Excess demand prediction for bike sharing systems.

One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different appro...

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Autores principales:	Xin Liu, Konstantinos Pelechrinis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4f85abe97d0141d6832edf73222ccaec
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id	oai:doaj.org-article:4f85abe97d0141d6832edf73222ccaec
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spelling	oai:doaj.org-article:4f85abe97d0141d6832edf73222ccaec2021-12-02T20:10:32ZExcess demand prediction for bike sharing systems.1932-620310.1371/journal.pone.0252894https://doaj.org/article/4f85abe97d0141d6832edf73222ccaec2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252894https://doaj.org/toc/1932-6203One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems' demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.Xin LiuKonstantinos PelechrinisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252894 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xin Liu Konstantinos Pelechrinis Excess demand prediction for bike sharing systems.
description	One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems' demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.
format	article
author	Xin Liu Konstantinos Pelechrinis
author_facet	Xin Liu Konstantinos Pelechrinis
author_sort	Xin Liu
title	Excess demand prediction for bike sharing systems.
title_short	Excess demand prediction for bike sharing systems.
title_full	Excess demand prediction for bike sharing systems.
title_fullStr	Excess demand prediction for bike sharing systems.
title_full_unstemmed	Excess demand prediction for bike sharing systems.
title_sort	excess demand prediction for bike sharing systems.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4f85abe97d0141d6832edf73222ccaec
work_keys_str_mv	AT xinliu excessdemandpredictionforbikesharingsystems AT konstantinospelechrinis excessdemandpredictionforbikesharingsystems
_version_	1718374997987164160

Excess demand prediction for bike sharing systems.

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