ABSTRACT
Public transport is one of the main infrastructures of a sustainable city. For this reason, there are many studies on public transportation which mostly answer the question of “when my next bus will arrive?”. However now when the public is under the restrictions of the Covid-19 pandemic and learning to live with new social rules such as “social distance” a new yet crucial question arise on public transportation: “how crowded my next bus will be?” To prevent the crowdedness in public transportation the traffic regulators need to forecast the number of passengers the day ahead. In this study, in cooperation with Synteda, we suggest a machine learning algorithm that forecasts the occupancy in a bus or tram the day ahead for each stop for a route. The input data is past passenger travel data provided by the Västtrafik AB which is the public transportation company in Gothenburg, Sweden. The hourly data for the precipitation and temperature also has been added to the forecasting method;the database of precipitation and temperature is obtained by the SMHI, Swedish Meteorological and Hydrological Institute. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.