Abstract
One of the techniques for the analysis of travel patterns on a public transport network is the clustering of the users movements, in order to identify movement patterns. This paper analyses and compares two different methodologies for public transport trajectory clustering: feature clustering and geospatial trajectory clustering. The results of clustering trip features, such as origin, destination, or distance, are compared against the clustering of travelled trajectories by their geospatial characteristics. Algorithms based on density and hierarchical clustering are compared for both methodologies. In geospatial clustering, different metrics to measure distances between trajectories are included in the comparison. Results are evaluated by analysing their quality through the silhouette coefficient and graphical representations of the clusters on the map. The results show that geospatial trajectory clustering offers better quality than feature trajectory clustering. Also, in the case of long and complete trajectories, density clustering using edit distance with real penalty distance outperforms other combinations.
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
Similar content being viewed by others
References
Adrián, H.C., Alfonso, S.P., Zanón, B.B., Raffaetà, A., Zanatta, F.: Discovery of tourists’ movement patterns in Venice from public transport data. In: SAC 2022: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 564–568. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3477314.3507355
Ansari, M.Y., Ahmad, A., Khan, S.S., Bhushan, G., Mainuddin: Spatiotemporal clustering: a review. Artif. Intell. Rev. 53(4), 2381–2423 (2020). https://doi.org/10.1007/s10462-019-09736-1
Beirão, G., Sarsfield Cabral, J.A.: Understanding attitudes towards public transport and private car: a qualitative study. Transp. Policy 14(6), 478–489 (2007). https://doi.org/10.1016/j.tranpol.2007.04.009
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAIWS 1994: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359–370. AAAI Press (1994). https://doi.org/10.5555/3000850.3000887
Besse, P., Guillouet, B., Loubes, J.M., François, R.: Review and perspective for distance based trajectory clustering. ArXiv e-prints (2015). https://doi.org/10.48550/arXiv.1508.04904
Bian, J., Tian, D., Tang, Y., Tao, D.: A survey on trajectory clustering analysis. ArXiv e-prints (2018). https://doi.org/10.48550/arXiv.1802.06971
Chen, L., Ng, R.: On the marriage of LP-norms and edit distance. In: VLDB 2004: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 792–803. VLDB Endowment (2004). https://doi.org/10.5555/1316689.1316758
Gutiérrez, A., Domènech, A., Zaragozí, B., Miravet, D.: Profiling tourists’ use of public transport through smart travel card data. J. Transp. Geogr. 88, 102820 (2020). https://doi.org/10.1016/j.jtrangeo.2020.102820
He, L., Trépanier, M., Agard, B.: Space–time classification of public transit smart card users’ activity locations from smart card data. Public Transport 13(3), 579–595 (2021). https://doi.org/10.1007/s12469-021-00274-0
Liu, Y., Cheng, T.: Understanding public transit patterns with open geodemographics to facilitate public transport planning. Transportmetrica A: Transp. Sci. 16(1), 76–103 (2020). https://doi.org/10.1080/23249935.2018.1493549
McInnes, L., Healy, J., Astels, S.: HDBSCAN: hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017). https://doi.org/10.21105/joss.00205
de Montréal, V.: Déplacements MTL Trajet - Site web des données ouvertes de la Ville de Montréal (2023). https://donnees.montreal.ca/dataset/mtl-trajet
Müllner, D.: Modern hierarchical, agglomerative clustering algorithms. ArXiv e-prints (2011). https://doi.org/10.48550/arXiv.1109.2378
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7
Tsafarakis, S., Gkorezis, P., Nalmpantis, D., Genitsaris, E., Andronikidis, A., Altsitsiadis, E.: Investigating the preferences of individuals on public transport innovations using the maximum difference scaling method. Eur. Transp. Res. Rev. 11(1), 1–12 (2019). https://doi.org/10.1186/s12544-018-0340-6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Adrian, H.C., Zanon, B.B., Alfonso, S.P., Dolezel, P. (2023). Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip Data. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-40725-3_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40724-6
Online ISBN: 978-3-031-40725-3
eBook Packages: Computer ScienceComputer Science (R0)