Abstract
To better understand users and their information demands, it is useful to divide them into user groups. These user groups can be assigned characteristics and mobility preferences. With the help of these parameters, the individual user can be better addressed. In this work a user model was created for the commuter and validated with mobility data. Based on this model, an analysis tool for mobility data was developed. The mobility data analysis tool was designed to identify commuter routes in the dataset. The analysis tool was tested using daily mobility data collected by student in 2018 using the app “MobiDiary”. The results of the analysis show that filtering the trips with the criteria “trip purpose” and “start time” can be a first approach identifying commuter trips. However, a more precise filtering of commuter routes is much more complex. The general findings of this work indicate, that the model trained on the labeled data set, where the participants provided trip purposes, needs to be aware of more parameters for being able to identify commuter trips only based on not labeled trip data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Böhm, F., et al.: Toolbox for analysis and evaluation of low-emission urban mobility. In: Krömker, H. (ed.) HCII 2020. LNCS, vol. 12213, pp. 145–160. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50537-0_12
Keller, C., Titov, W., Schlegel, T.: SmartMMI Analyseergebnisse. Modell- und kontextbasierte Mobilitätsinformationen auf Smart Public Displays und Mobilgeräten im Öffentlichen Verkehr. Hg. v. Hochschule Karlsruhe – Technik und Wirtschaft (HSKA). Institut für Ubiquitäre Mobilitätssysteme (IUMS). Karlsruhe. Online verfügbar unter (2018). https://smartmmi.de/smartmmi-leaflet-analyseergebnisse/. zuletzt geprüft am 18 Dec 2020
Lathia, N., Froehlich, J., Capra, L.: Mining public transport usage for personalised intelligent transport systems. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), Sydney, Australia, 13 December 2010–17 December 2010, pp. 887–892. IEEE (2010)
Nobis, C., Kuhnimhof, T.: Mobilität in Deutschland – MiD. Ergebnisbericht. BMVI, infas, DLR, IVT, infas 360. Bonn, Berlin (2018). Online verfügbar unter http://www.mobilitaet-in-deutschland.de/pdf/MiD2017_Ergebnisbericht.pdf. zuletzt geprüft am 10 Jan 2021
Stephan, K., Köhler, K., Heinrichs, M., Berger, M., Platzer, M., Selz, E.: Das Elektronische Wegetagebuch – Chancen und Herausforderungen einer Automatisierten Wegeerfassung Intermodaler Wege. In: Schelewsky, M., Jonuschat, H., Bock, B., Stephan, K. (eds.) Smartphones unterstützen die Mobilitätsforschung, pp. 25–45. Springer, Wiesbaden (2014). https://doi.org/10.1007/978-3-658-01848-1_3
Trefzger, M., Titov, W., Keller, C., Böhm, F., Schlegel, T.: A Context Aware Evaluation Tool for Individual Mobility. Karlsruhe University of Applied Sciences, Moltkestr. 30, 76133 Karlsruhe, Germany. Institute of Ubiquitous Mobility Systems (2019)
Xue, M., Wu, H., Chen, W., Goh, G.H.: Identifying tourists from public transport commuters. In: Macskassy, S. (Hg.) Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 24–27 August 2014, KDD. Association for Computing Machinery; ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Annual ACM SIGKDD Conference, pp. 1779–1788. ACM, New York (2014)
Entwicklung kontextadaptiver Erfassungsmethoden von Mobilitätspräferenzen der Fahrgäste im öffentlichen Verkehr. Karlsruhe University of Applied Sciences, Moltkestr. 30, 76133 Karlsruhe, Germany. Institute of Ubiquitous Mobility Systems (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gartner, T., Titov, W., Schlegel, T. (2021). Identifying Mobility Pattern of Specific User Types Based on Mobility Data. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1498. Springer, Cham. https://doi.org/10.1007/978-3-030-90176-9_68
Download citation
DOI: https://doi.org/10.1007/978-3-030-90176-9_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90175-2
Online ISBN: 978-3-030-90176-9
eBook Packages: Computer ScienceComputer Science (R0)