Progressive urban density affects city centers especially and results in growing congestion, lack of parking spaces, and increasing environmental costs of transportation, causing increased air pollutant emissions and noise. These phenomena reduce the attractiveness of the city and result in a degradation of the quality of life for its residents. In light of these phenomena, there is a clear need for intelligent management of urban space using new technologies that would be complementary to existing intelligent transportation systems. Expanding information resources obtained from mobile cameras will have a positive impact on increasing the efficiency of transportation management and use of limited space in city centers. It will also have an impact on reducing external transport costs and increasing the quality of logistics services provided in the city. The main aim of the paper is to develop a concept of a transport management system in cities using mobile vision systems mounted on unmanned aerial vehicles. The model will concern the cases of lane occupation by freight vehicles and the analysis of parking spaces in the city in order to improve their management. The results of the developed model will contribute to the automation of the parking space management process and increase the efficiency of the use of city parking space resources.

An increasing number of devices and their communication with each other generates huge amounts of data. The efficiency of processing such large and heterogeneous data is crucial for extracting the reliable and consistent information that is needed for the effective management of smart cities within the field of transport. Data heterogeneity and volume as well as its integration and analytics are big challenges for decision-makers. The development of urban agglomerations is largely dependent on the proper management of such data. Therefore, this paper explores the role of these data repositories, their acquisition from different sources, and the ways to combine them. The main goal of this paper is to propose a concept of Smart City management based on Big Data Analytics and technology related to UAVs (Unmanned Aerial Vehicle) which may reduce costs and resource consumption. The presented concept includes successive data generation and collection, data type identification, problem and requirement identification, filtering, classification, pre-processing, and data optimization, as well as decision support analysis. A key part of this analysis utilizes computer algorithms, such as Speeded Up Robust Features (SURF) and Thresholding and Blob detection, to develop a multi-camera image recognition system for freight transport management and logistics in smart cities. The objective is to design a system that optimizes the route planning and time of vehicle passage on selected road sections, ultimately leading to the reduction of emissions. During the study, data obtained from multiple sources were compared, and the analysis uncovered different results for the same assumptions. We discuss the reasons for these variances. Overall, the results obtained in the analysis indicated that it is necessary to correct the predictions of the multi-camera image recognition system with additional methods and algorithms.

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.

One of the key challenges for city authorities is to ensure an adequate quality of life for residents while promoting sustainable urban development. Achieving this balance is closely related to transport management which strongly affects urban quality of life, energy consumption, and resource savings. The aim of this article is to propose a new approach of assessing urban transport management quality, with a view to implement urban energy and resource-saving policies. The assessment procedure is based on the Six Sigma methodology and is illustrated using the example of the city of Szczecin for three selected routes. Travel data were obtained based on actual vehicle traffic using crowdsourcing methods. The capacity processes were assessed based on the potential capacity index and the actual capacity index, which characterise deviations in urban traffic from the best way to save energy and resources. Customer specification limits were set based on surveys assessing residents’ expectations regarding car travel times on the analysed routes. The results show that the methodology proposed in the article can be successfully used to assess urban transport management and to identify areas in need of improvement for sustainable transport panning.

Main aim of the paper is to use a single non-metric camera to support the determination of the position of. Authors propose to use the existing infrastructure of CCTV cameras mounted on bridges and wharves to determine the position of inland waterway vessels. Image from cameras giving the pixel coordinates of moving object is transformed to the geodetic data domain using a modified projective transformation method. Novel approach is to use of Sequential Projection Transformation (SPT) which additionally uses virtual reference points. The transformation coefficients calculated using the virtual points are used to determine the position of the vessels and are also simultaneously used to calibrate the industrial camera. The method has been verified under real conditions, and the results obtained are average 30% more accurate compared to the traditionally used projective transformation using a small number of real points.