Urban areas around the world are experiencing rapid urbanisation and steady increases in their population which have lead to large increases in the number of vehicles on the roads due to the increasing demand for services because of urbanisation. This increase in vehicle traffic has created a number of challenges, such as increased congestion on the roads, increased fuel consumption, increased carbon emissions into the atmosphere, increased amount of time travelling to a destination and an increasing concern regarding the safety of our roadways and the high number of accidents occurring as a result of vehicle traffic. Currently, the traditional traffic management approaches (using fixed traffic signals, manual monitoring of traffic conditions, and separate traffic control methods) used to manage vehicle traffic in cities are unable to keep pace with the rapid changes taking place within modern urban traffic systems. Traffic management approaches such as those currently being used are unable to respond to the ever-changing needs of urban traffic management systems in real time, nor do they function well in the event of unplanned occurrences such as congestion caused by rush hour traffic, accidents or road blockages and/or weather conditions.

 

In order to address these issues, this document presents a comprehensive Smart City Traffic Management System powered by Big Data.

Through the use of cutting-edge technology, this system leverages data for smarter traffic management by integrating numerous data sources including: an Internet of Things (IoT) based traffic sensor network, video surveillance systems, globally positioned satellite (GPS) vehicle systems, and mobile applications for capturing real-time information as well as historical information traffic data. Additionally, this system uses technologies such as the Apache Hadoop and Apache Spark Framework for both storing and processing vast amounts of data very quickly and performing analytical functions on the data in real-time (as it is received through many different channels).

 Keywords— Smart City, Big Data Analytics, Traffic Management, IoT, Machine Learning, Hadoop, Spark