In today's world, bike sharing systems are becoming in- creasingly common in all main cities around the world. To understand the spatiotemporal patterns of how people move by bike through the city of Milan, we apply func- tional data analysis to study the flows of a bike sharing mobility network. We introduce a complete pipeline to properly analyse and model functional data through a con- current functional-on-functional model taking into account the effects of weather conditions and calendar on the bike flows. In the end, we develop an interactive interface to explore the results of the analyses.
|Numero di pagine||22|
|Rivista||JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS|
|Stato di pubblicazione||Pubblicato - 2020|
- bike sharing, functional data, functional regression, mobility, network, time evolving graph