Research on urban public bicycle dispatching optimization method
- Published
- Accepted
- Subject Areas
- Data Mining and Machine Learning
- Keywords
- multi-objective optimization, public bicycle system, community discovery algorithm, regional scheduling workload, elite strategy
- Copyright
- © 2019 Fei et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Research on urban public bicycle dispatching optimization method. PeerJ Preprints 7:e27562v1 https://doi.org/10.7287/peerj.preprints.27562v1
Abstract
Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.
Author Comment
This is a submission to PeerJ Computer Science for review.
Supplemental Information
Main program code
Here is the Python code for the main program execution.
NSGA2 algorithm code
Here is the executable code of the NSGA2 algorithm.
Variance algorithm code
Here is the executable code of the variance algorithm
Pylouvain algorithm code
Here is the executable code for the pylouvain algorithm