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A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

Rayane El Sibai (), Khalil Challita (), Jacques Bou Abdo () and Jacques Demerjian ()
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Rayane El Sibai: Computer Science Department, Faculty of Sciences, Al Maaref University, Beirut 1002, Lebanon
Khalil Challita: Faculty of Natural and Applied Sciences, Notre Dame University-Louaize, Zouk Mosbeh 1200, Lebanon
Jacques Bou Abdo: College of Business and Technology, University of Nebraska at Kearney, Kearney, NE 68849, USA
Jacques Demerjian: LaRRIS, Faculty of Sciences, Lebanese University, Fanar 1202, Lebanon

Sustainability, 2021, vol. 13, issue 5, 1-18

Abstract: The benefits of having a Bike Sharing System (BSS) in a city are numerous. Among other advantages, it promotes a cleaner environment with less traffic and pollution. One major problem the users of such services encounter is that of full or empty stations, causing user dissatisfaction. The objective of this work is to propose a new user-based incentive method to enhance BSS performance. The proposed method relies on a spatial outlier detection algorithm. It consists of adapting the departure and arrival stations of the users to the BSS state by stimulating the users to change their journeys in view of minimizing the number of full and empty stations. Experiments are carried out to compare our proposed method to some existing methods for enhancing the resource availability of BSSs, and they are performed on a real dataset issued from a well-known BSS called Velib. The results show that the proposed strategy improves the availability of BSS resources, even when the collaboration of users is partial.

Keywords: outliers detection; spatial data mining; moran scatterplot; iterative algorithms; Bike Sharing Systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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