Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers

Forouzan Rezapur-Shahkolai; Malihe Taheri; Tahereh Etesamifard; Ghodratollah Roshanaei; Samane Shirahmadi

doi:10.1371/journal.pone.0238728

Peer Review History

Original SubmissionMay 7, 2020
9 Jun 2020 Decision Letter - Feng Chen, Editor PONE-D-20-13494 Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers PLOS ONE Dear Dr. Shirahmadi, 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 Jul 24 2020 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. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Feng Chen Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. 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 [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: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No 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: The paper investigates the dimensions of aberrant driving behaviors and their effects on the crash frequency and severity of drivers in Iran. The research topic is interesting and worth of investigation. However, the Pearson tests and logit regressions used are somewhat simple. More works on traffic frequency and severity modeling should be referred to and acknowledged in the Introduction Section, including: Investigating the impacts of real-time weather conditions on freeway crash severity: A Bayesian spatial analysis. International Journal of Environmental Research and Public Health, 2020, 17(8), 2768. Spatial joint analysis for zonal daytime and nighttime crash frequencies using a Bayesian bivariate conditional autoregressive model. Journal of Transportation Safety and Security, 2020, 12(4): 566-585. Jointly modeling area-level crash rates by severity: A Bayesian multivariate random-parameters spatio-temporal Tobit regression. Transportmetrica A: Transport Science, 2019, 15(2): 1867-1884. According to the description in the Sampling Section, the drivers selected can be grouped by geographical districts, which may result in spatial correlation across adjacent drivers. Accounting for the spatial correlation in the regression models would improve their estimation capacities and reduce model misspecification, as indicated in the following papers. Reviewer #2: The topic of this paper is interesting and the results are meaningful and useful. There are several suggestions to improve this paper. 1. In the abstract, “to develop safe driving behavior interventions” could be “to develop driving behavior interventions”. 2. “According to a report by the Traffic Police of Iran in 2014, human behavior (71%), technical defect (18%) and traffic engineering (11%) were effective in the traffic crashes (12).” The word effective is misleading here. 3. “et al” in several places of this paper is a typo. 4. The Pearson tests and logit regressions used in this paper are somehow a bit simple. Some papers on traffic safety modeling and experiments could be referred to and acknowledged in the Introduction. For example, the following ones. [1] Examining the safety of trucks under crosswind at bridge-tunnel section: A driving simulator study, Tunnelling and Underground Space Technology, 2019, 92, 103034. https://doi.org/10.1016/j.tust.2019.103034 [2] Chen, Feng; Chen, Suren; Ma, Xiaoxiang. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. 2018, JOURNAL OF SAFETY RESEARCH. 65: 153-159. [3] Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model, International Journal of Environmental Research and Public Health, 2019, 16(14) , 2632. [4] Investigating the Differences of Single- and Multi-vehicle Accident Probability Using Mixed Logit Model, Journal of Advanced Transportation, 2018, UNSP 2702360. ****** 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. 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.0238728.r001
Revision 1
5 Aug 2020 Author Response Author’s response to reviews Title: Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers Authors: Forouzan Rezapur-Shahkolai (forouzan.rezapour@gmail.com) Malihe Taheri (ma.taheri@umsha.ac.ir) Tahereh Etesamifard (tahere_health@rocketmail.com) Ghodratollah Roshanaei (gh.roshanaei@umsha.ac.ir) Samaneh Shirahmadi (shirahmadi_s@yahoo.com) Version: 1 Date: 2 August 2020 Author's response to reviews: see over We thank all the reviewers for their valuable feedback and taking the time to provide useful comments which improved our manuscript entitled “Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers”. Based on the constructive comments the following changes have been made. Response to Reviewer 1: 1. The research topic is interesting and worth of investigation. However, the Pearson tests and logit regressions used are somewhat simple. Response: Thank you for your valuable comment. We changed our analysis and from ordinal logistic regression model (proportional odds model (POM)) and the chi-squared score test for the proportional odds assumption in ordinal logistic was employed to see whether the main model assumption was violated or not. Also, multinomial logistic regression (multinomial regression) was used to predict a nominal dependent variable (injury variable as multinomial variable) given one or more independent variables. - In the abstract section: - We have deleted these sentences from abstract section: " Data were statistically analyzed using numerical indices, linear regression analysis, multiple regression analysis and Pearson correlation.", "Traffic crashes and the severity of related injuries were significantly correlated with DBQ dimensions" and added this sentences in this section: " Data were statistically analyzed using numerical indices, linear regression analysis, Pearson correlation, ordinal logistic regression model and multinomial logistic regression." and "Age, gender, educational level, driving experience and driving hours during the day were significantly associated with DBQ dimensions and severity of road traffic injuries. - In the method section: - We have deleted these sentences from data analysis section" In addition, due to the correlation between DBQ dimensions and the rate of traffic crashes and the resulting injuries in different studies, multiple-logistic regression model was performed to investigate this correlation.", and added this paragraphs - "Analysis of crash variable as ordinal response variable was performed using ordinal logistic regression model (proportional odds model (POM)). To estimate the Ln (odds) of being at or below the jth category, the PO model can be rewritten as: Thus, this model predicts cumulative logits J-1 response categories. By transforming the cumulative logits, we can obtain the estimated cumulative odds as well as the cumulative probabilities being at or below the jth category. The chi-squared score test for the proportional odds assumption in ordinal logistic was employed to see whether the main model assumption was violated or not. In this study ordered dependent variable consists of 5 ordinal categories (no traffic crash, one traffic crash, two traffic crashes, three traffic crashes, four or more traffic crashes). Also, multinomial logistic regression (multinomial regression) was used to predict a multinomial dependent variable (injury variable) given one or more independent variables. The 95% confidence interval (CI) and odds ratio were calculated. A Significance level of P <0.05 was considered in all statistical tests. In the result section: - We deleted Tables 2 and 4 and added two more tables. - We have added these paragraphs in the result section: "The results of PO ordinal logistic regression model were presented in Table 3. Based on chi-square test (X2=77.1, p-value<0.001) the model provides an appropriate fit. The Pseudo R2 (McFadden’s R2) in this model was 0.14 and Nagelkerke=0.1, suggesting that the relationship between the response variable and the predictors is small. The OR and the 95% confidence interval of the OR based on Table 3, unintentional violations and driving experience were statistically significant but other covariates are not. So for unintentional violations, one unit increases in unintentional violations, the odds of being in a lower level versus at or higher levels of crash is 1.14, given all of the other variables in the model are held constant. Also the OR of being at or below category of crash in individual with driving experience of <1 year versus individual in driving experience >10 years was 0.41, this means that the level of crash in low experience driving increases. The interpreting of threshold is as follows: Threshold (crash=0): This is the estimated cut point on the latent variable used to differentiate No crash from one and higher crash when values of the predictor variables are evaluated at zero. Subjects that had a value of -1.37 or less on the underlying latent variable that gave rise to our crash variable would be classified as no crash given they were for example male (when the female considered baseline) and had zero value at the covariates. Threshold (crash=1): This is the estimated cut point on the latent variable used to differentiate no and one crash from higher level of crash when values of the predictor variables are evaluated at zero. Subjects that had a value of 1.32 or greater on the underlying latent variable that gave rise to our crash variable would be classified as higher crash given they were male and had zero on other covariates. Subjects that had a value between -1.37 and 1.32 on the underlying latent variable would be classified as one crash and so on to other threshold." And" In table 4, in multinomial logistic regression model for response variable injure with 4 levels (no injury, mild injury, severe injury and fatal injury) in which “no injury” was considered as reference category. Based on chi-square test (X2=265.5, pvalue<0.001) the model provides an appropriate fit. The Pseudo R2 (McFadden’s R2) for this model was 0.3 and Nagelkerke=0.12 suggesting that the relationship between the response variable and the predictors is moderate. The results showed that in Mild injury compared to no injury, the variables gender, age, education, driving experience and driving duration a day were statistically significant. In severe injury compared to no injury, gender, education, fines and driving duration were statistically significant. Also, unintentional violations, age, education, driving experience, driving duration a day were statistically significant in fatal injury compared to no injury." In the discussion section: - We have added these paragraphs in discussion section: - “However, as in other studies the results of this study showed that in general, the severity of injury is lower in young drivers, which can be due to physiological differences, reduced cognitive and physical abilities to identify and respond appropriately in traffic emergencies with age." - “While the results of this study, like many studies, showed that the severity of injuries is higher in men than women. Results of the study by Russo et al. and Chen et al. showed that men suffer less severe injuries. Given that men are more likely than women to engage in high-risk driving behaviors such as speeding and aggressive driving, and are more likely to put themselves and other road users in dangerous situations, this difference may be due to difference between the number of men and women among participants, as 85.5% of the participants were male." - “On the other hand, studies have shown that with increasing level of education, a person's attention to the impact of high-risk behaviors (such as speeding) on RTIs and severity of RTIs will increase. Educated people also wear seat belts because of improving their perceived safety value, to avoid being harmed. In addition, educated people usually use safer and higher quality vehicles due to their better socio-economic status." - “The motivation for driving while fatigued may be due to overestimation of capabilities and miscalculation of the cost of outcome by the drivers." - “Evidence indicates an increase in aberrant driving behaviors and traffic offences during within the first three years after licensure. Driving experience can significantly affect drivers' perception of risk. Experienced drivers can have much more knowledge and skills to drive safely and appropriately in different traffic conditions. - The results of this study showed that high experienced drivers (>10 years) have a reduced probability of severe and fatal injuries compared with mid experienced drivers (2-5 years of driving experience). This result may be attributed to the improved driving skills and handling capabilities in traffic emergencies, among high experienced drivers. The effect of driving experience on driver injury severity indicated that drivers with mid experience should be continuously educated to reduce the severity of road traffic injuries." 2. More works on traffic frequency and severity modeling should be referred to and acknowledged in the Introduction Section, including: Investigating the impacts of real-time weather conditions on freeway crash severity: A Bayesian spatial analysis. International Journal of Environmental Research and Public Health, 2020, 17(8), 2768. Spatial joint analysis for zonal daytime and nighttime crash frequencies using a Bayesian bivariate conditional autoregressive model. Journal of Transportation Safety and Security, 2020, 12(4): 566-585. Jointly modeling area-level crash rates by severity: A Bayesian multivariate random-parameters spatio-temporal to bit regression. Transportmetrica A: Transport Science, 2019, 15(2): 1867-1884. Response: Thank you for your valuable comment. We have added some studies based traffic frequency and severity modeling in introduction, as recommended. References # 11-13. 3. According to the description in the Sampling Section, the drivers selected can be grouped by geographical districts, which may result in spatial correlation across adjacent drivers. Accounting for the spatial correlation in the regression models would improve their estimation capacities and reduce model misspecification, as indicated in the following papers. Response: Thank you for your valuable comment. GLMM model in this study is not appropriate because: Random effect model was used to model of correlated or clustered data (in longitudinal as well as matched set of observation). But in present study, the data was collected from cross-sectional study as random sample of population. This type of data is independent and it is no need to use the mixed effect model to analyze them. Response to Reviewer 2: 1. In the abstract, “to develop safe driving behavior interventions” could be “to develop driving behavior interventions”. Response: Thank you for your valuable comment. The sentence has been revised as: “The findings of this study can be utilized to develop driving behavior interventions among the drivers”. Page 3 line 60. 2. “According to a report by the Traffic Police of Iran in 2014, human behavior (71%), technical defect (18%) and traffic engineering (11%) were effective in the traffic crashes (12).” The word effective is misleading here. Response: We have deleted this word. Page 5, line 107. 3. “et al” in several places of this paper is a typo. Response: We have revised this word throughout the manuscript. 4. The Pearson tests and logit regressions used in this paper are somehow a bit simple. Response: Thank you for your valuable comment. We changed our analysis and from ordinal logistic regression model (proportional odds model (POM)) and the chi-squared score test for the proportional odds assumption in ordinal logistic was employed to see whether the main model assumption was violated or not. Also, multinomial logistic regression (multinomial regression) was used to predict a nominal dependent variable (injury variable as multinomial variable) given one or more independent variables. - In the abstract section: - We have deleted these sentences from abstract section" Data were statistically analyzed using numerical indices, linear regression analysis, multiple regression analysis and Pearson correlation.", "Traffic crashes and the severity of related injuries were significantly correlated with DBQ dimensions" and added this sentences in this section: " Data were statistically analyzed using numerical indices, linear regression analysis, Pearson correlation, ordinal logistic regression model and multinomial logistic regression." And "Age, gender, educational level, driving experience and driving hours during the day were significantly associated with DBQ dimensions and severity of road traffic injuries. - In the method section: - We have deleted these sentences from data analysis section" In addition, due to the correlation between DBQ dimensions and the rate of traffic crashes and the resulting injuries in different studies, multiple-logistic regression model was performed to investigate this correlation.", and added this paragraphs - "Analysis of crash variable as ordinal response variable was performed using ordinal logistic regression model (proportional odds model (POM)). To estimate the Ln (odds) of being at or below the jth category, the PO model can be rewritten as: Thus, this model predicts cumulative logits J-1 response categories. By transforming the cumulative logits, we can obtain the estimated cumulative odds as well as the cumulative probabilities being at or below the jth category. The chi-squared score test for the proportional odds assumption in ordinal logistic was employed to see whether the main model assumption was violated or not.In this study ordered dependent variable consists of 5 ordinal categories (no traffic crash, one traffic crash, two traffic crashes, three traffic crashes, four or more traffic crashes). Also, multinomial logistic regression (multinomial regression) was used to predict a multinomial dependent variable (injury variable) given one or more independent variables. The 95% confidence interval (CI) and odds ratio were calculated. A Significance level of P <0.05 was considered in all statistical tests. - In the result section: - We deleted Tables 2 and 4 and added two more tables. - We have added these paragraphs in result section: - "The results of PO ordinal logistic regression model were presented in Table 3. Based on chi-square test (X2=77.1, p-value<0.001) the model provides an appropriate fit. The Pseudo R2 (McFadden’s R2) in this model was 0.14 and Nagelkerke=0.1, suggesting that the relationship between the response variable and the predictors is small. The OR and the 95% confidence interval of the OR based on Table 3, unintentional violations and driving experience. were statistically significant but other covariates are not. So for unintentional violations, one unit increases in unintentional violations, the odds of being in a lower level versus at or higher levels of crash is 1.14, given all of the other variables in the model are held constant. Also the OR of being at or below category of crash in individual with driving experience of <1 year versus individual in driving experience >10 years was 0.41, this means that the level of crash in low experience driving increases. The interpreting of threshold is as follows: Threshold (crash=0): This is the estimated cut point on the latent variable used to differentiate No crash from one and higher crash when values of the predictor variables are evaluated at zero. Subjects that had a value of -1.37 or less on the underlying latent variable that gave rise to our crash variable would be classified as no crash given they were for example male (when the female considered baseline) and had zero value at the covariates. Threshold (crash=1): This is the estimated cut point on the latent variable used to differentiate no and one crash from higher level of crash when values of the predictor variables are evaluated at zero. Subjects that had a value of 1.32 or greater on the underlying latent variable that gave rise to our crash variable would be classified as higher crash given they were male and had zero on other covariates. Subjects that had a value between -1.37 and 1.32 on the underlying latent variable would be classified as one crash and so on to other threshold." And" In table 4, in multinomial logistic regression model for response variable injure with 4 levels (no injury, mild injury, severe injury and fatal injury) in which “no injury” was considered as reference category. Based on chi-square test (X2=265.5, pvalue<0.001) the model provides an appropriate fit. The Pseudo R2 (McFadden’s R2) for this model was 0.3 and Nagelkerke=0.12 suggesting that the relationship between the response variable and the predictors is moderate. The results showed that in Mild injury compared to no injury, the variables gender, age, education, driving experience and driving duration a day were statistically significant. In severe injury compared to no injury, gender, education, fines and driving duration were statistically significant. Also Unintentional violations, Age, education, driving experience, driving duration a day were statistically significant in fatal injury compared to no injury." - In the discussion section: - We have added these paragraphs in discussion section: - “However, as in other studies the results of this study showed that in general, the severity of injury is lower in young drivers, which can be due to physiological differences, reduced cognitive and physical abilities to identify and respond appropriately in traffic emergencies with age." - “While the results of this study, like many studies, showed that the severity of injuries is higher in men than women. Results of the study by Russo et al. and Chen et al. showed that men suffer less severe injuries. Given that men are more likely than women to engage in high-risk driving behaviors such as speeding and aggressive driving, and are more likely to put themselves and other road users in dangerous situations, this difference may be due to difference between the number of men and women among participants, as 85.5% of the participants were male." - “On the other hand, studies have shown that with increasing level of education, a person's attention to the impact of high-risk behaviors (such as speeding) on RTIs and severity of RTIs will increase. Educated people also wear seat belts because of improving their perceived safety value, to avoid being harmed. In addition, educated people usually use safer and higher quality vehicles due to their better socio-economic status." - “The motivation for driving while fatigued may be due to overestimation of capabilities and miscalculation of the cost of outcome by the drivers." - “Evidence indicates an increase in aberrant driving behaviors and traffic offences during within the first three years after licensure. Driving experience can significantly affect drivers' perception of risk. Experienced drivers can have much more knowledge and skills to drive safely and appropriately in different traffic conditions. - The results of this study showed that high experienced drivers (>10 years) have a reduced probability of severe and fatal injuries compared with mid experienced drivers (2-5 years of driving experience). This result may be attributed to the improved driving skills and handling capabilities in traffic emergencies, among high experienced drivers. The effect of driving experience on driver injury severity indicated that drivers with mid experience should be continuously educated to reduce the severity of road traffic injuries." Some papers on traffic safety modeling and experiments could be referred to and acknowledged in the Introduction. For example, the following ones. [1] Examining the safety of trucks under crosswind at bridge-tunnel section: A driving simulator study, Tunnelling and Underground Space Technology, 2019, 92, 103034. https://doi.org/10.1016/j.tust.2019.103034 [2] Chen, Feng; Chen, Suren; Ma, Xiaoxiang. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. 2018, JOURNAL OF SAFETY RESEARCH. 65: 153-159. [3] Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model, International Journal of Environmental Research and Public Health, 2019, 16(14) , 2632. [4] Investigating the Differences of Single- and Multi-vehicle Accident Probability Using Mixed Logit Model, Journal of Advanced Transportation, 2018, UNSP 2702360. Response: Thank you for your valuable comment. We have added some studies based traffic frequency and severity modeling in introduction, as recommended. References # 14-15. Attachments Attachment Submitted filename: Response to Reviewers.doc https://doi.org/10.1371/journal.pone.0238728.r002
24 Aug 2020 Decision Letter - Feng Chen, Editor Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers PONE-D-20-13494R1 Dear Dr. Shirahmadi, 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: (No Response) Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) 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 Response) 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: (No Response) 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: (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 #1: No Reviewer #2: No https://doi.org/10.1371/journal.pone.0238728.r003
Formally Accepted
31 Aug 2020 Acceptance Letter - Feng Chen, Editor PONE-D-20-13494R1 Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers Dear Dr. Shirahmadi: 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.0238728.r004

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .