ST-D3DDARN: Urban traffic flow prediction based on spatio-temporal decoupled 3D DenseNet with attention ResNet

Jing Chen; Guowei Yang; Zhaochong Zhang; Wei Wang

doi:10.1371/journal.pone.0305424

Peer Review History

Original SubmissionJanuary 26, 2024
5 Apr 2024 Decision Letter - Muhammad Bilal, Editor PONE-D-24-03623ST-D3DDARN: Urban Traffic Flow Prediction based on Spatio-temporal Decoupled 3D Densenet with Attention ResNetPLOS ONE Dear Dr. Wei, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 20 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions 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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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 ****** 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 ****** 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: This manuscript represents traffic flow as traffic frames inspired by video analysis in computer vision and proposes an end-to-end urban traffic flow prediction model, named Spatio-temporal Decoupled 3D Densenet with Attention ResNet (ST-D3DDARN). This model extracts multi-source traffic flow features and dynamically establishes global spatio-temporal correlations with attention residual network. The overall experiments in the manuscript are sufficient, I think there are still some areas that need revision. Specific suggestions are provided below： 1.Pay attention to some formatting in writing. There are many formatting issues with the manuscript. For example, the second paragraph in the introduction part should not be written in the top case. The title of Part 3 “Problem Definition and Analysis” lacks punctuation. Please carefully check. 2. There are multiple citation errors in the introduction section. [Error! Reference source not found.] appears repeatedly. Additionally, in the related work section, there are several repeated references, such as Lin et al. [1111], Pu et al. [3636]. Please resolve them. 3. Please note the title of the figure. In Analysis 3 of the Definition 4 , there are Fig1 and Fig 2, but the introduction part also includes the Fig 1 and Fig 2, which are not the same. In addition, Fig 3 refers to “Urban grid and definition of inflow and outflow”, as well as “The overall framework of the ST-D3DDARN model”, Please correct them. 4. The manuscript controls the parameters and efficiency of the model, where "Time each Epoch" in Table 6 refers to training time? If so, why is the model less time-consuming during training than all comparison models, but more time-consuming during testing? Actually, in practical use of the model, testing time is generally more important. 5. Dividing the transportation network into grids is an approximate method, does it affect prediction accuracy? There are many papers that use graph for traffic prediction, for example “Hierarchical Spatio-Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting”, “Knowledge fusion enhanced graph neural network for traffic flow prediction”, you can refer to them. Reviewer #2: Overall, in this paper, the authors have made significant contributions to increase the knowledge. There are a few grammar things that could be improved. Could you double-check the grammar mistakes? Abstract, introduction, contribution and conclusion should be aligned. ****** 6. 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 #1: Yes: Yong Zhang Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0305424.r001
Revision 1
13 May 2024 Author Response Response to editor: 1. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Response:Thank you for providing the links. We will ensure that our manuscript complies with PLOS ONE's style requirements, including file naming conventions. We will utilize the style templates provided by PLOS ONE for formatting the main body and the title, authors, and affiliations sections. When submitting the revised version, we will adhere to PLOS ONE's style guidelines to ensure our manuscript meets the journal's standards. 2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. Response:Thank you for bringing this to our attention. We will review the guidelines provided by PLOS ONE on code sharing. We understand the importance of making all author-generated code freely available without restrictions upon publication to promote reproducibility and facilitate reuse of the research. We have already upload experimental code and relevant documentation to https://github.com/761049669/ST-D3DDARN. 3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. Response:Thank you for bringing this to our attention. We will ensure that the grant information provided in both the 'Funding Information' and 'Financial Disclosure' sections match. In the resubmission, we will provide the correct grant numbers for the awards received for our study in the 'Funding Information' section to ensure accuracy and consistency. 4. Thank you for uploading your study's underlying data set. Unfortunately, the repository you have noted in your Data Availability statement does not qualify as an acceptable data repository according to PLOS's standards. At this time, please upload the minimal data set necessary to replicate your study's findings to a stable, public repository (such as figshare or Dryad) and provide us with the relevant URLs, DOIs, or accession numbers that may be used to access these data. For a list of recommended repositories and additional information on PLOS standards for data deposition, please see https://journals.plos.org/plosone/s/recommended-repositories. Response: Thank you for your remind, we have already upload the minimum data set used in experiments to https://github.com/761049669/ST-D3DDARN. 5. When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. Response: Thank you for the reminder. As for the data availability statement issues involved in this paper, all authors have agreed to share all code and data sets freely at present. Our code and dataset can be found on the https://github.com/761049669/ST-D3DDARN. Thank you again for your careful work on my manuscript and please contact me if you need any further information. Reviewer #1 This manuscript represents traffic flow as traffic frames inspired by video analysis in computer vision and proposes an end-to-end urban traffic flow prediction model, named Spatio-temporal Decoupled 3D Densenet with Attention ResNet (ST-D3DDARN). This model extracts multi-source traffic flow features and dynamically establishes global spatio-temporal correlations with attention residual network. The overall experiments in the manuscript are sufficient, I think there are still some areas that need revision. Specific suggestions are provided below： Response: Thanks for kindly reviewing our work. Please refer to the responses for our point-by-point replies. Point 1: Pay attention to some formatting in writing. There are many formatting issues with the manuscript. For example, the second paragraph in the introduction part should not be written in the top case. The title of Part 3 “Problem Definition and Analysis” lacks punctuation. Please carefully check. Response: We are sorry that our paper suffers from many formatting problems. We have carefully checked the paper for formatting issues and made the necessary changes. In the introduction, we changed the case form in several places. In addition, we modified the format of all titles according to the requirements of the journal. Thank you for your patience and we will ensure that the paper is in the right format. Point 2: There are multiple citation errors in the introduction section. [Error! Reference source not found.] appears repeatedly. Additionally, in the related work section, there are several repeated references, such as Lin et al. [1111], Pu et al. [3636]. Please resolve them. Response: Thank you for your careful review! We apologize for some minor mistakes that were inadvertently made in the text. We carefully checked the citations in the paper and fixed the citation errors that occurred in the introduction section, ensuring that each citation was correctly linked to the corresponding reference. In addition, we removed the repeatedly cited entries in the related work section to ensure the accuracy and consistency of the citations in the text. Point 3: Please note the title of the figure. In Analysis 3 of the Definition 4, there are Fig1 and Fig 2, but the introduction part also includes the Fig 1 and Fig 2, which are not the same. In addition, Fig 3 refers to “Urban grid and definition of inflow and outflow”, as well as “The overall framework of the ST-D3DDARN model”, Please correct them. Response: Thanks to the reviewer for reminding. We apologize for the reading problems caused by the confusion of figure numbers in the paper. We have renumbered the figure titles in the paper so that readers can clearly understand their content. Point 4: The manuscript controls the parameters and efficiency of the model, where "Time each Epoch" in Table 6 refers to training time? If so, why is the model less time-consuming during training than all comparison models, but more time-consuming during testing? Actually, in practical use of the model, testing time is generally more important. Response: Thank you for bringing up this important point. "Time each Epoch" refers to the time spent in each Epoch during training. Our model is less time-consuming during training compared to the comparison models, but more time-consuming during testing. This might seem counterintuitive. In fact, in order to establish the global spatial dependence of traffic flow, we add a self-attention mechanism to the model, and the parameters of the model are greatly increased due to the existence of multiple fully connected layers in the self-attention mechanism. In our experiments, we found that the large increase in the number of parameters caused by the addition of self-attention mechanism does not lead to a large increase in training time. However, it will cause a significant increase in testing time. We know that model training includes both forward and backward propagation, while model testing only includes forward propagation. The complexity of back propagation calculation is different due to different neural network structure in the model. Therefore, the reason for this phenomenon may be that the large number of parameters generated by the introduction of the self-attention mechanism in our model leads to a large increase in the time of the forward propagation, while its backpropagation complexity is low. In general, our model has little difference in test time compared with other models, and there is little difference in actual use. In addition, our model requires less computational resources during training and has higher flexibility for practical use. Point 5: Dividing the transportation network into grids is an approximate method, does it affect prediction accuracy? There are many papers that use graph for traffic prediction, for example “Hierarchical Spatio-Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting”, “Knowledge fusion enhanced graph neural network for traffic flow prediction”, you can refer to them. Response: Thank you for your valuable comments. The references you recommended put forward the HSTGCNT and KE-STGCN models, which show strong effectiveness and robustness in the traffic flow prediction of the road network with irregular topology structure, and their superiority is proved by experiments with the road network traffic flow data collected by sensors and the passenger flow data of subway stations. Dividing the traffic network into grids is a common approximation, and indeed may affect the accuracy of the prediction. However, our work focuses on transforming traffic trajectory data into grid data and then improving the prediction accuracy as much as possible. We mention in the conclusion that future work may involve dividing cities into irregular regions based on their functional characteristics and modeling urban traffic flows using GCN to learn spatio-temporal information better. In addition, inspired by this comment from you, we realized that we did not compare methods for modeling spatial grid information using GCN. Therefore, we add GCN+LSTM model in our comparison experiments. GCN+LSTM model is a combination model of temporal and spatial. In this method, a graph G = (V, E) is constructed, where V and E represent the set of vertices and edges respectively. Each vertex in a graph G represents a region, and an edge between two vertices indicates that two regions are adjacent. The experimental results are shown in Table 1 and Table 2. Table 1. Comparison of model prediction results Methods TaxiBJ BikeNYC RMSE MAE RMSE MAE HA 45.36 22.47 20.33 10.22 ARIMA 22.78 12.72 10.22 5.36 CNN+LSTM 17.29 9.87 5.48 2.83 GCN+LSTM 16.79 9.84 5.28 2.73 ST-ResNet 16.92 9.64 5.27 2.75 DeepSTN+ 16.29 9.27 4.66 2.43 ST-3DNet 16.73 9.69 5.22 2.62 3D-ConvLSTMNet 16.47 9.58 5.14 2.96 MS-ResCnet 15.60 9.15 4.82 2.48 MPCNN 15.77 9.23 5.02 2.47 ST-D3DDARN 14.98 8.91 4.49 2.25 Table 2. Inflow and outflow prediction results in TaxiBJ Methods Inflow Outflow RMSE MAE RMSE MAE HA 45.27 22.40 45.45 22.54 ARIMA 22.73 12.56 22.82 12.83 CNN+LSTM 17.27 9.85 17.32 9.90 GCN+LSTM 16.75 9.82 16.83 9.86 ST-ResNet 16.89 9.62 16.95 9.66 DeepSTN+ 16.26 9.24 16.32 9.30 ST-3DNet 16.67 9.66 16.78 9.74 3D-ConvLSTMNet 16.39 9.52 16.55 9.64 MS-ResCnet 15.56 9.08 15.64 9.15 MPCNN 15.73 9.19 15.81 9.26 ST-D3DDARN 14.86 8.81 15.11 8.99 Reviewer #2 Overall, in this paper, the authors have made significant contributions to increase the knowledge. Response: Thank you for participating in the review of our work. Point 1: There are a few grammar things that could be improved. Could you double-check the grammar mistakes? Response: Sorry for the grammar errors. As non-English speakers, the language is truly a problem for us. Thus, according to the reviewer’s suggestion, we first tried our best to check the manuscript carefully, and further, we made revisions by using advanced translation software available in the Chinese market, as well as grammar correction software. Additionally, we sought the assistance of a friend who is proficient in English for further editing. We wish the revised version is more readable than the previous one. Point 2: Abstract, introduction, contribution and conclusion should be aligned. Response: Thanks for the constructive suggestion. We have revised inconsistent representations in the paper to ensure consistency between the abstract, introduction, contribution, and conclusion. https://doi.org/10.1371/journal.pone.0305424.r002
30 May 2024 Decision Letter - Muhammad Bilal, Editor ST-D3DDARN: Urban Traffic Flow Prediction based on Spatio-temporal Decoupled 3D Densenet with Attention ResNet PONE-D-24-03623R1 Dear Dr. Wei, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Muhammad Bilal 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: All comments have been addressed Reviewer #3: All comments have been addressed ******** 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: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes Reviewer #3: Yes ****** 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: Yes Reviewer #3: Yes ****** 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: Yes Reviewer #3: Yes ****** 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: The authors have already addressed all the comments made at the time of initial review. It is now ready for publication. Reviewer #3: (No Response) ****** 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: Yes: Dr Muhammad Ramzan ******** https://doi.org/10.1371/journal.pone.0305424.r003
Formally Accepted
3 Jun 2024 Acceptance Letter - Muhammad Bilal, Editor PONE-D-24-03623R1 PLOS ONE Dear Dr. Yang, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Muhammad Bilal Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0305424.r004

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