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People use transport daily to get around and, traffic congestions are an inescapable condition in big cities. With the increasing number of self-driving and regular cars clogging in London roads excepted to double in the coming years, avoiding traffic congestions and collisions is a sustainable transport solution for both communities and governmental institutions.
Artificial Intelligence has boom-bust over the years and its methods are among the leading-edge methods used for transport-related predictions however the existing researches are still in their infancy thus a limited size of datasets and insufficient depth of artificial intelligence studies.
This paper proposes a comprehensive approach towards real-time traffic prediction by using different cutting-edge technologies including big data, Graphics Processing Units (GPUs) and Artificial Intelligence methods such as Data Visualisation, Deep Learning to train the system using Transport For London datasets.
The first section covers the literature review and background research, the second section describes the aims and objectives of the research, section three covers the methods that need to be used to train the system to detect and predict traffic. Section four breaks the project in a timeline and section five gives a summary of the research and suggestions on what future works to do in the area of study. The last part of the paper contains the referencing section where all the articles read so far have been cited using the Harvard reference style.
As a software engineer accustomed to programming and developing applications; PR or self-promotion, unfortunately, is not my key strength especially this part where I am required to write about myself which as it happens is probably the most challenging. My background is mainly in Software Development. I became passionately engaged and interested in Software Development for over six years now and recently developed an interested in Artificial intelligence.