PONE-D-22-18546An Attention Based Recurrent Learning Model for Short-term Travel Time PredictionPLOS ONE

Dear Dr. Chughtai,

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you very much for the beautiful manuscript. I think the paper is in good condition!

The idea presented in the attention section is very practical and logical.

Manuscripts and forms are written in beautiful formats.

Accept

Reviewer #2: Paper summary:

This paper proposed an attention-based GRU model for short-term travel time prediction, enabling GRU to learn the relevant context in historical time slots and update the weights of hidden states accordingly. The authors evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of the proposed model, the authors performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that the proposed model performed better than the existing deep learning time-series models in terms of RMSE, MAE, MAPE, and R^2.

Strengths:

1. This proposal is the first to use traffic flow as input with attention-based GRU to forecast travel time.

2. This proposal is well-structured, and the experiments are detailed and convincing.

Weaknesses:

1. I am wondering whether the overfitting of the original model will affect the results of this proposal.

2. The related work section is suggested to be divided into 2-3 subsections to make the structure of this section clearer.

Other comments:

1. It seems that attention based is missing a hyphen in “attention based”.

2. To overcome these limitations, two specialized variants of RNNs, Gated Recurrent Unit (GRU) [48] and Long Short-Term Memory (LSTM) [49] are developed. Consider inserting a comma to separate the elements.

3. RNNs, unlike MLPs and CNNs (feed-forward neural networks and take data all at once), act on data sequentially and are frequently employed in the Natural Language Processing (NLP) domain. It seems that there is a grammar mistake.

4. “This paper employed GRU with attention mechanism to process travel time sequences and forecast future TT.”. It seems that there is an article usage problem.

Reviewer #3: The authors proposed an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical time slots and update the weights of hidden states accordingly. This scheme thus solves the problem that the existing GRU does not consider the relationship between various historical travel time slots for traffic prediction. The main message, background and figures are generally clear and concise, but some comparisons with the state-of-the-art algorithms still need to be added in the experimental results section. I recommend improving the following points.

==Major concern==

1.The algorithm proposed in the article integrates the attention mechanism into the GRU to improve the prediction ability. And the comparison results with GRU and several other traditional algorithms are given in the article. In fact, in recent years, attention mechanism has been introduced into DNN to solve Travel Time Prediction, such as [1]. Therefore, it is hoped that the author can add new experiments to compare with such algorithms.

[1] Wu J, Wu Q, Shen J, Cai C. Towards attention-based convolutional long short-term memory for travel time prediction of bus journeys. Sensors. 2020;20(12):3354

==Minor concern==

1. On line 203 of the article, too many commas are printed.

2. References are poorly written. For example, the representation of page numbers, some are p.785-794, some are: 785-794. In addition, the reference is ended with a period, and some ends with a semicolon and a period. and many more.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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An Attention-based Recurrent Learning Model for Short-term Travel Time Prediction

PONE-D-22-18546R1

Dear Dr. Chughtai,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Xiyu Liu

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: (No Response)

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: (No Response)

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: (No Response)

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: (No Response)

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: (No Response)

Reviewer #3: The authors proposed an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical time slots and update the weights of hidden states accordingly.They have answered all my questions. I have no other comments.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

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