Traffic congestion occurs when traffic demand is greater than the available network capacity. It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and longer vehicular queueing. Congestion can also impose a negative impact on the society by decreasing the quality of life with increased pollution, especially in urban areas. To mitigate the congestion problem, traffic engineers and scientists need quality, comprehensive, and accurate data to estimate the state of traffic flow. Various types of data collection technologies have different advantages and disadvantages as well as data characteristics, such as accuracy, sampling frequency, and geospatial coverage. Multisource data fusion increases the accuracy and provides a comprehensive estimation of the performance of traffic flow on a road network. This paper presents a literature overview related to the estimation of congestion and prediction based on the data collected from multiple sources. An overview of data fusion methods and congestion indicators used in the literature for traffic state and congestion estimation is given. Results of these methods are analyzed, and a disseminative analysis of the advantages and disadvantages of surveyed methods is presented.

The localization of Unmanned Aerial Vehicles (UAVs) is a critical area of research, particularly in applications requiring high accuracy and reliability in Global Positioning System (GPS)-denied environments. This paper presents a comprehensive overview of short-distance localization methods for UAVs, exploring their strengths, limitations, and practical applications. Among short-distance localization methods, ultra-wideband (UWB) technology has gained significant attention due to its ability to provide accurate positioning, resistance to multipath interference, and low power consumption. Different approaches to the usage of UWB sensors, such as time of arrival (ToA), time difference of arrival (TDoA), and double-sided two-way ranging (DS-TWR), alongside their integration with complementary sensors like Inertial Measurement Units (IMUs), cameras, and visual odometry systems, are explored. Furthermore, this paper provides an evaluation of the key factors affecting UWB-based localization performance, including anchor placement, synchronization, and the challenges of combined use with other localization technologies. By highlighting the current trends in UWB-related research, including its increasing use in swarm control, indoor navigation, and autonomous landing, potential researchers could benefit from this study by using it as a guide for choosing the appropriate localization techniques, emphasizing UWB technology’s potential as a foundational technology in advanced UAV applications.

The paper analyses the real-time detection of incidents in road traffic. A general model is presented of an integral road traffic incident management system. The paper presents the major incident detection methods. The detection procedure on open highway sections has been dealt with in particular. Adequate mathematical model has been defined, as the base for the realisation of the estimators of the traffic flow condition variables. The proposed method is the Extended Kalman Filter. The final part of the paper deals with an example for the realisation of the Incident Management Decision Support System (IMDSS). KEY WORDS: intelligent transport system, incident management system, traffic model in the status space, theory of estimation, extended Kalman filter, automatic incident detection, decision support system

The limiting conditions of traffic in cities, together with the complex and dynamic traffic flows, require an efficient and systematic management and information provision for the traffic participants, with the goal to achieve better...

Advanced automatic traffic control systems and various other ITS (Intelligent Transport Systems) applications and services rely on real-time information from the traffic system. This paper presents the overview and general functions of different information sources which provide real-time information that are used or could be used in ITS. The objective is to formally define the quality of information sources suitable for ITS based on formal models of the traffic system and information sources. The definition of quality encompasses these essential factors: traffic system information that exists or may be requested, user requirements and attributes that describe the information sources. This provides the framework and guidelines for the evaluation of information sources that accounts for relevant factors that influence their selection for specific ITS applications. KEY WORDS: information source, information source quality, Intelligent Transport Systems (ITS), automatic traffic control