Abstract
Ambulances and firefighting cars for example, cannot fritter away time waiting for traffic signals to turn
green. These vehicles require a system that allows them to safely and quickly cross traffic lights. The
researcher proposes a vehicle identification model-based smart traffic lighting system (STLS). The
technology analyzes photos from cameras, counts the number of vehicles in each lane, and then determines
which lane should be given priority taking into consideration waiting period, and type of service to be
delivered by motorists. This study will use the CRISP-DM research approach to develop an accurate vehicle
identification model for the system's success. Several algorithms (YOLO, SSD, Mask R-CNN, SqueezeDet,
MobileNet and YOLOR) shall be implemented, and the best algorithm to produce the best car object
detection, with at least 95% accuracy score, recall score of 95%, F1-score of 95% and support score of 95%.
An algorithm for the system will also be designed in such a way that emergency service delivery vehicles
are given priority and congestion is minimized.
1.1. Background
Road accidents, fires, earthquakes, floods, and other calamities claim the lives of many people each year,
causing them to lose their homes and leave them with permanent injuries or physical disabilities. This is
exacerbated, if necessary, aid from emergency response vehicles such as ambulances or fire engines is delayed.
It makes a tremendous difference in saving people's lives when an emergency response vehicle is delayed by
a few minutes. According to Almuraykhi & Akhlaq (2019), 70% of the deaths may have been prevented if
rescue teams had acted sooner. A system for emergency response vehicles has been devised. The basic concept
is that when an emergency vehicle arrives at a traffic light, it will find it open and unaffected by other signals.
The mechanism operates nearly imperceptibly, that is, without attracting the attention of others. To detect an
oncoming vehicle, present systems use short-range technology (IR, optical sensors, RFIDs, Radar, and so on).
When a car approaches a traffic signal, these devices can open it. Due to the short time available to open the
required signal, there may be a conflict. Our proposed system, on the other hand, employs cameras that can
detect emergency service delivery vehicles from afar and make a timely judgment on how to handle traffic
lights at intersections along the path without interfering with other signals.
1.2. Statement of the problem
The lack of modern and dynamic smart traffic lights contributes to congestion and traffic accidents at traffic
lights junctions. This is evidenced by Qi et al. (2016) who reported that 30% to 60% of accident injuries and
up to one third of fatalities occur at intersections. Kanungo et al. (2014) concurred that congestion is a serious
problem and Barba et al. (2012) emphasized the need for road safety.
1.3. Aim
To design vehicle identification model for a dynamic smart traffic lights system which grant access to either
motorists in congestion or according to urgency-based priority, waiting period, and congestion level.
1.4. Objectives
1. To develop a model that can identify vehicles approaching the traffic lights.
2. To build an algorithm that can count the number of vehicles approaching the traffic lights.
3. To design an algorithm that make decisions on traffic lights signal values basing on waiting period,
traffic velocity at traffic intersections, traffic density, and vehicle service delivery type.
4. To enhance traffic management at traffic intersections through the use of smart traffic lightning system.
5. To notify motorists about congested ways along their routes.
6. To determine an optimal route for motorists especially emergency service delivery motorists.
1.5. Research questions
1. What parameters can be used to train the best model that can identify vehicles approaching the traffic
lights?
2. How to build an algorithm that determine the number of vehicles approaching the traffic lights?
3. What effect does waiting period, traffic velocity at traffic intersections, traffic density, and vehicle
service delivery type have on making decisions on traffic lights signal values?
4. What are the most effective techniques to enhance traffic management at traffic intersections
considering traffic lightning systems?
5. What are the best methods of notifying motorists about congested ways along their routes?
6. What determines an optimal route for motorists especially emergency service delivery motorists?
6.5. Significance of the study
The success of this research will be an advancement in technology while saving lives and efficiently making
use of time resource. Besides helping the public, the output of this research will contribute to the body of
knowledge as other scientists and researchers will make use of this research in their projects. Research dealing
with mobile vehicle networks, advancement in vehicle technologies, making traffic lights more intelligent,
smart cities may need to have a look at the output of this research. This research is of great use to all those
who may require data to be used for traffic monitoring at traffic lights. The data to be gathered in this research
maybe used in big data analytics for instance, the data can be used to predict the occurrence of an event which
require urgent response such as ambulance services and/or firefighting.
6.6. Literature review
Lee and Chiu (2020) emphasized the importance of developing intelligent systems as we move towards
building smart cities to reduce traffic congestion, and improve transport efficiency. Lee and Chiu (2020)
proposed a system which could allow vehicles to communicate in such a way that if an emergency vehicle is
detected, all vehicles are notified of its direction so that respective motorists can react accordingly. However,
this does not nullify the need for traffic lights and traffic lights would need to synchronize with the messages
communicated between vehicles. This is because it was found that decentralized systems are either not
reliable, very costly, complex and not flexible as concurred by Tchuitcheu et al. (2020).
Tchuitcheu et al. (2020) proposed a system that makes use of cameras to understand traffic flow in real-time.
It is of paramount importance to note that this approach is one of the nascent technologies required in modern
and dynamic world for visual machines. However, the researchers feel that, it would have been better if the
centralized system is combined and synchronized with a decentralized system. Adding waiting period as a
factor in deciding on which vehicles to cross the intersection first would also be beneficial to all motorists.
Oza et al. (2020) delved into security issues related to sensor and controller networks. Therefore, high-breed
systems of centralized systems with some independent systems require high security enforcement for them to
successfully achieve their main goals.
Hartanti et al. (2019) noted that traffic lights require proper timing which represent the real-time situations at
traffic intersections. Therefore, factors such as road width, number of vehicles and queue size needs to be
considered when granting access to cross intersections.
6.7. Methodology
This research will be based on the implementation of the Cross-Industry Standard Process for Data Mining
(CRISP-DM) research approach, which is an accepted standard and a non-industry-specific process model
that is utilized for many data mining projects (Schröer et al., 2021). Research techniques are methods that a
researcher employs when conducting research. These techniques include data gathering techniques, statistical
data analysis tools, and methods for assessing the accuracy of the researcher's findings (Heath, 2021).
Lawrence (2020) agrees that research methods refer to the researcher's approach to data collection. Data
collection methods, analysis methods, outcome evaluation and testing methods are all discussed in this
chapter.
6.7.1. To count the number of vehicles approaching the traffic lights junction
automatically.
A digital camera will be used to visualize vehicles approaching traffic lights intersections. The cameras will
be installed in such a way that they visualize all ways used by vehicles to cross the intersection. The camera
will be taking images and the images will be processed in real-time to give instant feedback signal. A model
is going to be built using supervised machine learning. The model will take all images taken from all four
ways approaching the intersection. The images will be compared to check the side and way with more vehicles
than the other. The side with more vehicles is then given priority to pass the intersection. The granting
procedure will follow the normal traffic lights principles of Red, Amber and Green, however, the timing will
be minimized. The algorithm will compare the two pairs of opposite sides and if the total number of vehicles
in one pair is more than that of the other pair and the waiting time for the other pair is less than ten (10)
seconds, the pair with more vehicles will be granted access to pass through the intersection. However, if the
number of vehicles for one pair is more than that of the other pair and the waiting period is above twenty
seconds for at least three vehicles, the three vehicles will be granted access to pass through the intersection.
6.7.2. To identify urgent vehicles such as ambulances and fire brigade motorists with
siren.
To achieve this objective, the combination of a microphone and a camera will be utilized to identify urgent
vehicles such as ambulances and fire brigades. These vehicles are normally found with a siren and strobing
lights. Therefore, the camera will be checking for strobing lights and the microphone will be listen to siren
sound. If both microphone and camera identify the urgent vehicle, the direction in which the vehicle is coming
from will be granted access or all traffic lights goes red to stop all vehicles and allow the urgent motorist. If
only siren is heard without observing the vehicle on the camera, all traffic lights go red to stop vehicles and
allow urgent motorist. If only the camera identifies the urgent vehicle without siren, the vehicle will be treated
as normal vehicles because it will not be on an urgent trip.
6.7.3. To measure vehicle’s distance from traffic lights automatically.
Ultrasonic sensors will be used to measure vehicle distance from traffic lights. Vehicle’s distance before
intersection is important because the researchers are trying to avoid instant stoppage of vehicles approaching
intersection. They must be shown Amber before Red signal to allow motorists to stop safely.
6.7.4. To decide on which cars to go first
To decide on which pair of ways is allowed to pass the intersection, the system uses the distance at which the
vehicles are from the intersection, waiting period, the number of vehicles and the availability of urgent
motorists approaching the intersection. Therefore, this objective depends on outcomes of the above objectives.
6.8. Proposed Research Organization
6.8.1. Chapter 1
This chapter details the background of the study, problem statement, research objectives, and significance of
the study. It also covers how the research is to be covered and some brief notes on literature review and
methodology. It also outlines how the whole research is organized.
6.8.2. Chapter 2
Reviewing literature is a crucial research activity required in all researches. This is because it gives the
researcher a broader eye on the problem, it helps the researcher with alternatives in terms of methods of solving
the problem at hand, it avoids reinventing the wheel, and guides the researcher in their researches. Chapter 2
will focus on literature covering artificial intelligence methods of automatically counting vehicles at traffic
lights, and other smart traffic lights systems. This chapter will review journal articles, books and reliable
internet sources. The aim of the chapter is to find the best methods of designing smart traffic lights and clarify
on research gaps found in literature. The conclusion of this chapter determines this research’s methodology
and product as well.
6.8.3. Chapter 3
In this chapter, the research technique, as well as the research design and philosophy, are explored. This
chapter also covers ethical considerations, sample sizes, and the target demographic. Heath (2021) describes
methodology as a method through which a researcher addresses a scientific problem in a systematic manner
by following a set of stages to arrive at a solution. The underpinning for the entire project is research
methodology, which supports the research methodologies (Lawrence, 2020). According to (Lawrence, 2020),
research methodology entails the examination of research methodologies, assumptions, and principles. This
chapter will focus on the implementation of the Cross-Industry Standard Process for Data Mining (CRISPDM) research approach, which is an accepted standard and a non-industry-specific process model that is
utilized for many data mining projects (Schröer et al., 2021). Research techniques are methods that a
researcher employs when conducting research. These techniques include data gathering techniques, statistical
data analysis tools, and methods for assessing the accuracy of the researcher's findings (Heath, 2021).
Lawrence (2020) agrees that research methods refer to the researcher's approach to data collection. Data
collection methods, analysis methods, outcome evaluation and testing methods are all discussed in this
chapter.
6.8.4. Chapter 4
In order to answer the study questions given in the first chapter of this research, this chapter is guided by the
prior literature review chapter and theoretical framework. This chapter focuses on the results of the study and
provides additional insight into the data acquired. The interpretation of the research findings will be aided by
this dialogue. The CRISP-DM method was used to collect images data from the internet, YouTube videos,
and movies, which was then analyzed to enhance model construction and ensure that no underfitting or
overfitting occurs. The findings are presented in the form of figures and tables to help the reader grasp the
information while also allowing tables and graphs to be easily related to the research objectives.
6.8.5. Chapter 5
Chapter 5 gives a summary of the research study, discussing the achievement of research objectives and areas
of future study. It will explain the contributions the research has made from a theoretical and practical view.
6.9. Work plan
Table 1¨Work-plan
Table 2 ¨Work-plan
Table 3 ¨Work-plan
Figure 1