Designing a Smart Transportation System: An Internet of Things and Big Data Approach

Big data analytics are widely used in many areas such as efficient designing and planning of smart transportation, smart control systems, smart cities, smart communities, and more. However, analyzing big data for smart control systems has many challenges and issues using conventional engineering techniques. These challenges include processing big data in real time, fast processing, and efficient decision and management. In this article, we design a model for analyzing transportation data with Hadoop along with Spark to handle real-time transportation data. The system is further divided into four layers: data collection and acquisition, network, data processing, and application. Each layer is designed in a way to process and manage data in a well-organized format. Similarly, the data is tested through Hadoop and Spark in the data processing layer. The data is disseminated to a smart community citizen using the proposed event and decision mechanism based on named data networking. The proposed system is tested for transportation datasets from various authentic sources. The results show processing of data and real-time dissemination with citizens in less possible time. The Hadoop ecosystem along with Spark generate highly accurate results. Further, the significance of the proposed architecture is that it can be used in generic vehicular network scenarios.