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Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm

  • 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.

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Metadaten
Author:Timo Stadler, Amit Sarkar, Jan DünnweberORCiDGND
DOI:https://doi.org/10.1007/978-3-030-84340-3_36
ISBN:978-3-030-84340-3
Parent Title (English):CISIM2021: 20th International Conference on Computer Information Systems and Industrial Management Applications, September 24-26 2021, Ełk, Poland
Publisher:Springer
Place of publication:Cham
Editor:Khalid Saeed, Jiří Dvorský
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2021
Release Date:2021/11/10
Tag:Prediction; Random Forest; Rural mobility; Transportation
GND Keyword:Öffentlicher Personennahverkehr; Ländlicher Raum; Verkehrsnachfrage; Prognosemodell
First Page:442
Last Page:453
Andere Schriftenreihe:Lecture Notes in Computer Science ; 12883
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Labor Parallele und Verteilte Systeme
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG