The predominant focus on individual motorized transport is neither sustainable nor socially just. One goal of a more sustainable design of the transport sector is to encourage people to use public transport.
One barrier for passengers to use public transport are heavily occupied vehicles and the uncertainty about whether an empty seat is available on the desired connection. In this paper, a model is presented that is able to forecast the occupancy of vehicles in public transport. This information can be provided to passengers to increase customer satisfaction.
Different sub models are presented, which differ according to their forecast horizon and the data sources used. The most important data source is data from automatic passenger counting systems collected in vehicles in the region of Northern Hesse during the project period of the research project U-hoch-3. After linking further data sources such as weather and timetable data, stratification characteristics are developed based on which occupancy states can be derived for future journeys. By linking the data with real-time data, the forecast quality can be significantly improved.
It is shown which influences the Covid-19 pandemic and the introduction of the 9 € ticket in Germany had on the model development and by which functions these changes in demand can be correctly represented by the model.
The results presented in this paper show that it is possible to reliably predict occupancy rates for vehicles in public transport.
- Public transport
- Data analysis
- Automatic passenger counting
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Bundes-Klimaschutzgesetz: KSG (2019). https://www.gesetze-im-internet.de/ksg/BJNR251310019.html. Accessed 07 Nov 2022
Umweltbundesamt: Treibhausgasminderungsziele Deutschlands (2022). https://www.umweltbundesamt.de/daten/klima/treibhausgasminderungsziele-deutschlands#nationale-treibhausgasminderungsziele. Accessed 11 Nov 2022
Aamaas, B., Borken-Kleefeld, J., Peters, G.P.: The climate impact of travel behavior: a German case study with illustrative mitigation options. Environ. Sci. Policy 33, 273–282 (2013)
Faulhaber, A.K., et al.: Development of a passenger assistance system to increase the attractiveness of local public transport. Sustainability 14, 4151 (2022)
NVV Homepage. https://www.nvv.de/der-nvv/ueber-den-nvv/. Accessed 07 Nov 2022
Lopez-Carreiro, I., Monzon, A., Lopez, E., Lopez-Lambas, M.E.: Urban mobility in the digital era: an exploration of travellers’ expectations of MaaS mobile-technologies. Technol. Soc. 63, 101392 (2020)
Vandewiele, G., Colpaert, P., Janssens, O., van Herwegen, J., Verborgh, R., Mannens, E.: Predicting train occupancies based on query logs and external data sources. In:: Barrett, R., Cummings, R., Agichtein, E., Gabrilovich, E. (Hg.) Proceedings of the 26th International Conference on World Wide Web Companion - WWW 2017 Companion. The 26th International Conference, Perth, Australia, 03–07 April 2017, pp. 1469–1474. ACM Press, New York (2017)
Pasini, K., Khouadjia, M., Ganansia, F., Oukhellou, L.: Forecasting passenger load in a transit network using data driven models. In: WCRR 2019, 12th World Congress on Railway Research (2019)
Wang, B., Wu, P., Chen, Q., Ni, S.: Prediction and analysis of train passenger load factor of high-speed railway based on LightGBM algorithm. J. Adv. Transp. 2021, 1–10 (2021)
Heydenrijk-Ottens, L., Degeler, V., Luo, D., van Oort, N., van Lint, J.: Supervised learning: predicting passenger load in public transport. In: CASPT Conference on Advanced Systems in Public Transport and TransitData, pp. 30–32 (2018)
Arabghalizi, T., Labrinidis, A.: Data-driven bus crowding prediction models using context-specific features. ACM Trans. Data Sci. 1(3), 1–33 (2020)
Vial, C., Gazeau, V.: Load passenger forecasting towards future bus transportation network. J. ICT Stand. 8, 185–198 (2020)
Jenelius, E.: (2020) Personalized predictive public transport crowding information with automated data sources. Transp. Res. Part C Emerg. Technol. 117, 102647 (2020)
Deutsche Bahn. Auslastungsinformationen: Wie voll wird mein Zug? https://www.bahn.de/service/buchung/auslastungsinformation. Accessed 11 Nov 2022
NV-ProVi. https://nv-provi.de/. Accessed 11 Nov 2022
RMV. https://www.rmv.de/c/de/informationen-zum-rmv/der-rmv/rmv-aktuell/auslastungsprognose. Accessed 11 Nov 2022
Project SAFIRA. https://www.vbb.de/presse/wissen-wie-voll-der-zug-ist-bevor-er-kommt-forschungsprojekt-safira-gestartet/. Accessed 11 Nov 2022
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Taylor and Francis, Hoboken (1988)
Konstantinos, G., Cats, O.: Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transp. Rev. 41(3), 374–392 (2021)
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Saake, S., Sommer, C. (2023). Design of a Forecasting Method for Occupancy Rates in Local Public Transport Based on Data from Automatic Passenger Counting Systems. In: Clausen, U., Dellbrügge, M. (eds) Advances in Resilient and Sustainable Transport. ICPLT 2023. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-031-28236-2_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28196-9
Online ISBN: 978-3-031-28236-2