A Study on the Utilization of Shared Bicycles in Comprehensive Passenger Transport Hub Area Based on Neural Network Evaluation

Authors

  • Yunhong Shao Development Planning Division, Zhejiang Institute of Communication, Hangzhou 311112, Zhejiang, China
  • Tao Zhang Rail Transit Department, Zhejiang Institute of Communication, Hangzhou 311112, Zhejiang, China
  • Min Hong Rail Transit Department, Zhejiang Institute of Communication, Hangzhou 311112, Zhejiang, China
  • Feiqiang Zhou Hangzhou Public Bicycle Transportation Service Development Co., Ltd, Hangzhou 311112, Zhejiang, China
  • Jing Zhou Rail Transit Department, Zhejiang Institute of Communication, Hangzhou 311112, Zhejiang, China

Abstract

As a new mode of short-distance travel, shared bicycles play an important role in providing rapid first-/last-mile connections and have high potential for being utilized in the service areas of integrated passenger transport hubs. However, shared-bicycle use around such hubs still faces challenges, including relatively low utilization, uneven vehicle distribution, and irrational dispatching, which call for improvement and optimization. Based on the 2025 operational data of the public bicycle system in a high-speed railway hub area, this study investigates shared-bicycle utilization using big-data analytics and neural-network methods, and conducts analysis with a backpropagation (BP) neural network algorithm. The research results showed that under the same conditions, when parking points were arranged according to the conventional method, the sharing cycle utilization rates of stations and residential areas at 7–9, 11–13 and 17–19 were 92.6% and 90.4%, 88.9% and 87.6%, 89.3% and 85.5% respectively; when parking points were arranged according to big data and BP neural network algorithm, the utilization rates of shared bicycles at 7–9, 11–13 and 17–19 in stations and residential areas were 95.1% and 94.3%, 91.9% and 90.2%, 93.7% and 94.1% respectively. The utilization rate of the latter was significantly higher than that of the former, indicating that big data and neural network methods can improve the utilization rate of shared bicycles.

Keywords: Bike sharing; neural network method; big data; rail transit station

Cite As

Y. Shao, T. Zhang, M. Hong, F. Zhou, J. Zhou, "A Study on the Utilization of Shared Bicycles in Comprehensive Passenger Transport Hub Area Based on Neural Network Evaluation", Engineering Intelligent Systems, vol. 34 no. 5, pp. 579-592, 2026.





Downloads

Published

2026-09-01

Issue

Vol. 34 No. 5 (2026): International Journal of Engineering Intelligent Systems

Section

Article

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.