Selection of Intersection Groups for Congestion Mitigation and Energy Conservation in Urban Road Engineering

Comments 1: Methodology relies on overly simplistic, indirect, and weakly justified relationships. Methodology is very poor and is not clear.

The study does not conduct original traffic simulation, field experiments, or emission modeling. Instead, it constructs a chain of indirect linear relationships.

Emission modeling is outdated, non-standard, and not generalizable.

Emission–speed relationships are taken from a single local study on Xinning Road, yet generalized to an entire urban network. Vehicle type composition, fuel types, fleet age, cold-start effects, and driving cycles are completely ignored.

Network efficiency metric lacks theoretical and practical grounding.

No realistic congestion mitigation strategies are modeled.

No comparison is made with established traffic performance indicators such as delay, travel time index, or throughput.

Results are not validated, statistically tested, or compared with established methods.

Response 1: Thank you very much for your careful review and professional comments.

(1) In this study, the urban core area of Xining City, Qinghai Province, was selected as the study area. Relevant experiments were conducted based on a Portable Emission Measurement System (PEMS) to collect data on vehicle exhaust emissions, speed, and other indicators. The relationship between vehicle emissions and traffic delay rate was derived. An article pertaining to this preliminary work has been published previously (see Reference [17]), and this dataset serves as the primary data source for the subsequent research and analysis presented in this paper.

(2) Due to constraints such as the limited number of traffic police in actual urban settings, it is necessary to identify more critical node groups for the specific process of traffic congestion mitigation. This study attempts to investigate this issue from the perspective of energy conservation and emission reduction. However, the expression of the relationship between vehicle emissions and factors such as vehicle speed and the degree of road congestion (including saturation) can vary due to the combined influence of different urban topographies, road gradients, the number and width of lanes, and the degree of intersection congestion. Specifically for this paper, we performed both polynomial and linear relationship fitting. Considering that the choice between these fittings does not affect the ranking of important node groups for the study area (Xining City, Qinghai Province), and given that the P-values for the employed linear approximation are all less than 0.05, meeting practical engineering requirements, we adopted the simpler linear approximation for our analysis.

(3) Traffic simulation can be categorized into macroscopic and microscopic traffic simulation. Specifically, as this study investigates the impact of node group selection on the energy conservation and emission reduction effects of the overall road network, it falls under the category of macroscopic traffic simulation in terms of scale. However, implementing this process within traffic simulation software, while also considering factors such as varying vehicle compositions, queue lengths, and different traffic congestion mitigation strategies and measures, presents a significant challenge. We look forward to a more in-depth discussion with you on this topic.

Comments 1: It is unclear how dependence (1) was obtained. Moreover, its coefficient values ​​are rather strange. Is this always true? Perhaps this dependence is relevant only for a selected region or even a city district?

Response 1: Thank you for your professional comments. Regarding Dependency (1): This relationship is derived from linear fitting based on the correspondence between average operating speed and Level of Service (saturation) for general urban road sections, as specified in the industry standard of the People's Republic of China, the Code for Design of Urban Roads (CJJ37-2012) (see Table 1). This is presented on Page 3, Table 1, Lines 121-122, and in Equation (1).

Comments 2: Expressions (2) and (3) provide a rough approximation. The deviations are significant. Approximation should be performed using polynomials.

Response 2: Thank you for your professional comments. As you pointed out, the polynomial approximation does yield results superior to those of the linear approximation (particularly for Equation (3)). We carefully compared the two approximation approaches and their respective impacts on the selection of key urban road node groups and the associated effects on energy conservation and emission reduction. Our analysis indicates that this choice does not affect the ranking results of important node groups (see Table 5). Furthermore, considering that the P-values for Dependencies (2) and (3) are both less than 0.05, thereby meeting engineering requirements, this study continues to employ the original Dependencies (2) and (3). The corresponding P-values have now been added following these dependencies, as shown on Page 4, Equations (2) and (3).

Comments 3: Further calculations using formulas (6) and (7) yield very approximate values ​​due to the lack of verification of formulas (2) and (3) – they do not reflect the actual values ​​in the graphs.

Response 3: Thank you for your professional comments. As can be seen from Dependencies (4) and (5), ，Therefore, by substituting into Equations (2) and (3), Dependencies (6) and (7) are obtained. The value of ​ in Dependency (5) is provided on Page 4, Lines 139-141.

Comments 4: Formula (8) is also approximate.

Response 4: Thank you for your professional comments. The reason for retaining the original approximation here is the same as that provided in the response to the first comment. However, the P-value for this approximation has now been added, as shown in Equation (8) on Page 5.

Comments 5: The conclusions following formula (8) are obvious – emissions decrease with increasing speed.

Response 5: Yes, vehicle emissions decrease as speed increases, which aligns with the combustion theory of internal combustion engines. This also confirms that traffic congestion can, to a certain extent, impact vehicle exhaust emissions in urban areas.

Comments 6: Formula (10) is not substantiated. Why is this?

Response 6: Thank you for your careful review. Equation (10) represents a fundamental concept in complex networks. The purpose of introducing this concept here is to lay the groundwork for subsequently characterizing the operational state of the entire network and its relationship with vehicle emissions. This objective and explanation have now been added on Page 5, Lines 160-162.

Comments 7: Formula (11) lacks upper and lower summation limits.

Response 7: Thank you for your careful review. The upper and lower limits of the summation have been added as requested, as shown in Equation (11) on Page 5.

Comments 8: The criteria for establishing alpha values ​​in the table are unclear. 4.

Response 8: Thank you for your professional comments. The alpha values corresponding to different traffic operation states in Table 4 are primarily proposed based on the industry standard of the People's Republic of China (main content shown in Table 1). The purpose is to characterize the impact of different node group selections on overall network vehicle emissions through changes in the overall network improvement efficiency. An explanation regarding this has now been added on Page 6, Lines 176-195.

Comments 9: Expressions (14) and (15) raise doubts. It's unclear why the relationships are linear. Where were they derived?

Response 9: Thank you for your professional comments. As shown in Equation (13), the relationship between the alpha values (saturation) and the improved overall network efficiency indicator  is highly complex, influenced by factors such as the network scale N and the distance between nodes ​. Here, based primarily on Equation (13) and combined with the urban core road network of Xining City, Qinghai Province, we derived the representative saturation values (0.2, 0.6, 0.8) corresponding to different network operation states and their associated baseline network efficiencies (0.1892, 0.0631, 0.0473). Furthermore, through linear fitting of these data points, we obtained an approximate relationship between the alpha values and .

Comments 10: The article's results rely heavily on the chosen estimation model using linear approximation. I have concerns about the validity of this assessment. The authors selected the coefficients and parameters in the model without scientifically substantiating the criteria.

I find the work interesting. However, the conclusions and recommendations are based on the authors' assumptions and are not substantiated. Scientific substantiation for the choice of relationships and coefficient values ​​is needed. Therefore, the results seem irrelevant and even far-fetched. However, the authors' general conclusions are quite clear.

Response 10: Thank you for your professional comments. Due to constraints such as the limited number of traffic police in actual urban settings, it is necessary to identify more critical node groups for the specific process of traffic congestion mitigation. This study attempts to investigate this issue from the perspective of energy conservation and emission reduction, and as you noted, this endeavor is indeed very interesting. However, the expression of the relationship between vehicle emissions and factors such as vehicle speed and the degree of road congestion (including saturation) can vary due to the combined influence of different urban topographies, road gradients, the number and width of lanes, and the degree of intersection congestion. Specifically for this paper, considering that the choice between polynomial and linear approximations does not affect the ranking of important node groups for Xining City, Qinghai Province, and given that the P-values for the employed linear approximations are all less than 0.05, thereby meeting practical engineering requirements, we adopted the simpler linear approximation for our analysis.

Comments 1: Relationship Between Saturation Degree and Vehicle Emissions.

Please explain the choice of the road network of this city as an example.

Response 1: Thank you for your careful review. The relationship between saturation and vehicle emissions is shown in Equation (9). The greater the saturation, the higher the vehicle emissions. This relationship is also reflected in the conclusions, as detailed in lines 286-288 on page 11.

Comments 2: Table 1. Correspondence between speed and saturation of road sections

Please explain what the data marked in red means.

Response 2: Thank you for your careful review. 0.77<α<1 represents saturated traffic flow and forced traffic flow, see Table 1 on page 3 for details.

Comments 3: Figure 1. Relationship between travel time delay rate and exhaust emissions of test

vehicles on each section of Xinning Road during the morning peak.

Please specify p-value for the regression equations to demonstrate the statistical differences between the coefficients of the equations and zero.

Response 3: Thank you for your professional comments. P-values have been added as required. Since all P-values are less than 0.05, it indicates that the influence of the independent variable on the dependent variable is statistically significant and should be retained in the model. See formulas (2) and (3) on page 4 for details.

Comments 4: Conclusion

Please briefly indicate whether these patterns can be observed in the road network of other cities in China and cities of other countries, not only in the studied urbanized area.

Response 4: Thank you for your professional comments. A comparison with the findings of relevant literature (where the study area was urban roads in Xiangyang City, Hubei Province, China) reveals that the relationship between vehicle emissions and factors such as vehicle speed and the degree of road congestion (including saturation) can vary due to the combined influence of different urban topographies, road gradients, the number and width of lanes, and the degree of intersection congestion. Furthermore, this paper represents the first exploration of the relationship between the selection of road intersection groups and energy conservation and emission reduction; no similar studies have been found in the existing literature.

Comments 1: Remove references from conclusion. 

Response 1: Thank you for your careful review. The corrections have been made as requested. Please refer to Page 3, Lines 101-104, and Page 11, Conclusion item 6.

Comments 1: For example, in formula (8) and throughout the text, powers of 10 are indicated in a superscript, while in Tables 5 and 6 they are inline.

Response 1: Thank you for your careful review. The corrections have been made as requested, as shown in Table 5 on Page 8 and Table 6 on Page 10.

Comments 2: The authors use a multiplication sign in the formulas as a dot, in Table 3, and in the tables below as a cross.

Response 2: Thank you for your careful review. The correction has been made as requested, as shown in Equation (8) on Page 5.

Comments 3: The authors don't use punctuation marks after formulas. Furthermore, before formulas (10), (11), and (13), the punctuation mark "period" is incorrect, when a "colon" is required.

Response 3: Thank you for your careful review. Punctuation has been added and revised as requested.

Comments 4: In formula (11) and beyond, the indication that "i is not equal to j" should be indicated after the formula, not within it. This is not accepted practice in mathematics.

Response 4: Thank you for your careful review. The corrections have been made as requested, as shown in Equation (11) and Equation (13) on Page 6.

Comments 5: The notation for short slashes in tables is unclear, for example, "28、 33、 45."

Response 5: Thank you for your careful review. The corrections have been made as requested, as shown in Table 5 on Page 8 and Table 6 on Page 10.