This paper proposes a novel analytical framework for passengers’ spatio-temporal behaviors in a large-scale train station in Paris, France. We focus on extracting the passengers’ behavioral patterns, considering their sequential movement between key locations, their length of stay, and the relationship between them. For this purpose, we employ the sequence alignment methods, applying them to a Wi-Fi access points data set. The sequence alignment methods were first introduced to analyze DNA or RNA strings of information in molecular biology. One of their significant aspects is their ability to consider the order (or sequence) of events, thus making them well suited for the analysis of passengers’ behaviors in spatio-temporal aspects.
|Traffic and Granular Flow 2019
|Veröffentlicht - 2019
- Integrated Digital Urban Planning