###### Abstract  
Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS. c 2020  
1  
<ccs2020>  
Traffic flow in specific road segments is closely associated with the number of vehicles in a given road segment at a given time and speed used for passing this road segment. Radical speed reduction may lead to traffic congestion, which has a negative impact on the environment, health of the inhabitants living in surrounding areas, and traffic fluency, which further impacts road users [2; 3; 4; 5]. Traffic prediction in association with the possibilities of processing new (and often open) data is currently one of the emerging areas of interest among researchers all over the world--see, e.g., [6; 7; 8; 9]. To predict traffic speed, Floating Car Data (FCD) [10] is mainly used, as well as other relevant data sources available in individual states across the world. The outputs of articles focused on traffic prediction contribute to the innovation of ATIS and ATMS, especially for the purpose of traffic monitoring, traffic congestion identification, and traffic flow modelling [11; 12]. This article emphasizes the opportunities of using various data sources in order to improve the route calculation in car navigation and associated fleet and workflow management systems. Car navigation users prefer a faster route over a shorter one that is suitable for the navigation of pedestrians. The minimization of time spent en route and the accurate estimation of arrival time are important features of car navigations, which mostly use real-time traffic information acquired from FCD for this purpose as well as other sensors (depending on the state and data provider). The issues concerning car navigation and the possibilities to apply the designed solution are as follows. Despite the fact that navigations use real-time traffic information to avoid congestion and significant reduction in speed in road segments through which the planned route leads, for routes with several waypoints or stops, real-time traffic information, along with continuous recalculation of the route en route, may not be sufficient. Provided that these waypoints can be visited in a random order (e.g., sightseeing tour, goods distribution, police patrol), it is necessary to know the traffic flow at the time of the expected passage through the given road segment at the time of route calculation. Based on this information, waypoints can be arranged one after another to minimize the risk of traffic congestion for the navigation user.