Exploiting real-time big data to empower smart transportation using big graphs

Abstract

The growing population in the metropolitan areas in this modern age requires more smart services of transportation. Achieving smart and intelligent transportation requires the use of millions of devices equipped with Internet of things (IoT) technology. On the other hand, graphs are the better way to represent the transportation infrastructure. The use of millions of IoT devices generates a huge volume of data, termed as Big Data, which results in the generation of big graphs. The processing of big graphs using current technology while taking the present real-time traffic information in order to generate graphs for real-time decision making is a challenging task. Therefore, in this paper, we proposed smart and intelligent transportation based on real-time traffic circumstances using graphs. It supports the municipalities to manage the traffic efficiently and facilitate the travelers' queries anytime, anywhere intelligently based on current traffic scenarios. The road sensor deployment and vehicular network are used to generate real-time traffic information producing Big Data. In addition, an architecture is proposed to efficiently process the real-time vehicular Big Data by using parallel processing systems and big graph processing technology. Various graph algorithms are used to respond the user queries smartly. Vehicular data of Madrid highway and Aarhus city of Denmark is used for analysis and evaluation by implementing the system using Giraph on top of Hadoop ecosystem. The results show the proposed system is efficient and cable to work in the real-time environment.