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Discovery of tourists' movement patterns in venice from public transport data

Published: 06 May 2022 Publication History

Abstract

The data collected by public transport tickets has become a valuable source of information for transportation analysis. There are numerous works that analyze them in case studies for subway, bus or train networks, but there are few studies referring to public transport in aquatic environments. In this paper, ticket validation data is used to analyze the movements of tourists in the centre of Venice where waterbuses are the principal public transport. The objective is to analyse the behavior of tourists and detect some relevant patterns. In order to attain this goal, first we build several complex networks which represent the flow of tourists between clusters of stops during different time periods of the day. This allows us to discover some common behaviours of tourists. In a second phase, we construct a set of trajectories by considering the sequences of validations for each user. By applying a hierarchical clustering algorithm, we detect the movement patterns of tourists, identifying which places they visit and in which order. For each cluster we define a representative, that illustrates visually the main routes followed by the tourists. This can represent a valuable information for the decision-maker of the local administration and public transport.

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Cited By

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  • (2023)Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip DataHybrid Artificial Intelligent Systems10.1007/978-3-031-40725-3_50(589-599)Online publication date: 5-Sep-2023

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          cover image ACM Conferences
          SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing
          April 2022
          2099 pages
          ISBN:9781450387132
          DOI:10.1145/3477314
          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          Publication History

          Published: 06 May 2022

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          Author Tags

          1. complex networks
          2. hierarchical clustering
          3. public transport
          4. spatio-temporal analyses
          5. trajectories

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          • Marie-Sklodowska Curie

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          Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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          • (2023)Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip DataHybrid Artificial Intelligent Systems10.1007/978-3-031-40725-3_50(589-599)Online publication date: 5-Sep-2023

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