Citation

Gao W, Hu X, Wang N. Transp. Res. D Trans. Environ. 2023; 117: e103657.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trd.2023.103657

PMID

unavailable

Abstract

The demand balance for dockless bike-sharing systems (DBS) has become an important concern for governments and operators. Due to its lack of fixed sites, DBS is significant heterogeneity. Existing studies pay little attention to the impact of cycling environments on DBS heterogeneity. This study applies the random forest algorithm and a cluster-based approach to quantify the effects of cycling environments on the heterogeneity of bike-sharing trips. A probabilistic model considering cycling environments is also constructed to predict the bike-sharing flows among communities. A case study from Huangpu District in Shanghai shows that approximately 6% of trips are reduced for each 1% increase in gradient. Additionally, 50.79% of bike-sharing trips are made on commercial land. The performance of the proposed prediction model considering cycling environments outperforms the state of the art work, and its prediction accuracy is 0.76. The modeling framework provides valuable proposals for bicycle facilities planning and bicycle dispatch.

Language: en

Keywords

Cycling environment; Dockless bike-sharing; Hierarchical clustering; Random forest; Spatio-temporal heterogeneity