**1. Introduction**

Efficiently functioning public transport has a significant positive impact on the entire transportation system performance through numerous aspects, such as the reduction of congestion, energy consumption, and emissions. In most cases, the fundamental elements of public transport are the bus transport subsystem. Currently, in addition to criteria such as punctuality, the frequency of departures, and the number of transfers, the comfort of travelling is an essential element for travelers. It may be significantly related to the degree of occupancy and capacity of the bus. An overcrowded bus may discourage travelers from choosing this mode of transport and induce him to use a private car despite the existence of many other facilities offered by a given public transport system. Therefore, the forecasting of bus passenger demand and the forecasting of bus occupancy at individual bus stops is currently an important research direction. The variability and cyclicity in passenger flows make

such forecasts extremely useful. From the perspective of transport system planners, they can be used for an optimal allocation of resources and bus types in relation to current demand. From the traveler's point of view, the use of this type of forecast can help to reduce waiting times at the bus stop as well as helping him choose the right departure time. The forecasting methods and techniques used in this area mainly concern long-term prediction. Given the above, the main goal of the article is to present the framework of the prediction subsystem for the Advanced Travel Information System (ATIS), which enables one to predict the onboard comfort level related to the actual bus occupation.

The article is organized as follows. The first section is a literature review. It describes the conceptual framework for the ATIS subsystem with the comfort level prediction module. It also discusses the potential application of the proposed approach. The main core of the article concerns the forecasts in the public transportation system; hence, the second part of the literature review deals with that area. Subsequently, the theoretical background of the Markov chain (MC) for the prediction model is presented, defining the comfort level states. The characteristics and benefits of applying the MC for an onboard bus comfort level prediction based on a real-life case study summarise the previous theoretical discussion.
