μs, exists during the green phases. On the
contrary, the releasing capacity during the red phases should be zero.
Thus, the departure N-curve regardless of arrival will look like as Figure 4.8.3
below: counts increase with the slope of μs during green, and remain the same during red.
Fig.4.8.3 Departure Curve for a Signal with a Releasing Capacity

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Saturated case of a traffic light occurs when the releasing capacity is fully used.
This case usually exists when the arriving demand is relatively large. The N-curve
representation of the saturated case is shown in the Figure 4.8.3.1.
Fig.4.8.3.1 Saturated Case at a Traffic Light
Unsaturated case of a traffic light occurs when releasing capacity is not fully used.
This case usually exists when the arriving demand is relatively small. The N-curve
representation of the unsaturated case is shown in the Figure 4.8.3.2. If there is a
bottleneck with a capacity of μb(<μs) downstream of the light, the ultimate departure of
the light-bottleneck system will be that of the downstream bottleneck.
Fig.4.8.3.2 Unsaturated Case at a Traffic Light with a Downstream Bottleneck

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4.9. DYNAMIC TRAFFIC ASSIGNMENT
Dynamic traffic assignment can also be solved using the N-curve. There are two main
approaches to tackle this problem: system optimum, and user equilibrium. This
application will be discussed further in the following section.
4.9.1 KERNER’S THREE-PHASE TRAFFIC THEORY
Kerner’s three-phase traffic theory is an alternative theory of traffic flow. Probably the
most important result of the three-phase theory is that at any time instance there is a range
of highway capacities of free flow at a bottleneck. The capacity range is between some
maximum and minimum capacities. The range of highway capacities of free flow at the
bottleneck in three-phase traffic theory contradicts fundamentally classical traffic theories
as well as methods for traffic management and traffic control which at any time instant
assume the existence of a particular deterministic or stochastic highway capacity of free
flow at the bottleneck.
4.9.2 TRAFFIC ASSIGNMENT
The aim of traffic flow analysis is to create and implement a model which would enable
vehicles to reach their destination in the shortest possible time using the maximum
roadway capacity as shown in figure 4.9.2. This is a four-step process:
1. Generation – the program estimates how many trips would be generated. For this,
the program needs the statistical data of residence areas by population, location of
workplaces etc.;
2. Distribution – after generation it makes the different Origin-Destination (OD)
pairs between the location found in step 1;
3. Modal Split/Mode Choice – the system has to decide how much percentage of the
population would be split between the difference modes of available transport,
e.g. cars, buses, rails, etc.;
4. Route Assignment – finally, routes are assigned to the vehicles based on
minimum criterion rules.
This cycle is repeated until the solution converges.

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Fig.4.9.2 The Four Step Travel Demand Model for Traffic Assignment
There are two main approaches to tackle this problem with the end objectives:
1) System optimum
2) User equilibrium
System Optimum: System Optimum is based on the assumption that routes of all
vehicles would be controlled by the system, and that rerouting would be based on
maximum utilization of resources and minimum total system cost. (Cost can be
interpreted as travel time.)
Hence, in a System Optimum routing algorithm, all routes between a given OD
pair have the same marginal cost. In traditional transportation economics, System
Optimum is determined by equilibrium of demand function and marginal cost function.
In this approach, marginal cost is roughly depicted as increasing function in
traffic congestion. In traffic flow approach, the marginal cost of the trip can be expressed
as sum of the cost(delay time, w) experienced by the driver and the externality(e) that a
driver imposes on the rest of the users. Suppose there is a freeway(0) and an alternative
route(1), which users can be diverted onto off-ramp. Operator knows total arrival

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rate(A(t)), the capacity of the freeway(μ_0), and the capacity of the alternative
route(μ_1).
From the time 't_0', when freeway is congested, some of the users start moving to
alternative route. However, when 't_1', alternative route is also full of capacity. Now
operator decides the number of vehicles(N), which use alternative route. The optimal
number of vehicles(N) can be obtained by calculus of variation, to make marginal cost of
each route equal.
Thus, optimal condition is T_0=T_1+∆_1. In this graph, we can see that the
queue on the alternative route should clear ∆_1 time units before it clears from the
freeway.
This solution does not define how we should allocates vehicles arriving between
t_1 and T_1, we just can conclude that the optimal solution is not unique. If operator
wants freeway not to be congested, operator can impose the congestion toll, e_0-e_1,
which is the difference between the externality of freeway and alternative route.
In this situation, freeway will maintain free flow speed, however alternative route
will be extremely congested.
User equilibrium: The user optimum equilibrium assumes that all users choose their
own route towards their destination based on the travel time that will be consumed in
different route options.
The users will choose the route which requires the least travel time. The user
optimum model is often used in simulating the impact on traffic assignment by highway
bottlenecks. When the congestion occurs on highway, it will extend the delay time in
travelling through the highway and create a longer travel time. Under the user optimum
assumption, the users would choose to wait until the travel time using a certain freeway is
equal to the travel time using city streets, and hence equilibrium is reached. This
equilibrium is called User Equilibrium, Wardrop Equilibrium or Nash Equilibrium.
The core principle of User Equilibrium is that all used routes between a given OD
pair have the same travel time. An alternative route option is enabled to use when the
actual travel time in the system has reached the free-flow travel time on that route.

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Fig.4.9.3 User equilibrium traffic model
For a highway user optimum model considering one alternative route, a typical
process of traffic assignment is shown in figure 4.9.3. When the traffic demand stays
below the highway capacity, the delay time on highway stays zero.
When the traffic demand exceeds the capacity, the queue of vehicle will appear on
the highway and the delay time will increase. Some of users will turn to the city streets
when the delay time reaches the difference between the free-flow travel time on highway
and the free-flow travel time on city streets.
It indicates that the users staying on the highway will spend as much travel time
as the ones who turn to the city streets. At this stage, the travel time on both the highway
and the alternative route stays the same.
This situation may be ended when the demand falls below the road capacity, that
is the travel time on highway begins to decrease and all the users will stay on the
highway. The total of part area 1 and 3 represents the benefits by providing an alternative
route. The total of area 4 and area 2 shows the total delay cost in the system, in which
area 4 is the total delay occurs on the highway and area 2 is the extra delay by shifting
traffic to city streets.

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4.10. TIME DELAY
Both User Optimum and System Optimum can be subdivided into two categories on the
basis of the approach of time delay taken for their solution:
1. Predictive Time Delay
2. Reactive Time Delay
Predictive time delay is based on the concept that the system or the user knows
when the congestion point is reached or when the delay of the freeway would be equal to
the delay on city streets, and the decision for route assignment is taken in time.
On the other hand, reactive time delay is when the system or user waits to
experience the point where the delay is observed and the diversion of routes is in reaction
to that experience. Predictive delay gives significantly better results than the reactive
delay method.
4.11. KERNER’S NETWORK BREAKDOWN (BM) PRINCIPLE
Kerner introduced an alternative approach to traffic assignment based on his
network breakdown minimization (BM) principle.
Rather than an explicit minimization of travel time that is the objective of System
Optimum and User Equilibrium, the BM principle minimizes the probability of the
occurrence of congestion in a traffic network. Under sufficient traffic demand, the
application of the BM principle should lead to implicit minimization of travel time in the
network.
4.12. VARIABLE SPEED LIMIT ASSIGNMENT
This is an upcoming approach of eliminating shockwave and increasing safety for the
vehicles. The concept is based on the fact that the risk of accident on a roadway increases
with speed differential between the upstream and downstream vehicles.
The two types of crash risk which can be reduced from VSL implementation are
the rear-end crash and the lane-change crash. Different approaches have been
implemented by researchers to build a suitable VSL algorithm.

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4.13. ROAD JUNCTIONS
A major consideration in road capacity relates to the design of junctions. By allowing
long "weaving sections" on gently curving roads at graded intersections, vehicles can
often move across lanes without causing significant interference to the flow.
However, this is expensive and takes up a large amount of land, so other patterns
are often used, particularly in urban or very rural areas.
Most large models use crude simulations for intersections, but computer
simulations are available to model specific sets of traffic lights, roundabouts, and other
scenarios where flow is interrupted or shared with other types of road users or
pedestrians.
A well-designed junction can enable significantly more traffic flow at a range of
traffic densities during the day. By matching such a model to an "Intelligent Transport
System", traffic can be sent in uninterrupted "packets" of vehicles at predetermined
speeds through a series of phased traffic lights.
The UK's TRL has developed junction modeling programs for small-scale local
schemes that can take account of detailed geometry and sight lines; ARCADY for
roundabouts, PICADY for priority intersections, and OSCADY and TRANSYT for
signals. Many other junction analysis software packages exist such
as Sidra and LinSig and Synchro.
4.14. KINEMATIC WAVE MODEL
The kinematic wave model was first applied to traffic flow by Lighthill and Whitham in
1955. Their two-part paper first developed the theory of kinematic waves using the
motion of water as an example. In the second half, they extended the theory to traffic on
“crowded arterial roads.” This paper was primarily concerned with developing the idea of
traffic “humps” (increases in flow) and their effects on speed, especially through
bottlenecks. The authors began by discussing previous approaches to traffic flow theory.
They note that at the time there had been some experimental work, but that “theoretical
approaches to the subject in their infancy.” One researcher in particular, John Glen
Wardrop, was primarily concerned with statistical methods of examination, such as space

86
mean speed, time mean speed, and “the effect of increase of flow on overtaking” and the
resulting decrease in speed it would cause. Other previous research had focused on two
separate models: one related traffic speed to traffic flow and another related speed to the
headway between vehicles.
The goal of Lighthill and Whitham, on the other hand, was to propose a new
method of study “suggested by theories of the flow about supersonic projectiles and of
flood movement in rivers.” The resulting model would capture both of the
aforementioned relationships, speed-flow and speed-headway, into a single curve, which
would “[sum] up all the properties of a stretch of road which are relevant to its ability to
handle the flow of congested traffic.” The model they presented related traffic flow to
concentration (now typically known as density).
They wrote, “The fundamental hypothesis of the theory is that at any point of the
road the flow q (vehicles per hour) is a function of the concentration k (vehicles per
mile).”
According to this model, traffic flow resembled the flow of water in that “Slight
changes in flow are propagated back through the stream of vehicles along ‘kinematic
waves,’ whose velocity relative to the road is the slope of the graph of flow against
concentration.” The authors included an example of such a graph; this flow-versus-
concentration (density) plot is still used today.
The authors used this flow-concentration model to illustrate the concept of shock
waves, which slow down vehicles which enter them, and the conditions that surround
them. They also discussed bottlenecks and intersections, relating both to their new
model.
For each of these topics, flow-concentration and time-space diagrams were
included. Finally, the authors noted that no agreed-upon definition for capacity existed,
and argued that it should be defined as the “maximum flow of which the road is capable.”
Lighthill and Whitham also recognized that their model had a significant limitation: it
was only appropriate for use on long, crowded roadways, as the “continuous flow”
approach only works with a large number of vehicles.

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Components of the kinematic wave model of traffic flow theory are:
The kinematic wave model of traffic flow theory is the simplest dynamic traffic
flow model that reproduces the propagation of traffic waves. It is made up of three
components: the fundamental diagram, the conservation equation, and initial conditions.
The law of conservation is the fundamental law governing the kinematic wave model:
The fundamental diagram of the kinematic wave model relates traffic flow with
density, as seen in figure 3 above. It can be written as:
Finally, initial conditions must be defined to solve a problem using the model. A
boundary is defined to be k(t, x), representing density as a function of time and position.
These boundaries typically take two different forms, resulting in initial value problems
(IVPs) and boundary value problems (BVPs).
Initial value problems give the traffic density at time t=0, such that k(0,x)= g(x) ,
where g(x) is the given density function. Boundary value problems give some
function g(t) that represents the density at the x=0 position, such that k(t, 0) = g(t) . The
model has many uses in traffic flow.
One of the primary uses is in modeling traffic bottlenecks, as described in the
following section.

88
Traffic bottleneck: Traffic bottlenecks are disruptions of traffic on a roadway caused
either due to road design, traffic lights, or accidents.
Fig.4.14 Pie-chart for various causes of congestion.
There are two general types of bottlenecks, stationary and moving bottlenecks.
Stationary bottlenecks are those that arise due to a disturbance that occurs due to a
stationary situation like narrowing of a roadway, an accident. Moving bottlenecks on the
other hand are those vehicles or vehicle behavior that causes the disruption in the vehicles
which are upstream of the vehicle.
Generally moving bottlenecks are caused by heavy trucks as they are slow
moving vehicles with less acceleration and also may make lane changes
Bottlenecks are important considerations because they impact the flow in traffic,
the average speeds of the vehicles. The main consequence of a bottleneck is an
immediate reduction in capacity of the roadway. The Federal Highway Authority has
stated that 40% of all congestion is from bottlenecks figure 4.14 shows the pie-chart for
various causes of congestion.

TRAFFIC IMPROVEMENTS FOR SMOOTH MOVEMENT OF TRAFFIC FLOW

TRAFFIC IMPROVEMENTS FOR SMOOTH MOVEMENT OF TRAFFIC FLOW

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