METRIC: Model and prediction of road traffic flow using drones
Title: METRIC: Model and prediction of road traffic flow using drones
SNIC Project: Berzelius-2022-38
Project Type: LiU Berzelius
Principal Investigator: Xiaoliang Ma <liang@kth.se>
Affiliation: Kungliga Tekniska högskolan
Duration: 2022-03-01 – 2022-09-01
Classification: 10299
Homepage: https://www.kth.se/profile/liang/page/metric-model-and-prediction-of-road-traffic-flow-using-drones
Keywords:

Abstract

Unmanned aerial vehicle (UAV), also called drone, is now considered as one of the most promising techniques for traffic management. It has potentials to be used as a technological platform for efficient and cost-effective data collection as well as for proactively solving problems of traffic incident and congestion. This project has its initial objectives to investigate and evaluate two essential technologies for drone-based traffic management platform i.e. real-time data streaming and image analysis techniques. In addition, traffic data, collected and processed by the drone-based system, will be used to estimate and predict traffic state information. The live video feed and derived traffic information will support the real-time decision makings in traffic control center. A demonstrator will be implemented for the incident management scenarios with the technologies.