Abstract
The Free-Floating Bike-Sharing system (FFBSS) connects users to public transit networks and is an important component of the “last-mile” transport network. However, the rapid development of the FFBSS in China has significantly increased the local municipal workload and deteriorated the public transport. To mitigate these negative impacts, the Chinese government has launched a pilot project of electronic fence spots, by selecting several bike-sharing parking spots from the existing ones to set up the virtual stations. Compared to the traditional public shared bicycles with fixed stations, the flexibility of choosing parking spots could dynamically cater the fast-changing traffic environment and facilitate the renting and returning for users and, therefore, render a more sparse and complicate parking spots network. In this paper, we study the location selection of bike-sharing parking points as a multidimensional problem, which considers not only the interests of users and stakeholders but also the environment and safety issues. We propose a multicriteria decision-making (MCDM) model including the analytic hierarchy process (AHP) and the weight-restricted data envelopment analysis (DEA) method to evaluate and determine the optimal bike-sharing parking points. Since the additional weight restrictions in the DEA method might lead to infeasible solutions, we introduce the weight restrictions feasibility theorem to avoid such infeasibility in the proposed weight-restricted DEA model, which has not been thoroughly studied in the literature. Specifically, a hyperplane adjusting model is developed to adjust the infeasible results. In the computational study, we evaluate 36 current parking spots in three regions in Beijing, China, to verify the rationality of the combined approach and put forward some managerial suggestions for this pilot project in Beijing, China.
Highlights
Academic Editor: Chi-Hua Chen e Free-Floating Bike-Sharing system (FFBSS) connects users to public transit networks and is an important component of the “last-mile” transport network
We argue that the weight-restricted data envelopment analysis (DEA) method which has been successfully applied in many scenarios is promising in handling the bike-sharing spot selection problem. e corresponding justification is presented in Section 3. e only paper we found that focused on a similar topic is [32]
Bike-sharing parking points evaluation and selection have been identified as an important problem that can affect the efficiency of company operation and social warfare
Summary
As mentioned by Garcıa-Palomares et al [10], the location selection of bike-sharing parking spots can be formulated as an MCDM problem, and MCDM models have been developed to assess bike-sharing stations. In most facility location researches, the AHP model is only used to generate qualitative input figures for the DEA model, where the different importance of the criteria to the system is not properly considered To overcome this issue, we argue that the weight-restricted DEA method which has been successfully applied in many scenarios is promising in handling the bike-sharing spot selection problem. The appropriate weights of criteria were calculated through an AHP procedure, and the additional weight constraints for the DEA model were generated by the AR technique. Due to the complexity in considering the qualitative information and discriminatively quantifying the criteria, we apply a hybrid method of the AHP technique and weight-restricted DEA within the MCDM framework to streamline the evaluation process of bike-sharing parking spots locations. A hyperplane adjusting model is developed to adjust the infeasible results in the hybrid approach
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