@inproceedings{StadlerSarkarDuennweber, author = {Stadler, Timo and Sarkar, Amitrajit and D{\"u}nnweber, Jan}, title = {Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm}, series = {CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland}, booktitle = {CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland}, editor = {Saeed, Khalid and Dvorsk{\´y}, Jiř{\´i}}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-84340-3}, doi = {10.1007/978-3-030-84340-3_36}, pages = {442 -- 453}, abstract = {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.}, subject = {{\"O}ffentlicher Personennahverkehr}, language = {en} }