## 2 Related Work
### Which Models Should Be Used to Predict Mode Choices? Traditionally, research related to the change in transport behavior is primarily supported by discrete choice models, like logit models, such as multinomial logit models, nested logit models, and mixed logit models. This is evidenced by numerous studies, such as the one conducted in Xi'an (China), in which the authors [PERSON] et al. [54] used a nested logit model to investigate the behavior of commuters in the joint choice of travel time and mode by considering factors such as socio-demographic and economic characteristics, household characteristics, and travel. The results of the study indicate that transport behavior is most influenced by household income and the distance traveled during the trip. In addition to the above, the authors [PERSON] and [PERSON] [55] used data obtained through a study conducted in Budapest (Hungary) to apply the multinomial logit model (MNL) and the nested logit model based on the utility function. By analyzing the results, it was found that the most important variables, namely the distance during the trip, then the time of the trip and its purpose, had the greatest influence on the choice of the mode of transportation. On the other hand, the authors [PERSON] et al. [56] found, using a cross-sectional and longitudinal structural equation model, that the use of public transport is most affected by the built environment, while the use of cars and bicycles is hardly affected. However, in recent years, machine learning has become more and more present in many scientific fields, which has also influenced the growth of interest in its application in modeling the behavior of individuals when choosing to travel [57]. Compared to statistical models that are based on a mathematically proven theoretical foundation, machine learning methods are non-parametric, relying on computers to examine data for their structure without any theory about what the underlying structure of the data is. This enables them to form more flexible modeling structures, which can often lead to greater predictive ability, i.e., greater accuracy of model predictions. In this regard, the modeling of travel mode choice can often be viewed as a classification problem, providing an alternative to logit models. In support of this, there are more and more studies that indicate that machine learning classifiers are more effective in modeling the travel behavior of individuals compared to logit models [58; 59; 60; 61; 62]. Also, among the machine learning models that were used in studies that dealt with the modeling of behavior when choosing a mode of transport, the models of artificial neural networks can be distinguished by their significant predictive capabilities [63; 64; 65]. For example, the results of a study conducted in Luxembourg by the authors [PERSON] et al. [66] indicate that when predicting the way individuals travel in the city, better results were given by the artificial neural network models (MLP) 80.12% and (RBF) 81.0% compared to the multinomial logit model (MNL), whose prediction accuracy was only 62%. The data used contained information on various attributes such as characteristics of individuals, data related to places of residence, and specifications of work and transport modes. Also, a study conducted only a few years later by [PERSON] et al. [59] confirms the very good results of predicting travel patterns in Luxembourg by artificial neural networks. Namely, with an accuracy of 81.1%, the multilayer perceptron neural network gave far better results compared to the radial basis function (79.2%), a support vector machine (67.7%), and multinomial logistic regression (64.7%). Similar results were obtained by [PERSON] et al. [67] within the framework of the conducted study, which applied four models of artificial neural networks (ANNs) and a multinomial logit model (MNL) to predict transportation behavior in Chicago (USA). The results show that ANN models outperform MNL, with a prediction accuracy around 80% compared to 70% for MNL.