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.
Author: | Timo Stadler, Amit Sarkar, Jan DünnweberORCiDGND |
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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): |