Effects of Object-Oriented Advance Guidance Signage on Lane-Changing Behaviors at the Mainline Toll Stations of Expressways

Round 1

Reviewer 1 Report

In this paper, the authors design three guidance signal plans, namely, OR, CMS, and VW&CMS to investigate the effects of ETC and MTC vehicles as guidance objects on the lane-changing behaviors of drivers for expressway mainline toll lanes.The manuscript is well organized, and the results enrich the existing theoretical research models and have certain theoretical value. Therefore, I think the paper can be accepted. In addition, the following are the few comments, which may be included while revision.

 

1. The simulation experiment only conducted experiments on 40 traffic participants, and the number of people was small, so the conclusion was not general.

2. Page 1, “According to the Manual on Uniform Traffic Control Devices for Streets and Highways of the United States [2]”, it’s not appropriate for the author to solve China 's problems with American standards.

3. The text in Figure 2 is recommended to be replaced with English.

4. Page 8, “However, no significant difference existed between the CMS and VW&CMS plans in the paired comparisons by LSD (p = 0.688)”. How did the authors come to this conclusion? The process should be given in more detail.

5. Table 4 should be placed after the first paragraph of section 3.1.2 for the convenience of the reader.

6. The authors should compare the research results in this paper with the existing researches, such as the conclusions of the effects of gender and age on driving performance etc, so as to make the paper more substantial.

7.  All of equations and the value of some key parameters should be given the corresponding references and practical significance.

8. What is the basis for the first item and the second item in the conclusion? Are they based on the research in this paper? However, no relevant discussion is found in the paper.

9. The list of references should be extended to include some recent papers.

1) Congestion and energy consumption of heterogeneous traffic flow mixed with intelligent connected vehicles and platoons, Physica A,Volume 609, 2023, 128331, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2022.128331.

2)  A multivalue cellular automata model for multilane traffic flow under lagrange coordinate.Computational and Mathematical Organization Theory. https://doi.org/10.1007/s10588-021-09345-w

 

Author Response

Response to Reviewer 1 Comments

Dear reviewer for our paper Sustainability-2073852:

Thank you very much for your letter and the comments from the referees about our paper submitted to Sustainability. We have learned much from the reviewer’ comments, which are fair, encouraging and constructive. After carefully studying the comments and your advice, we have made corresponding changes. The following is the answers and revisions we have made in response to the reviewer' questions and suggestions on an item-by-item basis.

 

Point 1: The simulation experiment only conducted experiments on 40 traffic participants, and the number of people was small, so the conclusion was not general.

Response 1: We understand the reviewer’s concern. In the study we recruited 43 participants. Due to motion sickness 40 complete participant data were collected (30 males and 10 females). Our study used a within-subjects experiment design. Each participant drove the scenario three times at three conditions (OR plan, CMS plan and VW&CMS plan). To make sure that 40 is an acceptable sample size, we calculated the required sample size by using G POWER program. Running a power analysis on matched samples t-test, the calculation with G POWER used an alpha of 0.05, a power of 0.80, an effect size of 0.50, and two tails, the required sample size for t-test would be 34. Based on the aforementioned assumptions, the sample size of 40 is adequate. Meanwhile, an advantage of GEE model is that it is tolerant of small sample size. For instance, Onninen et al. (2021) used GEE model to analyze the causes of sleepiness in shift-working tram with a sample size 23 shift-working tram drivers. Saifuzzaman et al. (2015) built the GEE model using a sample of 32 drivers to examine the impact of mobile phone use on car-following behavior of young drivers. Hang et al. (2022) also used GEE models to evaluate the efficacy of the in-vehicle warnings for the work zone with a sample size of 44.

We have added the explanation in the Participant section and the corresponding deficiencies in the limitation section as:

Page 5, line 223:

' The G POWER program was used to estimate the required sample size, a priori analysis power, considering effect size of 0.50, statistical power of 80%, significance level of 95% (α <0.05), totaling a sample size of 34. As a result, we recruited 40 participants with valid full driving licenses, including 30 male drivers and 10 female drivers. '

Page 14, line 559:

'Additionally, the experiment sample size is small. In the future, a larger sample size should be adopted to improve the generalizability of the experiment results. '

• Onninen, J., Pylkkönen, M., Hakola, T., Puttonen, S., Virkkala, J., Tolvanen, A., & Sallinen, M. (2021). The self-reported causes of sleepiness in shift-working tram and truck drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 78, 153-163.
• Saifuzzaman, M., Haque, M. M., Zheng, Z., & Washington, S. (2015). Impact of mobile phone use on car-following behaviour of young drivers. Accident Analysis & Prevention, 82, 10-19.
• Hang, J., Yan, X., Li, X., & Duan, K. (2022). In-vehicle warnings for work zone and related rear-end collisions: A driving simulator experiment. Accident Analysis & Prevention, 174,

 

Point 2: Page 1, “According to the Manual on Uniform Traffic Control Devices for Streets and Highways of the United States [2]”, it’s not appropriate for the author to solve China 's problems with American standards.

Response 2: We truly appreciate for your concerns. We have removed the relevant content about American standards.

 

Point 3: The text in Figure 2 is recommended to be replaced with English.

Response 3: Thanks for your comments very much. Because of the design of the sign board, we didn't replace the text with English, but we have made a more detailed English annotation on the Figure 2. The new figures are provided below:

Page 5, line 220:

Figure 2. Optimized guidance signage system.

 

Point 4: Page 8, “However, no significant difference existed between the CMS and VW&CMS plans in the paired comparisons by LSD (p = 0.688)”. How did the authors come to this conclusion? The process should be given in more detail.

Response 4: We appreciate the reviewer’s concern. In constructing GEE model with SPSS, least significant difference pairing comparison can be selected in the EM Means option to analyze the correlation between different schemes.

To make the paper clearer, we've added pertinent information about the paired comparisons by least significant difference (LSD) in 3.6. Statistical analysis:

Page 7, line 314:

 'For multiple comparisons of marginal means, least significant difference (LSD) pairwise comparisons were selected to analyze the effects of effects of each factor. '

 

Point 5: Table 4 should be placed after the first paragraph of section 3.1.2 for the convenience of the reader.

Response 5: We gratefully appreciate for your valuable comment. We have changed the position of the Table 4 and placed it after the first paragraph of section 4.1.2 for readers' convenience.

 

Point 6: The authors should compare the research results in this paper with the existing researches, such as the conclusions of the effects of gender and age on driving performance etc, so as to make the paper more substantial.

Response 6: Thank you for your important consideration. According to your nice suggestions, we have added to the discussion of the effects of gender and age on driving performance and added relevant conclusions. The modified part is shown as below：

Page 14, line 547:

' Although there was no significant difference between males and females in decision-making stage in this study, females were found in some research to be more cautious and secure than males in decision-making stage (Particularly in the reaction progress), better at comprehending the provided information and react accordingly [37], whereas in this study no significant difference was observed due to the limited amount of sign information. Compared with younger drivers, the old drivers may apply a low-er deceleration rate, which agrees with a previous study [38]. Furthermore, the cognitive impairment that comes with age makes it hard for drivers to make quick decisions [39], thus making the initial time interval of lane-changing closer to the toll plaza. Many studies have shown that with the increase of age, drivers' reaction ability de-creases, thus they have higher risk perception [40], so they may exhibit more cautious driving behavior [41][42].'

Page 14, line 582: '

4. Male drivers are safer than female drivers during lane-changing operation stage.
5. With an increase in age, drivers may have higher risk perception, they are more cautious during the lane-changing operation stage., such as a lower deceleration rate. '

 

Point 7: All of equations and the value of some key parameters should be given the corresponding references and practical significance.

Response 7: We understand the reviewer’s concern. The equations in chapter 3.2, we did not add the parameter values and the practical significance, since the calculation process of the TOPSIS algorithm does not contain them. Only the Premium degrees in the results has a range of 0 to 1, so we added the range of values and the practical significance.

Page 3, line 133:

' TOPSIS theories rely on distance (relative separation) from ideal and anti-ideal solutions. The relative distance value can range from 0 to 1, alternatives with a distance of 0 represent an anti-ideal solution, while those with a distance of 1 are genuinely an ideal solution. Due to its sound mathematical foundation, simplicity, ease of applicability, TOPSIS has been used extensively for a practical method for multi-criteria decision analysis [25]. '

Page 11, line 436:

' The performance ratings for each alternative against each attribute can be displayed in the form of a decision matrix [31]. Thus, the multiple objective decision matrix of the TOPSIS was formed as  in the three guidance plans. '

Page 11, line 444:

' Compared with various subjective weighting models, the biggest advantage of the entropy weight method is the avoidance of the interference of human factors on the weight of indicators, thus enhancing the objectivity of the comprehensive evaluation results. The definition of entropy is the expected value of the self-information of a variable [32]. The information entropy  was obtained according to equation 2. '

Page 12, line 460:

' The weighted-normalized decision matrix was composed of weighted ratings [31]. '

Page 12, line 467:

' Premium degrees were utilized to rank the competing alternatives, which values ranging from 0 to 1. A higher score value closer to 1 indicated better efficiency of the plan [33]. '

Meanwhile，We also add a more detailed description of the TOPSIS algorithm in Chapter 2 Previous research, and definitions of notation in 4.2.1. TOPSIS algorithm. to help the reader better understand. The specific contents are as follows:

 

Point 8: What is the basis for the first item and the second item in the conclusion? Are they based on the research in this paper? However, no relevant discussion is found in the paper.

Response 8: We apologize for the lack of clarity in the presentation of the conclusions. We have rearranged the first item and the second item in the conclusion, as shown below.

Page 14, line 571: '

1. With the increasing proportion of ETC vehicles and the number of ETC lanes increasing, the existing guiding systems are not sufficient to guide the vehicles, resulting in a more unsafe lane-changing process.
2. The complete guidance design plan (CMS) guidance signage (provided guidance information at 500 m and 1 km upstream of the toll station) can allow drivers to have shorter response time and earlier initial time interval and higher speed at the lane-changing progress. In addition, the proportion of drivers who successfully complete lane-changing in advance may increase. '

 

Point 9: The list of references should be extended to include some recent papers.

1) Congestion and energy consumption of heterogeneous traffic flow mixed with intelligent connected vehicles and platoons, Physica A,Volume 609, 2023, 128331, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2022.128331. 

2) A multi value cellular automata model for multi lane traffic flow under lagrange coordinate.Computational and Mathematical Organization Theory.

Response 9: Thanks for your comments very much. The papers you provided are very informative and we have incorporated them into the paper as shown below.

Page 4, line 160:

' As the toll station belongs to the road section with high traffic density, in order to prevent the toll station section from affecting the main road vehicle traffic due to high traffic density, more lanes were set up in the toll station section so as to improve the traffic capacity [26], therefore, the toll station consists of 13 channels (9 ETC channels and 4 MTC channels) [27]'

Page 6, line 276:

' Whether lane-changing depends on whether the driving conditions in the adjacent lane were better than the current lane [28]. When drivers change lanes earlier, they will be less likely to collide with other vehicles when approaching the toll plaza. '

Author Response File: Author Response.docx

Reviewer 2 Report

This study used a driving simulator to examine the effect of guidance signage on lane-changing behavior at toll stations in the expressway. The findings may be useful in designing a better sign at the expressway. However, I have the following concerns.

Major

1.      The research gaps were not clearly identified following the literature review.

2.      In line 187, you mentioned “data from the 40 participants, including 120 samples,” thus, I assume that each participant was involved in 3 experiments (i.e., OR, CMS, and VW&CMS). Then, the order of experiments is important. The order should be randomized to obtain unbiased results. How did you address this issue?

3.      The difference between the generalized estimation equation model and the standard regression model is unclear. The estimation tables seem the same as those by standard regression because no additional parameters were demonstrated. Could you demonstrate the model equation? In addition, model fitness measures such as pho² (or McFaddens’ R²) should be added.

 4.      I can not understand the logic and merit of the TOPSIS algorithm. The current description lacks the definition of notation; thus, it should be improved. In addition, the conclusion can be retrieved without TOPSIS results. Please consider deleting section 3.2.

5.      The discussion on “controlled mode” or “preattentive mode” (lines 344-345) is interesting. However, the authors did not conduct any questionnaire survey on participants to validate the discussion. The analysis is based only on the trajectory, but I suppose the experimental design should have been improved to collect participants’ attitudes.

6.      The relationship between some of the statements in the conclusion (lines 400-411) and the results is unclear.

Minor

1.      The abbreviation should be defined again in the main text (e.g., ETC and MTC).

2.      Detailed information on driving simulator experiments is missing. When and where were the experiments conducted?

3.      Line 259 states (p=0.819), but you mentioned “a significant difference.” Please check it.

4.      The definition of bars in figures 6-8 should be added. I suppose they were 95% confidence intervals and did not reveal significant differences. However, they could be standard deviation. 

Author Response

Response to Reviewer 2 Comments

Dear reviewer for our paper Sustainability-2073852:

We have carefully considered all comments from the reviewers and revised our manuscript accordingly. The manuscript has also been double-checked, and the typos and grammar errors we found have been corrected. In the following section, we summarize our responses to each comment from the reviewers. We believe that our responses have well addressed all concerns from the reviewers.

 

Point 1: The research gaps were not clearly identified following the literature review.

Response 1: We gratefully appreciate for your valuable comment. Based on previous research, few relevant studies have focused on evaluating the existing toll guidance sign system and develop appropriate and scientifically optimal signage designs. This study mainly intends to verify the three points: (1) With the increasing proportion of ETC vehicles, are the existing guiding systems still applicable? (2) To investigate whether drivers' lane-changing behavior significantly improves after obtaining advanced instructions. (3) Can voice warnings help drivers complete lane-changing more effectively? To address these questions, various plans of mainline guidance signage were designed considering the sign location, guidance content, and modality.

We have rewritten parts of the 2. Previous research to highlight the research gaps.

Page 3, line 142:

' Based on previous research, few studies have focused on optimizing guidance signage systems from the perspective of driver response modes. In reality, this issue restricts the efficient operation of toll stations. Therefore, pertinent research is essential to solve this issue.'

Page 3, line 147:

' Besides, the study determined the influence of driver characteristics (e.g., gender, age) on their lane-changing progress. Three specific points were considered in this study:

1)     With the increasing proportion of ETC vehicles, are the existing guiding systems still applicable? 2) To investigate whether drivers' lane-changing behavior significantly improves after obtaining advanced instructions.

3)     Can voice warnings help drivers complete lane-changing more effectively?

This study was expected to obtain the optimal design alternatives of guidance signage systems for expressway stations which would support the revision of related standards. '

 

Point 2: In line 187, you mentioned “data from the 40 participants, including 120 samples,” thus, I assume that each participant was involved in 3 experiments (i.e., OR, CMS, and VW&CMS). Then, the order of experiments is important. The order should be randomized to obtain unbiased results. How did you address this issue?

Response 2: Thanks for your comments. It is crucial that we comprehend the experiment's randomness. The three drives are randomized between them, the sequence of 3 driving scenarios of each subject was generated by a random function.

We have added the 3.4 Experimental procedure to clarify this issue in the revised paper:

Page 6, line 252:

'Each participant needs to drive in 3 scenarios and was allowed to have at least 30 minutes between two drives, the order of the 3 scenarios for each participant was created by a random function, to impede drivers from potential learning effects. '

 

Point 3: The difference between the generalized estimation equation model and the standard regression model is unclear. The estimation tables seem the same as those by standard regression because no additional parameters were demonstrated. Could you demonstrate the model equation? In addition, model fitness measures such as pho2 (or McFaddens’ R2) should be added.

Response 3: We understand the reviewer’s concern. The difference between the generalized estimation equation model and the standard regression model is that: The standard linear model is not applicable to dependent variables as categorical variables, While the Generalized Estimation Equation model (GEE) does not need to specify the whole distribution of the dependent variables, and it has no special requirements for the data type of independent variables. Moreover, GEE model can handle the imbalanced dataset, as GEE uses Quasi-likelihood Estimation Method to estimate parameters, which is specially used to deal with repeated measures including unbalanced panel data. It should be noted that we didn't set out to build an optimal predictive model equation to observe any patterns; rather, we only wanted to examine if the important elements generated significant impacts on the dependent variable or not, as a result, we didn’t add the model equation model fitness measures. Moreover, we found it quite common practice in many studies relating to traffic (Pawar et al., 2021; Onninen et al., 2021; Devlin, McGillivray, Charlton, Lowndes, & Etienne, 2012; Devlin, McGillivray, Charlton, Lowndes, & Etienne, 2012; Devlin et al., 2019).

We have added the difference between the generalized estimation equation model and the standard regression model in 3.6. Statistical analysis, as shown below.

Page 7, line 308:

' In contrast to standard linear equations, the Generalized Estimation Equation model (GEE) does not need to specify the whole distribution of the dependent variables, and it has no special requirements for the data type of independent variables. Moreover, this method is specially used to deal with repeated-measure data, including unbalanced panel data [26]. '

• Onninen, J., Pylkkönen, M., Hakola, T., Puttonen, S., Virkkala, J., Tolvanen, A., & Sallinen, M. (2021). The self-reported causes of sleepiness in shift-working tram and truck drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 78, 153-163.
• Pawar, N. M. , & Velaga, N. R. . (2021). Effect of time pressure on steering control of the drivers in a car-following situation. Transportation Research Part F: Traffic Psychology and Behaviour, 80, 218-236.
• Chang, H.-L., Woo, T. H., & Tseng, C.-M. (2006). Is rigorous punishment effective? A case study of lifetime license revocation in Taiwan. Accident Analysis & Prevention, 38(2), 269-276.
• Devlin, A., Beck, B., Simpson, P. M., Ekegren, C. L., Giummarra, M. J., Edwards, E. R., . . . Gabbe, B. J. (2019). The road to recovery for vulnerable road users hospitalised for orthopaedic injury following an on-road crash. Accident Analysis & Prevention, 132, 105279.
• Devlin, A., McGillivray, J., Charlton, J., Lowndes, G., & Etienne, V. (2012). Investigating driving behaviour of older drivers with mild cognitive impairment using a portable driving simulator. Accident Analysis & Prevention, 49, 300-307.

 

Point 4: I can not understand the logic and merit of the TOPSIS algorithm. The current description lacks the definition of notation; thus, it should be improved. In addition, the conclusion can be retrieved without TOPSIS results. Please consider deleting section 3.2.

Response 4: We understand the reviewer’s concern. As you are concerned, we did not have a clear description of the logic and merit of the TOPSIS algorithm, which resulted in not giving a clear understanding to the reader. TOPSIS theories rely on distance (relative separation) from ideal and anti-ideal solutions. The relative distance value can range from 0 to 1. Alternatives with a distance of 0 represent an anti-ideal solution, while those with a distance of 1 are genuinely an ideal solution. Due to its sound mathematical foundation, simplicity, ease of applicability, TOPSIS has been used extensively for practical method for multi-criteria decision analysis (Yeh, C., 2010).

For our approach of keeping TOPSIS results, we provide the following explanations.

Firstly, the response time, average speed and lane changing duration distance performed better under CMS plan, but the Initial time interval and average declaration rate performed better under VW&CMS plan. We want to obtain the optimal plans from CMS and VW&CMS plan by using a comprehensive evaluation method, so we chose the TOPSIS method based on the entropy weight method for the comprehensive evaluation. Secondly, the discussion of voice warning in Chapter 5 Discussion and the revised conclusion “Adding voice warning to the complete guidance design plan guidance sign-age may not be necessary, as the process of lane-changing behaviors was not significantly improved.” were also based on the result of TOPSIS algorithm. Moreover, we found it quite common to use TOPSIS to evaluate a few schemes with many indicators in many studies relating to traffic (Rosić, Pešić, Kukić, Antić, & Božović, 2017; Huang et al., 2020; Bian, Liang, Zhao, Li, & Yang, 2020).

As a result, we chose to keep TOPSIS results.

We also add a more detailed description of logic and merit of the TOPSIS algorithm in Chapter 2 Previous research, and definitions of notation in 4.2.1. TOPSIS algorithm. The specific contents are as follows:

Page 3, line 133:

' TOPSIS theories rely on distance (relative separation) from ideal and anti-ideal solutions. The relative distance value can range from 0 to 1, alternatives with a distance of 0 represent an anti-ideal solution, while those with a distance of 1 are genuinely an ideal solution. Due to its sound mathematical foundation, simplicity, ease of applicability, TOPSIS has been used extensively for a practical method for multi-criteria decision analysis [25]. '

Page 11, line 436:

'The performance ratings for each alternative against each attribute can be displayed in the form of a decision matrix [31]. Thus, the multiple objective decision matrix of the TOPSIS was formed as  in the three guidance plans. '

Page 11, line 444:

'Compared with various subjective weighting models, the biggest advantage of the entropy weight method is the avoidance of the interference of human factors on the weight of indicators, thus enhancing the objectivity of the comprehensive evaluation results. The definition of entropy is the expected value of the self-information of a variable [32] The information entropy  was obtained according to equation 2. '

Page 12, line 460:

'Then, we calculated the weighted normalized matrix, the weighted-normalized decision matrix was composed of weighted ratings [31]. '

Page 12, line 467:

'Premium degrees  were utilized to rank the competing alternatives, which values ranging from 0 to 1. A higher score value closer to 1 indicated better efficiency of the plan [33]. '

• Yeh, C. . (2010). The selection of multiattribute decision making methods for scholarship student selection. International Journal of Selection & Assessment, 11(4), 289-296.
• Bian, Y., Liang, K., Zhao, X., Li, H., & Yang, L. (2020). Evaluating the effectiveness of new-designed crosswalk markings at intersections in China considering vehicle-pedestrian interaction. Accident Analysis & Prevention, 139, 105498
• Huang, L., Zhao, X., Li, Y., Ma, J., Yang, L., Rong, J., & Wang, Y. (2020). Optimal design alternatives of advance guide signs of closely spaced exit ramps on urban expressways. Accident Analysis & Prevention, 138, 105465
• Rosić, M., Pešić, D., Kukić, D., Antić, B., & Božović, M. (2017). Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method. Accident Analysis & Prevention, 98, 277-286.

 

Point 5: The discussion on “controlled mode” or “preattentive mode” (lines 344-345) is interesting. However, the authors did not conduct any questionnaire survey on participants to validate the discussion. The analysis is based only on the trajectory, but I suppose the experimental design should have been improved to collect participants’ attitudes.

Response 5: Thank you very much for your insightful comments. We did a short questionnaire in the experimental phase and collected questionnaires on the participants' attitudes and on the evaluation of the authenticity of the scenes, which were as follows. Meanwhile, since the questionnaire was not comprehensive, we did not analyze the drivers' attitudes in depth. According to the results of the questionnaire (Part 2 Question 3), 95% of MTC drivers thought that the guidance signs in the CMS and VWCMS plans assisted drivers in selecting the correct lane. we will analyze the drivers' attitudes by setting a more comprehensive questionnaire in future studies.

Post-experimental questionnaire for drivers

ＩＤ：       Name：     Gender：    Age：    

 

Part 1

Please comment on the authenticity of the driving simulator. (5 is the most authenticity, 1 is not authenticity)

1. Authenticity of the road and surrounding buildings

□5  □4  □3  □2  □1

2. Authenticity of overall vehicle handling performance

□5  □4  □3  □2  □1

3. Authenticity when accelerating with the vehicle throttle

□5  □4  □3  □2  □1

4. Authenticity when using vehicle brakes to slow down

□5  □4  □3  □2  □1

5. Authenticity of steering wheel control during vehicle turns

□5  □4  □3  □2  □1

6. Authenticity of control during vehicle lane change

□5  □4  □3  □2  □1

7. Did you have any adverse reactions to the driving simulator during the driving process?

□It is very dizzy, it is difficult to continue to adhere to the experiment 

□A little dizzy, tolerable

□No dizziness, no reaction

 

Part2

Please make your comments based on your preview of the sign.

1. Do you change lanes early when you see a toll station approach sign?

□Yes  □No  

2. If your vehicle is equipped with ETC, which of the following information prompts will be most helpful for your lane selection

                   +Voice warning

a                            b                                c

3. If your vehicle is not equipped with ECT, which of the following information prompts will help you most in lane selection

                   + Voice warning

a                             b                               c

4. Where do you usually select lanes at toll booths when choosing ETC or MTC lanes?

□See the prompt sign to select in advance

□Select when approaching a toll booth

□Enter the toll station area and then select

5. Have you ever encountered dangerous situations when choosing ETC and MTC lanes at toll booths?_____

 

We have added the description of the experimental procedure regarding the questionnaire in 3.4. Experimental procedure and added the results of the questionnaire in Chapter 8. Discussion, as shown as below.

Page 6, line 257:

'Each participant was asked to complete the questionnaire about the evaluation of guide signs in this experiment. '

Page 13, line 512:

'Additionally, 95% of MTC drivers thought that the guidance signs in the CMS and VWCMS plans assisted drivers in selecting the correct lane, according to the results of the questionnaire. The overall higher speed, lower deceleration rate, longer lane-changing distance and positive attitudes towards signage as drivers approach the toll station suggested that the CMS plan can potentially contribute to traffic efficiency and safety. '

 

Point 6: The relationship between some of the statements in the conclusion (lines 400-411) and the results is unclear.

Response 6: We apologize for the lack of clarity in the presentation of the conclusions. We have rearranged the conclusion, as shown as below.

Page 14, line 571: '

1. With the increasing proportion of ETC vehicles and the number of ETC lanes increasing, the existing guiding systems are not sufficient to guide the vehicles, resulting in a more unsafe lane-changing process.
2. The complete guidance design plan (CMS) guidance signage (provided guidance information at 1 km and 500 m in front of the toll station) can allow drivers to have shorter response time and earlier initial time interval and higher speed at the lane-changing progress. In addition, the proportion of drivers who successfully complete lane-changing in advance may increase.
3. Adding voice warning to the complete guidance design plan guidance signage may not be necessary, as the process of lane-changing behaviors was not significantly improved. '

 

Point 7: The abbreviation should be defined again in the main text (e.g., ETC and MTC).

Response 7: Thanks for your comments. We have made the corresponding corrections as follows:

Page 1, line34: 'ETC' ==> ' electronic toll collection (ETC) '

Page 1, line 37: 'MTC' ==> ' manual toll collection (MTC) '

 

Point 8: Detailed information on driving simulator experiments is missing. When and where were the experiments conducted?

Response 8: Thank you very much for your insightful comments. The experiment was carried out in the key laboratory of special environment road engineering of Hunan province, from 9:00 a.m. to 11:30 a.m. and 2:00 p.m. to 5:00 p.m.

Page 6, line247: '

3.4. Experimental procedure

The experiment was carried out in the key laboratory of special environment road engineering of Hunan province, from 9:00 a.m. to 11:30 a.m. and 2:00 p.m. to 5:00 p.m. Upon arrival, all participants were informed regarding driving tasks and cautions. Before the formal experiment, participants were required to complete a practice drive for at least 5 minutes. Each participant needs to drive in 3 scenarios and was allowed to have at least 30 minutes between two drives, the order of the 3 scenarios for each participant was created by a random function, to impede drivers from potential learning effects. Participants were asked to do exactly as they drive daily. Additionally, participants were advised that if they felt any discomfort, they could quit the experiment at any time. The entire experiment lasted about 120 minutes. Each participant was asked to complete the questionnaire about the evaluation of guide signs in this experiment. After finishing the whole experiment, each participant could receive 200 RMB (about $30) as a reward. '

 

Point 9: Line 259 states (p=0.819), but you mentioned “a significant difference.” Please check it.

Response 9: Thank you so much for your careful check. We have revised related content and carefully checked the article. The missing references have been provided, as listed below.

Page 9, line369:

'The paired comparisons showed no significant difference between the CMS and VW&CMS plans (p = 0.819).'

 

Point 10: The definition of bars in figures 6-8 should be added. I suppose they were 95% confidence intervals and did not reveal significant differences. However, they could be standard deviation.

Response 10: Thanks for your comments very much. The definition of bars in figures 6-8 are standard deviation. We have provided meaning expressed by the figures as well as the tables.

Page 7, line323:

'Each table contained coefficient estimates, standard errors, Wald chi-square value and p-value, and each figure provided the mean and standard deviation of significant variables. '

 

Author Response File: Author Response.docx

Reviewer 3 Report

Dear Authors, I appreciate the quality of your research quite highly.

The research topic is very relevant. Tom Vanderbilt wrote about the problems of the complexity of optimal choice and lane change on the highway in his popular book-bestseller "Traffic. Why We Drive the Way We Do (and What It Says About Us)». I think that you are doing quite important research.

I have only four suggestions for improving your article:

1. It is necessary to add T. Vanderbilt's book to the bibliographic list.

2. It is necessary to slightly change the structure of the sections of the article. In particular, the Introduction section should be divided into two sections – the introduction (relevance, problems, etc.) and the results of the analysis of the status of the issue (analysis of previously performed works). Accordingly, the number of partitions will change from 5 to 6.

3. It is necessary to describe in more detail the process of identifying the value of the drivers' reaction time to the information signs.

4. Figures №№ 5... 8 of the article should be presented in a more favorable quality.

In addition, I found an error – a discrepancy in the number of experiments conducted – 120 (line 187) and 160 (line 228). Please correct this error.

Author Response

Response to Reviewer 3 Comments

Dear reviewer for our paper Sustainability-2073852:

Thank you very much for your important suggestions and constructive comments. We have addressed all the reviewers’ comments and suggestions in the revised paper. Each suggestion is shown below in Italic followed with our action to modify the paper accordingly.

 

Point 1: It is necessary to add T. Vanderbilt's book to the bibliographic list.

Response 1: We appreciate the useful book you recommended, which has a high reference value for this paper. We have incorporated this reference in the Introduction, as shown follows

Page 2, line 92:

' In a study on the complexity of optimal choices and lane-changing on the highway, it was noted that early lane-changing can have a positive effect on drivers [14]. As a result, different guidance methods should be provided to various types of drivers to help them complete the lane change earlier.'

 

Point 2: It is necessary to slightly change the structure of the sections of the article. In particular, the Introduction section should be divided into two sections – the introduction (relevance, problems, etc.) and the results of the analysis of the status of the issue (analysis of previously performed works). Accordingly, the number of partitions will change from 5 to 6.

Response 2: Thanks for your comments very much. We have revised the Introduction structure. We have divided the Introduction section into Chapter 1 Introduction and Chapter 2 Previous research. The specific contents of the literature reviewed is as follows:

Page 1, line 33:

1. Introduction

In China, electronic toll collection (ETC) has been widely implemented. As of October 2020, the ETC utilization rate of expressways exceeded 66% [1]. At the mainline toll stations of expressways, the large number of ETC vehicles has caused an increase in the number of ETC lanes and a decrease in the number of manual toll collection (MTC) lanes. However, the guidance signage systems in front of toll stations have not been updated with the change in the layout of channels. In the past, guidance signage instructed ETC vehicles to change lanes, and only a few ETC channels were enough to accommodate a modest number of ETC vehicles. However, due to an increase in the number of ETC vehicles, each toll station typically has more than three ETC lanes. Thus, MTC vehicles need to change lanes to the right to enter the MTC lanes, and ETC vehicles can enter the toll station straightly without lane-changing. In this situation, the objects to be guided become MTC vehicles. The guidance information in front of the toll station cannot effectively guide MTC and ETC vehicles, resulting in conflicts between ETC and MTC vehicles when changing lanes at the toll plaza. Without guidance directions, drivers (particularly those of MTC vehicles) upstream of the toll station cannot make choices improving traffic flow and safety at the toll station.

2. Previous research

This section focused on a brief overview of guidance design studies, drivers ' models of information processing for static signs, the influence of driver characteristics on driving behavior and some guidance efficacy of static signage systems related evaluation methods.

Much research has been conducted on the location and layout content of guidance signage in front of expressway toll stations. In terms of selecting the locations, according to the Road Traffic Signs and Markings (GB5768.2-2022) of China [2], the signs at 0 km, 50 0m, 1km and 2km upstream should provide information on the toll station, while the guidance information is only available at 0 m and 300 m upstream. In the research on the reaction operations of drivers to expressway exit signs, Zhao et al. [3] and Huang et al. [4] both pointed out that the 500m upstream of the expressway exit was a critical area for driving operations. The guidance information at 300 m based on the present standard may not be sufficient to complete the reaction procedures of drivers. Shu [5] performed a further questionnaire survey on expressway guide signs, and the results of the subjective evaluation showed that guide information was recommended to be placed at signs 1 km and 0.5 km in front of expressway exits. Advanced guidance information can improve safety and effectiveness in front of the toll station by lowering the possibility of emergency lane changing as vehicles approach the toll station. In terms of text and graphic design of signs, Latin American countries, such as Colombia and Mexico, advised using pictograms and required information to be simple [6][7]. Wang, J.-H., Hesar, S. G., and Collyer, C. E. [8] examined a set of statistical data about dynamic signs and discovered that most participants preferred graphic-assisted information signs to information signs with only simple text. To study the perceptions of drivers on pictograms, Cristea and Delhomme [9] developed an analysis of variance technique; they found that drivers had positive attitudes toward pictograms accompanying text and believed that such pictograms contributed to the understanding of information. Similarly, Shinar and Vogelzang [10] found that adding text to signs reduced the time spent on comprehending signs, especially unfamiliar ones.

Drivers have two modes of information processing for static signs. One is the “preattentive mode” [11], also called automatic response [12]. Drivers in this mode respond more instinctively, choosing to change lanes as soon as they observe guidance signs. The other is “controlled mode” [13], in which drivers think more after recognizing sign information and do not operate immediately. Multiple signage reminders are essential for drivers since some even operate after approaching toll stations. In a study on the complexity of optimal choices and lane-changing on the highway, it was noted that early lane-changing can have a positive effect on drivers [14]. As a result, different guidance methods should be provided to various types of drivers to help them complete the lane change earlier. For drivers in the preattentive mode, providing guidance information over a reasonable distance is adequate; for drivers in the controlled mode, clear directional guidance information should be repeatedly provided across a sizable distance.

Numerous studies have been conducted on the effects of the characteristics of drivers, such as gender and age, on driving performance. Yang [15] conducted static simulation experiments on 39 groups of combined expressway guidance signs, reporting that the cognitive time of drivers was related to their characteristics, such as age and driving experience. Yao et al. [16] used simulation experiments to evaluate the effectiveness of traffic guide signs; results showed that elderly drivers preferred signs with directional information, and a positive correlation existed between age and directional information preference. Zahabi et al. [17] investigated the effect of driver age on driver performance and attention allocation on expressway exit ramps and found that elderly drivers performed poorly (greater lane deviations) and adopted more conservative control strategies (more speed reductions and greater maximum deceleration levels) than middle-aged and young drivers. Lyu, Cao, Wu, Xu, and Xie [18] analyzed the effect of the gender of drivers on driving behaviors and found male drivers were more aware of risks and had more aggressive driving tendencies than female drivers.

As for the comprehensive evaluation of the guidance efficacy of static signage systems, Upchurch et al. [19] obtained 11 evaluation indexes using simulation experiments and evaluated 4 guidance sign schemes at the exit of a two-lane expressway using the weighted average method. Fitzpatrick et al. [20] adopted the expert evaluation method to assess the performance of six expressway guidance signs using data from desktop simulation tests. Liu et al. [21] introduced the information demands, comprehension levels and information content and established an ergonomic evaluation model of guidance sign layout. Based on the driving simulation experimental data, Zhao et al. [3] used the factor analysis method to reduce the dimension and performed a multivariate quantitative evaluation of two exit guidance signs. Xie and Jia [22] built a thorough evaluation model of traffic signs using the technique for order of preference by similarity to ideal solution (TOPSIS) based on the survey results. The computation of the weighted average method is very straightforward, but the equivalent exchange between the sub-objectives affects the process of simultaneous convergence of them to optimal or better, prolonging the optimization time, and occasionally making things worse [23]. Using the expert evaluation method, the amount of data is minimized by dimensionality reduction when it is too huge, which is convenient for calculation but is prone to be influenced by human subjective aspects. Using the factor analysis method, the factors affecting the common factors can be identified, the data can be streamlined, and the factor variables can be more comprehensible by rotation; however, the least-squares method is employed to calculate the factor scores, which is problematic for some datasets [24]. TOPSIS theories rely on distance (relative separation) from ideal and anti-ideal solutions. The relative distance value can range from 0 to 1, alternatives with a distance of 0 represent an anti-ideal solution, while those with a distance of 1 are genuinely an ideal solution. Due to its sound mathematical foundation, simplicity, ease of applicability, TOPSIS has been used extensively for a practical method for multi-criteria decision analysis [25].

In general, with rapid increases in the numbers of high-speed ETC users and ETC lanes, the architecture of the current static guidance signage systems cannot accommodate the demand of drivers for lane changing, and drivers in the automatic response and controlled modes cannot safely change lanes within a short distance. Based on previous research, few studies have focused on optimizing guidance signage systems from the perspective of driver response modes. In reality, this issue restricts the efficient operation of toll stations. Therefore, pertinent research is essential to solve this issue. In this study, we developed plans for different mainline guide signs, focusing on the layout design of guide signs and the placement of guide information. Besides, the study determined the influence of driver characteristics (e.g., gender, age) on their lane-changing progress. Two specific points were considered in this study:

1)      With the increasing proportion of ETC vehicles, are the existing guiding systems still applicable?

2)      To investigate whether drivers' lane-changing behavior significantly improves after obtaining advanced instructions.

3)      Can voice warnings help drivers complete lane-changing more effectively?

This study was expected to obtain the optimal design alternatives of guidance signage systems for expressway stations which would support the revision of related standards.

 

Point 3: It is necessary to describe in more detail the process of identifying the value of the drivers' reaction time to the information signs.

Response 3: We understand the reviewer’s concern. To obtain more accurate response time, we should have used the eye-movement data to identify the exact moment when the driver perceived the first toll station sign (the 2km sign). However, due the lack of eye-tracking equipment, the eye-movement data are not usable. Thus, we use a same start point (i.e. the 2km sign) to measure the relative response time for all participants, which is a common practice for driving simulation studies when eye-movement data are not available (e.g. Li, et al., 2019, Hang et al., 2022).

We have modified the description of the reaction time to better explain how it was measured, as shown as below.

Page 7, line 289:

' 'Limited by the lack of eye tracker, the study used the same start point (e.g. 2km sign) to measure the participants’ response time, i.e., the time interval from the moment when the driver passed the “2km” sign to the moment when the lane-changing started (in s).'

• Hang, J., Yan, X., Li, X., Duan, K., Yang, J., & Xue, Q. (2022). An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment. Journal of safety research, 80, 416-427.
• Li, X., Rakotonirainy, A., & Yan, X. (2019). How do drivers avoid collisions? A driving simulator-based study. Journal of safety research, 70, 89-96.

 

Point 4: Figures №№ 5... 8 of the article should be presented in a more favorable quality.

Response 4: Thanks for your comments very much. We have improved the clarity of the figures in the revised paper. The new figures are provided below:

Page 8, line 346:

Figure 5. WLCA proportion in different signage plans.

Page 9, line 375:

(a) Response time in different plans	(b) Initial time interval in different plans

Page 10, line 419:

(a) Average speed in different plans	(b) Average deceleration rate in different plans
(c) Lane-changing duration distance in different plans

Page 10, line 423:

 

Figure 8. Lane-changing duration distance influenced by gender.

 

Point 5: In addition, I found an error – a discrepancy in the number of experiments conducted – 120 (line 187) and 160 (line 228). Please correct this error.

Response 5: Thank you so much for your careful check. We have carefully checked and made the corresponding corrections as follows.

Page 7, line 319:

' The data of the 40 participants were obtained, which included 120 samples, from the driving simulator experiment. For the MTC vehicle, we observed 94 lane changes before the drivers reached the “toll station entrance” sign (i.e., lane-changing in advance) and 26 after the drivers passed the “toll station entrance” sign. Thus, 120 samples were employed to analyze WLCA, and 94 were used to analyze the other variables. '

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors revisied the paper carefully based on the reviewers' comments. I think it can be published in this journal.

Reviewer 2 Report

Thank you for the careful revision. I am satisfied with your revision.

Reviewer 3 Report

Dear Authors, thank you for the thorough revision of the article. The volume and quality of improvements is very significant.