| Project Detail |
Inefficient management of urban mobility leads to congestion with several socioeconomic and environmental adverse effects. Despite numerous research efforts and deployed technological solutions to address congestion, the problem still persists. One of the main reasons is attributed to the sparsity and low quality of traffic data. Unmanned Aerial Vehicles (UAVs) are an emerging, non-invasive, vision-based traffic sensing technology that offers economic viability and fast deployment. UAVs can swiftly and controllably move over disperse locations to capture road traffic data and simultaneously measure multiple traffic parameters. Despite these advantages, their applicability has so far been limited to occasional surveillance of road networks for live video monitoring or the extraction of historical traffic data.
URANUS proposes real-time, dynamic, and continuous UAV-based sensing of vehicular and pedestrian traffic and the use of the collected information for UM management. The envisioned paradigm features: (a) intelligent spatiotemporal sampling from UAVs, (b) the generation of complete spatiotemporal measurement sets with quantified uncertainty, (c) the development of suitable methodologies to close the loop, by jointly controlling the urban mobility (UM) and UAV networks for enhanced UM performance, and (d) the strategic selection of measured parameters. In this context, URANUS will design a solid framework for real-time urban mobility management targeting both vehicular and pedestrian traffic. The developed framework will include UM monitoring, UM control, and UAV operational planning methodologies, exploiting the unique URANUS sensing characteristics. Success of this project will transform our understanding of joint optimization between sensing, monitoring, and control, not only in UM management but also in other UAV-centric fields such as air pollution monitoring. |
