Intelligent Public Transport Prediction System Using Wireless Sensor Network: - A Survey

Abstract

Wireless Sensor Networks (WSNs) consist of a large number of self-powered, low-cost sensing devices that are interconnected through wireless ad hoc communication technologies. These networks enable real-time data collection, processing, and transmission across distributed environments, making them highly suitable for dynamic and large-scale applications such as transportation systems. In recent years, the integration of WSNs into Intelligent Transport Systems (ITS) has gained significant attention due to their ability to enhance traffic monitoring, management, and prediction. This survey paper presents a comprehensive overview of an Intelligent Public Transport Prediction System based on Wireless Sensor Networks. It discusses the fundamental concepts of WSNs, including their architecture, communication protocols, and network topologies, and examines how these components are applied in transportation environments. The study highlights the role of WSNs in collecting real-time traffic data such as vehicle density, speed, travel time, and environmental conditions, which are essential for accurate prediction and decision-making. Furthermore, the paper explores various applications of WSNs in public transport systems, including bus arrival time prediction, congestion detection, route optimization, and passenger information systems. It also reviews different design approaches and models used for traffic prediction, emphasizing the integration of data analytics and machine learning techniques to improve system accuracy and efficiency. In addition, this survey addresses key challenges associated with WSN-based transport systems, such as energy efficiency, network reliability, data latency, scalability, and security concerns. Potential solutions and recent advancements in communication technologies are also discussed to provide insights into future research directions. Overall, this paper demonstrates that the integration of WSNs into intelligent public transport prediction systems can significantly improve the quality, efficiency, and safety of urban mobility, while also contributing to sustainable transportation development.

Keywords: Wireless Sensor Networks, Intelligent Transport Systems (ITS), Public Transport Prediction, Traffic Monitoring, Machine Learning

DOI: 10.7176/JIEA/16-1-03

Publication date: April 30^th 2026