TY - CHAP A1 - Stadler, Timo A1 - Sarkar, Amitrajit A1 - Dünnweber, Jan ED - Saeed, Khalid ED - Dvorský, Jiří T1 - Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm T2 - CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland N2 - In recent years, mobility solutions have experienced a significant upswing. Consequently, it has increased the importance of forecasting the number of passengers and determining the associated demand for vehicles. We analyze all bus routes in a rural area in contrast to other work that predicts just a single bus route. Some differences in bus routes in rural areas compared to cities are highlighted and substantiated by a case study data using Roding, a town in the rural district of Cham in northern Bavaria, as an example. Data collected and we selected a random forest model that lets us determine the passenger demand, bus line effectiveness, or general user behavior. The prediction accuracy of the selected model is currently 87%. The collected data helps to build new mobility-as-a-service solutions, such as on-call buses or dynamic route optimizations, as we show with our simulation. KW - Transportation KW - Rural mobility KW - Prediction KW - Öffentlicher Personennahverkehr KW - Ländlicher Raum KW - Verkehrsnachfrage KW - Prognosemodell KW - Random Forest Y1 - 2021 SN - 978-3-030-84340-3 U6 - https://doi.org/10.1007/978-3-030-84340-3_36 SP - 442 EP - 453 PB - Springer CY - Cham ER -