Abstract The issue of incomplete data on the volume and types of freight transport in urban areas is well known, leading to difficulties in managing transport processes and hindering the development of strategic approaches for modern cities. To improve the management of freight flows, it is essential to distinguish freight vehicles from private and public transport. This paper investigates and develops methods for vehicle detection and tracking using cameras mounted on unmanned aerial vehicles (UAVs), aiming to enhance urban logistics data through image processing and analysis algorithms. A multi-camera vehicle detection and tracking algorithm was developed to support the analysis and evaluation of traffic flow efficiency in urban logistics, ultimately contributing to improved transportation management aligned with emission reduction principles.
Kujawski et al. (Wed,) studied this question.