Enhancing Urban Train Transportation through Context-Aware Applications with Wireless Sensor Network Support

Abstract

Urban train transportation systems face challenges in efficiency, safety, and passenger satisfaction. This study presents the design and implementation of context-aware applications supported by wireless sensor networks (WSNs) to address these challenges. By integrating WSNs into urban train infrastructures, real-time monitoring and data analysis are achieved, enabling predictive maintenance, improved operational efficiency, and enhanced passenger experiences. This research proposes integrating WSNs with context-aware applications to enhance real-time monitoring, predictive maintenance, and decision-making capabilities. WSNs enable comprehensive data collection from various components of the rail infrastructure, such as trains, stations, and tracks. Context-aware systems utilize this data to provide dynamic, situational responses, improving system efficiency, safety, and passenger experience.