Brisaboa, Nieves R.Fariña, AntonioGalaktionov, DaniilVarela Rodeiro, TirsoRodríguez, M. Andrea2024-11-152024-11-152022-01Brisaboa, N. R., Fariña, A., Galaktionov, D., Rodeiro, T. V., & Rodriguez, M. A. (2022). Improved structures to solve aggregated queries for trips over public transportation networks. Information Sciences, 584, 752-783. https://doi.org/10.1016/j.ins.2021.10.079http://hdl.handle.net/2183/40145This is the Accepted Manuscript. This version of the article has been accepted for publication after peer review. The Version of Record is available online at https://doi.org/10.1016/j.ins.2021.10.079.[Abstract]: We address the problem of storing and analyzing large datasets of passenger trips over public transportation networks that are of interest to network administrators trying to balance transportation offers (e.g., frequency of vehicles) according to the historical demand. We exploit the fact that all passenger trips made within the same vehicle share the same trajectories to reduce their redundancy and provide a representation, based on well-known compact data structures, that not only reduces the space requirements of the original passenger’s trajectories but also efficiently supports querying. Our solution uses two complementary representations: T-Matrices which excels at querying the aggregated network load, and TTCTR which represents all passenger trips and aims at counting the trips following a given pattern (i.e., how many passengers started/ended a trip at a given location or moved from a given location to another). In addition, we propose XCTR, a variant of TTCTR, which efficiently answers a wider range of queries at the cost of a moderate performance loss for some queries and some space overhead. Overall, our representation can handle a dataset of ten million trips within approximately 65% of its original size while supporting a wide range of queries in the order of microseconds.engAtribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Compact data structuresCompressionTrajectories on public transportationjournal articleopen access10.1016/j.ins.2021.10.079