A modeling framework for the dynamic management of free-floating bike-sharing systems
(2018) In Transportation Research Part C: Emerging Technologies 87. p.159-182- Abstract
Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution,... (More)
Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems.
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- author
- Caggiani, Leonardo ; Camporeale, Rosalia LU ; Ottomanelli, Michele and Szeto, Wai Yuen
- publishing date
- 2018-02-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Decision Support System, Dynamic fleet relocation, Free-floating bike sharing systems, Non-linear autoregressive neural network forecasting, Spatio-temporal clustering
- in
- Transportation Research Part C: Emerging Technologies
- volume
- 87
- pages
- 24 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85044115523
- ISSN
- 0968-090X
- DOI
- 10.1016/j.trc.2018.01.001
- language
- English
- LU publication?
- no
- id
- 56c16299-0bac-490d-9bd9-68f088c00ce7
- date added to LUP
- 2018-09-25 10:16:49
- date last changed
- 2022-04-25 17:06:00
@article{56c16299-0bac-490d-9bd9-68f088c00ce7,
abstract = {{<p>Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems.</p>}},
author = {{Caggiani, Leonardo and Camporeale, Rosalia and Ottomanelli, Michele and Szeto, Wai Yuen}},
issn = {{0968-090X}},
keywords = {{Decision Support System; Dynamic fleet relocation; Free-floating bike sharing systems; Non-linear autoregressive neural network forecasting; Spatio-temporal clustering}},
language = {{eng}},
month = {{02}},
pages = {{159--182}},
publisher = {{Elsevier}},
series = {{Transportation Research Part C: Emerging Technologies}},
title = {{A modeling framework for the dynamic management of free-floating bike-sharing systems}},
url = {{http://dx.doi.org/10.1016/j.trc.2018.01.001}},
doi = {{10.1016/j.trc.2018.01.001}},
volume = {{87}},
year = {{2018}},
}