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.

Discovery of Tourists’ Movement Patterns in Venice from Public Transport Data

Raffaeta A.;
2022

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.
SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5004671
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