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Peer-Review Record

Intercity Online Car-Hailing Travel Demand Prediction via a Spatiotemporal Transformer Method

Appl. Sci. 2021, 11(24), 11750; https://doi.org/10.3390/app112411750
by Hongbo Li 1, Jincheng Wang 2, Yilong Ren 1,3,4,* and Feng Mao 5
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2021, 11(24), 11750; https://doi.org/10.3390/app112411750
Submission received: 25 November 2021 / Revised: 6 December 2021 / Accepted: 8 December 2021 / Published: 10 December 2021

Round 1

Reviewer 1 Report

Good paper but assumes previous method knowledge of the reader. Could use more explanations.

The term "car-hailing" may be understood as hailing private cars. Consider using taxi-hailing or public car-hailing.

r78 define vanilla RNN

r94 LSTnet. All other references are LSTNet

r104 two issues vs r121 second challenge. Use same term.

r181 explain passenger flow in a grid. Passage presence at time t.

r188 AN STTnet. An?

r224 explain Query, Key and Value matrices

r225 use t and T?

(10) introduce dk

r319 introduce STT for ST-transformer

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

A very good article. I propose to add something about how renting an online vehicle and designing traffic for it together with the customer reduces congestion on the roads. I believe the rental system is designed to reduce the number of vehicles on the road. The key question is whether the planned road for the rented vehicles will not coincide with other rented vehicles. Every vehicle on the road is a chance for congestion. City managers are trying to discourage the use of their own vehicles in favor of rented ones. Thus, it is necessary to analyze whether the chance of congestion increases, or whether a large number of hired vehicles reduces the chance of congestion on the roads. 

Author Response

Please see the attachment

Author Response File: Author Response.docx

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