Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic
Round 1
Reviewer 1 Report (Previous Reviewer 2)
Comments and Suggestions for AuthorsThe paper should be the authors‘ reworking of the previously rejected paper 《Emission impacts of signal control on light-heavy-duty mixed traffic in intersections: optimization and empirical analysis》（sustainability-2095251）. The authors have answered my previous question.
1. Have the authors responded to and revised the comments on the previously rejected paper? What is the improvement of this paper compared to the previous one?
2. The authors of the previous paper are Jieyu Fan (the corresponding author), Jokhio Sarang, and this time Tian Li added as the corresponding author, please explain the contribution of each author to the paper.
Author Response
- Have the authors responded to and revised the comments on the previously rejected paper? What is the improvement of this paper compared to the previous one?
Response: Yes, I have made changes and responded to the most previous comments.
Firstly, I rewrote the INTRODUCTION, adding relevant literature. A more precise summary of the relevant literature.
Then I streamlined the data collection for the experimental portion and optimized the VSP model portion for both heavy-duty and light-duty vehicles.
In Table 2 Instantaneous emission data for VSP at 2kW/t partition, Bins are changed from 1 kW/t to 2 kW/t, so the table is streamlined and does not affect the calculations.
In “3 Optimization of signal control”, Rephrase the first paragraph in its entirety.
“The frequency of acceleration and deceleration states of vehicles at intersections surpasses that in regular roadway driving due to the random nature of vehicle arrivals [28]. Poorly designed intersection signal timing can trigger severe traffic flow oscillations, leading to a high occurrence of abrupt acceleration and deceleration behaviors in vehicle micro-operations [29]. Previous research indicates that these acceleration and deceleration processes contribute significantly to traffic emissions. Effectively mitigating motor vehicle acceleration and deceleration can yield substantial emission reductions. Notably, existing studies on traffic signal control have neglected emissions in mixed traffic flows comprising both light-duty vehicles and heavy-duty vehicles. This study uniquely prioritizes emissions from mixed traffic flows during the traffic signal control optimization process, with a specific focus on intersections as key points for vehicle emissions reduction.”
In the derivation of the formula, the parameters in the formula are re-varied to make it more accurate.
In “4 Results” and “5. Conclusion”, changes were made not only to the English writing, but also to the results, especially the processing of the data and the pictures of the results.
Add heavy-duty vehicle and light-duty vehicle before signal optimization and after signal optimization.
The figures were redrawn， to present the comparison of data results， every emission is in a single figue.
- The authors of the previous paper are Jieyu Fan (the corresponding author), Jokhio Sarang, and this time Tian Li added as the corresponding author, please explain the contribution of each author to the paper.
Response: Jieyu Fan collected data, idea of modelling, and wrote the paper, and Jieyu Fan was mainly responsible for the revision of the thesis.
Arsalan Najafi gave me a lot of help on English writing and the details of the paper.
Sarang is very good at VISSIM simulation, and the VISSIM simulation in this paper is jointly done by Jieyu Fan and Sarang. Sarang is responsible for running the VISSIM software.
Tian Li helped me to revise the English writting of this paper as a whole, and she also made major contributions in the model processing part, data processing and model modification, as well as the final data result processing. In view of the contribution of Tian Li, we decided to let Tian Li be the corresponding author.
Author Response File: 
Author Response.pdf
Reviewer 2 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsThe study investigates the influence of the emissions (CO, HC, and NOx) on traffic signal control at intersections, and then optimizes traffic signal control. Overall, it is an interesting study. However, there are several major concerns that need to be addressed before publication. Pls see my comments as follows.
1. As per the title of this study, pls explain how to optimize the emission impact of intersection signal control.
2. For equations (1)-(3), the symbols are needed to be interpreted.
3. How to obtain table 2?
4. For the example of Cao’an Highway-Jiasong North Road intersection, more details regarding data collection are suggested to be added. With respect to such specific location, how to obtain the emission data of light-duty vehicles and heavy-duty vehicles?
5. It is confusing that how to optimize the traffic signal control based on the vehicle emission.
6. The limitations and future directions of this study are strongly advised to be supplemented.
7. The writing needs more work. There are several grammar errors.
Comments on the Quality of English LanguageWhile most of sentences are easy to follow, there are several grammar errors that need to be fixed carefully.
Author Response
- As per the title of this study, pls explain how to optimize the emission impact of intersection signal control.
Response: Thank you for your summarizing comments, this is very important for this thesis and I have added to summarize how to optimize the impact of signal control at intersections on emissions.
Revised: We initiated our study by collecting real-time emissions data and traffic data in the field. With this data, VSP model was developed. We use VISSIM simulation to obtain instantaneous speed and acceleration data for vehicle sin the traffic flow. This data was then integrated with the VSP model to calculate traffic emission. We used the ratio of instantaneous emissions between light-duty vehicles and heavy-duty vehicles to convert vehicle factors in calculating traffic emissions. These adjusted parameters were used in signal control optimization model, introducing an emission-oriented approach to signal control. We further quantified and compared the changes in CO, HC, and NOx emissions from light-duty and heavy-duty vehicles before and after signal control optimization at a typical intersection in Shanghai. This study offers an effective method for reducing mixed traffic emissions through efficient signal control.
- For equations (1)-(3), the symbols are needed to be interpreted.
Response: Thanks for the suggestion, it's very useful to understand the formula and I've added the instructions.
Revised: v is the instantaneous speed m/s; a is the instantaneous acceleration m/s2; g is the acceleration of gravity and is set to be 9.81 m/s2; grade is the road gradient, %; n is the integer that divides the interval.
- How to obtain table 2?
Response: Thank you for your comments, I have added explanations and summaries in the paper.
Revised: Based on the VSP values and corresponding instantaneous emissions of exhausts (CO, HC, and NOx) detected by the OBEAS-3000 system, we average the instantaneous emission rates in the same VSP interval to obtain representative emission rates within each VSP interval. These results construct a relationship between the vehicle operating conditions (speed and acceleration), VSP values, and the corresponding emission rates for the different exhausts. There are remarkable differences in the emission rates of different exhausts in the same VSP interval.
- For the example of Cao’an Highway-Jiasong North Road intersection, more details regarding data collection are suggested to be added. With respect to such specific location, how to obtain the emission data of light-duty vehicles and heavy-duty vehicles?
Response: Thanks for your comments, I have added the way the data is collected and the details.
Revised: We selected the Cao’an Highway-Jiasong North Road intersection situated in Jiading District, Shanghai (Fig 1). To ensure the accuracy of the collected data, we employed drones to capture images at intersections during morning and evening peak hours from Monday to Friday. This comprehensive dataset on intersection conditions was then averaged for in-depth analysis. This urban accommodate a diverse traffic stream that includes a significant number of heavy-duty vehicles. By analyzing on-site traffic data alongside the lane design’s saturation traffic flow, we derived the saturation flow ratio for real intersection traffic conditions.
- It is confusing that how to optimize the traffic signal control based on the vehicle emission.
Response: Thanks for your input, I've added an explanation about how to optimize the traffic signal control in mixed traffic flow based on emission. It is mainly based on the emissions of heavy-duty vehicles and light-duty vehicles, calculates the conversion parameters of heavy-duty vehicles and light-duty vehicles (i.e., the emissions of one heavy-duty vehicle are equal to the emissions of N light-duty vehicles), substitutes the parameters into the signal timing formula, and calculates the signal timing.
Revised: The ratio of instantaneous emissions of light-duty vehicles and heavy-duty vehicles is used as the basis for the conversion of the emission coefficients of light-duty vehicles and heavy-duty vehicles, and in this study, according to the ratio of 1 heavy-duty vehicle to N light-duty vehicles, the coefficients are substituted into the signal timing formula, and the signal timing at the intersection is calculated. Based on the conversion of emission coefficients of different vehicles to determine the optimization method of signal control at intersections under a certain heavy vehicle ratio. In the empirical analysis, the changes of CO, HC and NOx emissions of light-duty vehicles and heavy-duty vehicles before and after signal control optimization are quantitatively analyzed based on VISSIM simulation data.
- The limitations and future directions of this study are strongly advised to be supplemented.
Response: Thank you for your comments, limitations and future research are really important to this paper, and with that in mind I have added a section to do an exploration.
Revised: 6. The limitations and future study
This study delves into the emissions of mixed traffic flows at peak-hour intersections, focusing on signal timing optimization and the application of vehicle conversion factors for both heavy-duty and light-duty vehicles. It also examines the emissions of heavy-duty and light-duty vehicles before and after signal timing optimization. Following studies could be in the areas:
(1) While acknowledging that the peak-hour period represents only a segment of overall traffic flow, our future work will address the impact of signal timing optimization on vehicle emissions during off-peak hours.
(2) Due to resource constraints, the current dataset is limited. Future research can overcome these limitations by expanding both the volume of data and the breadth of the investigation, allowing for a more comprehensive analysis of emissions from medium-sized vehicles, buses, and other vehicle models. While our present empirical analysis predominantly centers on urban road intersections, subsequent studies can extend to urban segments and the entire road network.
(3) Traffic emission research primarily informs the creation of urban traffic management and transportation planning. Subsequent research can use simulation methods, and consider emissions variations among different vehicle models and road network structures. This could constructe an expansive traffic emission network simulation tailored in cities.
- The writing needs more work. There are several grammar errors.
Response: Thank you for your comments, I have revised the English writing in my paper and highlighted in red.
Author Response File: Author Response.pdf
Reviewer 3 Report (New Reviewer)
Comments and Suggestions for AuthorsThe paper proposes signal timing adjustments at road intersections in the presence of heavy- and light duty vehicles, and how such adjustments affect the analysis of CO, HC and NOx emissions. The paper is well organized and informative. A question for a future paper remains about the impact of suggested adjustments in the face of stochastic demand. However, it would be useful to at least touch on this issue in this paper.
Comments on the Quality of English LanguageThe paper readability can benefit by using an English language editor.
Author Response
The paper proposes signal timing adjustments at road intersections in the presence of heavy- and light duty vehicles, and how such adjustments affect the analysis of CO, HC and NOx emissions. The paper is well organized and informative. A question for a future paper remains about the impact of suggested adjustments in the face of stochastic demand. However, it would be useful to at least touch on this issue in this paper.
Response: I sincerely appreciate your positive feedback on my paper. Your affirmation has boosted my confidence in pursuing further research on transportation emissions. Specifically, your insights into the potential impacts of proposed adjustments for stochastic demand have motivated me to delve deeper into this aspect in my upcoming research projects. Thank you for your invaluable encouragement.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsThe reviewer comments have been addressed, and the paper is recommended for publication.
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis study is interesting. The reviewer has several concerns that need to be addressed before publication.
1. As mentioned in the Section 1 introduction, “if traffic signals change drivers' driving behavior at intersections by reducing speed, then this will also lead to a relative reduction in pollutant emissions”, does the driving behavior of deceleration reduce the emissions definitely? Abrupt deceleration and idling for a long time always increase pollutant emissions, which is crucial but not considered in this study.
2. Given that the equipment has been used to continuously detect pollutant emissions and kinetic parameters, why use the VSP formula to rebuild the estimation relationship between kinetic parameters and emissions, since the VSP formula is always used with the MOVES (Motor Vehicle Emission Simulator) model and easily affected by the inconsistent vehicle mass with the used vehicle type in this study.
3. In Section 3, there lacks the explanation on how to divide the driving patterns of vehicles at intersections according to five stages, according to the distance, time, or speed?
4. Due to the lack of schematic diagram of the research intersection, it is hard to obtain more details not described clearly in the current paper.
5. The definition of vehicle conversion factor is not precise, it seems equal to the ratio of heavy-duty vehicles, however, it appears in a quantitative form.
6. Importantly, there seems no specific optimization method applied to achieve the minimum objective.
7. It lacks the definition of light and heavy duty vehicles, since the mixed traffic flow in a real scenario during the time period of peak hours always involves other types of vehicles.
8. Some grammar errors exist, and this article still needs to be polished.
Author Response
- As mentioned in the Section 1 introduction, “if traffic signals change drivers' driving behavior at intersections by reducing speed, then this will also lead to a relative reduction in pollutant emissions”, does the driving behavior of deceleration reduce the emissions definitely? Abrupt deceleration and idling for a long time always increase pollutant emissions, which is crucial but not considered in this study.
Response: Thank you for your careful review, it was an inaccurate statement on my part and has been changed to: if traffic signals reduce the time of vehicle delays or acceleration/deceleration times of vehicles, then this will also lead to a relative reduction in pollutant emissions.
- Given that the equipment has been used to continuously detect pollutant emissions and kinetic parameters, why use the VSP formula to rebuild the estimation relationship between kinetic parameters and emissions, since the VSP formula is always used with the MOVES (Motor Vehicle Emission Simulator) model and easily affected by the inconsistent vehicle mass with the used vehicle type in this study.
Response: Since the VSP is a representation of the relationship between vehicle operating speed and acceleration and emissions, the VSP-calibrated emission interval is used to facilitate the output of the vehicle's instantaneous operating speed and acceleration in the simulation to calculate instantaneous emissions.
- In Section 3, there lacks the explanation on how to divide the driving patterns of vehicles at intersections according to five stages, according to the distance, time, or speed?
Response: Thank you for your careful review, I have explained the five stages. It is: uniform motion before entering the intersection, deceleration, stop, acceleration, uniform motion after leaving the intersection
- Due to the lack of schematic diagram of the research intersection, it is hard to obtain more details not described clearly in the current paper.
Response: Thank you for your comments, I have added Figure 2 to show the intersection in situ.
- The definition of vehicle conversion factor is not precise, it seems equal to the ratio of heavy-duty vehicles, however, it appears in a quantitative form.
Response: I have explained the conversion factor, which in this case is one heavy vehicle equals the number of light vehicles. Modifications such as，
According to the headway, the conversion equivalents of the vehicle factors in the traffic flow are 2:1 for light and heavy vehicles (i.e. one heavy vehicle equals 2 light vehicles).
Therefore we optimize the signal timing according to different conversion factors. The vehicle conversion factor is the number of heavy vehicles equal to light vehicles, as in Table 3.
- Importantly, there seems no specific optimization method applied to achieve the minimum objective.
Response: Thank you for your suggestion, to which I have added. “This implies that the intersections with large heavy vehicle ratios will not gain the unlimited right of way as the conversion factor increases, considering the overall efficiency of the intersection. So as the conversion factor between heavy and light vehicles increases, the effective green light time reaches optimality, at which point we can use the emissions from the intersection to test whether the total emissions are minimized when the effective green light time reaches optimality.”
- It lacks the definition of light and heavy duty vehicles, since the mixed traffic flow in a real scenario during the time period of peak hours always involves other types of vehicles.
Response: Thank you for your careful review, I have added an explanation to the paper “The experimental vehicles were selected from light and heavy vehicles according to the research objectives. Light vehicles refer to M1, M2, and N1 vehicles with a gross mass of up to 3.5 tonnes, while heavy vehicles mainly refer to goods vehicles and passenger vehicles with a gross mass of more than 8 tonnes.”
- Some grammar errors exist, and this article still needs to be polished.
Response: Yes, I have made changes to the English grammar and phrasing in the paper.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe authors conducted emission detection experiments in Shanghai urban streets and then combined the VSP model with the VISSIM traffic simulation platform. They proposed a new idea of a vehicle coefficient conversion model based on instantaneous vehicle emissions, which breaks with the traditional conversion model based on headway time distance and provides an effective and innovative basis for optimizing signal timing at intersections.
One question, the authors used the traffic flow data of the Cao'an Highway - Jiasong North Road intersection in Jiading District, Shanghai. The mixed traffic flow consisted of light vehicles and heavy vehicles. However, new energy vehicles, especially electric vehicles, now account for an increasing proportion of light-duty vehicles in Shanghai. In the process of data analysis, the authors should indicate the proportion of new energy vehicles in the collected traffic flow data and process the data to remove the effect of new energy vehicles, for example, before conducting the analysis.
Author Response
The authors conducted emission detection experiments in Shanghai urban streets and then combined the VSP model with the VISSIM traffic simulation platform. They proposed a new idea of a vehicle coefficient conversion model based on instantaneous vehicle emissions, which breaks with the traditional conversion model based on headway time distance and provides an effective and innovative basis for optimizing signal timing at intersections.
One question, the authors used the traffic flow data of the Cao'an Highway - Jiasong North Road intersection in Jiading District, Shanghai. The mixed traffic flow consisted of light vehicles and heavy vehicles. However, new energy vehicles, especially electric vehicles, now account for an increasing proportion of light-duty vehicles in Shanghai. In the process of data analysis, the authors should indicate the proportion of new energy vehicles in the collected traffic flow data and process the data to remove the effect of new energy vehicles, for example, before conducting the analysis.
Response: This is a very, very valuable observation. When I first started my research I considered the proportion of trams in the traffic flow, but the intersections we researched were suburban intersections in Shanghai, where the proportion of new energy vehicles was very small. In the city center, where the proportion of new energy vehicles is larger, I will be looking at a separate topic, which is the impact of the proportion of new energy vehicles on traffic emissions.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors I do not recommend publication as the scientific value of the manuscript in its current form is questionable. The main reasons for my critical evaluation are as follows:
General remarks:
The manuscript should be rewritten and completed in order to clearly present the materials and methods, results and discussion.
Detailed remarks:
Section 1.
Instead of presenting some historical facts, a deeper insight should be made into the new works.
Section 2.
Emission data which are crucial for the idea presented in the paper are generally presented in an unclear manner. The technical parameters of the measuring device and the measurement conditions under which the emission data were collected are unknown. Moreover, the reader does not know either:
How were representative vehicles selected? What research objectives were used? How the data collected for the two light vehicles were merged into one data set? Why only one heavy vehicle was used and why the data set for a heavy vehicle is several times smaller? How the road slope effect was extracted from the heavy-duty vehicle measurement data to be representative of the zero-slope case?It is also not clear why the authors used the established formulas (2 and 3) given in other studies to calculate the VSP instead of equation (1) with the parameters of the vehicles used in this study. Instantaneous emissions are defined for individual VSP intervals by averaging the aggregated data over 1 second intervals, but the aggregation procedure is not defined. In addition, the reader should be informed how measurement uncertainty affected the instantaneous emission data given in Table 1. I think it had a serious impact on the results, e.g. looking at the data presented in the literature, one can expect an increase in the instantaneous CO and HC emissions as a result of the increase in fuel consumption when comparing the data for VSP bins from [0.1)to about [11.12).
Section 3 - Optimization
This chapter is hard to read. The description of the signal timing calculation is more or less chaotic. There are many mistakes: strange sentences like …based on the number of vehicles in light vehicles… wrong units and units abbreviations (cpu, vph), incorrect description of parameters (e.g. fg and fw are described both as correction factor for line width adjustment factor), subscripts etc. The authors do not explain why only three factors from a whole set of important factors are used to calculate the saturation flow. More importantly, the HV equivalent must be used when calculating the heavy-vehicle adjustment factor. What value of the equivalent was assumed in the calculations carried out by the authors? The reader does not know whether the minimum green time was taken into account in the calculations and what method was used to solve the minimization problem (5).
Section 4
This chapter presents the calculation results. First of all, the reader does not know what exactly the authors mean when they write: conversion of vehicle factors – conversion factors for heavy and light vehicles – vehicle type factor conversion and in Table 3 vehicle conversion factor.As I understand it, the idea is to pay more attention to heavy vehicles when calculating the timing of the signal, because pollutant emissions from HVs are several times higher than for light vehicles. Unfortunately, the article does not show how to introduce the aim into the objective function.
The authors used field data for the actual intersection, but they do not even show a diagram of the intersection. Signal times are calculated for different HDV:LDV ratios. However, the general characteristics of the traffic for the proposed solutions are not known. Changes in vehicle delays and overall service levels after applying the proposed signal times are not shown. In 4.4. the authors presented the emission values ​​calculated after applying the proposed signal times. In order to obtain data on instantaneous velocity and acceleration, the VISSIM package was used. As a standard, VISSIM uses stochastic vehicle generators to model traffic in the road network. This means that the model runs need to be repeated with a different Random Seed value. As a consequence, a set of simulation data is obtained. Therefore, the calculation results should be presented together with the calculated confidence intervals.
Since the material and methods are not presented as they should be in a scientific work, the results cannot be evaluated and commented on.
Author Response
Section 1.
Instead of presenting some historical facts, a deeper insight should be made into the new works.
Response: In response to the literature review, I have summarised the gaps in current research, as well as analyzed the research points that should be done.
Firstly, in the current signal control optimization, a combination of current traffic signal control optimization methods and micro-simulation tools assume that a travel cycle includes a constant proportion of free flow, rather than actual random traffic characteristics. Secondly, existing signal control optimization cannot adequately take into account the impact of traffic flow on emissions. In traffic control systems, simulation-based signal control optimization usually results in inaccurate data when the simulation model is complex or the optimization variables are large. Finally, current research has focused on the calculation of emissions from light vehicles or single traffic flows at road junctions. Although environmental benefits are considered, little research has been done on emissions from mixed traffic flows. Different heavy vehicle ratios in mixed traffic flow have different effects on emissions. In this research, the impact of signal control on traffic emissions at intersections is investigated from an emissions perspective by considering different heavy vehicle ratios and optimizing signal control at intersections using traffic delay reduction and emissions as quantitative indicators.
In addition, headway time spacing is generally used in signal control optimization at intersections to convert vehicle factors for light and heavy vehicles. The conversion method is mainly used to optimize signal control for reducing traffic delays and increasing traffic flow. However, in light of the current research findings on traffic emissions, there are still three shortcomings. First, most studies have only looked at emission trends for a single vehicle, but with the demand for traffic flow, there is a need to do further refinement of the factors influencing emissions for different vehicles. Second, due to the complex traffic scenarios and the large amount of data collected, there is currently no detailed classification of vehicle operating conditions, so the analysis of the impact of vehicle operating conditions on emissions is not deep enough. Third, no analysis of the emission patterns of different vehicle models under the same operating conditions has been constructed, and there is a lack of factors and models to quantify and evaluate the influence of mixed traffic flow emissions.
Section 2.
Emission data which are crucial for the idea presented in the paper are generally presented in an unclear manner. The technical parameters of the measuring device and the measurement conditions under which the emission data were collected are unknown. Moreover, the reader does not know either:
How were representative vehicles selected? What research objectives were used? How the data collected for the two light vehicles were merged into one data set? Why only one heavy vehicle was used and why the data set for a heavy vehicle is several times smaller? How the road slope effect was extracted from the heavy-duty vehicle measurement data to be representative of the zero-slope case?
It is also not clear why the authors used the established formulas (2 and 3) given in other studies to calculate the VSP instead of equation (1) with the parameters of the vehicles used in this study. Instantaneous emissions are defined for individual VSP intervals by averaging the aggregated data over 1 second intervals, but the aggregation procedure is not defined. In addition, the reader should be informed how measurement uncertainty affected the instantaneous emission data given in Table 1. I think it had a serious impact on the results, e.g. looking at the data presented in the literature, one can expect an increase in the instantaneous CO and HC emissions as a result of the increase in fuel consumption when comparing the data for VSP bins from [0.1)to about [11.12).
Response: Thank you for your careful review.
Equation 1 is the basic formula for calculating VSP. Equations 2 and 3 are the formulae for calculating VSP for heavy vehicles and light vehicles respectively, after bringing in the parameters of heavy and light vehicles in accordance with Equation 1. We set the road gradient in this study to 0.
Regarding this issue, I have added Table 1 to explain the parameters and types of experimental vehicles.
Table 1 Parameters of the experimental vehicle
|
Vehicle type/parameter |
Light vehicle |
Heavy vehicle |
|
|
Brand |
Volkswagen |
Harvard SUV |
FAW Jie Fang |
|
Total mass（KG） |
1285 |
1725 |
15790 |
|
Engine Displacement（L） |
1.6 |
2.0 |
6.6 |
|
Fuel type |
Petrol |
Petrol |
Diesel |
|
Emission standards |
State IV |
State IV |
State IV |
Because of the large number of vehicles in the traffic flow, the experimental vehicles selected in the article are representative vehicles, and then the vehicle parameters are input into the simulation where the instantaneous operational data for each vehicle in the traffic flow is output in order to calculate the traffic emissions.
Section 3 - Optimization
This chapter is hard to read. The description of the signal timing calculation is more or less chaotic. There are many mistakes: strange sentences like …based on the number of vehicles in light vehicles… wrong units and units abbreviations (cpu, vph), incorrect description of parameters (e.g. fg and fw are described both as correction factor for line width adjustment factor), subscripts etc. The authors do not explain why only three factors from a whole set of important factors are used to calculate the saturation flow. More importantly, the HV equivalent must be used when calculating the heavy-vehicle adjustment factor. What value of the equivalent was assumed in the calculations carried out by the authors? The reader does not know whether the minimum green time was taken into account in the calculations and what method was used to solve the minimization problem (5).
Response: I have modified the statement. ”Under ideal conditions, the optimum cycle and optimum green time are calculated based on the number of light vehicles, but when there is a significant proportion of heavy vehicles in the traffic flow, the impact of heavy vehicles on the traffic flow has to be taken into account.”
Thank you for your careful review, I have made changes to the unit abbreviations and subscripts in the article.
I have used saturation flow for the equivalent values in my calculations and have not considered the minimum green time in my calculations, but have considered the minimum green time and maximum green time in the later green time optimization.
Section 4
This chapter presents the calculation results. First of all, the reader does not know what exactly the authors mean when they write: conversion of vehicle factors – conversion factors for heavy and light vehicles – vehicle type factor conversion and in Table 3 vehicle conversion factor.
As I understand it, the idea is to pay more attention to heavy vehicles when calculating the timing of the signal, because pollutant emissions from HVs are several times higher than for light vehicles. Unfortunately, the article does not show how to introduce the aim into the objective function.
The authors used field data for the actual intersection, but they do not even show a diagram of the intersection. Signal times are calculated for different HDV:LDV ratios. However, the general characteristics of the traffic for the proposed solutions are not known. Changes in vehicle delays and overall service levels after applying the proposed signal times are not shown. In 4.4. the authors presented the emission values ​​calculated after applying the proposed signal times. In order to obtain data on instantaneous velocity and acceleration, the VISSIM package was used. As a standard, VISSIM uses stochastic vehicle generators to model traffic in the road network. This means that the model runs need to be repeated with a different Random Seed value. As a consequence, a set of simulation data is obtained. Therefore, the calculation results should be presented together with the calculated confidence intervals.
Response: I have explained the conversion factor, which in this case is one heavy vehicle equals the number of light vehicles. Modifications such as，
According to the headway, the conversion equivalents of the vehicle factors in the traffic flow are 2:1 for light and heavy vehicles (i.e. one heavy vehicle equals 2 light vehicles).
Therefore we optimize the signal timing according to different conversion factors. The vehicle conversion factor is the number of heavy vehicles equal to light vehicles, as in Table 3.
I have added Figure 2 to show the intersection in situ.
I have added Table 5 to illustrate the average speed and average delay after signal optimization.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsOverall this paper is of high quality and the modelling work appears sound. The discussion and explanations given are generally adequate for a paper of this type.
From the context, I assume that light duty vehicles have been assumed to be non-hybrid gasoline-powered models with well functioning aftertreatment (catalytic converters). This should be stated. In some markets, a significant proportion of light duty vehicles are diesel powered - this point can be mentioned, even if the current model does not take such vehicles into account. Furthermore, the authors might mention the topic of hybridisation of light (and even heavy) duty vehicles and how electric propulsion, engine shutdown/restarts etc might change the picture regarding the impact of signal control strategies. It could be explored to what extent the benefits of the presented signal control strategy (or indeed any signal control strategy) are universal in nature, regardless of the technical characteristics of the local light and heavy duty fleets (fuel type, exhaust aftertreatment system type, electrification [hybridisation], exhaust emissions standard, even vehicle size/mass). Such a topic is of course vast and very complex, but it could be mentioned in a short paragraph.
In terms of the language, some minor changes should be made to improve readability. There are some sentences which need to be connected to the preceding or following text, for example: "Fuel consumption of vehicles increases by an average of 10%. [continuation]"
Author Response
From the context, I assume that light duty vehicles have been assumed to be non-hybrid gasoline-powered models with well functioning aftertreatment (catalytic converters). This should be stated. In some markets, a significant proportion of light duty vehicles are diesel powered - this point can be mentioned, even if the current model does not take such vehicles into account. Furthermore, the authors might mention the topic of hybridisation of light (and even heavy) duty vehicles and how electric propulsion, engine shutdown/restarts etc might change the picture regarding the impact of signal control strategies. It could be explored to what extent the benefits of the presented signal control strategy (or indeed any signal control strategy) are universal in nature, regardless of the technical characteristics of the local light and heavy duty fleets (fuel type, exhaust aftertreatment system type, electrification [hybridisation], exhaust emissions standard, even vehicle size/mass). Such a topic is of course vast and very complex, but it could be mentioned in a short paragraph.
Response: Thank you for your careful review. Regarding this issue, I have added Table 1 to explain the parameters and types of experimental vehicles.
Table 1 Parameters of the experimental vehicle
|
Vehicle type/parameter |
Light vehicle |
Heavy vehicle |
|
|
Brand |
Volkswagen |
Harvard SUV |
FAW Jie Fang |
|
Total mass（KG） |
1285 |
1725 |
15790 |
|
Engine Displacement（L） |
1.6 |
2.0 |
6.6 |
|
Fuel type |
Petrol |
Petrol |
Diesel |
|
Emission standards |
State IV |
State IV |
State IV |
In terms of the language, some minor changes should be made to improve readability. There are some sentences which need to be connected to the preceding or following text, for example: "Fuel consumption of vehicles increases by an average of 10%. [continuation]"
Yes, I have made changes to the English grammar and phrasing in the paper.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors1. The authors do not carefully revised the manuscript based on the reviewer comments.
2. It lacks of the response to the reviewer comments. It is very important.
3. While this study focuses on the signal control optimization with the consideration of emission, traffic safety (such as traffic conflicts) should be accounted for in further studies. Some useful insights can be found in the following references.
A multivariate method for evaluating safety from conflict extremes in real time
Predicting real-time traffic conflicts using deep learning
Author Response
To: Editors and reviewers of Fundamental Research
From:
Jieyu Fan, ULM University
On behalf of all authors of # Sustainability-2095251
Subject: Response to peer-review comments of the submitted paper # Sustainability-2095251
Dear editors and the referees:
The authors sincerely appreciate your comments and suggestions for our submitted paper Sustainability-2095251 on “Impact of signal control on emissions from mixed light and heavy traffic at intersections”. We have considered the comments carefully and revised the manuscript accordingly, as per your valuable suggestions. Please find below the authors’ responses to the comments received from the referees of our paper.
The reviewers’ comments are marked in blue, and our corresponding responses are presented after the reviewers’ comments in black.
Round1
As mentioned in the Section 1 introduction, “if traffic signals change drivers' driving behavior at intersections by reducing speed, then this will also lead to a relative reduction in pollutant emissions”, does the driving behavior of deceleration reduce the emissions definitely? Abrupt deceleration and idling for a long time always increase pollutant emissions, which is crucial but not considered in this study.
Response: Thank you for your careful review, it was an inaccurate statement on my part and has been changed to: if traffic signals reduce the time of vehicle delays or acceleration/deceleration times of vehicles, then this will also lead to a relative reduction in pollutant emissions.
Quote:
One of the main results of this study showed that poor signal control strategies can lead to an increase in the proportion of vehicles violating speed regulations, which leads to an increase in emissions; on the other hand, if traffic signals change drivers' driving behavior at intersections by reducing speed reduce the time of vehicle delays or acceleration/deceleration times of vehicles, then this will also lead to a relative reduction in pollutant emissions.
Given that the equipment has been used to continuously detect pollutant emissions and kinetic parameters, why use the VSP formula to rebuild the estimation relationship between kinetic parameters and emissions, since the VSP formula is always used with the MOVES (Motor Vehicle Emission Simulator) model and easily affected by the inconsistent vehicle mass with the used vehicle type in this study.
Response: Since the VSP is a representation of the relationship between vehicle operating speed and acceleration and emissions, the VSP-calibrated emission interval is used to facilitate the output of the vehicle's instantaneous operating speed and acceleration in the simulation to calculate instantaneous emissions. Modifications as Quote.
Quote:
Vehicle emission patterns are highly dependent on vehicle dynamics during operations, which is a complex process. In this study, we adopt the well-known Vehicle Specific Power (VSP) model to establish the relationship between vehicle dynamics and instantaneous emissions of different exhausts. Making the best of the field data we have collected, we establish instantaneous emission models for both light vehicle and heavy vehicle in terms of CO, HC, and NOx for investigating the traffic emissions in the following contents. VSP is the instantaneous power per unit mass of a vehicle (kW/t), and the transient emissions of a vehicle are closely related to the VSP values. The formula of VSP is Eq. (1). As VSP is a representation of the relationship between vehicle operating speed and acceleration and emissions, VSP calibrated emission intervals are used to correspond to the output of the VSP caculated by vehicle's operating speed and acceleration in the simulation to calculate the instantaneous emissions of the vehicle during the simulation.
In Section 3, there lacks the explanation on how to divide the driving patterns of vehicles at intersections according to five stages, according to the distance, time, or speed?
Response: Thank you for your careful review, I have explained the five stages. It is: uniform motion before entering the intersection, deceleration, stop, acceleration, uniform motion after leaving the intersection
Quote:
The emissions produced by motor vehicles due to acceleration and deceleration at intersections are more serious. In order to study the driving states of vehicles at intersections, this paper divides the driving patterns of vehicles at intersections according to five stages (uniform motion before entering the intersection, deceleration, stop, acceleration, uniform motion after leaving the intersection), the upstream normal driving section, the deceleration section, the stopping section, the acceleration section, and the downstream normal driving section[33].
Due to the lack of schematic diagram of the research intersection, it is hard to obtain more details not described clearly in the current paper.
Response: Thank you for your comments, I have added Figure 2 to show the intersection in situ.
Quote:
We used the Cao'an Highway - Jiasong North Road intersection in Jiading District, Shanghai, as shown in Figure2. A large urban intersection with high morning peak and evening peak traffic flows and a mixed traffic flow consisting of a certain proportion of heavy vehicles. Based on the traffic data from the field study and the lane design's saturation traffic flow, the actual traffic's saturation flow ratio can be calculated, as shown in Table 3.
Fig 2 Caoan Highway - Jiasong North Road Intersection
The definition of vehicle conversion factor is not precise, it seems equal to the ratio of heavy-duty vehicles, however, it appears in a quantitative form.
Response: I have explained the conversion factor, which in this case is one heavy vehicle equals the number of light vehicles. Modifications as Quote.
Therefore we optimize the signal timing according to different conversion factors. The vehicle conversion factor is the number of heavy vehicles equal to light vehicles, as in Table 3.
Quote:
According to the headway, the conversion equivalents of the vehicle factors in the traffic flow are 2:1 for light and heavy vehicles (i.e. one heavy vehicle equals 2 light vehicles). However, from an emission point of view, the instantaneous emissions of a heavy vehicle are much higher than the instantaneous emissions of a light vehicle under the same road conditions and vehicle operating conditions.
The vehicle conversion factor is the number of heavy vehicles equal to light vehicles, as in Table 4.
Table 4 Signal timing with different vehicle conversion factors
|
Vehicle conversion factor |
Import |
Left-turn signal timing(S) |
Through signal timing(S) |
|
HDV:LDV=2:1 |
East-West |
29 |
42 |
|
South-North |
45 |
69 |
|
|
HDV:LDV =5:1 |
East-West |
23 |
48 |
|
South-North |
55 |
59 |
|
|
HDV:LDV =10:1 |
East-West |
19 |
48 |
|
South-North |
58 |
61 |
|
|
HDV:LDV =15:1 |
East-West |
16 |
47 |
|
South-North |
60 |
62 |
|
|
HDV:LDV =20:1 |
East-West |
15 |
47 |
|
South-North |
61 |
62 |
|
|
HDV:LDV =25:1 |
East-West |
14 |
47 |
|
South-North |
62 |
62 |
|
|
HDV:LDV =30:1 |
East-West |
13 |
47 |
|
South-North |
62 |
63 |
Importantly, there seems no specific optimization method applied to achieve the minimum objective.
Response: Thank you for your suggestion, to which I have added. “This implies that the intersections with large heavy vehicle ratios will not gain the unlimited right of way as the conversion factor increases, considering the overall efficiency of the intersection. So as the conversion factor between heavy and light vehicles increases, the effective green light time reaches optimality, at which point we can use the emissions from the intersection to test whether the total emissions are minimized when the effective green light time reaches optimality.”
Quote:
Firstly, the increase or decrease of the effective green time is related to the size of the heavy vehicle ratio at the intersection. The effective green light time will increase with the increase of the conversion factor for inlet lanes with a larger proportion of heavy vehicles, On the other hand, it will decrease with the increase of the conversion factor for inlet lanes with a smaller proportion of heavy vehicles, i.e. inlet lanes with a larger proportion of heavy vehicles will have more right of way. Secondly, although different conversion factors will correspond to different effective green light times, the trend of the effective green light time will gradually become slower as the conversion factor increases. This implies that the intersections with large heavy vehicle ratios will not gain the unlimited right of way as the conversion factor increases, considering the overall efficiency of the intersection. So as the conversion factor between heavy and light vehicles increases, the effective green light time reaches optimality, at which point we can use the emissions from the intersection to test whether the total emissions are minimized when the effective green light time reaches optimality.
It lacks the definition of light and heavy duty vehicles, since the mixed traffic flow in a real scenario during the time period of peak hours always involves other types of vehicles.
Response: Thank you for your careful review, I have added an explanation to the paper
Quote:
The experimental vehicles were selected from light and heavy vehicles according to the research objectives. Light vehicles refer to M1, M2, and N1 vehicles with a gross mass of up to 3.5 tonnes, while heavy vehicles mainly refer to goods vehicles and passenger vehicles with a gross mass of more than 8 tonnes.
Some grammar errors exist, and this article still needs to be polished.
Response: Yes, I have made changes to the English grammar and phrasing in the paper.
Round 2
- The authors do not carefully revised the manuscript based on the reviewer comments.
Response:I have made careful changes to one of the reviewer's items, please check “Quote” or the part of the paper marked in red for details of the requested changes.
- It lacks of the response to the reviewer comments. It is very important.
Response: I have re-explained the changes in detail and have listed the text changes separately.
- While this study focuses on the signal control optimization with the consideration of emission, traffic safety (such as traffic conflicts) should be accounted for in further studies. Some useful insights can be found in the following references.
A multivariate method for evaluating safety from conflict extremes in real time
Predicting real-time traffic conflicts using deep learning
Response: I have referenced two papers in the text.
Quote:
In signal optimisation, traffic conflicts, speed differences, traffic density, speed and safety proxies need to be taken into account[26]. Fu et al[27] studied the fit and dependence of these indicators. These methods utilise several optimization objective-specific methods, where multiple optimization objectives are eventually combined into a single objective so that the final resulting solution depends mainly on the underlying weight variables[28].
- Formosa N, Quddus M, Ison S, et al. Predicting real-time traffic conflicts using deep learning. Accident Analysis & Prevention, 2020, 136: 105429.
- 27. Fu C, Sayed T. A multivariate method for evaluating safety from conflict extremes in real time. Analytic methods in accident research, 2022, 36: 100244.
Author Response File: 
Author Response.docx
Reviewer 3 Report
Comments and Suggestions for AuthorsDear Authors, from my point of view, the most important questions remain unanswered. I did not find any significant correction in the manuscript.
Author Response
To: Editors and reviewers of Fundamental Research
From:
Jieyu Fan, ULM University
On behalf of all authors of # Sustainability-2095251
Subject: Response to peer-review comments of the submitted paper # Sustainability-2095251
Dear editors and the referees:
The authors sincerely appreciate your comments and suggestions for our submitted paper Sustainability-2095251 on “Impact of signal control on emissions from mixed light and heavy traffic at intersections”. We have considered the comments carefully and revised the manuscript accordingly, as per your valuable suggestions. Please find below the authors’ responses to the comments received from the referees of our paper.
The reviewers’ comments are marked in blue, and our corresponding responses are presented after the reviewers’ comments in black.
Section 1.
Instead of presenting some historical facts, a deeper insight should be made into the new works.
Response: In response to the literature review, I have summarized the gaps in current search, as well as analyzed the research points that should be done. Please see the quote for a detailed description. Perhaps my understanding is off, and if the text is inappropriate, please specify the direction of the changes.
Quote:
Firstly, in the current signal control optimization, a combination of current traffic signal control optimization methods and micro-simulation tools assume that a travel cycle includes a constant proportion of free flow, rather than actual random traffic characteristics. Secondly, existing signal control optimization cannot adequately take into account the impact of traffic flow on emissions. In traffic control systems, simulation-based signal control optimization usually results in inaccurate data when the simulation model is complex or the optimization variables are large. Finally, current research has focused on the calculation of emissions from light vehicles or single traffic flows at road junctions. Although environmental benefits are considered, little research has been done on emissions from mixed traffic flows. Different heavy vehicle ratios in mixed traffic flow have different effects on emissions. In this research, the impact of signal control on traffic emissions at intersections is investigated from an emissions perspective by considering different heavy vehicle ratios and optimizing signal control at intersections using traffic delay reduction and emissions as quantitative indicators.
In addition, headway time spacing is generally used in signal control optimization at intersections to convert vehicle factors for light and heavy vehicles. The conversion method is mainly used to optimize signal control for reducing traffic delays and increasing traffic flow. However, in light of the current research findings on traffic emissions, there are still three shortcomings. First, most studies have only looked at emission trends for a single vehicle, but with the demand for traffic flow, there is a need to do further refinement of the factors influencing emissions for different vehicles. Second, due to the complex traffic scenarios and the large amount of data collected, there is currently no detailed classification of vehicle operating conditions, so the analysis of the impact of vehicle operating conditions on emissions is not deep enough. Third, no analysis of the emission patterns of different vehicle models under the same operating conditions has been constructed, and there is a lack of factors and models to quantify and evaluate the influence of mixed traffic flow emissions.
Section 2.
Emission data which are crucial for the idea presented in the paper are generally presented in an unclear manner. The technical parameters of the measuring device and the measurement conditions under which the emission data were collected are unknown. Moreover, the reader does not know either:
How were representative vehicles selected? What research objectives were used? How the data collected for the two light vehicles were merged into one data set? Why only one heavy vehicle was used and why the data set for a heavy vehicle is several times smaller? How the road slope effect was extracted from the heavy-duty vehicle measurement data to be representative of the zero-slope case?
It is also not clear why the authors used the established formulas (2 and 3) given in other studies to calculate the VSP instead of equation (1) with the parameters of the vehicles used in this study. Instantaneous emissions are defined for individual VSP intervals by averaging the aggregated data over 1 second intervals, but the aggregation procedure is not defined. In addition, the reader should be informed how measurement uncertainty affected the instantaneous emission data given in Table 1. I think it had a serious impact on the results, e.g. looking at the data presented in the literature, one can expect an increase in the instantaneous CO and HC emissions as a result of the increase in fuel consumption when comparing the data for VSP bins from [0.1)to about [11.12).
Response: Thank you for your careful review.
The OBEAS-3000 is adapted and calibrated to the technical parameters for the road conditions in Chinese cities. In Section 4, I have marked the experimental location as the intersection of Caoan Highway - Jiasong North Road in Shanghai.
Quote:
In this study, the OBEAS-3000 portable emission tester was used to continuously detect the instantaneous emissions of CO, HC, and NOx, as well as vehicle operating conditions and the accurate position, speed, and acceleration of the vehicle through the GPS system. The OBEAS-3000 is adapted and calibrated to the technical parameters for the road conditions in Chinese cities. It output the instantaneous emission gas mass of the vehicle at different times and locations reflecting the quantitative relationship between the instantaneous emissions of speed and acceleration vehicles and providing a basis for the road network in providing a data basis for the measurement of traffic emissions.
Response: The choice of experimental vehicles was light and heavy duty, corresponding to my topic, and the choice of experimental vehicles was representative of the choice of light and heavy duty vehicles for data collection.
Quote:
The experimental vehicles were selected from light and heavy vehicles according to the research objectives. Light vehicles refer to M1, M2, and N1 vehicles with a gross mass of up to 3.5 tonnes, while heavy vehicles mainly refer to goods vehicles and passenger vehicles with a gross mass of more than 8 tonnes.
Response: Only one vehicle was used for each model to facilitate data collection, with a representative vehicle for each model, and the data collected was imported into VISSIM for simulation. Because of the large number of vehicles in the traffic flow, the experimental vehicles selected in the article are representative vehicles, and then the vehicle parameters are input into the simulation where the instantaneous operational data for each vehicle in the traffic flow is output in order to calculate the traffic emissions.
Equation 1 is the basic formula for calculating VSP. Equations 2 and 3 are the formulae for calculating VSP for heavy vehicles and light vehicles respectively, after bringing in the parameters of heavy and light vehicles in accordance with Equation 1. We set the road gradient in this study to 0.
Quote:
Wyatt et al.[29] provided model parameters of the VSP model for LDVs based on empirical data, and the VSP value of the light vehicle can be expressed by Eq. (2).
(2)
In this study, the effect of the slope is not considered because the experimental areas (Shanghai China) are plain without much variation in altitude, so is set to be 0. Due to the considerable distinctions in vehicular characteristics, the VSP formula for heavy vehicles is not the same as that for light vehicles. Referring to Barth et al.[30], this study uses the following VSP calculation formula for heavy vehicles considering vehicle weight, front-end cross-section, and other parameters regarding HDVs.
(3)
Response: Regarding this issue, I have added Table 1 to explain the parameters and types of experimental vehicles.
Quote:
Table 1 Parameters of the experimental vehicle
|
Vehicle type/parameter |
Light vehicle |
Heavy vehicle |
|
|
Brand |
Volkswagen |
Harvard SUV |
FAW Jie Fang |
|
Total mass（KG） |
1285 |
1725 |
15790 |
|
Engine Displacement（L） |
1.6 |
2.0 |
6.6 |
|
Fuel type |
Petrol |
Petrol |
Diesel |
|
Emission standards |
State IV |
State IV |
State IV |
Section 3 Optimization
This chapter is hard to read. The description of the signal timing calculation is more or less chaotic. There are many mistakes: strange sentences like …based on the number of vehicles in light vehicles… wrong units and units abbreviations (cpu, vph), incorrect description of parameters (e.g. fg and fw are described both as correction factor for line width adjustment factor), subscripts etc. The authors do not explain why only three factors from a whole set of important factors are used to calculate the saturation flow. More importantly, the HV equivalent must be used when calculating the heavy-vehicle adjustment factor. What value of the equivalent was assumed in the calculations carried out by the authors? The reader does not know whether the minimum green time was taken into account in the calculations and what method was used to solve the minimization problem (5).
Response: Thank you for your careful review, I have made changes to the unit abbreviations and subscripts in the article, and I have modified the statement.
Quote:
When there is a certain proportion of heavy vehicles in the lane, the saturation flow needs to be corrected according to the proportion of heavy vehicles. Let the proportion of heavy vehicles be HV, then the proportion of light vehicles is 1 - HV. The default gradient of the intersection in the study is G=0, so the correction factor for heavy vehicles is, the correction factor for lane width is, the correction factor for lane width is, the correction factor for non-motorized vehicles is , and the correction factor for the turning radius of the right turn lane is . Therefore the single-lane saturation flow rate for the inlet lane is derived as,
（10）
（11）
（12）
Where, is the saturation flow of a single lane in the straight lane, (veh/h); is the saturation flow of a single lane in the left turn lane, (veh/h); is the saturation flow of a single lane in the right turn lane, (veh/h).
Response: I have modified the sentences.
Quote:
Under ideal conditions, the optimum cycle and optimum green time are calculated based on the number of light vehicles, but when there is a significant proportion of heavy vehicles in the traffic flow, the impact of heavy vehicles on the traffic flow has to be taken into account.
Response: I have used saturation flow for the equivalent values in my calculations and have not considered the minimum green time in my calculations, but have considered the minimum green time and maximum green time in the later green time optimization.
Section 4
This chapter presents the calculation results. First of all, the reader does not know what exactly the authors mean when they write: conversion of vehicle factors – conversion factors for heavy and light vehicles – vehicle type factor conversion and in Table 3 vehicle conversion factor.
As I understand it, the idea is to pay more attention to heavy vehicles when calculating the timing of the signal, because pollutant emissions from HVs are several times higher than for light vehicles. Unfortunately, the article does not show how to introduce the aim into the objective function.
The authors used field data for the actual intersection, but they do not even show a diagram of the intersection. Signal times are calculated for different HDV:LDV ratios. However, the general characteristics of the traffic for the proposed solutions are not known. Changes in vehicle delays and overall service levels after applying the proposed signal times are not shown. In 4.4. the authors presented the emission values ​​calculated after applying the proposed signal times. In order to obtain data on instantaneous velocity and acceleration, the VISSIM package was used. As a standard, VISSIM uses stochastic vehicle generators to model traffic in the road network. This means that the model runs need to be repeated with a different Random Seed value. As a consequence, a set of simulation data is obtained. Therefore, the calculation results should be presented together with the calculated confidence intervals.
Since the material and methods are not presented as they should be in a scientific work, the results cannot be evaluated and commented on.
Response: I have explained the conversion factor, which in this case is one heavy vehicle equals the number of light vehicles.
Quote:
According to the headway, the conversion equivalents of the vehicle factors in the traffic flow are 2:1 for light and heavy vehicles (i.e. one heavy vehicle equals 2 light vehicles). However, from an emission point of view, the instantaneous emissions of a heavy vehicle are much higher than the instantaneous emissions of a light vehicle under the same road conditions and vehicle operating conditions.
Response: Therefore we optimize the signal timing according to different conversion factors. The vehicle conversion factor is the number of heavy vehicles equal to light vehicles, as in Table 4.
I have added Figure 2 to show the intersection in situ.
I have added Table 5 to illustrate the average speed and average delay after signal optimization.
Quote:
The vehicle conversion factor is the number of heavy vehicles equal to light vehicles, as in Table 4.
Table 4 Signal timing with different vehicle conversion factors
|
Vehicle conversion factor |
Import |
Left-turn signal timing(S) |
Through signal timing(S) |
|
HDV:LDV=2:1 |
East-West |
29 |
42 |
|
South-North |
45 |
69 |
|
|
HDV:LDV =5:1 |
East-West |
23 |
48 |
|
South-North |
55 |
59 |
|
|
HDV:LDV =10:1 |
East-West |
19 |
48 |
|
South-North |
58 |
61 |
|
|
HDV:LDV =15:1 |
East-West |
16 |
47 |
|
South-North |
60 |
62 |
|
|
HDV:LDV =20:1 |
East-West |
15 |
47 |
|
South-North |
61 |
62 |
|
|
HDV:LDV =25:1 |
East-West |
14 |
47 |
|
South-North |
62 |
62 |
|
|
HDV:LDV =30:1 |
East-West |
13 |
47 |
|
South-North |
62 |
63 |
We used the Cao'an Highway - Jiasong North Road intersection in Jiading District, Shanghai, as shown in Figure2. A large urban intersection with high morning peak and evening peak traffic flows and a mixed traffic flow consisting of a certain proportion of heavy vehicles. Based on the traffic data from the field study and the lane design's saturation traffic flow, the actual traffic's saturation flow ratio can be calculated, as shown in Table 3.
Fig 2 Caoan Highway - Jiasong North Road Intersection
The trend of curvers in the figure 3 shows that the effective green light times for each inlet lane at the intersection can be derived for heavy and light vehicles according to different vehicle conversion factors. The average speed and stopping delays at the intersection after signal control optimization are shown in Table 5.
Table 5 Average speed and average delay for different vehicle conversion factors
|
Vehicle conversion factor |
Vehicle type |
Average vehicle speed（km/h） |
Average delay（s） |
Average stoping delay（s） |
|
HDV:LDV =2:1 |
Light vehicles |
18.08 |
53.6 |
41.7 |
|
Heavy vehicles |
19.51 |
44.9 |
30.1 |
|
|
Average |
18.23 |
52.5 |
40.3 |
|
|
HDV:LDV =5:1 |
Light vehicles |
18.56 |
52.6 |
41.1 |
|
Heavy vehicles |
19.55 |
44.0 |
30.1 |
|
|
Average |
18.65 |
50.8 |
39.0 |
|
|
HDV:LDV =10:1 |
Light vehicles |
18.92 |
51.6 |
39.6 |
|
Heavy vehicles |
19.94 |
43.5 |
29.2 |
|
|
Average |
19.04 |
50.4 |
38.1 |
|
|
HDV:LDV =15:1 |
Light vehicles |
19.40 |
49.3 |
38.7 |
|
Heavy vehicles |
20.64 |
42.6 |
28.5 |
|
|
Average |
19.52 |
49.4 |
37.4 |
|
|
HDV:LDV =20:1 |
Light vehicles |
19.70 |
48.7 |
37.7 |
|
Heavy vehicles |
20.99 |
41.3 |
28.5 |
|
|
Average |
20.03 |
47.2 |
36.3 |
|
|
HDV:LDV =25:1 |
Light vehicles |
19.49 |
48.7 |
37.1 |
|
Heavy vehicles |
21.05 |
42.6 |
27.4 |
|
|
Average |
19.61 |
47.8 |
36.6 |
|
|
HDV:LDV =30:1 |
Light vehicles |
19.22 |
47.7 |
37.8 |
|
Heavy vehicles |
20.04 |
42.2 |
27.4 |
|
|
Average |
19.36 |
47.9 |
36.2 |
Author Response File: Author Response.docx
Round 3
Reviewer 1 Report
Comments and Suggestions for AuthorsThe reviewer comments have been addressed, and the paper is recommended for publication.
