Development of a Traffic Microsimulation Tool with the Incorporation
of Variations in Driver Behaviors
MAHMOUD JAHJOUH, UNEB GAZDER, RASHID ABDULRAHMAN ISMAEEL
Department of Civil Engineering,
University of Bahrain,
32038, Sakhir,
BAHRAIN
Abstract: - Traffic simulation is a field that is gaining popularity among researchers and practitioners due to
various benefits and applications. This research aims to develop a traffic microsimulation model using an
object-oriented approach. The proposed approach will also consider variations in driver behavior. The approach
was applied as a prototype on a busy four-leg intersection in Bahrain. The available data consisted of turning
movement counts on the intersection at three different times of the day. The tool applied the movement to
simulate the flow in each lane of the said intersection. It was observed that the proposed tool could simulate the
flow with reasonable accuracy without any evidence of bias which could result in under or over-estimation of
results. These encouraging results pave the way for further use of the tool for application on other types of road
segments and intersections. It is expected that this tool will provide valuable insights for road safety analysis.
Key-Words: - Traffic, Bahrain, Intersection, Error, Standard deviation, Simulation tool.
Received: March 5, 2024. Revised: September 11, 2024. Accepted: October 9, 2024. Published: November 27, 2024.
1 Introduction
Simulation refers to the replication of a real-life
system, or a part thereof, using mathematical
models and algorithms, [1]. It can be used to
generate numeric data [2] and graphic
representations (videos, pictures, and maps) [3], but
its primary advantage is the acquisition of data
related to a real-world problem in a manner that
efficiently uses time and resources. Simulation also
enables researchers to test alternative scenarios, of
events that may not currently exist, by changing
system parameters, [4]. Because of these
advantages, coupled with the rapid and vast
development of computational technology,
simulation methods, and techniques have gained
popularity in all fields of research and academic
studies, [5], [6].
The same holds true for the traffic context,
wherein travel patterns and vehicle trajectories are
simulated using models developed on the basis of
the observed behaviors of travelers including
drivers, passengers, pedestrians, and bicyclists, [7].
Traffic simulation can be classified on the grounds
of scale and scope into macroscopic and
microscopic simulations. Macroscopic simulation
deals with the travel patterns associated with an
entire region, which is divided into smaller zones to
estimate trip generation and its associated
characteristics, [8]. Microscopic simulation focuses
on the behavior of drivers and vehicles on the road,
including their movement patterns and interactions
with each other, [9].
Over the years, randomness, driver behaviors,
and the impact of such behaviors on vehicle
interactions, especially driver-related conflicts that
could lead to crashes, have become issues of prime
importance for researchers and students of traffic
engineering, [10]. The current research is a step
forward in this direction, with its proposal of an
object-based approach for developing a traffic
simulation program that incorporates the variation in
driver behavior and an automated conflict
monitoring tool that can help the authorities
evaluate the safety implications of different design
solutions. The inclusion of driver behavior can
enable the planners and designers to impact of
changes in infrastructure on the driver behavior and
in their interaction with each other, which has been
done through the use of driving simulators or
observational studies with test subjects, [11]. The
proposed approach was used to carry out a
microscopic simulation of traffic movement on a
four-legged intersection. The novelty of this study
lies in the methodology used for the aforementioned
incorporation, and it is expected to provide accurate
results on other segments of a highway system.
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2 Literature Review
Traffic microsimulation works on the basis of
several sub-models that are used to accurately
predict and implement the choices of individual
drivers with regard to lane change and car-following
behaviours, [12]. The development of simulation
software involves the following steps: defining the
scope of work, collecting data, developing a base
model, checking errors, developing a working
model, calibrating the model using field data, and
forecasting and analysis of traffic flow, [13]. Some
of the practical constraints to the development of
microsimulation software are those associated with
budgets, the availability of appropriate tools, the
accuracy of data, and the engagement of
stakeholders, [14]. Some well-known simulation
programs are VISSIM, AIMSUN, TransModeler
[15], and PARAMICS [16].
The main parameters for the development and
calibration of traffic microsimulation software
include vehicular flow, vehicular speed, vehicular
acceleration, headway, and spacing between
vehicles. These parameters are subject to change
depending on road conditions and the indigenous
behavior of drivers, [17]. Simulation software
enables the effective analysis of the effects of traffic
management strategies and devices on traffic flow
and capacity, [18]. It can be used to assess the
outcomes of strategies for mitigating traffic
congestion in specific zones or corridors, [15]. Such
software can also be employed to optimize plans
and strategies for emergency or evacuation
management, which is a critical yet scarcely
implemented initiative, [19]. An important aspect in
relation to the above-mentioned parameters is to
determine the underlying distributions that can best
correspond to actual driver behaviors, [20].
There have been attempts to integrate
simulation software with other models to
incorporate and analyze other parameters that affect
or are affected by traffic flow, such as network
protocols [21] parking choice [22], and emissions.
Given the multidimensional results derived from the
simulation programs, they have been used as
sources of valuable inputs for multicriteria decision-
making related to transport planning, [23].
The initial focus of simulation software has
been on car movement, for which mixed traffic
conditions are considered to ascertain the effects of
public transport, freight movement, or other road
users on traffic flow parameters, including volume,
speed, density, and travel time, [24]. Early models
treat other road users as simulation units that are
similar to vehicles but have different characteristics.
However, this approach has changed because of the
development of different sub-models that revolve
around individual road users or modes of transport
and are used in tandem with traffic simulation
programs, [25]. Other areas of concentration of
simulation software are intersections, specifically
with regard to conflict resolution and right of way.
This concentration is prompted by the significant
impact of these intersections on safety and
congestion, [26].
Microsimulation has been recognized as a
method that effectively caters to the sample size and
data availability issues pertaining to crash data
collection. This is especially true when analyzing
mitigation measures that require a before-and-after
analysis, which could take months before any
reasonable assessment can be made of the
effectiveness of improvements. Thus far, Time-To-
Collison (TTC) within microsimulation has been
readily used by researchers to generate data related
to conflicts, [27]. The primary data for such conflict
analysis is the vehicle trajectory data obtained from
observation of real-time traffic. Other important
safety parameters include Post Encroachment Time
[10], Deceleration Rate to Avoid Crashes, and the
Proportion of Stopping Distance, [28]. The primary
challenge in the use of microsimulation software for
safety analysis is its employability in online-based
heterogeneous traffic conditions, [10].
The advent of vehicle technology has come to a
point where connected and automated vehicles are
foreseen to be responsible for a significant share of
traffic in the near future. Considering this
development, the future of microsimulation seems
to be directed toward modeling the effects of these
vehicles on traffic flow, and efforts have already
been taken in this regard, [29]. Some of studies have
been devoted to the planning aspect of automated
vehicle sharing. Despite the fact that the
aforementioned issues are examined under the
macrosimulation approach, the impact of the
dynamic assignment of automated vehicles can be
explored using a microsimulation methodology,
[30].
The problem is that considerable time will pass
before these automated vehicles completely capture
traffic flows as it depends upon market conditions
and the acceptance of road users. Until that moment
and beyond, researchers cannot neglect the impact
of human intervention, whether in endeavors
involving traditional vehicles alongside automated
vehicles or those dedicated solely to the latter. This
requirement justifies the call for the continuous
development of simulation programs that can
accurately model the effects of variations in
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driver/human behaviors on traffic flow and, more
so, on the safety of road users.
3 Simulation Strategy
To achieve the aim of this research, and facilitate
future improvements and fine-tuning, a multi-object
traffic model is developed to simulate the behavior
of traffic and predict the performance of traffic
intersections under given parameters.
Since the “behavior” of each “component” of a
traffic simulation needs to be detailed and specified
in a simulation, the object-oriented philosophy was
implemented when defining the interaction logic of
each of those components. The object-oriented
programming approach allows easy manipulation of
these components and the construction of complex
components with multiple interaction layers out of
simple building blocks, which follows a “divide and
conquer” strategy.
A graphical summary of the main simulation
components is provided in Figure 1.
Fig. 1: Main simulation components
As previously mentioned, multiple objects are
used in the simulation of traffic behavior. The
various objects that are part of this simulation are
described in the following sections along with their
parameters and their abilities.
3.1 Node
The node is a point in two-dimensional space that
defines the start and end of a path segment. A node
can be further specialized in various subtypes such
as Traffic Generation Zones (T) and Traffic Control
Units (TCUs). Two parameters are required to
define a node, namely it’s coordinate and
coordinate. Furthermore, it has a traffic status .
This status for nodes is always an “unconditional
pass”. A node has no methods. A class view of the
“node” object is further shown in Table 1.
Table 1. Node object outline
Node (N)
Inherits from: N/A
Fields
:
Unconditional pass
Pass if unable to stop
Stop
3.2 Traffic Generation Zone (TGZ)
Based on nodes, TGZs are special types that are
responsible for generating the traffic during the
simulation. The traffic generation occurs at random
normally distributed time intervals.
Thus, a TGZ requires the average and
standard deviation of the generation time. They
also keep track of the last time a moving object was
generated , to calculate whether a new object is to
be generated. Furthermore, TGZs store all possible
routes along with their probabilities. Finally, a
traffic generation status is stored to denote
whether a moving object is to be generated in the
next time step. TGZs inherit the fields of nodes. It
also has a status update method that consists of
calculating a random time interval of generation
based on the normal distribution provided. If the
time interval is larger than the time of the last object
generation, its status is set to “Generate”. The class
outline is shown in Table 2.
Table 2. TGZ outline
Traffic Generation Zone (TGZ)
Inherits from: (N)
Inherited
Fields
:
Generate
Idle
Methods
UpdateStatus
3.3 Traffic Control Unit (TCU)
TCUs are an extremely important part of the
simulation based on nodes. TCUs can be standalone
or controlled in groups by an Intersection Control
Unit (ICU). TCUs control the traffic flow by
allowing or denying the passage of moving objects
through them.
A standalone TCU is one that does not need an
ICU as it is not connected to other TCUs. An
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example would be a timed pedestrian traffic light.
Other TCUs such as an intersection traffic light
system or an intersection yield sign would need an
ICU to relay information and allow or disallow the
passage of traffic.
3.4 Traffic Lights` (TL)
A traffic light is one of the TCUs based on nodes. It
also incorporates a moving object counter that
counts the number of vehicles passing through .
Those are further categorized into timed traffic
lights and controlled traffic lights. The object is
further outlined in Table 3.
Table 3. TL outline
Traffic Light (TL)
Inherits from: (N)
Inherited
Fields
3.5 Timed Traffic Lights (TTL)
A timed traffic light is one of the TCUs that does
not need an ICU to control its traffic status , but
relies on its own timer to switch between its
different phases. To facilitate this, TTLs store their
red phase duration , amber phase duration ,
green phase duration . Furthermore, it stores the
time when the phase last change to determine if a
phase change is needed. TTLs also have the status
update method, that checks whether enough time
has passed to switch from one phase to the next. The
object is further outlined in Table 4.
Table 4. TTL outline
Timed Traffic Light (TTL)
Inherits from: (TL)
Inherited
Fields
Methods
UpdateStatus
3.6 Controlled Traffic Lights (CTL)
Controlled CTL is a controlled TCU. Those
organize more complicated traffic situations at
intersections. The CTL is based on the TL and does
not add anything to the basic TL with exception to
be compatible with a Control Unit (CU), which
controls the of CTLs. CUs will be explained
later.
3.7 Traffic Signs (TS)
TSs control the flow of traffic at simple
intersections with lower traffic volume. Currently,
only two traffic signs are implemented in the
simulation: Yield All and Priority All. A curved
priority or stop signs are not yet implemented and
would be the subject of future work. A traffic sign
stores one field, namely the traffic sign type
.
The outline of TSs is shown in Table 5.
Table 5. TS outline
Traffic Sign (TS)
Inherits from: (N)
Inherited
Fields
3.8 Control Unit (CU)
CUs control and coordinate the status of TCUs to
allow for more complicated traffic scenarios such as
traffic light control of intersections. CUs come in
various forms such as timed CUs, sensor-actuated
CUs, traffic-aware CUs or a combination of the
aforementioned. Two ICUs are implemented in this
simulation, namely a Traffic Light Control Unit
(TLCU) and a Traffic Sign Control Unit (TSCU).
3.9 Traffic Light Control Unit (TLCU)
TLCUs control the phases of groups of traffic lights
at an intersection. Thus, a significant parameter for
all TLCUs is the CTL groups and sorting. This is a
sorted list of groups of CTLs that are to be
controlled together. The TLCU implemented in this
simulation is a timed TLCU. Thus, the TLCU cycles
through the sorted list to allow traffic for each group
while blocking traffic for all other groups. Some
interesting traffic scenarios can be realized by
carefully designing the TLCU groups. Another
parameter in a timed TLCU is the red phase
duration , amber phase duration , green phase
duration for each and intersection clearance
times .
Regarding the methods of TLCUs, it has a status
update method that checks if any needs to
change its phase similarly to what a TTL does, with
the difference being that a full group of CTLs is
controlled in a similar way as explained a TTL. The
class outline of TLCU is shown in Table 6.
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Table 6. TLCU class outline
Traffic Light Control Unit (TLCU)
Inherits from: (CU)
Fields
Methods
UpdateStatus
3.10 Traffic Sign Control Unit (TSCU)
TSCUs provide the current status of an intersection
that is controlled by TSs. In the current simulation,
an intersection can have a maximum of 4 road
branches, 2 priority and 2 yield co-linear branches.
The priority logic used in this simulation is as
follows:
Level 1 Priority: Moving objects on a
priority branch moving straight or right.
Level 2 Priority: Moving objects on a
priority branch moving left.
Level 3 Priority: Moving objects on a yield
branch moving straight or right.
Level 4 Priority: Moving objects on a yield
branch moving left.
Thus, the TSCU stores the traffic sign on each
of the branches as fields. Furthermore, it stores all
moving objects approaching the intersection as
subscribers , along with their expected arrival
times , clearing times and intended
direction of travel . Regarding the methods of
TSCU, it has a status update method that updates the
of each traffic sign based on the current
conditions of the intersection. An outline of a TSCU
is shown in Table 7.
Table 7. TSCU outline
Traffic Light Control Unit (TSCU)
Inherits from: (CU)
Fields
Methods
UpdateStatus
3.11 Path Segment (PS)
Path segments are used by moving objects to
navigate from their starting point to their end point.
Those segments can also be used to approximate
curved roads by means of discretization. PSs store
the top speed of the segment , the start node
, the end node and the road condition as
fields. The road conditions are used later to
calculate acceleration modifiers. An outline of
TLCU is shown in Table 8.
Table 8. TLCU outline
Traffic Light Control Unit (TLCU)
Inherits from: (CU)
Fields
:
Normal
Wet
Icy
3.12 Moving Object (MO)
To be able to simulate traffic, moving objects are
defined. Those are complex objects that are
comprised of a vehicle and a driver. It also takes
into consideration other factors that control how a
moving object behaves. Moving objects can also be
used to model pedestrians, but this has not yet been
implemented in this simulation.
Moving objects keep track of multiple
parameters that affect their behavior under traffic
conditions. It’s always aware of its own status ,
which could be accelerate, decelerate, decelerate to
match target speed, static. The acceleration and
deceleration are calculated based on the vehicle and
driver performance. Also, it is aware of its intended
travel direction: straight, turning left, turning right.
This intended travel direction simulates the
usage of travel direction indicators and is of
paramount importance for TSCUs to determine its
priority settings. Furthermore, the time of last status
update is kept, to simulate reaction times when
the status is to be changed. Other “situational
awareness” parameters relative to other CUs are
included, such as: distance to next CU , time to
next CU , minimum clearance from CUs
. Furthermore, parameters relative to other
moving objects for “collision detection and
avoidance” are stored, such as: distance to nearest
MO , time to nearest MO , minimum distance
from MO . Other kinematics parameters such
as current position , speed , acceleration
and heading are also stored. Finally, the trip
information of the moving object, which is a list of
path segments to follow, is stored.
Moving objects have sophisticated behavior that
is part of their abilities. Those abilities are
mentioned here and expanded upon later. These
include, but are not limited to, planning courses,
studying its current position, making decisions and
calculating acceleration and kinematics. Those
calculations and decisions are further influenced by
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the driver and vehicle that are part of the moving
object. MOs simulate deep decision-making
abilities, that are best described in a flow chart later.
3.13 Vehicle (V)
The mechanical part of a moving object, vehicles,
are described by parameters that define their
accelerating and decelerating abilities as well as
their top speed.
An acceleration ratio vs. speed ratio
profile is defined, along with the vehicle’s top
acceleration and top deceleration . There is no
deceleration profile, and it is assumed that the car is
always able to provide the maximum deceleration at
any given time regardless of the vehicle’s current
speed.
The vehicle itself has no abilities or behavior,
but is controlled by the moving object, which
couples the driver and the vehicle to produce
complex responses to a variety of traffic conditions.
3.14 Driver
The human factor in a moving object. It is key to
providing an accurate representation of the traffic
simulation. Various parameters are taken as
normally distributed random variables and are
sampled when the driver is initialized along with the
initialization process of the moving object.
A variety of driver types exist, namely:
distracted, normal and aggressive drivers, each with
his own set of statistical variables to be used in the
generation of his own behavior. Among others, the
following statistical variables are considered: the
average reaction time , the standard deviation of
the reaction time , the average performance ratio
, the standard deviation of the performance ratio
, average passing tolerance ratio and
standard deviation of the passing tolerance ratio
It is worth mentioning that the performance
ratio represents how hard the driver hits the
acceleration pedal, whereas the passing tolerance
ratio is the time the driver considers it to be
“acceptable” to pass an amber traffic light, which is
also referred to as dilemma zone.
4 Description of the Simulation
The simulation follows an incremental calculation,
where the global simulation parameters are defined,
such as the total simulation time , time
increment and maximum number of moving
objects .
The flowchart of the simulation, shown in
Figure 2, consists of an initialization phase,
followed by a time step iterator that continues
running until the simulation time is reached. During
each step of the time iterator, the simulation cycles
first through all traffic generation zones, generating
moving objects. This is followed by looping all
ICUs and standalone TCUs, updating their status
based on their own models. Finally, all MOs are
updated.
Fig. 2: Simulation process flowchart
As an example, the update process of a timed
standalone TCU and a time ICU are shown in
Figure 3 and Figure 4, respectively.
Fig. 3: Timed ICU Updating Flowchart
Fig. 4: Timed standalone TCU updating flowchart
The updating of each MO is a more
sophisticated three-step process, namely: Position
Analysis, Decision Making, Decision
Implementation, and Movement. A flowchart of this
process is shown in Figure 5.
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Fig. 5: Moving object updating flowchart
5 Study Area
This research is based in Bahrain wherein the
prototype of the simulation approach is applied.
Bahrain, an island country situated in the Arabian
Gulf, is renowned for its strategic location,
historical significance, and vibrant culture.
Geographically, it is positioned east of Saudi Arabia
and west of the Qatar peninsula. Despite its small
size, Bahrain boasts a bustling economy driven by
industries such as finance, tourism, and petroleum
processing, [31]. The traffic patterns in Bahrain
reflect its dynamic urban landscape, characterized
by a mix of modern highways and traditional
alleyways. The road network is well-developed,
with major thoroughfares connecting key cities and
landmarks, [32]. However, traffic congestion can be
a challenge during peak hours, particularly in urban
centers like Manama, the capital, [33].
What sets Bahrain apart is its rich cultural
heritage, exemplified by ancient archaeological sites
like the Bahrain Fort and the Barbar Temple.
Additionally, Bahrain's thriving art scene,
encompassing traditional crafts and contemporary
works, adds a unique dimension to its cultural
landscape [34]. In conclusion, Bahrain's strategic
location, coupled with its vibrant culture and
innovative approach to traffic management, makes it
a compelling subject for further study and
exploration.
6 Results and Discussion
The above-mentioned simulation methodology was
applied to simulate traffic on a four-leg intersection
in Bahrain. The selected intersection is located on
Shaikh Salman Highway at its intersection with
Salmabad city. As shown in Figure 6, this highway
is a key route in Bahrain and serves as a vital link
connecting various parts of the country. Stretching
from the capital city of Manama to the southern
regions, this highway plays a crucial role in
facilitating transportation and commerce. One of the
notable intersections along this highway is located
at Salmabad, an industrial area known for its
bustling commercial activities. The intersection at
Salmabad is a busy juncture, serving as a gateway to
numerous industrial facilities and commercial
establishments. Despite the heavy traffic volume,
efforts have been made to ensure smooth traffic
flow with modern traffic management systems and
infrastructure upgrades. Despite these efforts, the
intersection at Salmabad still incurs delays and
traffic jams on a daily basis.
Figure 7 shows the said intersection with the
details of the available traffic data. It is evident that
the highest volumes of traffic are for through
movement at the intersection of Shaikh Salman
highway, for reasons already mentioned above.
Secondly, the flow towards Salmabad is already
higher than on the other side due to the industries
which are there. The evening peak hours have the
highest total entering volume, although different
sides may have their respective high volume in other
hours.
Fig. 6: Shaikh Salman highway
The intersection shown in Figure 7 was drawn
in the simulation platform. Then the simulation
approach, as described above, was applied to have
flow for each movement (mentioned in Figure 7) in
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each lane. The movements having multiple lanes
were divided with a share in each lane. This was
done due to the absence of lane-wise distribution of
flow, in which case, any other assumption would
have under and over-estimated the results for
different lanes. The assumption of equal distribution
is expected to be a balanced approach. The error for
each lane was calculated as the absolute %
difference between the simulated and observed
values. Table 9 shows the average % error and its
standard deviation for each peak hour for each
direction.
Fig. 7: Intersection orientation and data
Table 9 shows that the errors are generally
under 10% while the standard deviation is less than
the average error which means the coefficient of
variation is less than 1. These indicators point to the
satisfactory performance of the simulation approach
according to the acceptable values from previous
studies [35], [36]. The highest error and standard
deviation were found to be for the AM peak hour in
the North to South direction. Looking at the
volumes (Figure 7) it does not seem to be due to any
specific trend in terms of flow as the volume in this
hour for this direction is not particularly high or
low. Moreover, the same direction has a much lower
error and standard deviation in different hours. Most
importantly, the simulation strategy gives very good
results for the highest and lowest cases of volumes
which are more critical. It was also observed that
some of the errors were positive, and others were
negative. These trends provide evidence of the
validity of the approach and encourage its use for
other cases and safety evaluation.
Table 9. Error values for the simulation
Move
ment
AM Peak
PM Peak
EV. Peak
Aver
age
%
error
Stand
ard
devia
tion
of %
error
Aver
age
%
error
Stand
ard
devia
tion
of %
error
Aver
age
%
error
Stand
ard
devia
tion
of %
error
South
to
North
24.2
4
19.16
4.46
2.45
3.88
2.48
North
to
South
6.74
6.19
9.78
5.15
10.3
1
5.54
West
to East
6.65
5.54
9.01
4.98
9.51
5.32
East to
West
1.54
0.18
1.48
0.57
0.75
0.55
7 Conclusions and Recommendations
This study aims to develop and implement an
object-oriented microsimulation for traffic flow that
incorporates the variation in driver behavior. This
has been done by considering the
deceleration/acceleration and spacing of the drivers.
A busy junction in Bahrain was taken as the first
case study to determine the applicability of the said
approach. A signal light controller was used to
simulate the effects of signal timing on the flow.
Sensers/counters were placed on each lane (in the
simulation) to have lane-wise flows.
The simulated results show proximity to the real
flows on almost all approaches for different timings.
The accuracy of the simulation model was judged
based on % error in simulated and actual values and
the standard deviation of the error for each
approach. Especially, the errors for the critical cases
(highest and lowest values) were especially low and
had smaller variations in them. These results are
encouraging for further use of the same
methodology in the future.
Based on these results, the future avenues of
research could include the following. The strategy
could be applied to different types of intersections
and highway segments. Another aspect of research
could be to test and evaluate the effects of different
design and mitigation strategies on driver behavior.
Surrogate safety measures could be used for this
Sh. Salmman Highway - Amman Avenue (Bahrain Gas)
AM PEAK : FLOW(6:30-7:30) MCC2017-014
PM PEAK : FLOW(13:45-14:45) SITE NO. TSX.174
EV. PEAK : FLOW(17:00-18:00) DATE : Sun.07/05/2017
Sh.Salmman Highway
EV. PEAK 1178 0358 3
PM PEAK 1054 0406 3
AM PEAK 744 0216 1
AM PM EV.
PEAK PEAK PEAK
1694 710 910
Amman 4 232 273 214
336 249 247
010
000
499 485 719 Amman Avenue
387 346 591
412 365 689
EV. PM AM
PEAK PEAK PEAK 24 113 0202 AM PEAK
17 121 0171 PM PEAK
11 163 4832 265
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WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2024.23.22
Mahmoud Jahjouh, Uneb Gazder,
Rashid Abdulrahman Ismaeel
E-ISSN: 2224-2872
233
Volume 23, 2024
purpose. The researchers aim to incorporate an
automated counter for these measures which would
make the safety analysis much more efficient and
easier than the existing tools.
Declaration of Generative AI and AI-assisted
Technologies in the Writing Process
During the preparation of this work the authors used
ChatGPT in order to improve the readability and
language of your manuscript, especially sections
related to the study area. After using this tool, the
authors reviewed and edited the content as needed
and take full responsibility for the content of the
publication.
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Rashid Abdulrahman Ismaeel
E-ISSN: 2224-2872
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Mahmoud Jahjouh, Uneb Gazder,
Rashid Abdulrahman Ismaeel
E-ISSN: 2224-2872
235
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Mahmoud Jahjouh carried out the simulation and
the optimization.
- Uneb Gazder was involved in data collection.
- Mahmoud Jahjouh Uneb Gazder and Rashid
Abdurahman Ismaeel were involved in writing the
initial draft of the paper.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No Funding was received for this research.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2024.23.22
Mahmoud Jahjouh, Uneb Gazder,
Rashid Abdulrahman Ismaeel
E-ISSN: 2224-2872
236
Volume 23, 2024
