Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model
Round 1
Reviewer 1 Report
This is a well written and organized paper. The topic and the findings are clearly presented and discussed. The results can give some qualitative features of HDVs and CAVs in fogy situations. Unfortunately, the findings cannot be verified by real traffic and weather conditions.
Some detailed comments:
Section 2.3: Please also take a look into the following paper regarding the so-called Time-Oriented Cellular (TOCA)
- Brilon; Wu, N.:
Cellular Automata for Highway Traffic Flow Simulation.
Proceedings of the 14th ISTTT, Jerusalem, Israel, July, 1997, Vol. II.Technion-Israel, Institute of Technology, Haifa, Israel, 1999
line 19: Is the limited visual distance the only relevant parameter for a fog situation? What is about the reaction time? Is the reaction time=1s = time of update step in the paper like it is in the STCA? This is a crucial issue for CAVs. The reaction time of CAVs may much shorter (about 0.1s).
Eqs. 1, 2, 3, 4, 7, and 9: What is the parameter t in the dominator in the last term Dxn(t)/t? Is it equal to the time Step =1s? Pleased clarify.
Line 257: The parameter dsafe for HDVs = 2vmax is ok but dsafe for CAV = 1vmax is a very strong assumption. The parameter dsafe influence the maximum capacity qmax directly. Can this value dsafe for CAVS be varied? E.g., dsafe =0.5vmax?
Section 3.2.2: The Game Theory applies also for HDVs. Indeed, the GT applies more for HDVs than for CAVs. Please give some discussion. Please explain and give an example how the payoff values of Pij and Qij are selected/obtained exactly (eqs. 14 pp).
Section 4.2: Figs. 4 and 5. The results are very promising. However, I wonder how significantly the parameter alpha and beta in GT (eq. 14) may influence the results.
Tab. 4: The comparison is parameter-related and depends on the presumptions made. Thus, in my opinion, the comparison does not show a better performance of GT because we don’t have a reference. We don’t know what the reality is. Here we only know that the GT (with default parameters) delivers a higher speed than the STCA without GT,
Lines 342-343: What is the target vehicle? Is it the SV? Please clarify “target vehicle”, “preceding vehicle”, and following vehicle” with regard to fig. 2.
Line 469: The presented Fundamental Diagrams are not triangular at all (cf. figs. 5 and 6).
Author Response
Dear Reviewer:
Manuscript ID: sustainability-1720141
Title: “Modeling HDVs and CAVs mixed Traffic Flow in Fog Environment on Two-lane Highway Based on Cellular Automata and Game Theory Mode
We wish to express our very deep appreciation, and the appreciation of all of us, for your patience, great efforts, and suggestions for our manuscript under missing figures. The comments and suggestions are valuable and very helpful for revising and improving our paper, as well as the important guidance to our research.
A point-to-point response to your comments can be see in the attachment, and the responses are in red. The modification is marked in red in the revised version.
We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And modified content is marked in red in the revised paper. We appreciate your warm work earnestly and hope that the correction will meet with approval. Thank you for your time and patience.
I look forward to receiving your letter.
Once again, we would like to thank you for the constructive comments and suggestions. Please feel free to contact us with any questions. We are looking forward to your reply.
Yours sincerely,
Ciyun Lin
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors model the mixed traffic flow in the fog weather on the two-lane highway and evaluate the influence of visibility level and CAVs market penetration on the traffic efficiency. The work of this paper is practical and logical. However, there are some problems to be further improved as well:
- There are some little writing errors in the manuscript, such as, in Abstract, line 15, the first word should be capitalized in the sentence. In Section 3.1.3, line 288, should be , the same as the notion in Section 3.1.1. What is the meaning of “the target vehicle” in line 344? Is it “SV”? Please give a clear definition of the subject before it is used.
- In the car-following models, the MIN functions for updating the speed of a vehicle in the next time step have some mistakes: should be in Equation (1); Equation (10) cannot ensure the CAV safety, since .
- In the lane-changing model, what is the physical meaning of Equation (14)? Why must FV accelerate or decelerate in the lane-changing game? There should be three types of decision-making for FV if it could maintain the current speed. As a result, the payoff matrix in Table 2 should be revised. Besides, why do the vehicle SV change lanes if ? How does the research gap mentioned in the last paragraph of Section 2.2 be filled in the proposed model?
- CAVs are assumed to perform lane-changing if Equations (11-13) are met, which means . But in Line 334, is another lane-changing probability. What’s the difference between them? Furthermore, this assumption could reduce the stability of the mixed traffic flow system. It is a good choice to examine the influence of on the traffic efficiency and show more complete research results.
- From Figure 4, it is hard to observe the stop-and-go phenomenon which is pointed out in Line 389. Please show reasonable evidence for it, or just remove it.
- The authors reviewed the mixed traffic flow model, including the car-following and lane-changing behaviors. The authors appear to have missed an entire body of work from recent years on interaction-aware motion planning based on machine learning This kind of method has been proven to be very helpful and thus needs to be added in this literature review, including but not limited to
Peng, B., Keskin, M. F., Kulcsár, B., & Wymeersch, H. (2021). Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning. Communications in Transportation Research, 1, 100017.
Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021). Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 10, 100057.
Olovsson, T., Svensson, T., & Wu, J. (2022). Future connected vehicles: Communications demands, privacy and cyber-security. Communications in Transportation Research, 2, 100056.
The authors need to compare machine-learning-based methods with game-theory-based methods to present the reason for choosing the latter one.
In general, I think this paper can be accepted after careful revision since the methods and results look appropriate and promising.
Author Response
Dear Reviewer:
Manuscript ID: sustainability-1720141
Title: “Modeling HDVs and CAVs mixed Traffic Flow in Fog Environment on Two-lane Highway Based on Cellular Automata and Game Theory Mode
We wish to express our very deep appreciation, and the appreciation of all of us, for your patience, great efforts, and suggestions for our manuscript under missing figures. The comments and suggestions are valuable and very helpful for revising and improving our paper, as well as the important guidance to our research.
A point-to-point response to your comments can be see in the attachment, and the responses are in red. The modification is marked in red in the revised version.
We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And modified content is marked in red in the revised paper. We appreciate your warm work earnestly and hope that the correction will meet with approval. Thank you for your time and patience.
I look forward to receiving your letter.
Once again, we would like to thank you for the constructive comments and suggestions. Please feel free to contact us with any questions. We are looking forward to your reply.
Yours sincerely,
Ciyun Lin
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors have addressed all my comments.
