Dynamic Traffic Optimization in Smart Cities (DTOS): Integrating OpenStreetMap, IoT, and Fog Computing

This paper presents an innovative approach to urban traffic management by integrating OpenStreetMap (OSM) with Dijkstra’s algorithm, augmented with the Internet of Things (IoT) and fog computing technologies. Our method leverages real-time traffic data gathered from IoT devices, such as smart cameras, to gain a comprehensive view of current traffic conditions. The data is promptly processed via fog computing, ensuring reduced latency, and enabling real-time adaptation to traffic changes. The application of Dijkstra’s algorithm to this dataset is a key innovation, optimizing traffic routes dynamically with updates from OSM. A critical aspect of our research is its application beyond conventional traffic management. The system is particularly beneficial in emergency scenarios, facilitating rapid medical response and supporting military operations that require swift and secure movement. Additionally, it proves advantageous in managing large-scale events by enabling efficient isolation of specific areas or locations as needed. This adaptability makes it a vital tool for city administrations, not only improving daily traffic flow but also enhancing public safety and operational efficiency in various critical situations. Our experimental simulations in urban settings show significant improvements in traffic management and congestion reduction. The findings suggest that this integrated approach is not only a practical solution for routine traffic control but also a versatile tool for managing emergencies, military movements, and special events. This research paves the way for future smart city innovations, highlighting the potential of advanced technology in creating sustainable, efficient, and safer urban environments.