Intelligent Traffic and Transportation Analysis Using SUMO, IoT, and Fuzzy Expert Systems
DOI:
https://doi.org/10.69996/jsihs.2026011Keywords:
Intelligent Transportation System, SUMO, IoT, Traffic Monitoring, Fuzzy Logic, Smart Cities, Transportation Analytics, Traffic SimulationAbstract
Rapid urbanization, population growth, and the continuous increase in vehicle ownership have intensified transportation and traffic management challenges worldwide. Conventional transportation systems often fail to efficiently manage traffic congestion, environmental pollution, fuel consumption, and real-time road monitoring. Intelligent Transportation Systems (ITS) integrated with Internet of Things (IoT), traffic simulation tools, and Artificial Intelligence (AI)-based decision support mechanisms provide promising solutions for addressing these challenges. This research paper presents an intelligent traffic and transportation analysis framework using Simulation of Urban MObility (SUMO), OpenStreetMap (OSM), IoT-based data collection, and a Fuzzy Logic Expert System (FLES). The proposed framework integrates real-world transportation data into a simulation environment for monitoring vehicle movement, traffic density, lane occupancy, speed variation, congestion levels, and emission analysis. Real traffic data were collected using quadcopter-assisted monitoring and imported into SUMO to generate realistic traffic scenarios. The experimental results demonstrate that the proposed system improves traffic monitoring accuracy, congestion detection efficiency, and route optimization. The framework also provides decision support for transportation planners and urban authorities. The study highlights the significance of intelligent transportation analytics for smart cities, sustainable transportation management, and real-time traffic control systems.
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