CHAPTER ONE
INRODUCTION
The main backbone of urban activities is the network of urban transportation. The urban
transportation network is usually designed to accommodate the transportation activities of urban
people. Due to increase in population and activities of land-use and transportation, the traffic
congestion rate begin to rise from one area to another until the whole area is fully congested with
traffic, thereby causing economic loss and environmental degradation.
To ease traffic congestion, identifying the state of road traffic should be considered first. The
sequence steps targeted traffic facilities or the traffic management measures.
Through the study of traffic congestion index, evaluation of road traffic condition can be more
objective which contribute to the traffic planning and management, also is important to select path
effectively and to reduce travel time for travelers.
This research work is to identify and access road traffic congestion measurement metrics at
different locations along the case study route and varied period of the day, which will help improve
on managing traffic congestion, and find a way to influence the policy, which can be replicated in
other part of the country with traffic congestion issues.
This work will also provide information on the measures to manage traffic on the case study route.
Furthermore, results of this study can be used to manage existing problems of traffic congestion
on IFO LOCAL GOVERNMENT B/S - IYANA ILOGBO B/S.
BACKGROUND OF STUDY
Traffic congestion not only affecting the human being, but also elevates the pollution. This study
seeks to Identify and access road traffic congestion measurement metric at different locations along
case study route and varied periods of the day, which will help improve on managing traffic
congestion, and as such influence policy, which can be replicated in other cities across the country.
Evaluating traffic congestion levels of road networks is important for traffic management and
control, since it could allow the corresponding agencies an accurately and clearly grasping of
network traffic operation status including the information and extent of congestion at different
locations, and at varied periods of the days.
Therefore, it is necessary to evaluate traffic congestion situations for urban road traffic networks
using applicable evaluation measures.
AIMS & OBJECTIVES
The study is based on traffic congestion and proposing a way to eradicate better still reduce the
traffic congestion on the case study route. The aims and objective of the study are as follows:

To determine the traffic volume and pattern.

To determine the vehicle classification.

To determine the factor that contribute to the congestion on the route.

To determine the day of the week the route is more congested.

To determine the most congested part/section of the route.

To determine the most appropriate means of measuring the level of congestion of the case
study route.
SIGNIFICANCE
The importance of this study is to pick a case study route, determine the level of congestion on the
route and to know the volume of vehicles that go through the case study route. Proffering solution
to the traffic congestion on the case study or find a way to reduce the rate of traffic congestion and
apply the solution to other congested route in the area.
The study on this particular route is given the task to ensure that a long lasting solution is given to
the traffic congestion on the cases study route.
CHAPTER 2
LITERATURE REVIEW
It has long been recognized that road traffic congestion can cause an economic externality in urban
areas and on trunk roads (see, for example Pigou, 1920, and Nash, 2007). Many economists from
Pigou (1920) onwards have analyzed this problem. It is argued in this review that there are
limitations in past analyses and that some of these stem from the emphasis of previous analyses
on flow as the basic cause of congestion rather than recognizing that flow, speed and density are
determined simultaneously.
As a policy response to solve this problem, in terms of economic efficiency, it is often proposed
to charge road users the marginal external congestion cost they impose (Mattsson, 2008). For
example, Goodwin (1995, p. 149) suggests that “charging people something for the external costs
of congestion and environmental damage will be more efficient than not charging them.” Likewise,
De Palma and Lindsey (2011, p. 1) explain that “Congestion pricing has a big advantage over other
travel demand management policies in that it encourages individuals and firms to adjust all aspects
of their behavior: number of trips, destination, mode of transport, time of day, route and so on, as
well as their longrun decisions on where to live, work and set up business.”
Santos et al., 2010, lists a number of problems with first best charging policies. It may be difficult
to measure the marginal external congestion cost. It is possible that other existing transport policies
may even lead to overcharging for congestion externalities, eg the subsidising of other modes of
transport. Policies in other related sectors, eg vehicle insurance (see Peirson, Skinner and
Vickerman (1998) and income taxes (see Verhoef, 2000) could also affect the efficiency of
congestion charging.
Adam Smith (1937) and Dupuit (1844 and 1849) were probably the first economists to consider
the economics of road transport and, to a certain extent, congestion, see Lindsey (2006). Pigou
(1920) was the first modern economist to consider the impact of tolls on congestion in a slightly
confusing example of two different types of roads between the points A and D where differential
taxation may “create an ‘artificial’ situation superior to the ‘natural’ one”, (p. 194). Pigou is
generally regarded as the discoverer of the concept of externalities though not using the term
specifically, see Bohm (2008). As the quotation suggests, Pigou does not explicitly explain the
causes of road congestion but hints at these causes.
Vickrey was perhaps the first economist to consider explicitly the economics of road congestion
and the underlying causes of congestion. Prior to the 1960s, it has been suggested that the causes
of (not the solutions to) road congestion were mainly considered by engineers and planners and
not economists, see Lindsey (2006, p. 303). Vickrey (1969) distinguished six causes of congestion:
simple traffic interactions; multiple traffic interactions; bottlenecks; trigger neck (caused by the
queues resulting from a bottleneck causing delay to vehicles not intending to use the bottleneck
facility); network and control congestion (resulting from a lack of traffic control measures) and
general density (this may well overlap with the other causes of congestion).
Mobility is crucial to functionality of cities as it affects their socio-economic activities. It is also a
fact that the economic development of a nation is closely linked to its transport system. Hindrance
to effective mobility is road traffic congestion, which the World Bank (1999) stated that it
constitutes about 54.5% of all noticeable urban transport externalities. This is as a result of the
ever increasing urbanization, human activities and the resultant heavy dependence on road
transportation that warrants increase in the number of vehicles, of different categories, on the road.
Of interest also is the difficulty of movements on inter-city roads and other major corridors due
largely to obstructions such as traffic crashes, broken down vehicles or certain land use activities
located along these corridors or sheer traffic volume exceeding the road network capacity during
festive seasons and some other major activities. The demand for transport especially in cities of
developing countries has been on the increase following the rapid socio-economic growth and
development of these countries. For instance, the rate of motor vehicle ownership and use is
growing faster than population in many places, with the vehicle ownership growth rates rising to
15 to 20 percent per year. (Odeleye 2008). Traffic management has been quite poor in many
developing countries, despite the growth in transport demand and supply. The resultant traffic
congestion has become impediment to our livability.
Road traffic congestion, according to Goodwin (1997) can be defined as the impedance vehicles
impose on each other, due to the speed-flow relationship, in conditions where the use of a
transport system approaches its capacity. Banjo (1984), also defined congestion as the saturation
of road network capacity due to regular and irregular reductions in service quality exemplified
by increased travel times, variation in travel times and interrupted travel. Olagunju (2011) simply
described road traffic congestion as a disproportion between the inflow and the outflow of
vehicles into and out of a particular space. This is also in line with Ogunsanya (2002)
conceptualization of road traffic congestion as a situation when urban road network could no
longer accommodate the volume of traffic on it.
As a policy response to solve this problem, in terms of economic efficiency, it is often proposed
to charge road users the marginal external congestion cost they impose (Mattsson, 2008). For
example, Goodwin (1995, p. 149) suggests that “charging people something for the external costs
of congestion and environmental damage will be more efficient than not charging them.” Likewise,
De Palma and Lindsey (2011, p. 1) explain that “Congestion pricing has a big advantage over other
travel demand management policies in that it encourages individuals and firms to adjust all aspects
of their behavior: number of trips, destination, mode of transport, time of day, route and so on, as
well as their long run decisions on where to live, work and set up business.”
CHAPTER THREE
METHODOLOGY
To measure the congestion level, several congestion measures have been developed considering
numerous performance on the case study route. Depending on these criteria, the congestion
measures can be categorized into five categories: (i) speed, (ii) travel time, (iii) delay, (iv) level of
services (LoS), and (v) congestion indices, as shown in Figure 3. Moreover, some measures are
used by the DOT-FHWA to quantify the congestion level annually. These federal congestion
measures are listed in Section 3.6. The congestion measures employed in other countries may
differ from the ones discussed in this paper will be discussed briefly in Section 3.7. Note that the
measures presented in this paper are not exhaustive.
Speed
Travel Time
Speed
Reduction
Index (SRI)
Delay
Level Of
Service
Volume to
Capacity ratio
(V/C)
Delay rate
Fig 4 Different Ways to Measure Congestion Rate
Federal
Congested
Hours
Dlay ratio
Travel rate
Speed
Performance
Index(SPI)
Congestion
Indices
Relative
Congestion
Index (RSI)
Travel Time
Index (TTI)
Planning Time
Index (PTI)
Speed
Speed Reduction Index (SRI)
Speed Reduction Index speed change between congested and free-flow conditions, as shown n
Equation (1) The SRI ratio is multiplied by 10 to keep the value of SRI in the range of 0 to 10.
Congestion occurs when the index value exceeds 4 to 5. Values less than 4 indicates non-congested
condition.
𝑆𝑅𝐼 = (1 − 𝑣 𝑎𝑐 ) ∗ 10
(1)
𝑣𝑓𝑓
Where SRI denotes the speed reduction index, vac indicates the actual travel speed, and vff means
the free-flow speed.
The free-flow speed generally refers to the average speed of the off-peak period. In practice,
the posted speed limit can also be considered as the free-flow speed. In the FHWA’s Urban
Congestion.
Report of 2019 the 85th percentile of the off-peak speed is considered as the free-flow speed.
According to the same report, the off-peak period is Monday through Friday, 9:00 a.m. to 4:00
p.m. and 7:00 p.m. to 10:00 p.m., and Saturday and Sunday 6:00 a.m. to 10:00 p.m.
Speed Performance Index (SPI)
SPI is developed to evaluate urban road traffic conditions. The value of SPI (ranging from 0 to100)
can be defined by the ratio between vehicle speed and the maximum permissible speed, as shown
in Equation (2). To measure the traffic state on the road with this index, the traffic state level can
be classified with three threshold values (25, 50, and 75). The classification criterion of the urban
road traffic state is shown in Table 1.
𝑆𝑃𝐼 = (𝑉𝑎𝑣𝑔/𝑉𝑚𝑎𝑥 )
(2)
Where SPI denotes the speed performance index, vavg indicates the average travel speed, and vmax
denotes the maximum permissible road speed.
Table 1. Speed Performance with Traffic State.
Speed Performance Index
Traffic State Level
Description of Traffic State
(0,25)
Heavy Congestion
Low average speed, poor road traffic state
(25,50)
Mild Congestion
Lower average speed, road traffic state bit
weak.
(50,75)
Smooth
Higher the average speed, road traffic state
better
(75,100)
Very Smooth
Higher average speed, road traffic state good.
Travel Rate.
Travel rate refers to the rate of motion for a particular roadway segment or trip that can be represented
by the ratio of the segment travel time by the segment length, as shown in Equation (3). The inverse of
speed can also be employed to quantify the travel rate.
𝑇𝑟𝑟 = 𝑇𝑡 /𝐿𝑠
(3)
Where, Trr denotes the travel rate, Tt is the travel time, and Ls indicates the segment length.
Delay
Delay Rate
The delay rate is the rate of time loss for vehicles operating during congestion for a specific
roadway segment or trip. It can be calculated by the difference between the actual travel rate and
the acceptable travel rate as
𝐷𝑟 = 𝑇𝑟𝑎𝑐 − 𝑇𝑟𝑎𝑝
(4)
Where, Dr is the delay rate, Trac is the actual travel rate, and Trap is the acceptable travel rate.
Delay Ratio
The delay ratio can be calculated by the ratio of delay rate and the actual travel rate as Equation
(5). It is used to compare the relative congestion levels on different roadways.
𝐷 = 𝐷𝑟 /𝑇𝑟𝑎𝑐 ,
(5)
Where D denotes the delay ratio, Dr is the delay rate, and Trac is the actual travel rate.
Level of Services (LoS)
The Highway Capacity Manual (HCM) adopts the LoS approach. Because of the simplicity,
LoS has become extremely popular in practice. The LoS can be determined by various traffic
quantities, such as density, speed, volume to capacity ratio, and maximum service flow rate. The
LoS of a roadway can be determined by the scale intervals of the volume-to-capacity ratio (V/C),
as shown in Table 2. The V/C ratio can be calculated by
𝑉
𝐶
= 𝑁𝑣 /𝑁𝑚𝑎𝑥 ,
(6)
Where, Nv is the spatial mean volume, and Nmax denotes the maximum number of vehicles that a
segment is able to contain as the capacity. It can be further quantified as
𝐿
𝑁𝑚𝑎𝑥 = (𝐿𝑠 ) ∗ 𝑁𝑙 ,
𝑣
(7)
Where Ls is the spatial segment length, Lv is the average vehicle length occupancy, and Nl is the
number of lanes. Lv includes vehicle length and safety distance. In general, it is assumed that
vehicle length is about 14 ft. (approximately 4.27 m), and safety distance is about 15 ft.
(approximately 4.57 m). Table 2. Level of service (LoS) based on the corresponding V/C ratio and
operating conditions.
LoS Class
Traffic State and Condition
V/C Ratio
A
Free Flow
0-0.60
B
Stable flow with unaffected flow
0.61-0.70
C
Stable flow but speed is affected
0.71-0.80
D
High density but stable flow
0.81-0.90
E
Traffic volume near or at capacity level with
0.91-1.00
low speed
F
Breakdown flow
>1.00
Congestion Indices
Relative Congestion Index (RCI)
RCI is the ratio of delay time and free-flow travel time (Tff). The RCI of 0 denotes a very low
congestion level, and the values greater than two (> 2) show a significant congestion level. RCI
can be calculated by
𝑅𝐶𝐼 = (𝑇𝑎𝑐 − 𝑇𝑓𝑓 )/𝑇𝑓𝑓 ,
(8)
Where Tac is the actual travel time, which is further quantified with the ratio of spatial length and
spatial mean speed. The free-flow travel time (Tff) can be calculated with the ratio of spatial length
and free-flow speed.
Road Segment Congestion Index (RI)
The degree of road segment congestion, denoted by Ri, can be measured by using the normal
road segment state and the duration of the non-congestion state in the observation period. The noncongestion state includes the traffic state where the speed performance index (SPI) is higher than
50. The Ri index value ranges between 0 and 1, and the smaller the value of Ri, the more congested
the road segment is.
𝑅𝑖 = (
𝑆𝑃𝐼𝑎𝑣𝑔
100
) ∗ 𝑅𝑁𝐶 , (9)
𝑅𝑁𝐶 = 𝑡𝑁𝐶 /𝑡𝑡
(10)
Where Ri is the road segment congestion index, SPIavg is the average of the speed performance
index. RNC and tNC denotes the proportion of non-congested state and the duration of the noncongested state, respectively. tt is the length of the observation period.
Federal Congestion Measures
In the reports of annual urban congestion trends from 2016 to 2018, published by the DOTFHWA, congested hours, travel time index, and planning time index were used as congestion
measures. These measures are described as follows.
Congested Hours
The congested hours represent the average number of hours during specified periods in which
road sections are crowded. Speeds less than 90% of the free-flow speed for weekdays (6:00 a.m.
to 10:00 p.m.) is considered as the congested state. For example, if most vehicles travel on a 54
mph average when the free-flow speed is 60 mph, it is considered as the congested state.
Travel Time Index (TTI)
TTI is proposed in the 2005 Urban Mobility Report. This index is quantified by comparing
travel time in the free-flow condition and the travel time in peak hours as
𝑇𝑝𝑝
𝑇𝑇𝐼 = 𝑇
𝑓𝑓
= 𝑉𝑓𝑓 /𝑉𝑝𝑝
(11)
Where Tpp is the peak period travel time, Tff is the free-flow travel time, vff is the free-flow travel
speed, and vpp is the peak period travel speed.
Planning Time Index (PTI)
PTI demonstrates the ratio of the 95th percentile travel time of the free-flow travel time. It is
computed during the a.m. (6:00 a.m. to 9:00 a.m.) and p.m. (4:00 p.m. to 7:00 p.m.) peak periods
on weekdays. A PTI of 1.60 means that travelers should anticipate an additional 60% of travel
time above the free-flow travel time to ensure on-time arrival 95% of the time (or most of the
time).