Enhancement of Smart Transportation SystemsUsing ITS-Enabled Analytics

Abstract

Intelligent Transportation Systems (ITS) have become central to modern urban planning, enabling real-time monitoring, predictive traffic management and automated decision making. As urban mobility grows increasingly complex, analytics-driven ITS frameworks offer improved safety, reduced congestion and optimized resource allocation. This research investigates an integrated ITS-enhanced
analytics model that incorporates multimodal data streams including vehicular sensors, traffic cameras, GPS traces and environmental IoT devices. Machine learning and deep learning techniques are deployed for traffic forecasting, anomaly detection, route optimization and incident prediction. A hybrid analytics pipeline combining time-series models, graph-based algorithms and deep spatiotemporal networks is developed and evaluated on benchmark transportation datasets. Results demonstrate substantial improvements in traffic-flow accuracy, travel-time estimation and congestion detection, with the integrated framework outperforming traditional ITS systems across several performance metrics. This study emphasizes the importance of data fusion, adaptive algorithms and system scalability while highlighting the structural limitations of existing infrastructure in developing regions. The findings indicate that ITS-enabled analytics can significantly enhance transportation planning, road safety management and urban mobility systems. Future work points toward AI-driven autonomous mobility ecosystems, edge-intelligent traffic control and privacy preserving vehicular networks that will shape next-generation smart cities.