Advancing Urban Traffic Monitoring in Smart Cities: A Field Experiment with UAV-Based System for Transport Planning and Intelligent Traffic Management

In Smart Cities (SCs), efficient traffic flow management in congested urban areas is crucial to promote sustainability, liveability, and accessibility. Big data can potentially provide valuable information to be used for mobility management; accordingly, integrating traditional traffic data collection methods to enrich the type of available data is to be pursued. In this perspective, recently the idea of using Unmanned Aerial Vehicles (UAVs) for traffic monitoring due to their flexibility, cost-efficiency, and reliability, emerged. This paper explores the potential of using UAV collected data - such as the one related to vehicle detection, tracking, and trajectory analysis - in the aim of calibrating microsimulation models for transport planning. A case study conducted in Catania, Italy, involved exploiting UAVs for deployment of various traffic detection technologies. The results highlighted the effectiveness of using UAVs, achieving high accuracy and efficiency in monitoring traffic conditions where traditional sensors often face limitations. Use of UAVs also allows continuous trajectory analysis, so enabling for example the detection of prohibited manoeuvres and identifying design strategies to improve road safety. Furthermore, the study demonstrated that a single UAV can effectively monitor large intersection areas, reducing the need for use of multiple more traditional sensors. This research lays the groundwork for a prototype that integrates diverse data sources to create harmonized traffic datasets, contributing to the development of a Digital Twin of Mobility (DTmob). The latter will be a valuable tool to improve traffic monitoring and control systems, supporting resilience, efficiency, and better service provision in SCs.