Intelligent Transportation Systems need methods to automatically monitor the road traffic, and especially track vehicles. The researcher concentrated on developing an algorithm in tracking traffic offenders using Intelligent Traffic System (ITS). Traffic in intersections is more variable, with multiple entrance and exit regions. The researcher describes an extension to intersections of the feature-tracking algorithm described. Vehicle features are rarely tracked from their entrance in the field of view to their exit. The researcher algorithm can accommodate the problem caused by the disruption of feature tracks. It is evaluated on video sequences recorded on four different intersections. The aim of this projects is to provide an algorithm for tracking traffic offenders using intelligence transport system. The methodology adopted for this research work is the Structured System Analysis and Design Methodology (SSADM), which was chosen by the researcher due to its numerous benefits. The
Background of the study
The concept of smart city
began in the early 1990s with the rising of new technologies of mobiles and
wireless networks; in addition to the huge development in the internet
technologies such as semantic web and internet of things (Adler and Blue, 2018).
The intelligent transportation system is
considered as one of the major applications of the smart city. Our proposed
system may compose of many technologies such as: Video Image Detection Systems,
Vehicular Ad Hoc Networks (VANETs) and Mobile phone tracking, and Global
Position System (GPS) (Boselly
and Ulberg, 2013). Using these technologies with artificial
intelligence could be creating an intelligence tracking system that take a
decision of tracking offenders. There are many goals that could be achieved
through this proposed system; such as changing the traffic conditions to reduce
the amount of trip time and the time that cars spend idling which decreases the
fuel consumption thus will cause decrease the amount of carbon monoxide emissions (Ahmed, 2012).
The other dynamic control
signals adjusts the timing and phasing of lights according to limits that are
set in controller programming, while the proposed system adjusts the timing and
phasing by the traffic lights itself designed a smart city framework for VANETs
that includes intelligent traffic lights that transmit warning messages and
traffic statistics (Ali
and Hassanein, (2009). Bayly, Regan, and Hosking, (2016)]
used a genetic algorithm and traffic emulator, developed in JAVA, to represent
dynamic traffic conditions.
Hartenstein and Laberteaux, (2010)
presented an overview image of processing and analysis tools used for traffic
applications on traffic monitoring and automatic vehicle guidance. Hartenstein and Laberteaux, (2010),
proposed an unsupervised vehicle‟s tracking and recognition methods for urban
Traffic surveillance in a distributed cooperative manner.
According to Guvenc and Chong, (2009) automated traffic system
is based on a genetic algorithm that receives inputs from the video image
detection system which will make a decision and determines the greens light
time to minimize the congestions and flow of traffic jam.
Recent governmental activity
in the area of ITS is further motivated by an increasing focus on homeland
. Many of the proposed ITS systems also involve surveillance
the roadways, which is a priority of homeland security. Funding of many
systems comes either directly through homeland security organisations or with
their approval (Ahmed, 2012). Further, ITS can play a role in the rapid mass
of people in urban centers after large casualty
events such as a result of a natural disaster or threat. Much of the
infrastructure and planning involved with ITS parallels the need for homeland
security systems (Ahmed, 2012).
In the developing
, the migration from rural to urbanized
has progressed differently. Many areas of the
developing world have urbanized
without significant motorization
and the formation of suburbs
. A small portion of the population can afford automobiles
but the automobiles greatly increase congestion in these multimodal transportation
They also produce considerable air pollution
pose a significant safety risk, and exacerbate feelings of inequities in the
society (Atev et al 2015).