Paper Info
Title
Big Data Mining for Smart Cities: Predicting Traffic Congestion using Classification
Abstract
This paper provides an analysis and proposes a methodology for predicting traffic congestion. Several machine learning algorithms and approaches are compared to select the most appropriate one. The methodology was implemented using Data Mining and Big Data techniques along with Python, SQL, and GIS technologies and was tested on data originating from one of the most problematic, regarding traffic congestion, streets in Thessaloniki, the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> most populated city in Greece. Evaluation and results have shown that data quality and size were the most critical factors towards algorithmic accuracy. Result comparison showed that Decision Trees were more accurate than Logistic Regression.
Year
DOI
Venue
2020
10.1109/IISA50023.2020.9284399
2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA
Keywords
DocType
ISSN
Data Mining,Big Data,Machine learning,Smart Cities,Prediction,Classification,Traffic Congestion
Conference
2379-3732
ISBN
Citations 
PageRank 
978-1-6654-2229-1
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Aristeidis Mystakidis100.34
Christos Tjortjis217324.40