Urban road carbon emission calculation method for dynamically calibrating working conditions

Abstract

The invention provides an urban road carbon emission calculation method for dynamically calibrating working conditions, which comprises the following steps: referring to an MOVES model, constructing basic carbon emission factors of working condition units of different vehicle types, calculating second-by-second working conditions based on small sample GPS data, dividing an optimal speed interval by adopting an improved NSGA-II algorithm, and obtaining working condition distribution of different speed intervals. And dynamically updating the working condition correction parameters of the carbon emission factor library. And on the basis of the dynamic carbon emission factor library, urban road dynamic carbon emission calculation is realized in combination with the travel speed and the statistical flow. According to the method, working condition correction factors of different speed intervals are dynamically calibrated through small sample GPS data, traffic state characteristics are reserved, meanwhile, the requirement for vehicle operation data during dynamic carbon emission calculation is lowered, and the problem that working condition data in dynamic characteristics of a carbon emission factor library are difficult to obtain in the past is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic environment and motor vehicle carbon emission, and in particular to a method for calculating carbon emission of urban roads under dynamic calibration conditions. Background Art

[0002] With the rapid development of cities, the deterioration of the ecological environment is becoming increasingly serious. At this stage, urban motor vehicle emissions have become an important factor affecting urban air quality. In order to accurately estimate the characteristics of motor vehicle emissions, developed countries in Europe and the United States have successively developed motor vehicle emission models suitable for macro, medium and micro scenarios since the last century.

[0003] According to the calculation principle of carbon emissions, it can be divided into two categories: average speed and driving conditions. Among them, models based on average speed, such as the Mobile Source Emission Factor Model (MOBILE) and the Computer Programme to Calculate Emissions from Road Transport (COPERT), use relatively fixed working conditions and emission rates during calculation, which makes the model less local and unable to reflect the actual driving status of the vehicle. Models based on driving conditions, such as the International Vehicle Emission Model (IVE) and the Motor Vehicle Emissions Simulator (MOVES), divide the working conditions into multiple micro units (bins) and reflect the actual operating status of the vehicle through the distribution of working conditions. This method can more realistically reflect the actual operating status of the vehicle, but the model has high requirements for the accuracy of the data collected on the vehicle's operating status, and the collection is difficult. Therefore, the current localized carbon emission factor library is mostly a static library with fixed environment and working condition correction, which cannot meet the calculation accuracy requirements of dynamic carbon emissions.

[0004] Many cities have increasingly higher requirements for the accuracy and timeliness of transportation carbon emissions calculations. How to build a localized, precise and dynamic carbon emission factor library remains a key issue. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the MOVES model has high requirements for vehicle operation status data and is difficult to collect, so as to meet the calculation accuracy requirements of dynamic carbon emissions.

[0006] In order to achieve the above-mentioned invention object, the technical solution of the present invention is to provide a method for calculating carbon emissions of urban roads under dynamic calibration conditions, which includes the following steps:

[0007] 1. Classify the vehicle dataset according to vehicle weight and vehicle type. Vehicle weight includes heavy vehicles and non-heavy vehicles. Vehicle types include small passenger cars, medium-sized passenger cars and small trucks. Label the fuel type of each type of vehicle in the obtained vehicle weight and vehicle type classification set. The fuel types of each labeled vehicle type are gasoline and diesel.

[0008] According to the working condition division method of the MOVES model, the VSP-bin working condition interval of non-heavy-duty vehicles and the VSP-bin working condition interval of heavy-duty vehicles are obtained;

[0009] According to the MOVES model, the CO2 emissions of each vehicle type in the corresponding operating range are matched. 2 Emission factors, construct basic emission factors;

[0010] Based on the small sample second-by-second GPS data of each vehicle model, combined with the VSP parameter calculation formula of the corresponding vehicle model, the VSP parameters of each vehicle model and the corresponding VSP-bin operating range classified by acceleration and speed are calculated respectively;

[0011] Based on the small sample second-by-second GPS data, the instantaneous working condition and travel speed of different vehicles of each model are calculated, and the travel speed is used as the speed division index of the speed interval. Combined with the working condition interval division standard of each model, the working condition distribution of each vehicle is calculated;

[0012] Determine the number of speed interval categories based on historical operating data and road traffic flow service level division;

[0013] According to the principle of the highest similarity of the speed interval classification number and the working condition distribution in each interval in the speed division index, the target constraint is performed with the minimum mean square error of the total speed interval and the minimum mean square error of the maximum speed interval as the goal;

[0014] Based on the improved NSGA-Ⅱ algorithm, the optimal small sample speed interval division of each vehicle model is calculated, and the optimal small sample speed interval of each vehicle model is obtained;

[0015] The average proportion of each VSP-bin operating condition interval within the optimal small sample speed range of each vehicle model is taken as the typical operating condition of the corresponding interval;

[0016] According to typical working conditions, the corresponding working condition correction parameters are updated to obtain dynamic working condition correction factors;

[0017] Combined with the impact coefficient of historical ambient temperature on emissions, determine the temperature range and the corresponding temperature correction factor;

[0018] Determine the degradation correction factor by combining the historical vehicle mileage changes with years and the actual service life;

[0019] A localized dynamic carbon emission factor library is constructed based on the basic emission factors of each vehicle type and the corresponding VSP-bin operating range, the speed range and the dynamic operating correction factors of the corresponding range, the temperature correction factors and the degradation correction factors;

[0020] Based on the vehicle type statistical flow and average travel speed in the vehicle dataset and the localized dynamic carbon emission factor library, the urban road carbon emissions under dynamic calibration conditions are calculated.

[0021] Preferably, the small sample second-by-second GPS data also includes the real-time speed, acceleration and slope of the vehicle.

[0022] Preferably, when the corresponding vehicle type is a non-heavy-duty vehicle, the VSP parameter calculation formula is:

[0023]

[0024] Where v is the instantaneous speed of the non-heavy vehicle, a is the instantaneous acceleration of the non-heavy vehicle, g is the acceleration of gravity, sinθ reflects the road slope characteristics, and w is the preset fixed mass parameter corresponding to the non-heavy vehicle model; w 1 、w 2 、w 3 is a constant parameter for different vehicle models.

[0025] Preferably, when the corresponding vehicle type is a heavy-duty vehicle, the VSP parameter calculation formula is:

[0026]

[0027] v 1 is the instantaneous speed of the heavy vehicle; a 1 is the instantaneous acceleration of the heavy vehicle; M is the vehicle mass of the heavy vehicle (t); f scale is a fixed quality coefficient; Z 1 , Z 2 , Z 3 is a constant parameter for different vehicle models.

[0028] Preferably, when the ambient temperature is less than 14°C, the correction coefficient in the temperature correction factor is 1.008, when the ambient temperature is between 14 and 35°C, the correction coefficient in the temperature correction factor is 1, and when the ambient temperature is greater than 35°C, the correction coefficient in the temperature correction factor is 1.507.

[0029] Preferably, the degradation correction factor T in the degradation correction factor is m Calculate using the following formula:

[0030]

[0031] In the formula, m is the model, year is the age of the vehicle, is the ratio of vehicles of model m and age year to the total number of vehicles of model m, is the degradation correction factor corresponding to the vehicle age year.

[0032] Preferably, the calculation formula for the travel speed in the speed interval division is as follows:

[0033]

[0034] In the formula, is the travel speed of vehicle veh of model m on road section l, is the travel time of vehicle veh of model m on road section l, is the collected speed of vehicle veh of model m on road section l in t seconds.

[0035] Preferably, the formula for the mean square error of the total speed interval and the maximum speed interval is as follows:

[0036]

[0037] In the formula, The speed range V of vehicle type m j The average value of the operating condition proportion in the lower VSP-bin interval i; bin_num is the number of operating condition intervals.

[0038] Preferably, the calculation formula for the dynamic carbon emission factor of each vehicle model in the localized dynamic carbon emission factor library is as follows:

[0039]

[0040] Where, T m is the degradation correction factor of vehicle type m; O is the temperature correction factor; is the speed range V of vehicle type m j Dynamic operating condition correction factor for the lower VSP-bin interval i; For vehicle type m using fuel oil in speed range V j The basic carbon emission factor for the medium operating interval m; bin_num is the number of operating intervals.

[0041] Preferably, the calculation formula for the average travel speed of the vehicle type m is as follows:

[0042]

[0043] In the formula, is the travel speed of vehicle veh in vehicle type m; The flow rate of vehicle type m on road L.

[0044] Preferably, the calculation formula for urban road carbon emissions is as follows:

[0045]

[0046] In the formula, CO of road L 2 Emission (g), L len is the length of the road L.

[0047] The technical solution of the present invention proposes a method for calculating carbon emissions of urban roads with dynamically calibrated working conditions. With reference to the MOVES model, the basic carbon emission factors of working condition units of different vehicle models are constructed, and the working conditions are calculated second by second based on small sample GPS data. The improved non-dominated sorting genetic algorithm with elite strategy (NSGA-Ⅱ) is used to divide the optimal speed range, and the working condition distribution of different speed ranges is obtained, and the working condition correction parameters of the carbon emission factor library are dynamically updated. Based on the dynamic carbon emission factor library, combined with the travel speed and statistical flow, the dynamic carbon emission calculation of urban roads is realized. This method dynamically calibrates the working condition correction factors of different speed ranges through small sample GPS data, retains the traffic state characteristics, and reduces the requirements for vehicle operation data during dynamic carbon emission calculation, solving the problem of difficulty in obtaining working condition data in the dynamic characteristics of the carbon emission factor library in the past. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for calculating carbon emissions from urban roads under dynamic calibration conditions according to an embodiment of the present invention;

[0049] Figure 2 This is a dynamic emission factor library table for model m of light vehicles in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0051] An embodiment of the present invention provides a method for calculating carbon emissions of urban roads under dynamic calibration conditions, comprising the following steps:

[0052] Step S1: Construction of dynamic carbon emission factor library.

[0053] This step takes into account that vehicle weight and vehicle type, fuel type, degradation coefficient, temperature and operating conditions are the main factors affecting motor vehicle emissions. Therefore, the design uses a small sample of GPS data to dynamically calibrate the operating condition correction parameters, combined with temperature parameters and degradation correction coefficients, to build a dynamic carbon emission factor library based on the basic emission factor library. The specific steps are as follows:

[0054] Step S1-1: Classify vehicle type and fuel type.

[0055] Vehicles are divided into small passenger cars, medium-sized passenger cars, large passenger cars, small trucks, medium trucks and large trucks according to their weight and type. Among them, small passenger cars, medium-sized passenger cars and small trucks are non-heavy-duty vehicles, while large passenger cars, medium trucks and large trucks are heavy-duty vehicles. Fuel types are divided into gasoline and diesel.

[0056] Step S1-2: Construct a basic carbon emission factor library.

[0057] 1) Classification of working condition characteristics.

[0058] The vehicle specific power (VSP) parameter is calculated based on the vehicle's real-time speed, acceleration and slope. Its physical meaning is the power output by the engine per unit mass (1t) moved. It can better reflect the vehicle's energy consumption and emission characteristics.

[0059] The VSP parameter calculation formula for each type of vehicle in the non-heavy vehicle group is:

[0060]

[0061] Where v is the instantaneous speed of the non-heavy vehicle; a is the instantaneous acceleration of the non-heavy vehicle; g is the acceleration due to gravity; sinθ reflects the road slope characteristics (0 on flat ground); W is the preset fixed mass parameter corresponding to the non-heavy vehicle model (different models have different values); w 1 、w 2 、w 3 is a constant parameter for different vehicle models.

[0062] The VSP parameter calculation formula for each type of vehicle in the heavy-duty vehicle group is:

[0063]

[0064] In the formula, v 1 is the instantaneous speed of the heavy vehicle; a 1 is the instantaneous acceleration of the heavy vehicle; M is the vehicle mass of the heavy vehicle (t); f scale is a fixed quality coefficient; Z 1 , Z 2 , Z 3 is a constant parameter for different vehicle models.

[0065] Referring to the working condition division method of the MOVES model, the working condition intervals are divided according to vehicle acceleration, speed and VSP. The non-heavy-duty vehicle working condition is divided into 23 intervals, and the heavy-duty vehicle working condition is divided into 29 intervals.

[0066] The operating range of non-heavy-duty vehicles is shown in Table 1, and the operating range of heavy-duty vehicles is shown in Table 2.

[0067] Table 1. Non-heavy-duty vehicle operating range

[0068]

[0069] Table 2. Heavy-duty vehicle operating range

[0070]

[0071]

[0072] 2) Basic carbon emission factor.

[0073] The open database in the MOVES model stores CO 2 The basic emission factor is matched according to the matching relationship between the United States and the current vehicle classification to obtain the CO emissions of each vehicle model in different VSP-bin intervals. 2 The emission factor is used as the basic carbon emission factor. For example, the basic carbon emission factor of vehicle m in VSP-bin operating range i is

[0074] Step S1-3: Correction parameters according to working conditions.

[0075] Based on small sample GPS data, the instantaneous working condition of the motor vehicle is calculated, the speed range is divided using the improved NSGA-Ⅱ algorithm, and the working condition correction parameters of different speed ranges are calibrated.

[0076] Step S1-3-1: Dynamic operating condition calculation.

[0077] Step S1-3-1-1: Calculate the second-by-second operating conditions of a small sample.

[0078] The small sample second-by-second GPS data contains the vehicle's instantaneous speed, location information, and other data. Assuming that the vehicle's acceleration remains constant within 1 second, the acceleration a t is the current vehicle speed v t The velocity v at the previous moment t-1 According to different vehicle models, VSP is calculated and the corresponding VSP-bin interval is calculated second by second.

[0079] Step S1-3-1-2: Speed ​​interval division based on improved NSGA-Ⅱ algorithm.

[0080] 1) Extract the travel speed and the corresponding operating condition distribution.

[0081] Calculate the travel speed of vehicle veh of model m on road section l

[0082]

[0083] In the formula, is the travel time of vehicle veh of model m on road section l, is the collected speed of vehicle veh of model m on road section l in t seconds.

[0084] Calculate the ratio of each working condition to the total travel time:

[0085]

[0086] In the formula, The vehicle model m is on the road section l. i The corresponding time proportion, is the operating interval number of vehicle veh of model m on road section l for t seconds, is the travel time of vehicle veh of model m on road section l.

[0087] 2) Determine the number of speed interval categories.

[0088] Based on the previous working condition data survey and the four-level service level classification of road traffic flow, it is proposed to divide the vehicle speed range into four intervals: V 1 , V 2 , V 3 , V 4 Corresponding speed intervals [V min ,V th_1 ), [V th_1 ,V th_2 ), [V th_2 ,V th_3 ), [V th_3 ,V max ].

[0089] 3) Determine the optimization objectives and constraints.

[0090] For the speed range V j The mean square error of the distribution of the vehicle type m in the interval is calculated as f(V j ).

[0091]

[0092] In the formula, is the speed range V of vehicle type m jThe average value of the operating condition proportion in the lower VSP-bin interval i; bin_num is the number of the operating condition interval, which is 23 for light vehicles and 29 for non-light vehicles.

[0093] The goal of speed interval division is to make the operating condition distribution within the interval most similar. Therefore, the calculation targets the minimum mean square error of the total speed interval and the minimum mean square error of the maximum speed interval:

[0094]

[0095] According to the definition of speed interval division, the calculation constraints are:

[0096]

[0097] 4) Calculate the optimal speed interval division based on the improved NSGA-Ⅱ algorithm.

[0098] According to the dual optimization objectives and computational constraints, the optimal speed interval division threshold calculation process is as follows:

[0099] (1) Set the calculation parameters, set the population size to Num, the evolutionary generation to gen_max, the current evolutionary generation to gen, and select the real number encoding according to the research objectives;

[0100] (2) Initial population P 0 , and perform non-dominated sorting, initialize individual rank values, gen = 0, and randomly generate the initial population;

[0101] (3) Using tournament selection method, select P gen Select Num individuals to enter the mating pool. The selection operation is based on the ra□□ value and the crowding distance. For two individuals, the individual with the smaller sequence value is selected. When the sequence values ​​are the same, the larger the crowding distance, the better the population diversity, and the individual with the larger crowding distance is selected. Perform crossover and mutation operations on the individuals in the mating pool to generate a new population Q gen ;

[0102] (4) P gen With Q gen Merge to produce a total R of size 2*Num gen ;

[0103] (5) R gen Classify and select Num individuals through sorting and elite retention strategy to form a new generation of population P gen+1 , maintain the population size as Num, where the elite retention strategy uses the sum of the mean square error of the total speed interval as the fitness value. The smaller the value, the better the fitness;

[0104] (6) Determine whether the population end condition is met. If gen < gen_max, then gen = gen + 1, return to (3); otherwise, output the non-dominated individuals in P gen+1 ;

[0105] (7) Select the solution with the largest mean square deviation and the smallest value of the speed interval from (6) as the speed interval division threshold {V th_1 , V th_2 , V th_3}, and divide the speed interval into [V min , V th_1 ), [V th_1 , V th_2 ), [V th_2 , V th_3 ), [V th_3 , V max .

[0106] Step S1-3-1-3: Calculate the typical working condition distribution of each speed interval.

[0107] Take the average proportion of each VSP-bin interval within the speed interval as the typical working condition of the corresponding interval. The typical proportion of vehicle model m in working condition interval i within speed interval V j is denoted as

[0108] Step S1-3-2: Dynamically calibrate the working condition correction parameters.

[0109] Based on the typical working condition distribution of each speed interval calculated from the small sample data, update the dynamic calibration working condition correction parameters of the corresponding VSP-bin intervals for each speed interval. That is, the dynamic calibration correction parameter of vehicle model m in working condition interval i within speed interval V j is Calculate the carbon emission factor of each speed interval V j by weighted calculation according to the working condition proportion

[0110]

[0111] where is the basic carbon emission factor of vehicle model m in working condition interval i within speed interval V j .

[0112] Step S1-4: Temperature parameter.

[0113] Temperature will have a certain impact on the emission rate. Referring to the influence coefficient O of ambient temperature on emissions obtained from WLTC condition tests at different ambient temperatures in previous studies, when the ambient temperature is less than 14°C, the correction coefficient is 1.008, when the ambient temperature is between 14 and 35°C, the correction coefficient is 1, and when the ambient temperature is greater than 35°C, the correction coefficient is 1.507.

[0114] Step S1-5: Deterioration correction parameters.

[0115] During the actual use of the vehicle, the emission characteristics of the vehicle will further deteriorate. The MOVES model stores the degradation coefficient T under different vehicle age distributions according to the vehicle age, which is divided into vehicle ages less than 3 years, 4-5 years, 6-7 years, 8-9 years, 10-14 years, 15-19 years and more than 20 years. In the study, degradation corrections were made based on the changes in the vehicle's actual mileage over the years and the actual years of use.

[0116] Vehicle age / year Correction factor 0～3 1 4～5 2.5 6～7 4.4 8～9 6.2 ＞10 10.8

[0117] The age distribution of each model is obtained based on the statistical yearbooks of each region. The proportion of vehicles with model age m years to the total number of vehicles of this model is The degradation correction factor corresponding to the vehicle age year is: Then the degradation correction coefficient of vehicle type m is:

[0118]

[0119] Step S1-6: Localize the dynamic carbon emission factor library.

[0120] Based on the above steps, a localized dynamic carbon emission factor library is obtained. Taking light vehicles as an example, the dynamic emission factor library of vehicle type m is as follows: Figure 2 shown.

[0121] Vehicle type m with fuel type oil in speed range V j Dynamic carbon emission factor

[0122]

[0123] Step S2: Calculation of road dynamic carbon emissions.

[0124] Real-time road carbon emissions are calculated based on vehicle type statistics, travel speed and dynamic carbon emission factors. Here, checkpoint data is used as an example.

[0125] Step S2-1: Calculate the flow rate of classified vehicle types.

[0126] The collected vehicle types in the checkpoint data are classified into vehicle types according to the working conditions, and the vehicle type flow of road objects is counted. The flow of vehicle type m on road L is

[0127] Step S2-2: Calculate the travel speed.

[0128] The time difference between vehicles passing through the checkpoint is travel_time veh , the distance of the road section is L len , the travel speed of vehicle veh travel_speed veh It is the ratio of the road section distance to the travel time difference.

[0129] Calculate the average travel speed of vehicle type m on road L:

[0130]

[0131] Step S2-3: Calculation of road dynamic carbon emissions.

[0132] 1) Determine the dynamic carbon emission factor for each vehicle model.

[0133] According to step S2-2, the average travel speed of the vehicle model is calculated and the speed range to which it belongs is determined.

[0134] 2) Calculate dynamic road carbon emissions.

[0135]

[0136] In the formula, CO of road L 2 Emissions (g).

[0137] The above description is a specific embodiment of the present invention and the technical principles used. If the changes made according to the concept of the present invention do not exceed the spirit covered by the description and drawings, they should still fall within the scope of protection of the present invention.

[0138] The beneficial effects of the embodiments of the present invention are as follows: based on small sample GPS data, the real-time working condition characteristics of motor vehicles are calculated, the speed intervals are divided using the improved NSGA-Ⅱ algorithm, and the working condition correction parameters of different speed intervals are calibrated, thereby retaining the real-time working condition characteristics. When calculating dynamic carbon emissions, the travel speed is used as the expression of the single vehicle working condition to obtain the dynamic carbon emission factor of the corresponding speed interval, which reduces the requirements for vehicle operation data, and combines statistical traffic to obtain road dynamic carbon emissions.

Claims

1. A method for calculating carbon emissions from urban roads under dynamic calibration conditions, characterized in that: The following steps are involved: The vehicle dataset is classified according to vehicle weight and vehicle type. Vehicle weight includes heavy vehicles and non-heavy vehicles. Vehicle types include small passenger cars, medium-sized passenger cars and small trucks. The fuel type is annotated for each type of vehicle in the obtained vehicle weight and vehicle type classification set. The fuel types of each annotated vehicle type are gasoline and diesel. According to the working condition division method of the MOVES model, the VSP-bin working condition interval of non-heavy-duty vehicles and the VSP-bin working condition interval of heavy-duty vehicles are obtained; According to the MOVES model, the CO2 emission factors of the corresponding operating conditions of each vehicle model are matched to construct the basic emission factors; Based on the small sample second-by-second GPS data of each vehicle model, combined with the VSP parameter calculation formula of the corresponding vehicle model, the VSP parameters of each vehicle model and the corresponding VSP-bin operating range classified by acceleration and speed are calculated respectively; Based on the small sample second-by-second GPS data, the instantaneous working condition and travel speed of different vehicles of each model are calculated, and the travel speed is used as the speed division index of the speed interval. Combined with the working condition interval division standard of each model, the working condition distribution of each vehicle is calculated; Determine the number of speed interval categories based on historical operating data and road traffic flow service level division; According to the principle of the highest similarity of the speed interval classification number and the working condition distribution in each interval in the speed division index, the target constraint is performed with the minimum mean square error of the total speed interval and the minimum mean square error of the maximum speed interval as the goal; Based on the improved NSGA-Ⅱ algorithm, the optimal small sample speed interval division of each vehicle model is calculated, and the optimal small sample speed interval of each vehicle model is obtained; The average proportion of each VSP-bin operating condition interval within the optimal small sample speed range of each vehicle model is taken as the typical operating condition of the corresponding interval; According to typical working conditions, the corresponding working condition correction parameters are updated to obtain dynamic working condition correction factors; Combined with the impact coefficient of historical ambient temperature on emissions, determine the temperature range and the corresponding temperature correction factor; Determine the degradation correction factor by combining the historical vehicle mileage changes with years and the actual service life; A localized dynamic carbon emission factor library is constructed based on the basic emission factors of each vehicle type and the corresponding VSP-bin operating range, the speed range and the dynamic operating correction factors of the corresponding range, the temperature correction factors and the degradation correction factors; Based on the vehicle type statistical flow and average travel speed in the vehicle dataset and the localized dynamic carbon emission factor library, the urban road carbon emissions under dynamic calibration conditions are calculated.

2. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: The small sample second-by-second GPS data also includes the real-time speed, acceleration and slope of the vehicle.

3. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: When the corresponding vehicle type is a non-heavy-duty vehicle, the VSP parameter calculation formula is: Where v is the instantaneous speed of the non-heavy vehicle, a is the instantaneous acceleration of the non-heavy vehicle, g is the acceleration of gravity, sinθ reflects the road slope characteristics, W is the preset fixed mass parameter corresponding to the non-heavy vehicle model; w1, w2, w3 are constant parameters of different vehicle models.

4. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: When the corresponding vehicle type is a heavy-duty vehicle, the VSP parameter calculation formula is: v1 is the instantaneous speed of the heavy vehicle; a1 is the instantaneous acceleration of the heavy vehicle; M is the vehicle mass of the heavy vehicle (t); f scale is a fixed mass coefficient; Z1, Z2, Z3 are constant parameters for different vehicle models.

5. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: When the ambient temperature is less than 14°C, the correction coefficient in the temperature correction factor is 1.008; when the ambient temperature is between 14 and 35°C, the correction coefficient in the temperature correction factor is 1; when the ambient temperature is greater than 35°C, the correction coefficient in the temperature correction factor is 1.

507.

6. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: The degradation correction factor T in the degradation correction factor m Calculate using the following formula: In the formula, m is the model, year is the age of the vehicle, is the ratio of vehicles of model m and age year to the total number of vehicles of model m, is the degradation correction factor corresponding to the vehicle age year.

7. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: The calculation formula of the travel speed in the speed interval division is as follows: In the formula, is the travel speed of vehicle veh of model m on road section l, is the travel time of vehicle veh of model m on road section l, is the collected speed of vehicle veh of model m on road section l for t seconds.

8. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: The formula for the mean square error of the total speed interval and the maximum speed interval is as follows: In the formula, is the speed range V of vehicle type m j The average value of the operating condition proportion in the lower VSP-bin interval i; bin_num is the number of operating condition intervals.

9. The method for calculating carbon emissions of urban roads under dynamic calibration conditions according to claim 1, characterized in that: The calculation formula for the dynamic carbon emission factor of each vehicle model in the localized dynamic carbon emission factor library is as follows: Where, T m is the degradation correction factor of vehicle type m; O is the temperature correction factor; is the speed range V of vehicle type m j Dynamic operating condition correction factor for the lower VSP-bin interval i; For vehicle type m using fuel oil in speed range V j The basic carbon emission factor for the medium operating interval m; bin_num is the number of operating intervals.

10. The method for calculating carbon emissions from urban roads under dynamic calibration conditions according to claim 1, characterized in that: The calculation formula for urban road carbon emissions is as follows: In the formula, is the CO2 emission of road L (g), L len is the length of the road L.

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