Journal of Transportation Technologies, 2012, 2, 248-259 Published Online July 2012 (
Performance Evaluation of Intelligent Adaptive Traffic
Control Systems: A Case Study
Saeed Samadi1, Ali Pajoumand Rad2, Farhad Mohammad Kazemi3, Hamed Jafarian2
1Department of EE, Research Institute of Food Science & Technology, Mashhad, Iran
2Mashhad Traffic Control Center, Mashhad Traffic and Transportation Organization, Mashhad, Iran
3Department of Computer Engineering, Payame Noor University, Mashhad, Iran
Received April 12, 2012; revised May 13, 2012; accepted June 3, 2012
Mashhad, the second largest city in Iran, like many other big cities, is faced with increasing traffic congestion owing to
rapidly increasing population and annual pilgrimage. In recent years, Mashhad traffic and transportation authorities
have been challenged with how to manage the increasing congestion with limited budgets for major roadway construc-
tion projects. Mashhad has recognized the need to improve the existing system capacity to get th e most out of their cur-
rent transportation system infrastructures. Since most of the delay ti mes occur at signalized intersectio ns, using an intel-
ligent control system with proper cap abilities to overcome the growing traffic requirements is reco mmen ded. Fo llowing
comprehensive studies carried out with the aim of developing the Mashhad traffic control center, the SCATS adaptive
traffic control system was introduced as the selected intelligent control system for integrating signalized intersections.
The first intersection was equipped with this system in 2005. This paper describes the results of a field evaluation in
which fixed actuated-coordinated signal timings are compared with those dynamically computed by SCATS. The ef-
fects of this system on optimizing fuel consumption as well as reducing air pollutants are fully discussed. It is found that
SCATS consistently reduced travel times and the average delay per stopped or appro a ching vehicle. The positive impact
of adaptive traffic control systems on fuel consumption and air pollution are also highlighted.
Keywords: ITS; Adaptive Traffic Control; SCATS; Measure of Effectiveness; Delay; Travel Time
1. Introduction
Intelligent Transportation Systems, or ITS, can be de-
fined as the application of computing, information, and
communications technologies to the real-time manage-
ment of vehicles and networks involving the movement
of people, goods, and services. When integrated into the
transportation system’s infrastructure, and into vehicles
themselves, these technologies relieve congestion, im-
prove safety, and enhan ce productivity. Intellig ent Trans-
portation Systems (ITS) encompasses a broad range of
wireless and wire line communications-based informa-
tion and electronics technologies [1].
Advanced Traffic Management System (ATMS) is a
major subsystem of ITS, and real-time traffic control is
considered to be one of the main operational processes of
ATMS, with the aim of implementing adaptive traffic
systems (Figure 1). The objects of this process are: 1) To
minimize congestion while maximizing the movement of
people and goods; 2) To improv e the flow of traffic; and
3) To manage travel demand and prioritize it. The net-
work of streets and roads plays an important role in ur-
ban traffic systems, and the quality of managing this
network determines the success or failure of the whole
system. Therefore, intersection control has its specific
and important place as the most effective parameter in-
fluencing the quality o f controlling street networks [2].
This paper describes the results of a field evaluation in
which fixed actuated-coordinated signal timings are com-
pared with those dynamically computed by the SCATS
adaptive traffic control system. Travel times, travel time
stopped, delays, and number of stops were collected by
driving probe vehicles on the major selected routes, and
information was extracted from traffic surveillance video
cameras. Then, the main measures of effectiveness which
are used in investigating the traffic condition of intersec-
tions are intr oduced, and the methodolog y of the study is
also described. Finally, the results of studies carried out
before and after using SCATS at selected intersections
are fully analyzed.
2. Signalized Intersections Control Systems
2.1. Pre-Time System
It is the simplest method for controlling the traffic load
opyright © 2012 SciRes. JTTs
Figure 1. Outline of intelligent transportation systems.
of an intersection. In this system, regardless of traffic
load, timing and phasing procedures are predetermined
and recorded in the local memory of the system. The
timing is set based on the statistics of the traffic load over
differen t hours of a day and different days of a year. Ob-
viously, this system lacks the capability of showing an
appropriate response to traffic load variations. Especially
when the traffic load does not follow a specific pattern, it
cannot calculate and ap ply proper timing at intersections.
2.2. Central Pre-Time System
The next step in improving timing at intersections is to
try to establish communication b etween intersections and
the traffic control center. In this case, as all systems can
communicate with a single control center, it is possible to
change the timing and correct it via a single source (the
traffic control center). Therefore, we can change the
timing of intersections and improve their efficiency using
the ITS system’s peripheral equipment, like installed
traffic cameras or the reports of supervisors.
2.3. Central Intelligent System
This is the newest signalized intersection control method
in which the timing and phasing parameters are continu-
ously adjusted to accommodate the real conditions of
traffic. The system could be aware of the real traffic con-
ditions via vehicle detectors installed in intersections.
There are various types of detectors, like inductive loop,
radar, or image detectors, of which the inductive detector
is the cheapest one. As shown in Figure 2, inductive
loop detectors consist of one simple coil whose induction
magnitude varies as the number of vehicles in an inter-
section changes.
Figure 2. Inductive loop sensors.
The variations of induction are detected by the devices
installed and are used for determining the traffic indexes
of all four arms of an intersection. In this way, based on
the instant information of a given in tersection, the timing
of each arm is calculated by central software and is ap-
plied immediately to the system in order to improve the
system’s efficiency. Figure 3 shows the differences be-
tween fixed-time and intellig ent adaptive systems in con -
trolling intersection s at different hours of a day.
The system sends its different traffic information to
central software. It is also able to optimally control the
traffic arteries and to coordinate the next uninterrupted
intersections. In this way, we can benefit from the green
wave concept in intersections within a given route
through decreasing the delay and stoppage times of vehi-
cles in the route.
Sydney Coordinated Adaptive Traffic System (SCATS)
Copyright © 2012 SciRes. JTTs
is a two-level, hierarchical traffic adaptive signal control
system developed in Australia in the early 1980s by the
Roads and Traffic Authority (RTA) [3]. SCATS uses
information from vehicle detectors located in each lane
immediately in advance of the stop line to adjust signal
timings in response to variations in traffic demand and
system capacity. SCATS acts as a heuristic feedback
system, adjusting signal timings based on the changes in
traffic flows during previous cycle(s). Two basic mea-
sures from detectors are used to adjust signal timings:
degree of saturation (DS) and traffic flow. Both are mea-
sured each cycle. They serve to calculate cycle lengths,
splits, and offsets for the following cycle. The SCATS
strategy assumes that higher cycle lengths increase inter-
section capacity and splits proportional to approach de-
mand, and provide longer offsets for increased traffic
volumes. For saturated and over-saturated traffic condi-
tions, SCATS usually abandons the concept of splits
proportional to saturation and provides more green for
higher traffic flows on major roads. For more informa-
tion about SCATS logic, refer to the relevant literature
As shown in Figure 4, SCATS can be deployed in the
field with traffic signal controllers. A central computer
Figure 3. Comparison of the performance of fixed-time and intelligent (adaptive) systems.
Figure 4. How SCATS works.
Copyright © 2012 SciRes. JTTs
running the SCATS algorithms processes DS and traffic
flows from selected detectors in the system and adjusts
signal timings in real time. The new signal timings are
then sent to local controllers via the communication
server and are implemented in the field. SCATS can de-
ploy various levels of responsiveness when selecting the
best signal timings (i.e. Masterlink, Flexilink, Isolated,
Master Isolated and Fixed Time). If communication be-
tween the central and field components fails, pre-time
signal timings from local controllers are implemented.
Each regional computer can manage up to 250 intersec-
tions. A SCATS system can have up to 64 regional
computers. Up to 100 users can connect to a SCATS
central manager at the same time and up to 30 users can
connect to a single regional computer simultaneously.
4. Study Methodology
In order to define the impacts of a system on improving
traffic conditions in a given signalized intersection or in
an initial street, we first should determine traffic mea-
sures of effectiveness (MOE) and then evaluate the im-
pacts of the system on the defined measures.
In general, before and after studies are carried out with
the aim of evaluating the quality of traffic flow within a
given route, monitoring the variations of total travel and
delay times, and determining major delay types. The out-
comes of these studies are used to compare practical
conditions of the route before and after improving an
intersection or a route and optimizing the controlling
parameters [8-12]. Usually these studies are done with
the help of probe vehicles and statistical techniques.
According to the results of studies, total travel times
within a given route as well as the average delay times of
vehicles could be considered as two main parameters in
determining the effectiveness of a given system or me-
thod in a signalized intersection. After computing these
parameters in an intersection we can calculate other re-
lated parameters like the number of stops and the prob-
able improvements in fuel consumption and air pollution
Before calculating travel and delay times we should
select the route that is going to be subjected to our stu-
dies as well as all controlling points. The most appropri-
ate times for this study are: early morning, evening, and
non-peak times of traffic in routes that are known to have
the max imu m tr aff ic load . C ase studies at other hours are
also possible, i f nec essa ry.
The studies should be carried out in appropriate
weather conditions in order to avoid the impacts of bad
weather conditions. Since car accidents and unusually
heavy traffic may result in unacceptable results, all travel
that happens in these conditions should be eliminated.
Once smooth traffic conditions have been reestablished,
the study should be restarted. In summary, the studies
should be carried out under usual and typical traffic con-
4.1. Travel Time
In order to measure travel times along the routes ending
at the selected intersections, we use a probe vehicle that
follows the traffic current. In this case, the vehicle should
show a behavior similar to other vehicles, i.e., it should
move or stop like other vehicles. This condition should
be met in order to simulate the average motion of vehi-
cles within a given route. In this method, two statistics
men calculate the following parameters and record them
in specific forms using two stopwatches as well as a dis-
tance-measuring device:
Travel Time (TT) (seconds): the time within which
the test vehicle travels from the start of the route to its
Delay Time (DT): the time during which the vehicle
has to stop because of route closure or reduce its
speed below 10 km/hr.
Running Time (RT) (seconds): the total time con-
sumed for the travel (regardless of delay times),
which is derived from Equati on (1):
RT = TT D (1)
Running Speed (RS) (km/hr): th e av e r ag e s pe ed of the
test vehicle during its motion (regardless of delay
time), which is derived from Equation (2):
RS = Distance/RT (2)
To calculate necessary movements for meeting the sta-
tistical requirements of before and after studies, we
should run the following algorithm:
Estimate the count of go and back movements. After
calculating the speeds of a group of movements, the real
value of the difference between the first and the second
movements, the second and the third and so on, is com-
puted in order to define the exact count of movements.
Then, the derived values are added together and the new
obtained value is divided by the count of differences (N –
1). The average range of movement speed will be d erived
through Equation (3) for initial information calculating
purposes. R = S/(N – 1) (3)
RS = the mean of movement speed (miles per hour);
R = the mean range of movement speed (miles per
S = the sum of the absolute magnitudes of differences;
N = the number o f r equire d tests.
1) Again, define the necessary movements via Table 1.
This time use the calculated average range.
2) Add other movements if necessary.
3) Consider experts’ and engineers’ points of view in
Copyright © 2012 SciRes. JTTs
order to adapt the count of movements with real condi-
tions and eliminate non-logical values.
4.2. Average Delay Time in an Intersection
This measure is used to estimate the role of an intersec-
tion in facilitating traffic flow, i.e., it defines how vehi-
cles arrive at the intersection and cross it or arrive at the
intersection and change their lane. This index optimally
gives detailed information about stoppage and delay
times within all four arms of intersections. This study
is based on counting the number of stopped vehicles in
a given intersection during continuous and uninterrup-
ted intervals. The mean time interval selected is 15 sec-
onds; however, other intervals can be selected. The time
intervals should be selected so that it does not result in
multiple cycles of traffic lights. For example, if a traffic
light has time cycles like 45, 60, 75, 90, 105, 120, 13 5 or
150, we recommend 13 seconds as the time interval of
study; otherwise, 15 seconds would be a proper time in-
To start the study, the first statistics man counts and
records the number of stopped vehicles within each in-
terval. We can use a stopwatch to let him know the end
of the interval. When a vehicle stops in the intersection
for more than one interval, we should con sider more than
one delay time for it. In other words, a vehicle that has
stopped in the intersection during successive intervals is
counted in each of the intervals.
The second statistics man has a separate form and re-
cords the number and volume of the vehicles in the in-
tersection. He classifies them to the stopped and moving
categories within one interval. If we add the stoppage
time of vehicles continually and count all stopped and
moving vehicles, this will result in a considerable delay
time. In case the study is carried out at an intersection
equipped with traffic lights, the number of the vehicles
which are going to stop (behind the stopped vehicles
column) should consist of fully stopped vehicles. The
vehicles that enter the intersection and cross it without
stopping should be recorded in the non-stopped or mov-
ing vehicles column.
The object of this study is to measure indexes like total
delay time, mean delay time for each non-stopped vehi-
cle, mean delay time for moving vehicles, and the per-
centage of stopped vehicles.
5. Study Results
At the end of this study, those intersections of Mashhad
city equipped with the intelligent adaptive system were
selected as our study locations (Figure 5). In order to in-
vestigate the impacts of this system more effectively, we
tried to select roads on which some of their intersections
have been equipped with SCATS system. Finally, the
selected roads and intersections were studied in two main
sections as follows:
Fixed Time-Pre-Time versus SCATS control;
Coordinated versus Local control.
The first section was carried out in the intersections
equipped with the adaptive system, and we investigated
the differences between fixed time and the SCATS sys-
tem in parameters like travel time, delay time in inter-
secttions, and so on. These differences were calculated
and reported in three in tervals, i.e., early morning, en d of
night, and non-peak times of traffic. The second section
of the study was carried out in the in tersections equipped
with an adaptiv e system with the aim of investigatin g the
effects of central and coordinated control in adjacent
The results obtained were reported for three time in-
tervals, i.e., early morning, end of night, and non-peak.
Table 2 lists the selected intersections. The study was
carried out as follows:
At first we studied the selected intersections, and the
rate of vehicles crossing them at different times of a day
was determined through the data extracted from the
SCATS system. Then, the peak and non-peak times of the
intersections were determined using the extracted data,
and the technical study phase was started with respect to
all necessary considerations. Prior to gathering statistical
Table 1. The approximate count and the minimum travels required for travel time study with a reliability of >95%.
The minimum count of movements versus permissible error
The average range of movement speedRa (mph) ±1.0 mph ±2.0 mph ±3.0 mph ±4.0 mph ±5.0 mph
2.5 4 22 2 2 2
5.0 8 4 3 2 2
10.0 21 8 5 4 3
15.0 38 14 8 6 5
20.0 59 21 12 8 6
aIf the value of R has not been mentioned above, recalculate it by interpolation.
Copyright © 2012 SciRes. JTTs
Figure 5. Selected ro ute s on Mashhad map.
data from the selected routes, periodic inspections were
carried out along these routes, and traffic load conditions
were determined in those periods. Finally, the values of
travel and delay times were calculated at early morning,
end of night, afternoon, and non-peak times.
Table 3 shows the summary results of the study car-
ried out on travel and delay times in three main roads
consisting of six intersections. Accord ing to the tab le, the
SCATS adaptive system shows evident improvements.
Also, in Figure 6 comparative charts are shown of delay
parameters for individual intersections and for all paths
on average. After measuring the decreased rate of travel
and delay times along each route as well as the parame-
ters of movement components using the recorded infor-
mation, we calculated fuel consumption and pollutant
emission rates as well as improvement percentage in
each route. Table 4 shows the results. As seen in this
table, fuel consumption and CO and HC pollutant emis-
sions were improved considerably so that for each vehi-
cle per one kilometer, the fuel consumption rate de-
creased by 22, 15, and 47 milliliters respectively in
Sajjad Boulevard, Ferdowsi Boulevard, and Jomhoori-
e-Eslami Boulevard.
Regarding the number of vehicles crossing the inter-
sections during one day, we can calculate the daily opti-
mized rate of fuel consumption [13]. For example, the
total number of vehicles crossing Ferdowsi Boulevard
during 24 hours of a day is estimated around 85,000.
Therefore, we can calculate the optimized rate of fuel
consumption as follo ws:
Daily optimization rate of fuel consumption in Fer-
dowsi Boulevard = the average of the optimized rates of
the fuel consumption of vehicles × the total vehicles
crossing this route within 24 hours = 85 ,000 (vehicle/day)
× 15 (mlit/vehicle) = 1275 (lit/day).
On the other hand, if we extend this argument to the
total number of vehicles crossing the mentioned inter-
sections, we see a reduction in fuel consumption of 1200,
1300, and 2900 lit/day respectively in Sajjad Boulevard,
Ferdowsi Boulevard, and Jomhoori Boulevard. Moreover,
as shown in Figure 7, the amount of the produced float-
ing particles, i.e., CO and UHC (unburned hydrocarbons),
are considerably decreased.
Finally, we can conclude that parameters like the qua-
lity of a smooth traffic flow, vehicles’ speed increase,
and delay time decrease, are all effective in significantly
improving air pollution and gasoline consumption. There
is just one negative parameter, i.e., the increase of NOx
emissions, which increases as the vehicles’ speed in-
creases, though it has a less negative impact on the envi-
ronment. As we know, the production rate of NOx pol-
lutant is higher in CNG fuels compared to gasoline [14];
however, owing to the decrease of other factors, includ-
ing fuel consumption and CO pollutant, our government
is serious about using this fuel.
6. Performance Improvement Suggestions
On many roads of Mashhad city, traffic problems are
generated from a few common sources. Since most routes
suffer common problems, we can introduce general
Table 2. Selected intersections and their study objects.
Study Object Route Name The Selected Route Item
Jomhoori-Parvin 1 Determining the optimizing rate of SCATS system compare d
with pre-time case, before and after installation studies
Jomhoori Blvd.
Ferdowsi-Bahar 3 Determining the effectiveness of optimizing SCATS parameters and
investigating the green wave concept implemented in Ferdowsi Boulevard
Ferdowsi Blvd.
Bahar-Sajjad 5 Determining the optimizing rate of SCATS system compared with
pre-time case, before and after installation studies
Sajjad Blvd.
Copyright © 2012 SciRes. JTTs
Table 3. Summary results; Comparison table of delay parameters for all intersections times of traffic.
Morning Peak Evening Peak Normal Noon
Average & Delay
Parameters/Time of Day SCATS offSCATS on Changes (%)SCATS offSCATS onChanges (%)SCATS off SCATS
on Changes (%)
Jomhoori Blvd.
Average Delay Per
Stopped Vehicle 33.3 31.0 –6.9 33.0 30.0 –9.0 28.1 25.1 –10.7
Average Delay Per
Approach Vehicle 22.5 19.1 –15.1 22.3 18.8 –15.9 17.2 14.2 –17.4
Average Travel Time
of East to West Path 179.1 170.0 –5.1 186.0 176.6 –5.1 154.8 143.5 –7.3
Average Travel Time
of West to East Path 202.1 145.0 –28.3 190.1 179.6 –5.5 134.1 131.8 –1.7
Ferdowsi Blvd.
Average Delay Per
Stopped Vehicle 33.0 31.5 –4.5 34.9 33.0 –5.4 32.5 30.9 –4.9
Average Delay Per
Approach Vehicle 22.1 20.7 –6.3 22.7 19.0 –16.3 20.8 19.1 –8.2
Average travel time
of East to West path 138.1 125.8 –8.9 143.3 139.5 –2.7 125.5 123.7 –1.4
Average travel time
of West to East path 143.6 141.5 –1.5 144.0 119.5 –17.0 128.1 122.3 –4.5
Sajjad Blvd.
Average Delay Per
Stopped Vehicle 34.3 31.1 –9.3 33.6 30.4 –9.5 29.9 27.6 –7.7
Average Delay Per
Approach Vehicle 25.6 23.4 –8.6 25.8 23.0 –10.9 20.0 16.3 –18.5
Average Travel Time
of East to West Path 192.3 167.8 –12.7 319.6 282.5 –11.6 87.3 87.8 0.6
Average Travel Time
of West to East Path 143.5 137.0 –4.5 192.1 160.5 –16.4 100.6 70.0 –30.4
Average Parameters for All Routes
Average Delay Per
Stopped Vehicle 33.5 31.2 –7.0 33.8 31.1 –7.9 30.2 27.9 –7.6
Average Delay Per
Approach Vehicle 23.4 21.1 –10.0 23.6 20.3 –14.2 19.3 16.5 –14.5
Average Travel Time
of East to West Path 169.8 154.5 –9.0 216.3 199.5 –7.8 122.5 118.3 –3.4
Average Travel Time
of West to East Path 163.1 141.2 –13.4 175.4 153.2 –12.7 120.9 108.0 –10.7
Note: The unit of “the mean of travel times sum”, “the mean of delay times sum” and “the mean of run times sum” indexes is seconds. The unit of “the mean of
travel speed sum” and “the mean of run speed sum” is km/hr (kilometer per hour).
solutions for removing similar traffic knots. Based on the
results of field studies and observations, previous sec-
tions introduced separate solutions for removing the traf-
fic problems of each route. In this section, we suggest
some general solutions enabling optimize use of the
SCATS central adaptive control system as follows:
6.1. The Geometrical Parameters of Signalized
Intersections Should Be Corrected
Since in intersections whose geometrical dimensions
have been corrected vehicles move in canalized right turn,
str ai g ht forward, and left turn rou tes, they w ill undou bt edl y
encounter each other less. Thus, in these intersections the
adaptive system can play a significant role in improving
traffic indexes through applying optimized timing on the
arms of the intersections. This correction has another
positive impact, which is the canalization of pedestrian
routes so that by assuming that they pay attention to traf-
fic lights, which has been considered in SCATS system,
this can decrease convergence between vehicles and
pedestrians, which in turn will cause the intersections to
be ev acua ted more quickly.
6.2. The Phasing of Intersections Should Be
In signalized intersections the convergence of right turn
and straightforward movements reduces the speed of mo-
tion and increases delay times. Since the SCATS system
is equipped with appropriate sensors at the entrance of
intersections, it is capable of identifying traffic problems
by analyzing the effects of the generated traffic knot on
the flow of intersections, and, obviously, it will correct
Copyright © 2012 SciRes. JTTs
Copyright © 2012 SciRes. JTTs
Figure 6. Comparative charts of delay parameters for all intersections.
Table 4. The impacts of SCATS on fuel consumption and air pollution.
Location Time Condition Fuel
CO Emissions
(gr) HCX Emissions
(gr) NO Xemissions
Decrease (%)
Decrease (%)
HC Emissions
Decrease (%)
Morning Peak Before 0.404 55.689 4.953 1.879 3.7 6.2 5.9
After 0.389 52.256 4.661 1.988
Evening Peak Before 0.412 56. 652 5.026 1.841 5.2 11 10.3
After 0.39 50.445 4.51 2.06
Normalnoon Before 0.387 48.678 4.344 2.082 5.6 4.4 4.5
After 0.366 46.524 4.146 2.143
Morning Peak Before 0.503 73.059 6.396 2.194 9.2 17.2 15.8
After 0.456 60.519 5.383 2.653
Evening Peak Before 0.498 71. 971 6.304 2.213 4.2 6.1 6
After 0.477 67.57 5.927 2.323
Normal Noon Before 0.439 57. 741 5.127 2.697 6.6 12.8 11.7
After 0.41 50.328 4.53 3.106
Morning Peak Before 0.37 60.917 5.328 0.796 5.8 6.5 6.7
After 0.348 56.938 4.972 0.803
Evening Peak Before 0.432 78.794 6.786 0.755 15.9 21.9 21.3
After 0.363 61.539 5.342 0.77
Normal Noon Before 0.286 39.49 3.552 1.07 8.8 15.9 14.5
After 0.261 33.218 3.035 1.276
Copyright © 2012 SciRes. JTTs
Copyright © 2012 SciRes. JTTs
Figure 7. Comparison of fuel consumption and air pollution reduction at different times of day using SCATS.
timing in this condition. In the cases that left turn move-
ments are dominant in an intersection, it is advised that
the left turn movements be separated from straight for-
ward ones if possible in order to establish a smooth traf-
fic flow in this type of intersection. Of course, this sepa-
ration will lead to the increasing of phase numbers, the
red time duration of each phase, and probably the in-
creasing of the delay time of each stopped vehicle. It will,
however, eventually lead to a smoother traffic flow, be-
cause the use of the adaptive control system, which ba-
lances the timing of each phase with respect to the traffic
load within each arm of intersection, will finally decrease
travel and delay times. So, it is advised to determine the
considerations in separate phases, provided that we have
sufficient space and capacity and technical traffic con-
siderations are being considered.
6.3. Drivers and Pedestrians Should Have
Respect for the Law
The adaptive control of signalized intersections is only
one parameter among several important parameters that
are considered in optimizing the traffic conditions of
routes. Respect for the traffic laws and the promotion of
the traffic culture of drivers and pedestrians is one of the
most effective parameters resulting in a smooth traffic
flow and improvement of traffic indexes.
Here we enlist in summary the chief topics to be ob-
served by citizens:
Drivers should select their appropriate lane before
they arrive at intersections.
They should observe pedestrians paths and drive in
the specified phases for pedestrians with respect to
traffic lights.
They should not park their cars within the intersec-
tion’s limits.
Taxi cabs should not take on or let off passengers
after and before the intersection’s limits.
They should not double park in drive bands.
They should pay attention to traffic lights and should
drive at safe speed limits.
It is necessary to organize marginal auto parks and
assign sufficient parking spaces outside signalized
intersections and heavy traffic roads.
6.4. The System Parameters Should Be
Optimized Continuously
SCATS is an intelligent system and adapts itself with
traffic flow; however, determining all of its parameters
requires presumptions that are determined by the system
designer. Predefined factors should be reviewed con-
tinuously, such as the minimum and maximum timings,
or the programs that set the proper cycle length, or the
percentage of each phase in each route. It is possible to
know the traffic conditions in pr evious hours and days as
well as the volume of data extracted by induced sensors
using peripheral software like the SCATS traffic reporter.
This kind of software provides other valuable informa-
tion like the saturation percentage of each route. These
data, along with filed observations carried out with the
aim of getting information from traffic officers, can make
an appropriate base for optimizing the main parameters
for intersection control purposes.
6.5. Drivers Should Be Informed of Traffic
Displaying the traffic conditions of signalized intersec-
tions using the information derived from induced sensors
is one of the SCATS system’s capabilities. This advan-
tage makes it possible to know the traffic conditions of
each arm of an intersection as well as the traffic condi-
tions of the routes ending at those intersections. There-
fore, we could use this advantage and inform drivers of
the traffic conditions of routes and enable them to select
optimum routes in order to avoid heavy traffic loads on
specific routes. Drivers can be informed by the operator
via news channels, through the Internet, and via display-
ing the traffic map of city, which is presented by the
7. Conclusions
To summarize the results of this before and after study, it
is shown that in most selected routes and intersections
the SCATS adaptive system has positive impacts on
travel and delay times. However, the effectiveness of this
system differs from route to route and depends on the
conditions of a given route. In other words, at main
routes with a high volume of traffic, lowering the delay
and vehicle stoppage time has a higher priority for the
intelligent traffic control system. Although, this may
temporarily increase the delay in other routes, but re-
garding traffic volume of each route it will increase
overall performance of the system. The obtained results,
however, show that more attention should be paid to the
quality of defining the parameters of the adaptive system
in the design and maintenance phases.
It should be mentioned that this study has been carried
out on routes with heavy traffic cond itions. In this type of
route, the intersection commanding system and its speci-
fied timing are not sufficient for reducing traffic load,
and other parameters may play significant role for this
purpose. An intersection adaptive control system, as an
effective parameter, can significantly improve the traffic
indexes of intersections, which in turn will improve the
traffic conditions of routes, but this improvement does
not solely depend on this parameter.
Some helpful suggestions for performance improve-
ment of the system are also given at the end of the paper.
8. Acknowledgements
This evaluation was funded jointly by Mashhad Traffic
and Transportation Organization and Iran Ministry of
Science, Research & Technology.
[1] L. Figueiredo, et al., “Towards the Development of Intel-
ligent Transportation Systems,” IEEE Proceedings of In-
telligent Transportation Systems, Oakland, 25-29 August
2001, pp. 1206-1211.
[2] N. Gartner, et al., “Development of Advanced Traffic
Signal Control Strategies for Intelligent Transportation
Systems: Multilevel Design,” Transportation Research
Record, No 1494, 1995, pp. 98-105.
[3] P. R. Lowrie, “SCATS, Sydney Co-Ordinated Adaptive
Traffic System: A Traffic Responsive Method of Control-
ling Urban Traffic,” Roads and Traffic Authority NSW,
Darlinghurst, 1990.
[4] R. Akcelik, M. Besly and E. Chung, “An Evolution of
SCATS Master Isolated Control,” Proceedings of the
19th ARRB Transport Research Conference (T ransport
98), 1998, pp. 1-24.
[5] C. J. Khisty, “Transportation Engineering: An Introduc-
tion,” 2nd editon, Prentice-Hall, Upper Saddle River, 1998.
[7] R. Ghaman, D. Gettman, L. Head and P. B. Mirchandani,
“Adaptive Control Software for Distributed Systems,”
Copyright © 2012 SciRes. JTTs
Copyright © 2012 SciRes. JTTs
28th Annual Conference of the Industrial Electronics So-
ciety, 5-8 November 2002, pp. 3103-3106.
[8] H. D. Robertson and J. E. Hummer, “Manual of Trans-
portation Engineering Studies,” Prentice-Hall, Upper Sad-
dle River, 1994.
[9] K. Fehon and R. Chong, “Adaptive Traffic Signal System
for Cupertino California,” 4th Asia-Pacific Transporta-
tion Development Conference, April 18-20, 2003.
[10] F. Zhu, G. Li, Zh. Li, C. Chen and D. Wen, “A Case
Study of Evaluating Traffic Signal Control Systems Us-
ing Computational Experiments,” IEEE Transaction on
Intelligent Transportation Systems, Vol. 12, No. 4, 2011,
pp. 1220-1226. doi:10.1109/TITS.2011.2157691
[11] F. Hu, et al., “Field Evaluation of SCATS Control System
in Las Vegas,” Proceedings of the 11th International
Con f erence of Chinese Transportation Profess i on a l s, 2011,
pp. 3963-3973. doi:10.1061/41186(421)397
[12] M. Kergaye, et al., “Comparison of before-after versus
off-on Adaptive Traffic Control Evaluations: Park City,”
Case Study, 2006.
[13] “MUTS: Manual on Uniform Traffic Studies,” 2000.
[14] P. F. Everall, “The Effects of Road and Traffic Condi-
tions on Fuel Consumption,” Road Research Laboratory,