A Dynamic Estimation Method, Device, and Medium for the Carbon Emission Reduction Effect of Bus Lanes

Abstract

The present invention discloses a method, device, and medium for dynamically estimating the carbon emission reduction effect of bus lanes, including: estimating the mileage of bus lines traveling on bus lanes and the line mileage traveling on general lanes, so as to classify the types of bus lines and divide the bus lines into first bus lines mainly traveling on bus lanes and second bus lines mainly traveling on general lanes; according to the types of bus lines, using the influencing factors at the bus stop level, bus link level, and bus line level as covariates, taking each bus line as a sample, and using a dynamic emission reduction effect estimation model to estimate the conditional emission reduction index and average emission reduction index of bus lanes, so as to quantify the global and local emission reduction effects of the carbon emission factor per passenger. Combining cross-city bus data and introducing random effects at the regional level, a multi-level dynamic emission reduction effect estimation model is constructed to reveal the differences in the emission reduction effects of bus lanes between different cities.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban transportation carbon emission reduction, and particularly relates to a method, device, and medium for dynamically estimating the carbon emission reduction effect of bus lanes. Background Art

[0002] With the accelerating urbanization process, the problems of traffic congestion and carbon emissions are becoming increasingly severe. As a core component of the urban transportation system, improving the carbon emission efficiency of public transportation is of great strategic significance for achieving a low-carbon city. In recent years, bus lanes, as an effective measure to improve public transportation efficiency, have been widely used. The setting of bus lanes can effectively reduce the delay of bus vehicles, improve the attractiveness of public transportation, and thus reduce the usage frequency of private vehicles, indirectly reducing the overall carbon emissions of urban transportation. The per-passenger bus carbon emission factor (PBF), as an evaluation index of relative carbon emission efficiency, is more suitable for evaluating the emission reduction benefits across time and regions. However, quantifying the specific emission reduction effect of bus lanes on PBF still faces many challenges.

[0003] Existing research mainly focuses on the following aspects:

[0004] (1) Existing research mainly focuses on the improvement effect of bus lanes on traffic flow and operation efficiency, while there is a lack of dynamic quantification research on carbon emission reduction effects.

[0005] (2) Existing research on the emission reduction effect of bus lanes mostly uses over-assumed simulation environments and lacks estimation methods based on real data.

[0006] (3) Some studies attempt to analyze the carbon emission effect of bus lanes using fixed time periods or static methods, but ignore the impact of different time periods, different line characteristics, and urban differences on the emission reduction effect.

[0007] (4) There is less analysis of the differences in the emission reduction effects of bus lanes set in different regions or cities, and there is a lack of multi-level emission reduction effect modeling across cities. Summary of the Invention

[0008] In view of the deficiencies of the prior art, embodiments of the present invention provide a method, device, and medium for dynamically estimating the carbon emission reduction effect of bus lanes.

[0009] In a first aspect, embodiments of the present invention provide a method for dynamically estimating the carbon emission reduction effect of bus lanes, the method comprising:

[0010] Estimate the mileage of a bus line traveling on a bus lane and the line mileage traveling on a general lane, so as to classify the types of bus lines.

[0011] According to the type of bus line, with the influencing factors at the bus stop level, bus link level, and bus line level as covariates, and each bus line as a sample, estimate the emission reduction effect of the unit passenger carbon emission factor of the bus lane.

[0012] In a second aspect, an embodiment of the present invention provides an electronic device, including:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the above-mentioned dynamic estimation method for the carbon emission reduction effect of the bus lane.

[0016] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program realizes the above-mentioned dynamic estimation method for the carbon emission reduction effect of the bus lane when executed by a processor.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instructions, and the computer program / instructions realize the above-mentioned dynamic estimation method for the carbon emission reduction effect of the bus lane when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0019] Figure 1 It is a flowchart of the dynamic estimation method for the carbon emission reduction effect of the bus lane provided by the embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the CRE distribution under the line turnover volume, fleet size, and arterial road ratio provided by the embodiment of the present invention;

[0021] Figure 3 It is a DVI analysis result diagram of the DRE model provided by the embodiment of the present invention;

[0022] Figure 4 It is a schematic diagram of an electronic device provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] It should be noted that, without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0025] As Figure 1 shown, the embodiment of the present invention provides a method for dynamically estimating the carbon emission reduction effect of a bus lane, and the method includes the following steps:

[0026] Step S1, estimate the mileage of the bus line running on the bus lane and the mileage of the line running on the general lane, so as to classify the types of bus lines and classify the bus lines into the first bus line mainly running on the bus lane and the second bus line mainly running on the general lane.

[0027] Furthermore, the bus GIS line data usually does not directly include the mileage information of the bus lane. Therefore, in this example, the bus line is matched with the bus lane to estimate the mileage of the bus lane corresponding to the target bus line; specifically, it includes the following sub-steps:

[0028] Step S101, obtain the bus GIS line data, and obtain the set of bus lanes according to the bus GIS line data ; for each bus line in the set of bus lanes , set a distance threshold , and divide the bus line into several sub-line segments according to the distance threshold ; the expression is as follows:

[0029]

[0030] In the formula, represents the length of the bus line ; in this example, the distance threshold can be set to 5 meters or 10 meters.

[0031] It should be noted that when the distance threshold is small enough, the spatial position of each sub-line segment can be approximately represented by the midpoint of each sub-line segment.

[0032] Step S102, obtain the bus GIS line data, and obtain the bus line Bus lane mileage and the proportion of bus lane mileage .

[0033] Specifically, for a sub-route segment , obtain the bus lane segment k closest to the midpoint of this sub-route segment ;

[0034] Calculate the perpendicular projection distance from the midpoint to the bus lane segment k ; if the perpendicular projection distance is less than the total lane width of the bus lane segment k, it is determined that the bus is traveling on the bus lane on the sub-route segment ; otherwise, it is determined that the bus is traveling on the general lane on the sub-route segment ;

[0035] Traverse each sub-route segment to determine whether the bus is traveling on the bus lane or the general lane on each sub-route segment

[0036] Accumulate the lengths of the sub-route segments traveling on the bus lane to obtain the bus lane mileage of the bus route , and the expression is as follows

[0037]

[0038] In the formula represents the length of the sub-route segment of the bus route .

[0039] Furthermore, calculate the proportion of the bus lane mileage of the bus route , and the expression is as follows :

[0040]

[0041] Step S103: Traverse all bus routes to obtain the bus lane mileage and the proportion of bus lane mileage corresponding to each bus route. Cluster the bus lane mileage and the proportion of bus lane mileage corresponding to all bus routes, and divide the bus routes into the first bus routes mainly traveling on the bus lane and the second bus routes mainly traveling on the general lane

[0042] Step S2: According to the type of bus line, using the bus stop-level influencing factors, bus link-level influencing factors, and bus line-level influencing factors as covariates, and taking each bus line as a sample, estimate the emission reduction effect of the per-passenger carbon emission factor of the bus lane.

[0043] Furthermore, the bus stop-level influencing factors include: stop density, dynamic population density within the stop service area, working POI density, and residential POI density; the bus link-level influencing factors include: bus link speed, real-time passenger load rate of the link, and proportion of road categories of the link; the bus line-level influencing factors include: fleet size, vehicle size, line departure interval, bus-bus line connectivity index, bus-subway line connectivity index, and line turnover volume.

[0044] It should be noted that in an ideal situation, the best way to evaluate the emission reduction effect is to compare the scenarios with and without the implementation of the bus lane measure, that is, to calculate the conditional emission reduction effect (CRE) under the covariate f. Its formula can be expressed as:

[0045]

[0046] In the formula, T represents the control variable, T = 0 indicates that the current bus line is the first bus line mainly running on the bus lane, and T = 1 indicates that the current bus line is the second bus line mainly running on the general lane.

[0047] Since it is impossible to simultaneously observe the PBF values of the bus line in these two states, this poses a significant challenge to the estimation of the emission reduction effect of the bus lane. For this reason, this example proposes a dynamic emission reduction effect estimation model (DRE model), which uses a data-driven method and takes the bus stop-level influencing factors, bus link-level influencing factors, and bus line-level influencing factors as covariates to characterize the potential factors affecting PBF from multiple dimensions; among them, for each time period t and covariate f, calculate the conditional emission reduction effect of the bus lane, and the expression is as follows:

[0048]

[0049] In the formula, the numerator represents the weighted covariance of the result residual and the treatment residual, and the denominator represents the weighted variance of the treatment residual; represents the sample size, is the per-passenger carbon emission factor of the bus line within the time period t; represents the kernel weight of the covariate f within the time period t, and the samples with higher weights contribute more to the conditional emission reduction effect CRE under the covariate f.

[0050] It should be noted that the dynamic emission reduction effect estimation model (DRE model) fits the outcome model and the propensity score model, thereby enhancing the robustness of the causal effect estimation. Even if either the outcome model or the propensity score model is correctly specified, the estimation results can still remain consistent.

[0051] Specifically, Indicates exclusion of The period after samples The main effect of is expressed as follows:

[0052]

[0053] Indicates exclusion of The period after samples The propensity score within is expressed as follows:

[0054] .

[0055] Furthermore, in order to standardize the actual impact of bus lanes on the carbon emission factor per passenger, the conditional reduction index (CRI) is proposed, which is expressed as follows:

[0056]

[0057] in, represents the conditional emission reduction index under the condition of covariate f in period t, PBF Indicates during the period Internal covariates The PBF estimate when no bus lane is set up under the condition is expressed as follows:

[0058]

[0059] in, is a weighting coefficient used to enhance the robustness of the estimation.

[0060] Furthermore, to capture the variation of CRI with specific covariates The nonlinear law of change is used to identify the key parameter values when maximizing carbon efficiency, and the Gaussian additive model (GAM) is used to fit the CRI. This model can flexibly model the nonlinear effects of multiple covariates, and the form of expression is as follows:

[0061]

[0062] in, is the model intercept, Represents the covariate The smoothing function estimate of are independent and identically distributed Gaussian error terms, reflecting the random perturbations of the model fitting. To evaluate the credibility of the fitting results, a 95% confidence interval is introduced to quantify the fitting results, thereby characterizing the uncertainty of the model under different covariate values. Specifically, for the covariate in the sample the confidence interval is calculated by the formula:

[0063]

[0064] where is the standard error of the GAM model fitting. The introduction of this confidence interval not only improves the interpretability of the model results but also provides a credible range for actual policy-making.

[0065] To measure the overall impact of the bus lane setting on PBF, this example defines the average reduction effect (ARE) as:

[0066]

[0067] Since the PBF values under and states cannot be directly obtained, the following formula is used to estimate :

[0068]

[0069] In addition, to further standardize the emission reduction potential of ARE relative to the scenario without bus lanes, this example defines the average reduction index (ARI):

[0070]

[0071] To enhance the stability and flexibility of the estimation, this example adopts the R-Learner framework to optimize the estimation process by decoupling the treatment effect and confounding factors. The optimization objective function is as follows:

[0072]

[0073] where is the regularization term, used to reduce overfitting and lower the model complexity.

[0074] Furthermore, Table 1 summarizes the dynamic ARI estimation values of the dedicated bus lanes.

[0075] Table 1: Summary Table of Dynamic ARI Estimation Values of Bus Lanes

[0076]

[0077] As can be seen from Table 1 above, the ARI p-values for the time periods of 08:00 - 09:00 and 18:00 - 19:00 are both lower than 0.05, indicating that during these two time periods, the bus-only lane has a more significant effect on reducing emissions; while the p-values for the time periods of 07:00 - 08:00 and 17:00 - 18:00 are higher than 0.05, indicating that their emission reduction effect is relatively limited. This highlights the dynamic characteristics of the carbon emission reduction effect of the bus-only lane. Since the bus-only lane is mainly set in urban areas, many commuters from the suburbs have not entered the city during the 07:00 - 08:00 time period, resulting in a low utilization rate of the bus-only lane. Therefore, the carbon emission reduction effect during this time period is not as obvious as that in other peak time periods. In view of this, it is recommended that the bus-only lane should be dynamically allocated according to actual needs, rather than being reserved specifically for buses only during peak time periods.

[0078] Figure 2 shows a schematic diagram of the conditional emission reduction effect CRI distribution under line turnover volume, fleet size, and arterial road proportion; from Figure 2 it can be seen that during peak time periods (07:00–09:00 and 17:00–18:00), the passenger turnover rate and the CRI curve show an inverted "V" shape. Specifically, when the passenger turnover rate is lower than 10 4 pax×km / h, the CRI value increases, indicating an improvement in carbon emission efficiency; while when the passenger turnover rate exceeds 10 4pax×km / h, the CRI value decreases, reflecting the reduction in emission efficiency. This trend may be due to the addition of more buses or articulated buses on high-turnover routes, resulting in increased congestion in dedicated lanes. The poor maneuverability of articulated buses further exacerbates this problem and reduces the operational efficiency of the bus system. To meet these challenges and maintain carbon emission reduction benefits, measures such as expanding the number of dedicated lanes or optimizing bus route configuration in high-turnover areas are recommended. The CRI curve of bus fleet size also presents an inverted "V" shape. In particular, when the fleet size is between 60 and 70 vehicles, the CRI value will be temporarily stable or slightly increase, which may be due to the optimization of bus route design and scheduling, which temporarily alleviates the congestion in dedicated lanes. In contrast, the CRI curve of the proportion of main roads shows a continuous upward trend. As the proportion of main roads increases, the number of intersection conflict points decreases, thereby improving the traffic efficiency of dedicated lanes. The impact of departure interval on CRI values in different time periods also varies. During the 07:00–08:00 and 17:00–18:00 periods, when the departure interval is less than 10 minutes, the CRI curve rises sharply, while when the interval is 10 to 20 minutes, the CRI value drops significantly. This is because passenger demand is low during these periods and frequent departures lead to increased carbon emissions. In contrast, during the 08:00–09:00 period, due to high passenger demand, frequent departures help reduce carbon emissions, so the CRI curve generally decreases. When the departure interval exceeds 20 minutes, the CRI curve tends to be stable or slowly increases in all periods. Although longer departure intervals help reduce the carbon emissions of buses, they will affect the service quality of the bus system. In summary, the optimal key parameters for maximizing carbon efficiency are: passenger turnover rate is about 10 4 pax×km / h, the fleet size is about 40 vehicles, the proportion of main roads is maximized, and the departure interval is about 10 minutes.

[0079] Furthermore, in order to improve the interpretability of the dynamic emission reduction effect estimation model (DRE model), this example proposes a dynamic variable importance index (DVI). The dynamic variable importance index (DVI) aims to quantitatively evaluate the contribution of different covariates to the model prediction results at different time periods. Specifically, the dynamic variable importance index (DVI) randomly disrupts the value of a covariate (while keeping other covariates unchanged), and then recalculates the prediction performance of the DRE model, including the coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE). On this basis, the magnitude of the performance degradation of the DRE model is calculated to obtain the DVI value of the covariate. The more significant the performance degradation, the greater the contribution of the covariate to the prediction results of the DRE model. In order to ensure that the analysis results are more stable and reliable, this example performs multiple bootstrap resamplings on the selected time periods and covariates to obtain a comprehensive DVI evaluation result.Figure 3 Shows the specific results of DVI analysis. According to the analysis results, the impact of passenger turnover on the DRE model is particularly significant during the two time periods of 07:00 - 09:00 and 17:00 - 18:00, and the DVI values are 13.4%, 17.4% and 15.9% respectively. In contrast, during the 18:00 time period, the variable importance of the main road proportion is the highest, and its DVI value reaches 16.5%.

[0080] Furthermore, the method includes:

[0081] Step S3, calculate the difference in emission reduction effects between cities.

[0082] The distribution of bus lanes, traffic flow, lane design and bus system operation characteristics vary significantly among different cities or regions, which may lead to heterogeneity in emission reduction effects. By collecting GIS data of bus lanes, bus line and passenger flow data in multiple cities or regions. Integrate covariates at the station level (such as station density, facility conditions), link level (such as road network density, main road proportion), line level (such as turnover, fleet size) and city level (total economic volume, population). Use a multi-city multi-level dynamic emission reduction effect estimation (HDRE) model, introduce a random effect term at the regional or city level, and describe the difference in emission reduction effects between different cities. The HDRE model can be expressed as:

[0083]

[0084] Where is the th line in the city during the time period is the covariate matrix, including station, link, line and city variables, represents the fixed intercept term, that is, the expected basic emission reduction effect level under the condition that all covariates take zero values, represents the covariate matrix in which the fixed effect coefficients corresponding to each variable, reflecting the marginal impact of each covariate on the emission reduction effect, is the random effect at the city level, used to describe the heterogeneity between different cities, is the random effect of line and time period, used to capture the emission reduction differences between lines and the dynamic changes of time periods, is the error term. Based on the analysis of emission reduction effects in multiple cities or regions, use the latent class mixture model (LCMM), combine city factors and emission reduction effects, divide cities or regions into several latent classes (such as high-efficiency emission reduction cities, low-efficiency emission reduction cities), and analyze the emission reduction patterns of each class of cities, so as to formulate targeted bus lane management strategies for different types of cities.

[0085] Correspondingly, the present application further provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic estimation method for the carbon emission reduction effect of the bus lane as described above. As Figure 4 shown, it is a hardware structure diagram of any device with data processing capabilities where the dynamic estimation method for the carbon emission reduction effect of the bus lane provided by the embodiment of the present invention is located. In addition to Figure 4 the processors, memory, and network interfaces shown, any device with data processing capabilities where the device in the embodiment is located usually may further include other hardware according to the actual functions of the any device with data processing capabilities, which will not be elaborated here.

[0086] Correspondingly, the present application further provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the dynamic estimation method for the carbon emission reduction effect of the bus lane as described above is implemented. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), an SD card, a Flash Card, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the any device with data processing capabilities, and may also be used to temporarily store data that has been output or will be output.

[0087] The above embodiments are only used to illustrate the design concept and features of the present invention, and the purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design concepts disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A dynamic estimation method for the carbon emission reduction effect of bus lanes, characterized in that The method includes: estimating the mileage of a bus line running on a bus lane and the mileage of the line running on a general lane, so as to classify the types of bus lines; according to the type of bus line, using the bus stop-level influencing factors, bus link-level influencing factors, and bus line-level influencing factors as covariates, and taking each bus line as a sample, estimating the emission reduction effect of the unit passenger carbon emission factor of the bus lane; wherein, the process of estimating the mileage of a bus line running on a bus lane and the mileage of the line running on a general lane includes: obtaining bus GIS line data; obtaining all bus lines and the set of bus lanes according to the bus GIS line data; dividing each bus line into a number of equidistant sub-line segments; for a sub-line segment, obtaining the bus lane segment closest to the midpoint of the sub-line segment; calculating the vertical projection distance from the midpoint to the bus lane segment; if the vertical projection distance is less than the total lane width of the bus lane segment, it is determined that the bus runs on the bus lane in this sub-line segment; otherwise, it is determined that the bus runs on the general lane in this sub-line segment; traversing each sub-line segment to determine whether the bus runs on the bus lane or the general lane in each sub-line segment; accumulating the lengths of the sub-line segments running on the bus lane to obtain the bus lane mileage of the current bus line; traversing all bus lines to obtain the bus lane mileage corresponding to each bus line.

2. The dynamic estimation method for the carbon emission reduction effect of a bus-only lane according to claim 1, characterized in that, The process of classifying the types of bus lines includes: clustering the bus lane mileage and the proportion of bus lane mileage corresponding to all bus lines, and classifying the bus lines into the first bus lines mainly running on the bus lane and the second bus lines mainly running on the general lane.

3. A dynamic estimation method for the carbon emission reduction effect of a bus lane according to claim 1, characterized in that, The bus stop-level influencing factors include: stop density, dynamic population density within the stop service area, working POI density, and residential POI density; The bus link-level influencing factors include: bus link speed, real-time passenger occupancy rate of the link, and proportion of road categories of the link; The bus line-level influencing factors include: fleet size, vehicle size, line departure interval, bus-bus line connectivity index, bus-subway line connectivity index, and line turnover volume.

4. A dynamic estimation method for the carbon emission reduction effect of a bus lane according to claim 1, characterized in that The process of estimating the emission reduction effect of the unit passenger carbon emission factor of the bus lane includes: calculating the conditional emission reduction effect of the bus lane based on the sample size, the kernel weight of the covariate f in period t, the unit passenger carbon emission factor of the bus line in period t, the main effect in period t after excluding the i-th sample, the control variable corresponding to the i-th sample, and the propensity score in period t after excluding the i-th sample; wherein, when the i-th sample is the first bus line mainly running on the bus lane, T = 0; when the i-th sample is the second bus line mainly running on the general lane, T = 1; and / or Calculate the average emission reduction effect of the bus lane based on the sample size, the per-passenger carbon emission factor of the bus line in period t, the main effect in period t after excluding the i-th sample, the control variable corresponding to the i-th sample, and the propensity score in period t after excluding the i-th sample.

5. A dynamic estimation method for the carbon emission reduction effect of a bus lane, as claimed in claim 4, wherein The process of estimating the per-passenger carbon emission factor emission reduction effect of the bus lane further includes: Calculate the conditional emission reduction index based on the conditional emission reduction effect of the bus lane and the estimated value of the per-passenger carbon emission factor when the bus lane is not set under the condition of covariate f in period t. and / or, Calculate the average emission reduction index based on the average emission reduction effect of the bus lane, the sample size, the weighting coefficient, and the per-passenger carbon emission factor of the bus line in period t. and / or, Calculate the dynamic variable importance index of a covariate by randomly shuffling the values of the covariate.

6. The dynamic estimation method for the carbon emission reduction effect of a bus lane according to claim 1, characterized in that The method further includes: Obtain the carbon emission reduction effect of the bus lane across cities, including: Obtain the carbon emission reduction effect of the bus lane in each city based on covariates, random effects at the city level, random effects of bus lines and periods, and error factors.

7. An electronic device, characterized in that, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to execute the dynamic estimation method for the carbon emission reduction effect of the bus lane as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by the processor, implements the dynamic estimation method for the carbon emission reduction effect of the bus lane as described in any one of claims 1-6.

9. A computer program product, comprising a computer program / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the dynamic estimation method for the carbon emission reduction effect of the bus lane as described in any one of claims 1-6.

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