Dynamic estimation method and device for carbon emission reduction effect of bus lane, and medium

Abstract

The invention discloses a dynamic estimation method and device for the carbon emission reduction effect of a bus lane and a medium, and the method comprises the steps: estimating the mileage of a bus line running on the bus lane and the mileage of a bus line running on a common lane; the types of the bus routes are divided, so that the bus routes are divided into a first bus route mainly running on the bus lane and a second bus route mainly running on the common lane; and according to the types of the bus lines, taking bus station level, bus link level and bus line level influence factors as covariables, taking each bus line as a sample, and adopting a dynamic emission reduction effect estimation model to estimate a condition emission reduction index and an average emission reduction index of the bus lane so as to quantify global and local emission reduction effects of carbon emission factors of unit passengers. In combination with cross-city bus data, a regional hierarchy random effect is introduced, and a multi-level dynamic emission reduction effect estimation model is constructed to reveal the difference of the emission reduction effects of bus lanes between different cities.

Description

Technical Field

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

[0002] With the acceleration of urbanization, traffic congestion and carbon emissions are becoming increasingly serious. 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 have been widely used as an effective measure to improve the efficiency of public transportation. The establishment of bus lanes can effectively reduce bus delays, increase the attractiveness of public transportation, and thus reduce the frequency of use of private vehicles, indirectly reducing the overall carbon emissions of urban transportation. The bus carbon emission factor (PBF) per passenger is a relative carbon emission efficiency evaluation indicator that is more suitable for cross-time and cross-regional emission reduction benefit assessment. However, there are still many challenges in quantifying the specific emission reduction effect of bus lanes on PBF.

[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 operating efficiency, while there is a lack of dynamic quantitative research on the carbon emission reduction effect;

[0005] (2) Existing studies on the emission reduction effects of bus lanes mostly use overly hypothetical simulation environments and lack estimation methods based on real data;

[0006] (3) Some studies have attempted to use fixed time periods or static methods to analyze the carbon emission effects of bus lanes, but have ignored the impact of different time periods, different route characteristics, and differences between cities on emission reduction effects;

[0007] (4) There is little analysis on the differences in the emission reduction effects of bus lanes 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 in the prior art, the embodiments of the present invention provide a method, device, and medium for dynamically estimating the carbon emission reduction effect of a bus lane.

[0009] In a first aspect, an embodiment of the present invention provides a method for dynamically estimating the carbon emission reduction effect of a bus lane, the method comprising:

[0010] Estimate the mileage of bus routes on bus lanes and the mileage of routes on ordinary lanes, so as to classify bus routes into different types;

[0011] According to the type of bus lines, the bus stop-level influencing factors, bus link-level influencing factors, and bus line-level influencing factors are used as covariates, and each bus line is taken as a sample to estimate the carbon emission factor reduction effect per passenger of bus lanes.

[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 that can be executed 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 method for dynamically estimating 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 having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for dynamically estimating the carbon emission reduction effect of bus lanes.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for dynamically estimating the carbon emission reduction effect of bus lanes.

[0018] Compared with the prior art, the present invention has the following beneficial effects: BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0020] Figure 1 is a flow chart of a method for dynamically estimating the carbon emission reduction effect of a bus lane provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of CRE distribution under line turnover, fleet size and trunk road ratio provided by an embodiment of the present invention;

[0022] Figure 3 is a DVI analysis result diagram of the DRE model provided by an embodiment of the present invention;

[0023] Figure 4It is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that, in the absence of conflict, the features in the following embodiments and implementations may be combined with each other.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamically estimating the carbon emission reduction effect of a bus lane, the method comprising the following steps:

[0027] Step S1, estimating the mileage of the bus line running on the bus lane and the mileage of the bus line running on the ordinary lane, so as to classify the bus lines into a first bus line mainly running on the bus lane and a second bus line mainly running on the ordinary lane.

[0028] Furthermore, the bus GIS route data usually does not directly include the mileage information of the bus lanes. Therefore, in this example, the bus routes are matched with the bus lanes to estimate the mileage of the bus lanes corresponding to the target bus routes. Specifically, the following sub-steps are included:

[0029] Step S101, obtain bus GIS route data, and obtain bus lane set according to bus GIS route data ; For bus lane collection Each bus route , set the distance threshold , according to the distance threshold Bus routes Divided into several sub-line segments; the expression is as follows:

[0030]

[0031] In the formula, Indicates bus routes ; in this example, the distance threshold Can be set to 5m or 10m.

[0032] It should be noted that when the distance threshold When the value is small enough, the spatial position of each sub-circuit segment can be approximately represented by the midpoint of the sub-circuit segment.

[0033] Step S102, obtain bus GIS route data, obtain bus routes Mileage of bus lanes and the proportion of bus lane mileage .

[0034] Specifically, for the sub-line segment , get the distance to the sub-line fragment midpoint The nearest bus-only road segment k;

[0035] Calculate midpoint Vertical projection distance to bus-only road segment k ; If the vertical projection distance If the total lane width is smaller than that of the bus-only road segment k, then the bus is judged to be in the sub-route segment. The bus is traveling on the bus lane; otherwise, the bus is judged to be in the sub-route segment Driving on the ordinary lane;

[0036] Traverse each sub-route segment and determine whether the bus is traveling on a bus lane or a common lane in each sub-route segment;

[0037] Accumulate the length of the sub-route segments running on the bus lane to obtain the bus route Mileage of bus lanes , the expression is as follows:

[0038]

[0039] In the formula, Indicates bus routes Sub-line fragment Length.

[0040] Furthermore, bus routes are calculated The proportion of bus lanes , the expression is as follows:

[0041]

[0042] Step S103, traverse all bus routes, obtain the bus lane mileage and bus lane mileage ratio corresponding to each bus route. Cluster the bus lane mileage and bus lane mileage ratio corresponding to all bus routes, and divide the bus routes into a first bus route that mainly travels on the bus lane and a second bus route that mainly travels on the ordinary lane.

[0043] Step S2, according to the type of bus line, taking 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 carbon emission reduction effect per passenger of the bus lane.

[0044] Furthermore, the bus stop-level influencing factors include: station density, dynamic population density in the station service area, work POI density, and residential POI density; the bus link-level influencing factors include: bus link speed, link real-time passenger load factor, and link road category ratio; 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.

[0045] It should be noted that, ideally, the best way to evaluate the emission reduction effect is to compare the scenarios of whether or not bus lane measures are implemented, that is, to calculate the conditional emission reduction effect (CRE) under the covariate f. The formula can be expressed as:

[0046]

[0047] Wherein, T represents the control variable, T=0 means that the current bus route is the first bus route that mainly travels on the bus lane, and T=1 means that the current bus route is the second bus route that mainly travels on the ordinary lane.

[0048] Since it is impossible to observe the PBF values ​​of bus lines in these two states at the same time, this poses a significant challenge to the estimation of the emission reduction effect of bus lanes. To this end, this example proposes a dynamic emission reduction effect estimation model (DRE model), which uses a data-driven approach and takes bus station-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, the conditional emission reduction effect of bus lanes is calculated, and the expression is as follows:

[0049]

[0050] 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 number of samples, is the bus route within time period t Carbon emission factor per passenger; It represents the kernel weight of covariate f in period t. Samples with higher weights contribute more to the conditional emission reduction effect CRE under covariate f.

[0051] 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.

[0052] Specifically, Indicates exclusion of The period after samples The main effect of is expressed as follows:

[0053]

[0054] Indicates exclusion of The period after samples The propensity score within is expressed as follows:

[0055] .

[0056] 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:

[0057]

[0058] 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 is expressed as follows:

[0059]

[0060] in, is a weighting coefficient used to enhance the robustness of the estimation.

[0061] 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:

[0062]

[0063] in, is the model intercept, Represents the covariate The smoothing function estimate of is an independent and identically distributed Gaussian error term, reflecting the random disturbance of model fitting. In order 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 at The calculation formula is:

[0064]

[0065] in, is the standard error of the GAM model fitting. The introduction of this confidence interval not only improves the explanatory power of the model results, but also provides a credible range for actual policy making.

[0066] In order to measure the overall impact of bus lanes on PBF, this example defines the average emission reduction effect (ARE) as:

[0067]

[0068] Since it is not possible to directly obtain and The PBF value under the state is estimated by the following formula :

[0069]

[0070] In addition, to further normalize the emission reduction potential of ARE relative to the scenario without bus lanes, this example defines the average emission reduction index (ARI):

[0071]

[0072] To enhance the stability and flexibility of the estimation, this example uses the R-Learner framework to optimize the estimation process by decoupling treatment effects and confounding factors. The optimization objective function is as follows:

[0073]

[0074] in, is a regularization term used to reduce overfitting and reduce model complexity.

[0075] Furthermore, Table 1 summarizes the dynamic ARI estimates for dedicated bus lanes.

[0076] Table 1: Summary of dynamic ARI estimates for bus lanes

[0077]

[0078] As shown in Table 1 above, the ARI p-values ​​for the 08:00-09:00 and 18:00-19:00 periods are both lower than 0.05, indicating that the bus lanes have a more significant effect on reducing emissions during these two periods; while the p-values ​​for the 07:00-08:00 and 17:00-18:00 periods are higher than 0.05, indicating that their emission reduction effects are relatively limited. This highlights the dynamic characteristics of the carbon emission reduction effect of dedicated bus lanes. Since bus lanes are mainly set up in urban areas, many commuters from the suburbs have not yet entered the city during the 07:00-08:00 period, resulting in a low utilization rate of dedicated bus lanes. Therefore, the carbon emission reduction effect during this period is not as obvious as other peak periods. In view of this, it is recommended that dedicated bus lanes should be dynamically allocated according to actual needs, rather than just reserved for buses during peak hours.

[0079] Figure 2 A schematic diagram showing the distribution of conditional emission reduction effect CRE under line turnover, fleet size and trunk road ratio is shown; Figure 2 It can be seen that during the peak hours (07:00–09:00 and 17:00–18:00), the passenger turnover rate and CRI curve present an inverted “V” shape. Specifically, when the passenger turnover rate is less than 10 4 pax×km / h, the CRI value increases, indicating that carbon emission efficiency improves; and 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.

[0080] 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 The specific results of the DVI analysis are shown. According to the analysis results, the impact of passenger turnover on the DRE model is particularly significant in the two periods of 07:00-09:00 and 17:00-18:00, with DVI values ​​of 13.4%, 17.4% and 15.9% respectively. In contrast, in the 18:00 period, the variable importance of the proportion of main roads is the highest, with a DVI value of 16.5%.

[0081] Furthermore, the method comprises:

[0082] Step S3, calculating the difference in emission reduction effects among cities.

[0083] The distribution of bus lanes, traffic flow, lane design and bus system operation characteristics in different cities or regions vary significantly, which may lead to heterogeneity in emission reduction effects. By collecting GIS data of bus lanes, bus routes 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, proportion of arterial roads), line level (such as turnover, fleet size) and city level (total economy, population size). Adopt a cross-city multi-level dynamic emission reduction effect estimation (HDRE) model, introduce random effect terms at the regional or city level, and characterize the differences in emission reduction effects between different cities. The HDRE model can be expressed as:

[0084]

[0085] in It is City Lines in the period The emission reduction effect is the covariate matrix, including site, link, route and city variables, represents the fixed intercept term, that is, the expected basic emission reduction effect level under the condition that all covariates are zero. Represents the covariate matrix The fixed effect coefficients corresponding to the variables in reflect the marginal impact of each covariate on the emission reduction effect. is a random effect at the city level, used to characterize heterogeneity among different cities, is the random effect of route and time period, which is used to capture the difference in emission reduction between routes and the dynamic changes in time period. is the error term. Based on the analysis of emission reduction effects in multiple cities or regions, a latent mixture model (LCMM) is used to combine urban factors and emission reduction effects, divide cities or regions into several potential categories (such as high-efficiency emission reduction cities and low-efficiency emission reduction cities), and analyze the emission reduction mode of each type of city, so as to formulate targeted bus lane management strategies for different types of cities.

[0086] Accordingly, the present application also provides an electronic device, comprising: 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 above-mentioned method for dynamically estimating the carbon emission reduction effect of bus lanes. Figure 4 As shown, a hardware structure diagram of any device with data processing capability for the method for dynamically estimating the carbon emission reduction effect of bus lanes provided by an embodiment of the present invention is shown, except Figure 4 In addition to the processor, memory and network interface shown, any device with data processing capability in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capability, which will not be described in detail.

[0087] Accordingly, the present application also provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by the processor, the method for dynamically estimating the carbon emission reduction effect of the bus lane as described above is implemented. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium can also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and can also be used to temporarily store data that has been output or is to be output.

[0088] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their 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 ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A method for dynamically estimating the carbon emission reduction effect of bus lanes, characterized in that: The method comprises: Estimate the mileage of bus routes on bus lanes and the mileage of routes on ordinary lanes, so as to classify bus routes into different types; According to the type of bus lines, the bus stop-level influencing factors, bus link-level influencing factors, and bus line-level influencing factors are used as covariates, and each bus line is taken as a sample to estimate the carbon emission factor reduction effect per passenger of bus lanes.

2. The method for dynamically estimating the carbon emission reduction effect of a bus lane according to claim 1, characterized in that: The process for estimating the number of miles of bus routes that travel on bus lanes and the number of miles of bus routes that travel on regular lanes includes: Get bus GIS route data; Obtain all bus routes and bus lanes according to bus GIS route data; Divide each bus route into a number of equally spaced sub-route segments; For a sub-route segment, obtain the bus-only road segment closest to the midpoint of the sub-route segment; calculate the vertical projection distance from the midpoint to the bus-only road segment; if the vertical projection distance is less than the total lane width of the bus-only road segment, determine that the bus is traveling on the bus-only lane on the sub-route segment; otherwise, determine that the bus is traveling on the ordinary lane on the sub-route segment; Traverse each sub-route segment and determine whether the bus is traveling on the bus lane or the ordinary lane in each sub-route segment; accumulate the length of the sub-route segments traveling on the bus lane to obtain the bus lane mileage of the current bus route; Traverse all bus routes and obtain the bus lane mileage corresponding to each bus route.

3. A method for dynamically estimating the carbon emission reduction effect of a bus lane according to claim 1 or 2, characterized in that: The process of classifying bus routes includes: The bus lane mileage and bus lane mileage ratio corresponding to all bus routes are clustered, and the bus routes are divided into the first bus route that mainly runs on the bus lane and the second bus route that mainly runs on the ordinary lane.

4. The method for dynamically estimating the carbon emission reduction effect of a bus lane according to claim 1, characterized in that: The bus station level influencing factors include: station density, dynamic population density in the station service area, work POI density, and residential POI density; The bus link level influencing factors include: bus link speed, link real-time passenger load factor, and link road category ratio; 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.

5. The method for dynamically estimating the carbon emission reduction effect of a bus lane according to claim 1, characterized in that: The process of estimating the carbon emission factor per passenger of bus lanes to reduce emissions includes: The conditional emission reduction effect of bus lanes is calculated based on the number of samples, 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; when the i-th sample is the first bus line that mainly travels on the bus lane, T=0; when the i-th sample is the second bus line that mainly travels on the ordinary lane, T=1; and / or, The average emission reduction effect of bus lanes is calculated based on the sample size, the carbon emission factor per passenger of the bus line in time period t, the main effect in time period t after excluding the i-th sample, the control variables corresponding to the i-th sample, and the propensity score in time period t after excluding the i-th sample.

6. The method for dynamically estimating the carbon emission reduction effect of a bus lane according to claim 5, characterized in that: The process of estimating the carbon emission reduction effect per passenger of bus lanes also includes: The conditional emission reduction index is calculated based on the conditional emission reduction effect of bus lanes and the estimated carbon emission factor per passenger when bus lanes are not set up under the condition of covariate f in time period t; and / or, The average emission reduction index is calculated based on the average emission reduction effect of bus lanes, sample size, weighting coefficient, and carbon emission factor per passenger on bus lines in time period t. and / or, By randomly shuffling the value of a covariate, the dynamic variable importance index of the covariate is calculated.

7. The method for dynamically estimating the carbon emission reduction effect of a bus lane according to claim 1, characterized in that: The method further comprises: Capture the carbon reduction effects of dedicated bus lanes across cities; including: Based on covariates, random effects at the city level, random effects of bus routes and time periods, and error factors, the carbon emission reduction effect of bus lanes in each city is obtained.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed 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 method for dynamically estimating the carbon emission reduction effect of the bus lane as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the method for dynamically estimating the carbon emission reduction effect of a bus lane according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for dynamically estimating the carbon emission reduction effect of a bus lane as described in any one of claims 1-7 is implemented.

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