Research on urban public bicycle dispatching optimization method

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  Hangzhou Dianzi University, Hangzhou, China
DOI
10.7287/peerj.preprints.27562v1
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
Fei L, Yang Y, Shihua W, Yudi X, Hong M. 2019. Research on urban public bicycle dispatching optimization method. PeerJ Preprints 7:e27562v1

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.

DOI: 10.7287/peerj.preprints.27562v1/supp-1

NSGA2 algorithm code

Here is the executable code of the NSGA2 algorithm.

DOI: 10.7287/peerj.preprints.27562v1/supp-2

Variance algorithm code

Here is the executable code of the variance algorithm

DOI: 10.7287/peerj.preprints.27562v1/supp-3

Pylouvain algorithm code

Here is the executable code for the pylouvain algorithm

DOI: 10.7287/peerj.preprints.27562v1/supp-4