Serin, FarukAlisan, YigitErturkler, Metin2025-03-232025-03-2320220263-22411873-412Xhttps://doi.org/10.1016/j.measurement.2022.111403https://hdl.handle.net/11486/6450The increase in population and the crowding of cities bring along transportation problems. Thus, people are directed to public transportation to reduce the burden on transportation. Being informed correctly about the arrival time at the stops attracts passengers. In this study, machine learning methods with three-layer architecture were used to predict bus arrival time. The first layer processes the measured data and gives the prediction results of actual data. In the second layer, the residuals are predicted at the specified depth. In the third layer, the results of the previous two layers are integrated with three different approaches to calculate the final prediction. The case study was carried out on the data obtained from Istanbul public transportation and various machine learning methods were applied to the data using the traditional and the three-layer architecture. The experimental results showed that the three-layer architecture provided successful results with approximately 2.552 MAPE.eninfo:eu-repo/semantics/closedAccessBus arrival timeMachine learningTime seriesPredictionPublic transportationArticle19810.1016/j.measurement.2022.1114032-s2.0-85131733468Q3WOS:000817167100004Q1