Dept: INFORMATION TECHNOLOGY File: Word(doc) Chapters: 1-5 Views: 15


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 security. Many of the proposed ITS systems also involve surveillance of 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 evacuation 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 world, the migration from rural to urbanized habitats 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 systems. They also produce considerable air pollution, pose a significant safety risk, and exacerbate feelings of inequities in the society (Atev et al 2015).

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