2124 2021 eng 442 453 conferenceobject Springer Cham 0 2021-10-31 -- -- 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. CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland 978-3-030-84340-3 10.1007/978-3-030-84340-3_36 publish Lecture Notes in Computer Science ; 12883 peer-reviewed Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG Timo Stadler Amitrajit Sarkar Jan Dünnweber eng uncontrolled Transportation eng uncontrolled Rural mobility eng uncontrolled Prediction deu swd Öffentlicher Personennahverkehr deu swd Ländlicher Raum deu swd Verkehrsnachfrage deu swd Prognosemodell eng uncontrolled Random Forest Computerprogrammierung, Programme, Daten Fakultät Informatik und Mathematik Research Center for Artificial Intelligence - RCAI Labor Parallele und Verteilte Systeme Digitale Transformation