Development and research of algorithms for determining user preferred public transport stops in a geographic information system based on machine learning methods

The paper considers a problem of determining the user preferred stops in a public transport recommender system. The effectiveness of using various machine learning methods to solve this problem in a system of personalized recommendations is compared, including a support vector method, a decision tree, a random forest, AdaBoost, a k-nearest neighbors algorithm, and a multi-layer perceptron. The described traditional methods of machine learning are also compared with the method proposed herein and based on an estimate calculation algorithm. The efficiency and the effectiveness of the proposed method are confirmed in the work.