A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Xin Fu; Hongwei Meng; Xue Wang; Hao Yang; Jianwei Wang

doi:10.1371/journal.pone.0263030

Peer Review History

Original SubmissionOctober 25, 2021
17 Nov 2021 Decision Letter - Feng Chen, Editor PONE-D-21-34056A Hybrid Neural Network for Driving Behavior Risk Prediction Based on Distracted Driving Behavior DataPLOS ONE Dear Dr. Meng, 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 Jan 01 2022 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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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: Partly 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 study proposes a driving risk prediction algorithm based on a popular Recurrent Neural Network architecture, LSTM. The proposed algorithm is demonstrated by comparing with several selected baseline models. The research idea is excellent, but some concerns need to be addressed for the potential publication. The major comments are listed below. 1, In the study, driving risk is categorized into 0, 1, and 2. Please clearly introduce the definition of driving risk level in the manuscript. 2, Please introduce the input data of DBRPNN, and provide a sample data as the illustration. 3, To my understanding, the purpose of predicting driving behavior risk is to provide timely warning information for avoiding potential safety issues. Thus, the predicted information is valid for a short-term period. However, in the study, the authors present the prediction for 30 mins, 60 mins, 90 mins, and 120 mins. Please explain why the authors select these four time-intervals for performance demonstration. 4, Figure 3, “Shaanxi Province” should be a typo. Figure 1 should be replaced by a high-resolution figure to improve the readability. 5, The manuscript contains considerable language issues. Please conduct a proofreading and polish the manuscript accordingly. Reviewer #2: This manuscript presents an interesting and meaningful piece of research, a method to predict distracted driving behavior is proposed. I think with the increasingly rich data of the Internet of Things, more risk collection data can provide better early warning support. The paper is well written and I have the following comments: (1) The literature review is obviously not comprehensive enough. In terms of risk prediction, behavioral prediction and flow prediction have made abundant research progress, but they are not well reflected in the review section. (2) Why are risk levels divided into three categories in clustering? The paper is not clear on this point. (3) If the classification criteria of risk level are changed, will the accuracy of the results change? (4) "The driving behavior of this type of vehicle is difficult to measure by predicting The results, And every time a driving behavior occurs, timely measures should be taken." Why is that? (5) The reference in this study needs further expansion, several papers fousing on prediciton using deep learning modles may be useful for their further studies. [1]Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data”. Computer-Aided Civil and Infrastructure Engineering, 2021, 1-21. DOI: 10.1111/mice.12688. [2]Multi- community passenger demand prediction at region level based on spatio-temporal graph convolutional network”, Transportation Research Part C: Emerging Technologies, Vol.124, 102951, 2021, 1-18. I hope these questions can contribute to the further improvement of this manuscript. ****** 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: No 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. 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Revision 1
24 Dec 2021 Author Response Reply Letter Dear Editor, We quite appreciate your favorite consideration and the reviewers’ insightful comments concerning our manuscript entitled “A Hybrid Neural Network for Driving Behavior Risk Prediction Based on Distracted Driving Behavior Data”. Those comments are very valuable and helpful for improving the quality and readability of our paper, as well as the important guiding significance to our future researches. The modifications are marked in blue, red colors according to each reviewer, respectively in the revised manuscript and this reply letter. Moreover, detailed point-by-point responses to all of the comments from reviewers are provided. Here, a brief overview and summary of the revisions are shown as follows. Based on the comments from the Reviewers and Editor, the manuscript has been well revised. We hope this revised manuscript has addressed your concerns, and look forward to hearing from you. Sincerely, Hongwei Meng Response to Editor’s Comments Academic Editor’s Comments: 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. Response: Thank you very much for the comments and the chance to improve our manuscript. We have checked the manuscript and corrected the content, the grammar, and the figures. The modifications corresponding to Reviewer 1’s comments are marked in blue, Reviewer 2's are marked in red in the revised manuscript and this reply letter. Detailed point-by-point responses to your comments are provided below. 1, Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Response: We have made our manuscripts meet the style requirements of PLOS ONE, including file naming requirements. 2, Thank you for stating the following in the Acknowledgments Section of your manuscript: "The research is supported by Key R&D Project of the Ministry of Science and Technology of China (Granted No. 2020YFC1512004)." We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: "There are no financial conflicts of interest to disclose." Please include your amended statements within your cover letter; we will change the online submission form on your behalf. Response: Thank you very much. We have removed the funding information from the manuscript and have included our amendment statement in the cover letter. (3) We note that Figure 3 in your submission contain [map/satellite] images which may be copyrighted. Response: The map image in Figure 3 is generated from OSM and is open source. OpenStreetMap: https://www.openstreetmap.org/ Response to Reviewer #1’s Comments Comments to the Author: This study proposes a driving risk prediction algorithm based on a popular Recurrent Neural Network architecture, LSTM. The proposed algorithm is demonstrated by comparing with several selected baseline models. The research idea is excellent, but some concerns need to be addressed for the potential publication. The major comments are listed below. 1, In the study, driving risk is categorized into 0, 1, and 2. Please clearly introduce the definition of driving risk level in the manuscript. 2, Please introduce the input data of DBRPNN, and provide a sample data as the illustration. 3, To my understanding, the purpose of predicting driving behavior risk is to provide timely warning information for avoiding potential safety issues. Thus, the predicted information is valid for a short-term period. However, in the study, the authors present the prediction for 30 mins, 60 mins, 90 mins, and 120 mins. Please explain why the authors select these four time-intervals for performance demonstration. 4, Figure 3, “Shaanxi Province” should be a typo. Figure 1 should be replaced by a high-resolution figure to improve the readability. 5, The manuscript contains considerable language issues. Please conduct a proofreading and polish the manuscript accordingly. Response: We feel great thanks for your professional review work on our article. As you are concerned, there are several problems that need to be addressed. According to your comments, the manuscript has been revised. The modifications corresponding to your comments are marked in blue. Detailed point-by-point responses to your comments are provided below. 1, In the study, driving risk is categorized into 0, 1, and 2. Please clearly introduce the definition of driving risk level in the manuscript. Response: Thank you again for your positive comments and valuable suggestions to improve the quality of our manuscript. In our study, driving risk is divided into 0, 1, and 2 by K-means. Level 0 indicates that the vehicle or area is in a low-risk state at this time and no action is required. Level 1 indicates that the vehicle or area is in a medium-risk state and measures should be taken according to the situation. Level 2 indicates that the vehicle or area is in a high-risk state and immediate measures are required. We have added an introduction to the definition of driving risk level in the revised manuscript, shown as follows. Level 0 indicates that the vehicle or area is in a low-risk state at this time and no action is required. Level 1 indicates that the vehicle or area is in a medium-risk state and measures should be taken according to the situation. Level 2 indicates that the vehicle or area is in a high-risk state and immediate measures are required. 2, Please introduce the input data of DBRPNN, and provide a sample data as the illustration. Response: We are very grateful for your comments. It is necessary to display the input of DBRPNN, which will make our paper clearer. The input data of DBRPNN includes risk level, the time of a day, driving time, and driving mileage. The risk level is divided into 0, 1, and 2. The time of the day is divided into four time periods: 0: 00 to 6:00, 6:00 to 12:00, 12:00 to 18:00, 18:00 to 24:00, respectively, using 0, 1, 2, and 3 to indicate. The driving time is the length of time the driver drives the vehicle. The driving mileage is the distance the driver drives the vehicle. Driving time and driving mileage are used to measure the fatigue state of the driver. We provide sample data as an illustration in the revised manuscript, as shown below. The sample data is shown in Table 3. Table 3. The Input Data Structure of DBRPNN. Field Name Field Type Data Example Risk Level Int 0 The Time of a Day Int 3 Driving Time Float 8.77 Driving Mileage Float 318.2 3, To my understanding, the purpose of predicting driving behavior risk is to provide timely warning information for avoiding potential safety issues. Thus, the predicted information is valid for a short-term period. However, in the study, the authors present the prediction for 30 mins, 60 mins, 90 mins, and 120 mins. Please explain why the authors select these four time-intervals for performance demonstration. Response: Thank you for your comments. We think this is an excellent suggestion. Some similar research practices summarize the data into four different time intervals: 15 minutes interval, 30 minutes interval, 45 minutes interval, and 60 minutes interval. When we divide our data into 15 minutes interval, the number of periods during which distracted driving behavior occurs is less than 30% of the total number of periods. The data of the five vehicles with the largest number of distracted driving behaviors are shown in the table below. When there are too few non-zero data, predictability is low, so we choose a time interval of 30 minutes and above. Vehicle ID Number of Periods During Which Distracted Driving Behavior Occurred Total Number of Periods 16759 2513 8640 16924 2509 33028 2412 20635 2339 33024 2281 We have revised the corresponding content and supplemented the relevant references, shown as follows. To observe the prediction effects under different periods, this paper refers to some similar research practices [41, 42], combines the distracted driving behavior data according to vehicle ID or area ID, and summarizes it into four different time intervals: 30 minutes interval, 60 minutes interval, 90 minutes interval, and 120 minutes interval. When the time interval is less than 30 minutes, the scale of time units containing distracted driving behavior will be small. 42. Ma X, Yu H, Wang Y, et al. Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory. Plos One, 2015, 10. 4, Figure 3, “Shaanxi Province” should be a typo. Figure 1 should be replaced by a high-resolution figure to improve the readability. Thanks for your careful checks. The spelling of "aa" is indeed confusing. According to the English naming standard of Chinese cities, the English name of this province is "Shaanxi Province". In the revised manuscript, Figure 1 has been replaced by a high-resolution figure as follows. Fig 1. Driving Behavior Risk Prediction Neural-Network (DBRPNN) Architecture. 5, The manuscript contains considerable language issues. Please conduct a proofreading and polish the manuscript accordingly. We sincerely thank you for careful reading. We have tried our best to proofread and polish the revised manuscript. And here we did not list the changes but marked them in blue in the revised paper. Response to Reviewer #2’s Comments Comments to the Author: This manuscript presents an interesting and meaningful piece of research, a method to predict distracted driving behavior is proposed. I think with the increasingly rich data of the Internet of Things, more risk collection data can provide better early warning support. The paper is well written and I have the following comments: (1) The literature review is obviously not comprehensive enough. In terms of risk prediction, behavioral prediction and flow prediction have made abundant research progress, but they are not well reflected in the review section. (2) Why are risk levels divided into three categories in clustering? The paper is not clear on this point. (3) If the classification criteria of risk level are changed, will the accuracy of the results change? (4) "The driving behavior of this type of vehicle is difficult to measure by predicting The results, And every time a driving behavior occurs, timely measures should be taken." Why is that? (5) The reference in this study needs further expansion, several papers fousing on prediciton using deep learning modles may be useful for their further studies. [1]Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data”. Computer-Aided Civil and Infrastructure Engineering, 2021, 1-21. DOI: 10.1111/mice.12688. [2]Multi- community passenger demand prediction at region level based on spatio-temporal graph convolutional network”, Transportation Research Part C: Emerging Technologies, Vol.124, 102951, 2021, 1-18. Response: Thank you again for your positive comments and valuable suggestions to improve the quality of our manuscript. According to your comments, the manuscript has been revised. The modifications corresponding to your comments are marked in red. Detailed point-by-point responses to your comments are provided below. (1) The literature review is obviously not comprehensive enough. In terms of risk prediction, behavioral prediction and flow prediction have made abundant research progress, but they are not well reflected in the review section. Response: Thank you for your nice comments on our article. This makes our literature review more comprehensive. In Related Work, we introduce distracted driving behavior, methods for predicting distracted driving behavior and traffic accidents, and related neural networks. We have supplemented the content of relevant behavioral prediction and flow prediction in relevant work, shown as follows. In terms of risk prediction, behavioral prediction and flow prediction have made abundant research progress. Tang proposed a forecasting framework named the spatiotemporal gated graph attention network to predict the urban traffic flow based on license plate recognition data [19]. In addition, Pu uses historical data to predict road surface friction [20, 21]. Tang used a geographically weighted Poisson quantile regression model to study the spatial heterogeneity and estimated the spatial impact on crash frequency [22]. We have supplemented the relevant references, shown as follows. 19. Tang J, Zeng J. Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Computer-Aided Civil and Infrastructure Engineering, 2021(7). 20. Pu Z, Liu C, Shi X, et al. Road surface friction prediction using long short-term memory neural network based on historical data. Journal of Intelligent Transportation Systems, 2020(1):1-12. 21. Pu Z, Cui Z, Wang S, et al. Time-aware gated recurrent unit networks for forecasting road surface friction using historical data with missing values. IET Intelligent Transport Systems, 2020, 14(4):213-219. 22. Tang J, Gao F, Liu F, et al. Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression. Accident Analysis & Prevention, 2020, 148:105833. 29. Tang J, Liang J, Liu F, et al. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transportation Research Part C Emerging Technologies, 2021, 124(10):102951. (2) Why are risk levels divided into three categories in clustering? The paper is not clear on this point. Response: We think this is an excellent suggestion. Thank you for pointing out our omissions. We use Silhouette Coefficient to evaluate the effect of K-means clustering. The results are shown in table 5. It can be seen from the table that the results are best when the number of clusters is three. Table 5. The Clustering Effect of K-means. Number of Clusters Silhouette Score 2 0.8193 3 0.8200 4 0.8033 5 0.8085 We have supplemented relevant content in the revised manuscript, shown as follows. This paper uses Silhouette Coefficient to evaluate the clustering effect of K-means, and the results of different values of k are shown in Table 5. Table 5. The Clustering Effect of K-means. Number of Clusters Silhouette Score 2 0.8193 3 0.8200 4 0.8033 5 0.8085 (3) If the classification criteria of risk level are changed, will the accuracy of the results change? Response: Thank you for your nice comments. If the classification criteria of risk level are changed, the accuracy of the results will change. The results are shown in the table below. Number of Risk Levels Accuracy Weighted-Recall 2 0.9167 0.9169 3 0.9146 0.9156 4 0.8458 0.8355 5 0.7562 0.7566 (4) "The driving behavior of this type of vehicle is difficult to measure by predicting The results, And every time a driving behavior occurs, timely measures should be taken." Why is that? Response: Thank you for your comment, and our reply is as follows. Distracted driving behavior of this type of vehicle rarely occurs. The prediction results of distracted driving behavior of some of these vehicles are shown in Fig 7. The distracted driving behavior of this type of vehicle is difficult to predict. So we need to remind them in time when they are distracted driving. (a) (b) Fig 7. The Prediction Result of the Risk Level. (a) Vehicle ID 16975. (b) Vehicle ID 16822. (5) The reference in this study needs further expansion, several papers fousing on prediciton using deep learning modles may be useful for their further studies. [1]Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data”. Computer-Aided Civil and Infrastructure Engineering, 2021, 1-21. DOI: 10.1111/mice.12688. [2]Multi- community passenger demand prediction at region level based on spatio-temporal graph convolutional network”, Transportation Research Part C: Emerging Technologies, Vol.124, 102951, 2021, 1-18. Response: We sincerely appreciate the valuable comments. These papers are very helpful for our current and further research. We have checked the literature carefully and added more references in the Related Work part in the revised manuscript, shown as follows. 19. Tang J, Zeng J. Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Computer-Aided Civil and Infrastructure Engineering, 2021(7). 20. Pu Z, Liu C, Shi X, et al. Road surface friction prediction using long short-term memory neural network based on historical data. Journal of Intelligent Transportation Systems, 2020(1):1-12. 21. Pu Z, Cui Z, Wang S, et al. Time-aware gated recurrent unit networks for forecasting road surface friction using historical data with missing values. IET Intelligent Transport Systems, 2020, 14(4):213-219. 22. Tang J, Gao F, Liu F, et al. Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression. Accident Analysis & Prevention, 2020, 148:105833. 29. Tang J, Liang J, Liu F, et al. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transportation Research Part C Emerging Technologies, 2021, 124(10):102951. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0263030.r002
11 Jan 2022 Decision Letter - Feng Chen, Editor A Hybrid Neural Network for Driving Behavior Risk Prediction Based on Distracted Driving Behavior Data PONE-D-21-34056R1 Dear Dr. Meng, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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, Feng Chen Academic Editor PLOS ONE Additional Editor Comments (optional): 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 #1: All comments have been addressed Reviewer #2: 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 #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 #1: No Reviewer #2: 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 #1: Yes Reviewer #2: 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 #1: (No Response) Reviewer #2: In this revision, authors responded all my concerning comments, and I think the quality of the paper has improved largely and it can be accepted for its current condition. ****** 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 #1: No Reviewer #2: No https://doi.org/10.1371/journal.pone.0263030.r003
Formally Accepted
14 Jan 2022 Acceptance Letter - Feng Chen, Editor PONE-D-21-34056R1 A Hybrid Neural Network for Driving Behavior Risk Prediction Based on Distracted Driving Behavior Data Dear Dr. Meng: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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. Feng Chen Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0263030.r004

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