Dynamic Rebalancing of the Free-Floating Bike-Sharing System

Round 1

Reviewer 1 Report

1. Figure 1 is a bit ugly. It is recommended to use a vivid picture rather than simple words.

2. The method seems like a trivial work. The authors should specify the originality.

3. I think some machine learning methods can also accomplish this task. To this end, I suggest the authors give some more comparisons if possible.

4. Although this paper is easy to follow, it needs further improvements, such as some pictures are not clear, and some mathematical expressions are not well defined. I would like to recommend the authors carefully proofread this paper and correct all the typos in the revision.

Author Response

Thank you for your comments，please see the attachment.

Author Response File: Author Response.doc

Reviewer 2 Report

The abstract promises the presentation of a new method for rebalancing the free-floating bike-sharing systems in the article. The idea of rebalancing bikes in the bike-sharing system is welcomed because users mostly travel in one direction during the morning rush hour and in the opposite direction during the evening rush hour. The disbalance in the system is evident and accessible bikes can be unreachable for customers during the day.
The article starts by presenting the theoretical method of how to realize rebalancing, better to say how to classify the positions of bikes. The square grid of the space is created and rules quantitating the disbalance of outcoming and incoming bikes are introduced.
The weak point I found in the rebalancing itself. I prefer a more precise formulation of the conclusion in the application example. The example is really nicely processed and shows us enough days for deep analysis. The application of the classification is clear. But the result is shadowed for me.

Author Response

Thank you for your comments, please see the attachment.

Author Response File: Author Response.doc