## 2 Related Work
### Which Models Should Be Used to Predict Mode Choices?
The study by [PERSON] and [PERSON] [68] also confirms these previous findings, indicating that the radial basis function (RBF) can better predict the rate of realization of trips in Akura (Nigeria) than the regression model (MLR). Modeling results showed that RBF showed higher accuracy, with an R2 value of 0.947 and RE of 0.391, compared to MLR, with an R2 value of 0.589 and RE of 0.875. Also, the results indicate that the number of household members, the number of employed household members, the number of students in the household, the number of household members over the age of 12, and the number of household members who have a driver's license represent variables that have a significant impact on trip generation rates. In addition to the above, the authors [PERSON] et al. [69] applied models such as an extreme learning machine (ELM), support vector machine (SVM), and multilayer perceptron neural network for their research to predict the behavior of students who choose the mode of transportation on the way to school in the cities of Al-Khobar and Dhahran in the Kingdom of Saudi Arabia. Multilayer perceptron stands out with the highest prediction accuracy (98.99%), followed by extreme learning machines (97.30%) and support vector machines (89.53%). Also, the results indicate that the most sensitive variables that influence the choice of travel method are household income, travel time, and parents' education level. These results are also confirmed by the study by [PERSON] et al. [58] conducted in San Francisco (California), which applied two machine learning models (decision tree (DT) and neural network) and a multinomial logit model to model the choice of travel mode. Like the previously presented results, the artificial neural network gave the best prediction results (88.0% and 78.2%, respectively) in comparison to the decision tree (86.0% and 76.8%, respectively) and the multinomial logit model (86.7% and 72.9%, respectively). Also, the most significant variables for the choice of travel mode include household size, number of vehicles in the household, gender, having a driver's license, travel time, time of day, and travel costs. In addition to the above, binary logistic regression (BLR) was also used in the study by [PERSON] et al. (2020), and gave worse prediction results compared to a multilayer perceptron neural network. Namely, with an accuracy of 85%, the multilayer perceptron correctly predicted and detected significant variables during the acceptability of electric microvehicles in Belgrade (Serbia). The results indicate that the respondents' attitude toward current environmental pollution, frequency of using modes of transport such as bicycles and motorcycles, mileage traveled, and personal income have the greatest influence on acceptability. On the other hand, the results of a study conducted in the city of Tongling (China) indicate that artificial neural networks do not always give the best results. Namely, the authors [PERSON] et al. (2020) used the extended structure of the naive Bayesian model to predict the travel mode choice, which achieved better performance for its prediction accuracy of 75.4% compared to the artificial neural network model, whose prediction accuracy was 72.2%. The previously presented studies show that the prediction accuracy of the logit model is not always a significant and reliable method. This is indicated by the obtained results of the studies, which highlight the limitations of traditional models in terms of complexity, and at the same time, the assumption of linearity, which represents a significant disadvantage when there is a large sample volume. On the other hand, although based on nonlinear relations, models like support vector machines and decision trees face limitations in terms of their need to form a large number of kernel tricks and decision trees, which often require manual adjustments and cannot always cover all nonlinear relations.