## 1 Introduction and Background  
Due to its geostrategic location, Slovenia is one of the European transit countries. Two important trans-European corridors (road and rail) run through Slovenian territory: the Mediterranean corridor (Seville-Barcelona-Venice-Ljubljana-Budapest) and the Baltic-Adriatic corridor (Ravenna-Venice-Graz-Brno-Gdansk). Thus, significant incidents and high traffic densities along these corridors affect not only traffic flows in the country where they occur, but also traffic flows in neighbouring EU countries: Italy, Austria, Hungary, and Croatia. Driver behaviour cannot be predicted with certainty, although drivers do behave predictably to some degree, with certain patterns that can be experientially identified and used in a feedback loop to modify input data to generate trips. In this case, traffic, also called traffic demand, encounters various forms of resistance that determine how it is distributed across the road network [1, 2]. It is understandable that AHTMSs (Active Highway Traffic Management Systems) are effective, but not always as desired or intended by the traffic control theory [3; 4]. Therefore, it is necessary to look for new approaches to encourage drivers to adopt consistent and safe traffic behaviour [5]. Significant changes are expected in this area in the near future, as the use of Advanced Vehicle Assistance Systems (ADASs) will enable drivers to more easily understand what the road infrastructure and other vehicles are telling them at any vehicle location and not just at the location of Variable Message Signs (VMSs). Indeed, the next generation of vehicle data collection technologies will be able to provide information about the lanes vehicles are using, which is currently only a wish of the systems mentioned above. Various factors influence the capacity utilisation of the highway network. The choice of lane is one of the traffic factors that affect the capacity utilisation of the directional lane of a highway. Lane change areas and the modelling of this phenomenon are a challenge for traffic analysts as well as for autonomous vehicle developers [6; 7; 8]. Generally, drivers change lanes due to differences in speed, such as to overtake slower vehicles, due to obstacles in the lane they are using, or the selfishness of certain drivers who \"keep the left lane for themselves\" [9]. Lane-changing traffic flow can be characterised by lane-changing models [10; 11]. The empirical models described by [PERSON] [12] are more or less based on [PERSON]'s model of road traffic behaviour [13]. Considering traffic flow rate \(q\) and density \(k\), the lane distribution of the two characteristics, i.e., \(LFR_{i}\) and \(LDR_{i}\), is the ratio between the corresponding value \(q_{i}\) and \(k_{i}\) on the traffic lane \(i\) and the total for the overall carriageway \(q_{tot}\) and \(k_{tot}\)[14]. Mathematical models usually specify the number of lane changes [15]; however, in practice, we are not very good at imagining the latter because we cannot yet track individual vehicles. A higher frequency of lane changes results in a greater general heterogeneity of highway traffic flow. When we talk about AHTMS and real-time road traffic information systems, the reliability of the data and the corresponding robust short-term forecast play a very important role [16]. Prediction is usually based on models that learn on the basis of the characteristic patterns of traffic conditions given by various characteristics of traffic flow in time and space. Their importance is related to typical events in the past, which remain relevant until the traffic demand is known in advance and this will be determined only by the announcement of the arrival of an individual vehicle in the future. The usual presence of different categories of vehicles is reflected in the heterogeneity of the traffic flow structure. The degree of heterogeneity in Slovenia is currently expressed by the share of heavy vehicles in the traffic flow in an individual traffic lane of a directional carriageway.