What is it about?
Hangzhou’s public bicycle system is one of the famous PBS with distinguished performance. Here is a case study focusing on Hangzhou PBS. Analyses the public bicycle trip patterns and imbalance of self-service stations. Propose the spatial O-D clustering algorithm for redistribution regions partitioning. Introduces the neural network based short-term prediction of riding demand of self-service stations. The bicycle redistribution model is then outlined and implemented in Hangzhou’s PBS.
Featured Image
Why is it important?
It is a new approach of the dynamic redistribution for the large scale PBS like Hangzhou. It has been also deployed in practice and got verified by the application of several cities.
Perspectives
I hope more practical PBS in other cities can benefit from our dynamic redistribution approach described in our paper.
Hongzhao Dong
ITS Joint Research Institute， Zhejiang University of Technology
Read the Original
This page is a summary of: Vehicle scheduling approach and its practice to optimize public bicycle redistribution in Hangzhou , IET Intelligent Transport Systems, May 2018, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-its.2017.0274.
You can read the full text:
Contributors
The following have contributed to this page
