###### Abstract
Without such information, the decision-making of the navigation is only based on the current traffic information. However, when calculating the route, the navigation does not consider facts, such as the deterioration of the traffic situation in 2 h in the area of the planned waypoint through the city center, which would make it more suitable to visit the given point at a sooner or later time. Another advantage of such enrichment of traffic information for car navigations is the ability to estimate the arrival time to the final destination more accurately. In order to deal with the aforementioned issues, it is crucial to process various data in the first step and to create a model which will supply relevant traffic information arising from the prediction of traffic conditions and from other data affecting the traffic fluency in the given country. The next step is to implement this model into the selected navigation system. This article deals with the first step only. This article aims to design a prediction model of the expected speed of passing each of approximately 20,500 selected road segments out of the road network of the Czech Republic on a given weekday at a specific hour. For this purpose, historical data acquired from FCD, information on traffic restrictions, and information gained from traffic detectors, CCTVs, and electronic tolls was processed with respect to the traffic in the Czech Republic. The designed solution can help various ITS, e.g., advanced car (or truck) navigation applications, as well as workflow and fleet management systems in business areas focused on B2C and B2B relationships and used in transport logistics [13]. The designed model enables the improvement of route calculation within the aforementioned ITS, which will be able to use predicted traffic speed in a given road segment and the time when the passage through this road segment is expected. This way, ITS can select a faster route for its user without burdening the given road segment(s) by further traffic, which will make a secondary contribution to traffic fluency and decrease the ecological and health-related burden in locations with highly used road segments. The prediction model will mainly serve as a supplementary one which will amend or enrich traffic information commonly used for route calculation in car navigation systems. The model will ensure information on the predicted speed and thus the expected traffic flow in the given road segment at the given time. In practice, the navigation application will use this information on the predicted speed in the given road segment only if a quality prediction is relatively highly likely based on the data available. Our results represent the first step toward a robust model, which will process various data sources concerning traffic and weather and predict traffic fluency in the Czech Republic that can be used by navigation systems in practice. The remainder of this article is organized as follows: In Section 2, we present the current perspectives and approaches used in the area of traffic prediction. Section 3 andSection 4 focus on the methodology and collection, pre-processing, and exploration of data. Section 5 introduces the design of the prediction model, and the model is validated further in Section 6. The conceptual design of using the designed solution in the area of navigation is described in Section 7. Section 8 concludes the article with discussion and future research directions.