2016
DOI: 10.3390/su8121299
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Bidirectional Incentive Model for Bicycle Redistribution of a Bicycle Sharing System during Rush Hour

Abstract: Abstract:Redistribution is an important part of operational activities in a bicycle sharing system (BSS). This paper proposes that there are two types of users in a BSS: leisure travelers and commuters. The operators and the government are adopting the bidirectional incentive model (BIM) to improve their service level of redistribution. That is, the BIM stimulates leisure travelers to actively respond to bicycle resetting needs of the system; on the other hand, it guides commuters by encouraging them to avoid … Show more

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Cited by 22 publications
(12 citation statements)
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References 29 publications
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“…Phua et al analyzed customer service, technical challenges, and customer trust in Airbnb [51]. Li et al studied the operation strategy of a dock bike-sharing program based on game theory, and provided an optimal strategy to maximize the revenue of the program [52,53]. However, few studies have considered the problems of the DBSP operations from the perspective of system.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Phua et al analyzed customer service, technical challenges, and customer trust in Airbnb [51]. Li et al studied the operation strategy of a dock bike-sharing program based on game theory, and provided an optimal strategy to maximize the revenue of the program [52,53]. However, few studies have considered the problems of the DBSP operations from the perspective of system.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Their simulations based on historical data show that even in the case of low cooperation degrees from customers, bike sharing systems can attain reasonable service levels by involving users into the balancing tasks and employing simple communication mechanisms with users. In [15], a bidirectional reward system is proposed based on three possible scenarios. This means that users whose trips may unbalance the system are penalized, while users whose trips help to balance the system are rewarded.…”
Section: Related Workmentioning
confidence: 99%
“…[44] propose a deep reinforcement learning algorithm to solve a incentive-based rebalancing problem, which consider both spatial and temporal features in the system. Other related research includes [45][46][47][48][49][50]. Almost all of these studies conduct analyses on SBBS systems.…”
Section: Literature Reviewmentioning
confidence: 99%