IoT-Based Context-Aware Intelligent Public Transport System in a Metropolitan Area

Abstract

The public transportation system (PTS) in a metropolitan area is a nonlinear, dynamic, and complex system. Managing and providing suitable public transportation services are difficult. In this article, we propose an Internet of Things-based intelligent PTS (IoT-IPTS) in a metropolitan area. An IoT is used to interconnect transportation entities, such as vehicles, commuters (mobile phones), routes (sensors), roadside units (RSUs), etc., in a metropolitan area. The IoT provides the seamless connectivity between different networking technologies whenever the commuters or vehicles move from one location to another location. Hence, IoT provides the suitable seamless public transportation services in the metropolitan area. In addition, we have used context information of transportation entities, such as routes condition, traffic density, number of routes available, traffic congestion, vehicles' movement, and their mobility, which are stored in the cloud. The stored context information in cloud along with the IoTs are used to find the relevant routes, alternative modes, departure times, and many more for providing public transportation services in a metropolitan area. The proposed IoT-IPTS makes use of static and mobile agents with the emergent intelligence technique (EIT) for collecting, analyzing, and sharing context information. The analyzed context information is used to form the policies to provide the best available public transportation services to the commuters in a metropolitan area. The software-defined network is used to enable the cloud computing and EI network to manage the public transportation services to the commuters.