A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Profiling tourists' use of public transport through smart travel card data
Abstract Data collected through smart travel cards in public transport networks have become a valuable source of information for transport geography studies. During the last two decades, a growing body of literature has used this sort of data source to study the behaviour of public transport users in cities and regions around the world. However, its use has been scarce in contexts where public transport demand is highly influenced by the activities of the tourist sector. Therefore, it remains to be seen whether these data can be leveraged to optimize the supply of public transport. In this article, data drawn from the Camp de Tarragona automated fare collection system extracted during 2018 are used to study tourists' use of public transport in Costa Daurada (Catalonia, Spain). This is a popular coastal destination with a high concentration of visitors during the summer period. The analysis focuses on the use of the T-10, a multipersonal transport fare with no time limitations on its use which makes it appealing for tourists. Model-based clustering has been applied to identify different clusters of passengers according to their activity and spatial profiles. Differences between profiles are significant and, as a result, this study allowed the validation of a method that could be replicated in other contexts, as it provides highly useful information for public transport policy and mobility management.
Highlights Smart card data are used to define public transport passenger profiles. The study is based on a coastal tourist destination during peak season. The analysis focuses on travellers using a multipersonal transport fare. Model-based clustering is used to identify passenger profiles. The method provides useful information for public transport policy and management.
Profiling tourists' use of public transport through smart travel card data
Abstract Data collected through smart travel cards in public transport networks have become a valuable source of information for transport geography studies. During the last two decades, a growing body of literature has used this sort of data source to study the behaviour of public transport users in cities and regions around the world. However, its use has been scarce in contexts where public transport demand is highly influenced by the activities of the tourist sector. Therefore, it remains to be seen whether these data can be leveraged to optimize the supply of public transport. In this article, data drawn from the Camp de Tarragona automated fare collection system extracted during 2018 are used to study tourists' use of public transport in Costa Daurada (Catalonia, Spain). This is a popular coastal destination with a high concentration of visitors during the summer period. The analysis focuses on the use of the T-10, a multipersonal transport fare with no time limitations on its use which makes it appealing for tourists. Model-based clustering has been applied to identify different clusters of passengers according to their activity and spatial profiles. Differences between profiles are significant and, as a result, this study allowed the validation of a method that could be replicated in other contexts, as it provides highly useful information for public transport policy and mobility management.
Highlights Smart card data are used to define public transport passenger profiles. The study is based on a coastal tourist destination during peak season. The analysis focuses on travellers using a multipersonal transport fare. Model-based clustering is used to identify passenger profiles. The method provides useful information for public transport policy and management.
Profiling tourists' use of public transport through smart travel card data
Gutiérrez, Aaron (author) / Domènech, Antoni (author) / Zaragozí, Benito (author) / Miravet, Daniel (author)
2020-07-29
Article (Journal)
Electronic Resource
English
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