Abstract
In order to solve a series of problems caused by the disorderly cycling of bicycle-sharing, the modeling problem, forecasting the needs of the shared bicycle and selecting the location for the bicycle-sharing parking point, is optimally solved by the improved algorithm. It provides an important theoretical basis for the management planning of bicycle-sharing. First, a bicycle-sharing demand forecasting model based on Canopy-Kmeans is proposed according to a large number of bicycle-sharing data collected. Then, the multi-objective location models for bicycle-sharing parking point is established with the minimum construction cost and the shortest total travel distance as the objective function according to demand points and requirements for planning parked points. It can effectively ensure the effectiveness and rationality of the location. Finally, the improved NSGA-II algorithm is used to solve the location model proposed in this paper. The results show that the model proposed in this paper can provide a more scientific basis for decision-makers to choose parking points for bicycle-sharing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
DeMaio, P.: Will smart bikes succeed as public transportation in the United States. J. Public Transp. 7(2), 23–27 (2004)
Garcia-Palomares, J.C., Gutierrez, J., Latorre, M.: Optimizing the location of stations in bike-sharing programs: a gis approach. Appl. Geogr. 35(1), 235–246 (2012)
Chemla, D., Meunier, F., Calvo, R.W.: Bike sharing systems: Solving the static rebalancing problem. Discret. Optim. 10(2), 120–146 (2013)
Deng, L.F., Xie, Y.H., Huang, D.X.: Research on shared cycling facility planning based on space-time data of cycling. Planner 33(10), 82–88 (2017)
Zhao, M.: Research on analysis and optimal scheduling of shared single cycle network. Shandong University of Science and Technology (2017)
Mccallum, A., Nigam, K., Ungar, L.H.: Efficient clustering of high-dimensional data sets with application to reference matching. In: ACM SIGKDD, pp. 169–178 (2000)
Li, Y., Zheng, Y., Zhang, H.: Traffic prediction in a bike-sharing system. In: SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 33 (2015)
Santoni, M., Santoni, M., Meenen, M.: Incentivizing users for balancing bike sharing systems. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 723–729 (2015)
Kaspi, M., Raviv, T., Tzur, M.: Bike-sharing systems: User dissatisfaction in the presence of unusable bicycles. IISE Trans. 49(2), 144–158 (2017)
Caulfield, B., O’Mahony, M., Brazil, W.: Examining usage patterns of a bike-sharing scheme in a medium sized city. Transp. Res. Part A Policy Pract. 100, 152–161 (2017)
Xu, P.F., Wang, L., Guan, Z.Y.: Evaluating brush movements for Chinese calligraphy: a computer vision based approach. In: International Joint Conferences on Artificial Intelligence (IJCAI), pp. 1050–1056 (2018)
Gong, M.G., Jiao, L.C., Yang, D.D.: Research on evolutionary multi-objective optimization algorithms. J. Softw. 20(2), 271–289 (2009)
Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (2002)
Tomoiagă, B., Chindriş, M., Sumper, A.: Pareto optimal reconfiguration of power distribution systems using a genetic algorithm based on NSGA-II. Energies 6(3), 1439–1455 (2013)
Acknowledgements
This work is supported by the Hangzhou Science & Technology Development Project of China (No. 20162013A08) and the Zhejiang Provincial Natural Science Foundation of China (No. LY16F020010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, G., Shi, Y., Xu, H., Zhang, B. (2018). A Method for Locating Parking Points of Shared Bicycles Based on Clustering Analysis. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_27
Download citation
DOI: https://doi.org/10.1007/978-981-13-2922-7_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2921-0
Online ISBN: 978-981-13-2922-7
eBook Packages: Computer ScienceComputer Science (R0)
