Bayesian Sequential Learning and Decision Making in Bike‐Sharing Systems

ABSTRACT In this article, we introduce modeling strategies for sequentially learning various types of demand uncertainty in bike‐share networks and propose methods for optimal station inventory management. Our approach is motivated by a real bike‐share network in Seoul, South Korea, with 40,000 bike...

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Veröffentlicht in:	Applied stochastic models in business and industry 2024-11, Vol.40 (6), p.1675-1688
Hauptverfasser:	Aktekin, Tevfik, Kim, Bumsoo, Novoa, Luis J., Zafari, Babak
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis Bicycles decision analysis Decision making Inventory management Metropolitan areas network optimization Parameters ride‐share economy state space State space models uncertainty modeling
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