• Energy Research

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.