## 2 Background  
For the prediction of traffic at a given road segment or a given network, two basic indicators, determined while using various sensors for traffic measurement, are mainly used [14; 15]: Traffic flow (e.g., number of vehicles per minute) and traffic speed (mean of the observed vehicle speeds). These indicators can be extended further by other ones, directly measurable by sensors or calculable to the end, e.g., occupancy and traffic density. In terms of time, traffic prediction is divided into a short-term and long-term traffic prediction. As for the short-term prediction, mainly real-time data are processed, and the prediction duration is in minutes or hours. As for the long-term prediction, long-term historical data are mainly used [16], and the prediction duration is in days or months. However, the given time framework can vary slightly depending on the authors and the tasks being solved, see [8; 17; 18]. From the perspective of transport managers and road users, traffic speed plays an important role for ITS as it reflects the traffic state of the road. Based on this (current value and short-term prediction), transport managers can adjust the permeability through the individual segments of the road network and lower the risk of traffic congestion. People using car navigation can use real-time information (current traffic speed in a given road segment) during their trip to avoid congestion, or they can use speed profiles (connected with long-term prediction) when planning the trip. There is a large number of various approaches or methods for traffic prediction, or more precisely, for the prediction of its indicators (flow, speed, density, travel time). These can be divided into three areas, i.e., data-driven statistical methods, machine learning methods, and deep learning methods [17; 19]. Alternatively, they can also be divided into the following areas: Parametric approaches, nonparametric approaches, and deep learning-based approaches [8; 18; 20; 21]. Deep learning-based solutions can be classified among machine learning or nonparametric approaches. However, this area has been classified separately in specialized literature. As for parametric approaches in the area of short-term traffic prediction, time series and linear regression models are commonly designed. These approaches assume a priori structure of prediction model [22]. Typical models are autoregressive integrated moving average (ARIMA) forecast models [23; 24] and other variants focused on specific seasonal conditions, e.g., [25; 26; 27]. Other approaches focus on statistics, including Kalman filters or Markov models. Many distinctive models have been introduced in this area, e.g., the constant acceleration model, constant speed model, Simulation of Urban Mobility (SUMO) model, intelligent driver model, and others [22; 28]. The specialized literature has also stated practical issues associated with seasonal ARIMA models connected to outlier detection and parameter estimation [21]. In a long term perspective, the parametric approach can successfully deal with common (repeated) traffic conditions [20]. Especially in a short-term perspective, due to the fact that traffic data can be random, varied, and nonlinear, according to the authors of [19], parametric approaches based on linear relationships (like ARIMA) are unsuitable for the analysis of nonlinear traffic data. Prediction accuracy with such models under these conditions is lower than with nonparametric approaches [8]. On the other hand, this disadvantage of parametric models is balanced in a long-term perspective where even these models can reach quality results. Another advantage is better manageability for vast road networks, as well as the robustness and explicability of the parametric models. The aforementioned issues associated with the dynamics and nonlinear character of traffic data, especially in a short-term perspective, are overcome satisfactorily with nonparametric methods [20] and ensemble learning [29].