Letelier, BenjaminBrisaboa, Nieves R.Gutiérrez-Asorey, PabloParamá, José R.Varela Rodeiro, Tirso2024-12-022024-12-022023-09-20Letelier, B., Brisaboa, N.R., Gutiérrez-Asorey, P., Paramá, J.R., Rodeiro, T.V. (2023). Compacting Massive Public Transport Data. In: Nardini, F.M., Pisanti, N., Venturini, R. (eds) String Processing and Information Retrieval. SPIRE 2023. Lecture Notes in Computer Science, vol 14240. Springer, Cham. https://doi.org/10.1007/978-3-031-43980-3_25978-3-031-43979-7978-3-031-43980-30302-97431611-3349http://hdl.handle.net/2183/40447This congress was held in Pisa, Italy, September 26–28, 2023[Abstract]: In this work, we present a compact method for storing and indexing users’ trips across transport networks. This research is part of a larger project focused on providing transportation managers with the tools to analyze the need for improvements in public transportation networks. Specifically, we focus on addressing the problem of grouping the massive amount of data from the records of traveller cards as coherent trips that describe the trajectory of users from one origin stop to a destination using the transport network, and the efficient storage and querying of those trips. We propose two alternative methods capable of achieving a space reduction between 60 to 80% with respect to storing the raw trip data. In addition, our proposed methods are auto-indexed, allowing fast querying of the trip data to answer relevant questions for public transport administrators, such as how many trips have been made from an origin to a destination or how many trips made a transfer in a certain station.eng© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AGCompressionPublic TransportTrip analysisconference outputopen access