Mystakidis, Aristeidis

2020-06-17T00:00:33Z

https://repository.ihu.edu.gr//xmlui/handle/11544/29638

Smart cities

Machine learning

Big data

masterThesis

free

School of Science and Technology, MSc in Mobile and Web Computing

This dissertation was written as a part of the MSc in Mobile and Web Computing at the International Hellenic University. These days, the volume of data information in the globe always seems to grow continuously with no turning back point. With enormously powerful machines, computers, phones and tablets saving information that earlier would be trashed is too simple. Affordable multi-terabyte drives make it very easy to delay choices over what to do with all this information. Users and companies are just purchasing another drive and saving everything. Increasingly popular electronics document users' decisions, financial choices, market habits, trips and photos. Users can access data from all way around the world, almost every record in a system, database or framework. The Internet and social media overwhelm more and more users with data information. Furthermore, more and more data are been stored regarding cities and urban areas. This information is critical for automatizing several procedures in these areas such as road traffic control. With urban living increased exponentially the last century, road traffic congestion has become one the most significant problems of this era. There is no panacea, but as far as the solution for this problem is concerned, analyzing the congestion data for future traffic prediction could do a significant difference. The current thesis is a presentation, analysis and construction of a model for predicting the traffic congestion for Tsimiski street in the city of Thessaloniki using data mining and machine learning algorithms, along with python, sql and gis technologies

Baltagiannis, Agamemnon

Berberidis, Christos

ihu