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A dynamic pricing scheme with negative prices in dockless bike sharing systems

A dynamic pricing scheme with negative prices in dockless bike sharing systems

Jie, Zhang, Zhiwei, Wang and Meng, Meng ORCID: 0000-0001-7240-6454 (2019) A dynamic pricing scheme with negative prices in dockless bike sharing systems. Transportation Research Part B: Methodological, 127. pp. 201-224. ISSN 0191-2615 (doi:

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To achieve bike relocation1 through travellers’ spontaneous behaviour in dockless bike sharing systems, an innovative dynamic pricing scheme with negative prices is introduced. In normal situation, users pay a positive price to operators for using a bike. However, when imbalanced distribution of bikes occurs in the system, users who cycle from the oversupplied area to undersupplied area will receive monetary reward from the operator, i.e., negative pricing applies. A user equilibrium dynamic traffic assignment model is developed to capture travellers’ mode-path choice behaviour in response to the proposed dynamic pricing strategy. Travellers can either use a single transportation mode (e.g. walking, cycling and bus) or take multiple modes to complete their trips. The user equilibrium travel pattern is formulated as a variational inequality problem and then solved by a path-flow swapping algorithm. Two numerical examples are conducted to demonstrate that the proposed dynamic pricing strategy with negative prices is effective in terms of attracting users as well as achieving a more balanced bike repositioning, especially when the number of bikes provided in the system is limited.

Item Type: Article
Uncontrolled Keywords: dynamic pricing strategy, negative price, dockless bike sharing, dynamic user equilibrium, bike relocation/repositioning
Subjects: H Social Sciences > HG Finance
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Last Modified: 26 Jul 2020 01:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: REF 1

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