*2.2. Prediction Methods in Public Transportation*

The main problem in public transportation, which researchers are trying to model and predict, is passenger demand. It has a direct influence on the efficiency of the public transport system, raising the competitiveness of this mean of transport and, lastly, fulfilling the expectations of passengers and encouraging them to choose this type of transport.

The problem of bus occupancy level forecasting is quite an important aspect, particularly for decision-makers. This is why there is an abundance of articles addressing this issue. A plurality of methods whose aim is to deal with this kind of forecasting shows an interest attached to the seriousness of this issue. Table 2 shows selected methodologies, types, modes of transport and the studies in which they were presented.


**Table 2.** Passenger demand and flow prediction studies in public transport.

According to Table 2, it is evident that there were not so many papers that tried to solve the passenger prediction problem using the Markov Chain method. Most of the presented methods are used to predict the passenger flow or demand in the short-term. The Markov chain method appears mostly in a combination of Markov and Grey models to forecast the passenger flow or as a hybrid model—the Back Propagation neural network and Markov model—to forecast the daily passenger volume in the rail transit station. This shows the existence of different fields of research on the most accurate methods for forecasting the passenger demand or flow to improve public transportation efficiency directly.

There are certain areas in public transportation in which prediction can be beneficial during not only the organisation process but also the adaptation to real conditions. These may include, besides the passenger demand or flow prediction, the bus arrival time [36] prediction. From the public transport organiser's point of view, the passenger structure prediction in the transportation corridor may also be useful. For this purpose, D. Wang, X. Sun and Y. Li utilized the Markov process [37]. In order to meet the expectations of public transport users, an important area of prediction is public transport trip flows [38].
