A dynamic pricing scheme with negative prices in dockless bike sharing systems. (September 2019)

• Record Type: Journal Article
• Authors: Zhang, J.; Meng, M.; Wang, David, Z.W.
• Is Part Of: Transportation research. Volume 127(2019)
• Page Start: 201
• Page End: 224
• Publication Date: 2019-09
• Subjects: Dynamic pricing strategy -- Negative price -- Dockless bike sharing -- Dynamic user equilibrium -- Bike relocation/bike repositioning; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2019.07.007
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We introduce a dynamic pricing strategy with negative prices into bike sharing systems. This is the first study to deal with the bike relocation or bike repositioning problem using user-based approach in dockless bike sharing system. A dynamic traffic assignment model and its solution algorithm are developed to capture travellers' mode-path choice behaviour in response to the proposed dynamic pricing strategy. A systematic comparison among the traditional positive price strategy, free price strategy and the proposed strategy is analysed. Abstract: To achieve bike relocation 1 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.