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Spatio-temporal clustering and forecasting method for free-floating bike sharing systems

Caggiani, Leonardo ; Ottomanelli, Michele ; Camporeale, Rosalia LU and Binetti, Mario (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:
author
; ; and
publishing date
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}},
}