Spatio-temporal clustering and forecasting method for free-floating bike sharing systems
(2017) 19th International Conference on Systems Science, ICSS 2016 In Advances in Intelligent Systems and Computing 539. p.244-254- Abstract
Free-floating bike sharing systems are an emerging new generation of bike rentals, that eliminates the need for specific stations and allows to leave a bicycle (almost) everywhere in the network. Although free-floating bikes allow much greater spontaneity and flexibility for the user, they need additional operational challenges especially in facing the bike relocation process. Then, we suggest a methodology able to generate spatio-temporal clusters of the usage patterns of the available bikes in every zone of the city, forecast the bicycles use trend (by means of Non-linear Autoregressive Neural Networks) for each cluster, and consequently enhance and simplify the relocation process in the network.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f6e86ede-9c94-4e18-ae85-e20de96bbb76
- author
- Caggiani, Leonardo ; Ottomanelli, Michele ; Camporeale, Rosalia LU and Binetti, Mario
- publishing date
- 2017-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bike sharing systems, Fleet relocation, Forecasting, Free-floating, Spatio-temporal clustering, Usage patterns
- host publication
- Advances in Systems Science - Proceedings of the International Conference on Systems Science 2016, ICSS 2016
- series title
- Advances in Intelligent Systems and Computing
- volume
- 539
- pages
- 11 pages
- publisher
- Springer
- conference name
- 19th International Conference on Systems Science, ICSS 2016
- conference location
- Wroclaw, Poland
- conference dates
- 2016-09-07 - 2016-09-09
- external identifiers
-
- scopus:84996598745
- ISSN
- 2194-5357
- ISBN
- 9783319489438
- DOI
- 10.1007/978-3-319-48944-5_23
- language
- English
- LU publication?
- no
- id
- f6e86ede-9c94-4e18-ae85-e20de96bbb76
- date added to LUP
- 2018-09-25 10:18:36
- date last changed
- 2022-03-25 04:12:31
@inproceedings{f6e86ede-9c94-4e18-ae85-e20de96bbb76, abstract = {{<p>Free-floating bike sharing systems are an emerging new generation of bike rentals, that eliminates the need for specific stations and allows to leave a bicycle (almost) everywhere in the network. Although free-floating bikes allow much greater spontaneity and flexibility for the user, they need additional operational challenges especially in facing the bike relocation process. Then, we suggest a methodology able to generate spatio-temporal clusters of the usage patterns of the available bikes in every zone of the city, forecast the bicycles use trend (by means of Non-linear Autoregressive Neural Networks) for each cluster, and consequently enhance and simplify the relocation process in the network.</p>}}, author = {{Caggiani, Leonardo and Ottomanelli, Michele and Camporeale, Rosalia and Binetti, Mario}}, booktitle = {{Advances in Systems Science - Proceedings of the International Conference on Systems Science 2016, ICSS 2016}}, isbn = {{9783319489438}}, issn = {{2194-5357}}, keywords = {{Bike sharing systems; Fleet relocation; Forecasting; Free-floating; Spatio-temporal clustering; Usage patterns}}, language = {{eng}}, month = {{01}}, pages = {{244--254}}, publisher = {{Springer}}, series = {{Advances in Intelligent Systems and Computing}}, title = {{Spatio-temporal clustering and forecasting method for free-floating bike sharing systems}}, url = {{http://dx.doi.org/10.1007/978-3-319-48944-5_23}}, doi = {{10.1007/978-3-319-48944-5_23}}, volume = {{539}}, year = {{2017}}, }