CN115908071B - Method, device, equipment and medium for measuring and calculating carbon emission of urban traffic trip - Google Patents
Method, device, equipment and medium for measuring and calculating carbon emission of urban traffic trip Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a medium for measuring and calculating carbon emission of urban traffic travel, which comprise the following steps: dividing space units of the area to be measured and calculated, and acquiring travel total amount of all traffic modes based on mobile phone signaling data; calculating to obtain the travel amount corresponding to each traffic mode according to the travel total amount and the sharing rate of each traffic mode; correcting the average travel straight line distance by using each correction coefficient obtained through calculation to obtain average travel correction distances of public transportation travel modes and self-driving travel modes; calculating corresponding travel turnover according to the average travel correction distance, and calculating corresponding energy consumption according to each travel turnover; and calculating the corresponding carbon emission according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission. The embodiment of the invention can be effectively applied to the areas lacking the fine passenger flow data of the traffic division mode, and can realize the measurement and calculation of the carbon emission of urban traffic.
Description
Technical Field
The invention relates to the technical field of carbon emission measurement and calculation, in particular to a method, a device, equipment and a medium for measuring and calculating carbon emission of urban traffic.
Background
Global climate control and green transformation are important challenges for development in the coming thirty years, and in the carbon emission structure of China, the traffic field is an important main body and accounts for about 10% of the total carbon emission, wherein urban traffic accounts for 7% -8% of the total carbon emission, and the urban traffic is one of the main contribution fields of carbon emission. Under the background, the carbon emission measuring and calculating method based on urban traffic travel is an important basic work for promoting traffic low-carbonization development and accelerating traffic consumption and emission reduction.
At present, the carbon emission measuring and calculating method based on urban traffic travel is a relatively common method for measuring the intensity of various urban traffic activities and further calculating the consumed energy and the discharged carbon dioxide. However, this method has two major difficulties. Firstly, the requirement on basic data is higher, the activity intensity data of multiple traffic modes is required to be refined as a support, and for areas lacking part of the activity data of the traffic modes, the simulation data such as traffic models can be generally relied on, so that the actual carbon emission measurement and calculation are difficult to realize. Secondly, the space characteristics of the carbon emission are not fully reflected, the traditional traffic carbon emission measurement and calculation based on the statistical data can only acquire the value of the carbon emission, but the space distribution condition of the carbon emission is difficult to quantify, or the quantification of the carbon emission result is too dependent on the space unit of the original data acquisition, and the flexibility and the fineness on the space scale are lacking in application.
Disclosure of Invention
The embodiment of the invention aims to provide a carbon emission measuring and calculating method, device, equipment and medium for urban traffic travel, which can be effectively applied to areas lacking fine passenger flow data in a traffic division mode by taking fine-granularity and wide-coverage mobile phone signaling data as main data bases, and is flexible in space statistics, and is suitable for measuring and calculating the carbon emission of urban traffic travel in different scales such as measuring and calculating communities, towns and the like.
In order to achieve the above object, an embodiment of the present invention provides a method for measuring and calculating carbon emission in urban traffic, including:
dividing space units of an area to be calculated, and acquiring travel total of all traffic modes in each space unit based on mobile phone signaling data of the area to be calculated; wherein, the transportation mode at least comprises: automobiles, buses, urban rail transit and motorcycles;
obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating to obtain a travel amount corresponding to each traffic mode in each space unit according to the travel total amount and each sharing rate;
calculating the average travel straight line distance of each space unit, and carrying out linear regression analysis by using the average travel straight line distance and the pre-acquired actual travel length of the public transportation travel mode and the average travel straight line distance and the pre-acquired actual travel length of the self-driving travel mode to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode;
Correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode;
calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance, and calculating the energy consumption corresponding to each traffic mode in each space unit according to each travel turnover;
and calculating the carbon emission corresponding to each traffic mode in each space unit according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission.
As an improvement of the above solution, the dividing the space units in the area to be measured and obtaining the travel total amount of all traffic modes in each space unit based on the mobile phone signaling data of the area to be measured includes:
acquiring mobile phone signaling data of a user in an area to be calculated;
identifying the preprocessed mobile phone signaling data to obtain a starting point position and an ending point position of each trip of a user;
dividing the space units of the region to be calculated, summarizing the travel quantity obtained based on the starting point position and the end point position of each travel of the user into corresponding space units, and constructing an OD table; wherein the OD table comprises: the initial travel amounts of all traffic modes between the starting point space unit i, the end point space unit k and the starting point space unit i to the end point space unit k; wherein i is more than or equal to 1, i is an integer, k is more than or equal to 1, and k is an integer;
Obtaining initial travel total of all traffic modes in each space unit by using the OD table;
acquiring the actual travel total amount of all traffic modes in the area to be calculated;
and according to the OD table, taking the actual travel total amount as a reference, carrying out equal-ratio sample expansion on the initial travel total amount of all the traffic modes in each space unit, and obtaining the travel total amount of all the traffic modes in each space unit.
As an improvement of the above solution, the obtaining the sharing rate corresponding to each traffic mode in each space unit, and calculating, according to the total travel amount and each sharing rate, the travel amount corresponding to each traffic mode in each space unit includes:
acquiring the motorized traffic mode travel amount in the actual travel total amount of all traffic modes in the area to be calculated;
and acquiring the travel volume ratio of the bus, the travel volume ratio of the automobile, the travel volume ratio of the urban rail transit and the travel volume ratio of the motorcycle in the travel volume of the motorized traffic mode.
Calculating to obtain the travel amount duty ratio of the new energy automobile and the travel amount duty ratio of the traditional fuel automobile according to the travel amount duty ratio of the automobile, the duty ratio of the new energy automobile in the area to be calculated and the duty ratio of the traditional fuel automobile in the area to be calculated;
Calculating to obtain the travel amount ratio of the new energy bus and the travel amount ratio of the traditional energy bus according to the travel amount ratio of the bus, the new energy bus retention ratio in the to-be-calculated area and the traditional energy bus retention ratio in the to-be-calculated area;
and multiplying the travel amount duty ratio by the new energy automobile sharing rate, the traditional fuel automobile sharing rate, the new energy bus sharing rate, the traditional energy bus sharing rate, the urban rail transit sharing rate and the motorcycle sharing rate to obtain the travel amount corresponding to each traffic mode in each space unit.
As an improvement of the above solution, the calculating the average travel straight line distance of each space unit, and performing linear regression analysis by using the average travel straight line distance and the actual travel length of the public transportation travel mode, and the average travel straight line distance and the actual travel length of the self-driving travel mode, to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode, includes:
calculating a centroid linear distance between the centroid of the starting point space unit i and the centroid of the end point space unit k based on the OD table;
Calculating the average travel straight line distance of each space unit according to the centroid straight line distance and the initial travel quantity;
acquiring the actual travel length of a public transportation travel mode and the actual travel length of a self-driving travel mode;
taking the average travel straight line distance as an independent variable, and performing linear regression analysis by taking the actual travel length of the public transportation travel mode as a dependent variable to obtain a correction coefficient of the public transportation travel mode;
and taking the average travel straight line distance as an independent variable, and performing linear regression analysis by taking the actual travel length of the self-driving travel mode as a dependent variable to obtain a correction coefficient of the self-driving travel mode.
As an improvement of the above solution, the calculating, according to each of the average travel correction distances, a travel turnover corresponding to each traffic mode in each space unit includes:
the corresponding travel turnover of the automobile, the bus and the motorcycle in each space unit is calculated through the following steps:
setting a vehicle conversion coefficient corresponding to each traffic mode according to the passenger capacity of each traffic mode;
dividing each travel amount by a corresponding vehicle conversion coefficient to obtain travel times corresponding to each traffic mode in each space unit;
Multiplying each travel number by the corresponding average travel correction distance to obtain travel turnover corresponding to each traffic mode in each space unit;
calculating the travel turnover corresponding to the urban rail transit in each space unit through the following steps:
and multiplying the average travel correction distance of the urban rail transit by the travel quantity of the urban rail transit to obtain the travel turnover quantity corresponding to the urban rail transit in each space unit.
As an improvement of the above scheme, the energy consumption corresponding to each traffic mode in each space unit is calculated according to each travel turnover:
determining the main fuel type corresponding to each traffic mode according to the characteristics of the traffic modes;
according to the main fuel type, obtaining an energy consumption coefficient corresponding to each traffic mode;
multiplying each energy consumption coefficient by the corresponding travel turnover to obtain the energy consumption corresponding to each traffic mode in each space unit.
As an improvement of the above-mentioned aspect, the calculating the carbon emission amount corresponding to each traffic pattern in each space unit according to each energy consumption amount includes:
According to the main fuel type, acquiring an emission factor of each traffic mode;
for traffic with electricity as the primary fuel type:
multiplying each emission factor by the corresponding energy consumption to obtain the carbon emission corresponding to each traffic mode in each space unit;
for traffic modes with fuel other than electricity as the primary fuel type:
and converting the energy consumption of each traffic mode into standard coal consumption, and multiplying the obtained standard coal consumption by a corresponding emission factor to obtain the carbon emission corresponding to each traffic mode in each space unit.
To achieve the above object, an embodiment of the present invention further provides a carbon emission measurement device for urban traffic travel, including a controller configured to:
dividing space units of an area to be calculated, and acquiring travel total of all traffic modes in each space unit based on mobile phone signaling data of the area to be calculated; wherein, the transportation mode at least comprises: automobiles, buses, urban rail transit and motorcycles;
obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating to obtain a travel amount corresponding to each traffic mode in each space unit according to the travel total amount and each sharing rate;
Calculating the average travel straight line distance of each space unit, and carrying out linear regression analysis by using the average travel straight line distance and the pre-acquired actual travel length of the public transportation travel mode and the average travel straight line distance and the pre-acquired actual travel length of the self-driving travel mode to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode;
correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode;
calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance, and calculating the energy consumption corresponding to each traffic mode in each space unit according to each travel turnover;
and calculating the carbon emission corresponding to each traffic mode in each space unit according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission.
To achieve the above object, an embodiment of the present invention further provides a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the carbon emission measurement method for urban traffic travel as described above when the computer program is executed.
To achieve the above object, embodiments of the present invention also provide a computer-readable storage medium including a stored computer program; wherein the computer program, when run, controls the device in which the computer readable storage medium resides to perform the carbon emission measurement method for urban traffic travel as described above.
Compared with the prior art, the carbon emission measuring and calculating method, device and equipment for urban traffic travel and the medium provided by the embodiment of the invention have the following beneficial effects:
(1) The invention takes the mobile phone signaling data as the main data base, and can be effectively applied to the scene of the refined traffic data loss. Based on mobile phone signaling data and in combination with supplementary data such as urban traffic running annual report, carbon emission measurement and calculation of urban multi-mode traffic can be achieved, the requirement on a data source is relatively low while accurate measurement is achieved, and the defect that the prior art excessively depends on fine traffic flow data is overcome.
(2) The measurement result of the invention can embody the spatial distribution characteristic of carbon emission and is applicable to spatial units with different scales. In the carbon emission measurement of urban traffic, the spatial distribution characteristics of carbon emissions are also important consideration factors in low-carbon traffic planning besides the total amount of carbon emissions. The measuring and calculating method provided by the invention can not only obtain the total carbon emission of urban traffic travel, but also grasp the spatial distribution characteristics of the urban traffic travel due to the high degree of spatialization of the mobile phone signaling data. Meanwhile, the method is flexible in space statistics, and is applicable to measurement and calculation of urban traffic travel carbon emission of different scales such as grids, traffic cells, towns and the like.
(3) The method and the device can be used for evaluating the carbon reduction effect and the space difference of the traffic mode under different policy situations. The traditional calculation method based on the statistical data can only calculate the total carbon reduction amount of the whole area, but the method can refine the specific spatial distribution of the carbon reduction amount, and the calculation parameters related to the method are various, comprise travel distance, total travel amount, new energy vehicle sharing rate, vehicle energy types and the like, and are beneficial to developing effect evaluation of different types of carbon reduction policies.
Drawings
FIG. 1 is a flow chart of a method for measuring and calculating carbon emission of urban traffic travel provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of the total travel amount of all traffic patterns in each space unit according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of a linear regression analysis provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of an average travel correction distance provided by an embodiment of the present invention;
FIG. 5 is a schematic view of total carbon emissions from urban transportation according to an embodiment of the present invention;
fig. 6 is a block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the method for measuring and calculating carbon emission of urban traffic comprises the following steps:
s1, dividing space units of an area to be calculated, and acquiring travel total of all traffic modes in each space unit based on mobile phone signaling data of the area to be calculated; wherein, the transportation mode at least comprises: automobiles, buses, urban rail transit and motorcycles;
s2, obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating to obtain a travel amount corresponding to each traffic mode in each space unit according to the travel total amount and each sharing rate;
s3, calculating the average travel straight line distance of each space unit, and performing linear regression analysis by using the average travel straight line distance and the pre-acquired actual travel length of the public transportation travel mode, and the average travel straight line distance and the pre-acquired actual travel length of the self-driving travel mode to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode;
s4, correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode;
S5, calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance, and calculating the energy consumption corresponding to each traffic mode in each space unit according to each travel turnover;
and S6, calculating the carbon emission corresponding to each traffic mode in each space unit according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission.
Specifically, step S1 of dividing the space units of the area to be calculated, and obtaining the travel total amount of all traffic modes in each space unit based on the mobile phone signaling data of the area to be calculated includes:
s11, acquiring mobile phone signaling data of a user in an area to be calculated;
it is understood that the area to be calculated is a city.
S12, identifying the preprocessed mobile phone signaling data to obtain a starting point position and an ending point position of each trip of the user;
it can be appreciated that the preprocessing includes cleaning and screening invalid data in the signaling data of the mobile phone; the method comprises the steps of carrying out identification of user residence behavior and travel behavior on the preprocessed mobile phone signaling data through a preset time threshold value to obtain travel total amount of all traffic modes in the area to be calculated; for example, with 30 minutes as a time threshold, when the residence time displayed at any two points in the mobile phone signaling data of a certain user exceeds 30 minutes, the behavior of the user is indicated to belong to trip behavior.
S13, dividing the space unit of the area to be calculated, and summarizing the travel quantity obtained based on the starting point position and the end point position of each travel of the user into corresponding space units to construct an OD table; wherein the OD table comprises: starting point space unit number, ending point space unit number, and travel amount from starting point space unit to ending point space unit;
for example, a 500m grid is taken as a space unit, the area to be measured and calculated is divided, and the travel amounts obtained based on the starting point position and the end point position of each travel of the user are summarized into the corresponding space units, so as to construct an OD table as shown in table 1:
TABLE 1OD Table
S14, obtaining initial travel total amounts of all traffic modes in each space unit by using the OD table;
it can be understood that the "travel amount between the start space unit and the end space unit" field of the OD table is summed to obtain the initial travel total amount of all traffic modes in the area to be calculated; the OD table is summed according to the number of the starting space unit, that is, the space unit where the travel starting point is located is summed to obtain an initial travel total amount of all traffic modes in each space unit, for example, the initial travel amounts of all traffic modes in the starting space unit 1 are summed, and the initial travel amounts of all traffic modes in the starting space unit 1 to the end space unit 2, the initial travel amounts of all traffic modes in the starting space unit 1 to the end space unit 3, the initial travel amounts of all traffic modes in the starting space unit 1 to the rest of the end space units, that is, the initial travel total amount of all traffic modes in the starting space unit 1, that is, the initial travel total amount of all traffic modes in the 1 st space unit is obtained. It is to be understood that the various values for the ith spatial element, collectively referred to below, are all the various values that indicate that the line start point is located in the ith spatial element.
S15, acquiring actual travel total amounts of all traffic modes in the area to be calculated;
the actual travel total amount of all traffic modes in the area to be calculated is obtained through urban traffic operation annual report;
s16, according to the OD table, taking the actual travel total amount as a reference, carrying out equal-ratio sample expansion on the initial travel total amount of all the traffic modes in each space unit, and obtaining the travel total amount of all the traffic modes in each space unit.
In the embodiment of the invention, the Guangzhou city is taken as the area to be calculated, the space units of the Guangzhou city are divided, and the travel total amount of all traffic modes in each space unit is obtained based on the mobile phone signaling data of residents of the Guangzhou city, as shown in fig. 2.
Specifically, step S2 of obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating, according to the total travel amount and each sharing rate, a travel amount corresponding to each traffic mode in each space unit includes:
s21, acquiring motorized traffic mode travel amounts in the actual travel total amount of all traffic modes in the area to be calculated;
it can be understood that the motorized traffic mode travel amount in the actual travel total amount of all traffic modes in the area to be calculated is obtained through urban traffic operation annual report;
S22, obtaining the travel volume ratio of the bus, the travel volume ratio of the automobile, the travel volume ratio of the urban rail transit and the travel volume ratio of the motorcycle in the travel volume of the motorized traffic mode.
It can be understood that the travel volume ratio of buses, the travel volume ratio of automobiles, the travel volume ratio of urban rail transit and the travel volume ratio of motorcycles in the motorized traffic mode are obtained through urban traffic operation annual messages;
s23, calculating to obtain the travel amount duty ratio of the new energy automobile and the travel amount duty ratio of the traditional fuel automobile according to the travel amount duty ratio of the automobile, the duty ratio of the new energy automobile in the area to be calculated and the duty ratio of the traditional fuel automobile in the area to be calculated;
it can be understood that the ratio of the new energy automobile holding quantity is the ratio of the new energy automobile holding quantity to the automobile holding quantity in the to-be-calculated area, and under the condition of lacking better actual measurement data, the ratio is used as a substitute index of the ratio of the new energy automobile traveling quantity to the automobile traveling quantity, and the ratio is multiplied by the automobile traveling quantity ratio to obtain the traveling quantity ratio of the new energy automobile under the automobile traveling quantity ratio; the ratio of the traditional fuel automobile is the ratio of the traditional fuel automobile in the automobile holding quantity in the area to be calculated, under the condition of lacking better actual measurement data, the ratio is used as a substitute index of the ratio of the traditional fuel automobile travel quantity to the automobile travel quantity, and the ratio is multiplied by the automobile travel quantity ratio to obtain the travel quantity ratio of the traditional fuel automobile under the automobile travel quantity ratio.
S24, calculating to obtain the travel amount ratio of the new energy bus and the travel amount ratio of the traditional energy bus according to the travel amount ratio of the bus, the new energy bus retention ratio in the to-be-calculated area and the traditional energy bus retention ratio in the to-be-calculated area;
it can be understood that the ratio of the new energy bus reserve is the ratio of the new energy bus reserve in the to-be-calculated area, and under the condition of lacking better actual measurement data, the ratio is used as a substitute index of the ratio of the new energy bus reserve to the bus reserve, and the ratio is multiplied by the bus reserve ratio to obtain the new energy bus reserve under the bus reserve; the ratio of the traditional energy bus is the ratio of the traditional energy bus in the to-be-calculated area to the bus holding amount, under the condition of lacking better actual measurement data, the ratio is used as a substitute index of the ratio of the traditional energy bus travel amount to the bus travel amount, and the ratio is multiplied by the bus travel amount ratio to obtain the travel amount ratio of the traditional energy bus under the bus travel amount ratio.
And S25, taking the travel amount duty ratio as a sharing rate, and multiplying the travel total amount by the sharing rate of the new energy bus, the sharing rate of the traditional fuel automobile, the sharing rate of the new energy bus, the sharing rate of the traditional energy bus, the sharing rate of urban rail transit and the sharing rate of the motorcycle respectively to obtain the travel amount corresponding to each traffic mode in each space unit.
Specifically, the travel amount corresponding to each traffic pattern in each space unit is calculated according to the following formula:
P ij ＝P i ×r ij
wherein P is ij Is the travel quantity of the jth traffic mode in the ith space unit, P i Is the travel total amount of all traffic modes in the ith space unit, r ij Is the sharing rate of the j-th traffic mode in the i-th space unit.
Specifically, step S3 is to calculate an average travel straight line distance of each space unit, and perform linear regression analysis by using the average travel straight line distance and an actual travel length of a public transportation travel mode, and the average travel straight line distance and an actual travel length of a self-driving travel mode, to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode, and the method includes:
s31, calculating the centroid linear distance between the centroid of the starting point space unit i and the centroid of the end point space unit k based on the OD table;
S32, calculating the average travel straight line distance of each space unit according to the centroid straight line distance and the initial travel amount;
specifically, the average travel straight line distance within each spatial unit is calculated according to the following formula:
wherein d i Is the average travel straight line distance of the ith spatial cell,is the centroid linear distance from the centroid of the start spatial unit i to the centroid of the end spatial unit k, +.>Is the initial travel amount of all traffic modes from the starting point space unit i to the end point space unit k.
S33, acquiring the actual travel length of the public transportation travel mode and the actual travel length of the self-driving travel mode;
it can be understood that the web crawler technology is used, travel tracks among different space units are respectively crawled based on the Goldmap API aiming at two travel modes of public transportation and self-driving, and the actual travel length of the public transportation travel mode and the actual travel length of the self-driving travel mode are obtained through calculation.
In practice, the real travel distance of the user can be calculated directly through the crawler, but because the data size is too large, if the full coverage of the data with a larger range (such as the whole Guangzhou city range) is difficult to realize, a certain amount of space unit samples are randomly and uniformly selected in the measuring and calculating area to develop the crawler and calculate the correction coefficient as much as possible, and then the correction coefficient obtained according to the samples is applied to the whole measuring and calculating area to correct the average travel straight line distance of each space unit, so that the average travel correction distance of each space unit in the measuring and calculating area is obtained.
S34, taking the average travel straight line distance as an independent variable, and performing linear regression analysis by taking the actual travel length of the public transportation travel mode as the dependent variable to obtain a correction coefficient of the public transportation travel mode;
and S35, taking the average travel straight line distance as an independent variable, and performing linear regression analysis by taking the actual travel length of the self-driving travel mode as the dependent variable to obtain a correction coefficient of the self-driving travel mode.
Illustratively, as shown in fig. 3, linear regression analysis is performed by using the average travel straight line distance and the actual travel length of the public transportation travel mode, and the average travel straight line distance and the actual travel length of the self-driving travel mode, so as to obtain a correction coefficient of 1.61 of the public transportation travel mode and a correction coefficient of 1.35 of the self-driving travel mode.
Specifically, in step S4, the correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode, including:
s41, multiplying the correction coefficient of the public transportation travel mode by the average travel straight line distance to obtain an average travel correction distance of the public transportation travel mode;
S42, multiplying the correction coefficient of the self-driving travel mode by the average travel linear distance to obtain an average travel correction distance of the self-driving travel mode;
it can be understood that the public transportation travel modes comprise buses and urban rail transit, the self-driving travel modes comprise automobiles and motorcycles, and as shown in fig. 4, the average travel correction distance of the two travel modes of buses and self-driving is obtained. In consideration of the fact that the actual travel track of the user between two space units is not a straight line connecting the two space units, the embodiment of the invention corrects the average travel straight line distance calculated according to the mobile phone signaling data, and the obtained travel correction distance is closer to the actual travel distance of the user.
Specifically, in step S5, calculating a travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance includes:
the corresponding travel turnover of the automobile, the bus and the motorcycle in each space unit is calculated through the following steps:
setting a vehicle conversion coefficient corresponding to each traffic mode according to the passenger capacity of each traffic mode;
illustratively, the vehicle scaling factors are as shown in Table 2:
Table 2 vehicle scaling factor values
| Traffic pattern | Conversion coefficient of vehicle | Vehicle number conversion unit |
| Traditional bus | 25 | Person/vehicle |
| New energy bus | 25 | Person/vehicle |
| Traditional car | 2 | Person/vehicle |
| New energy car | 2 | Person/vehicle |
| Motorcycle | 1 | Person/vehicle |
Dividing each travel amount by a corresponding vehicle conversion coefficient to obtain travel times corresponding to each traffic mode in each space unit;
and multiplying each travel number by the corresponding average travel correction distance to obtain travel turnover corresponding to each traffic mode in each space unit.
Specifically, the travel turnover corresponding to each traffic mode in each space unit is calculated according to the following formula:
PK ij ＝P′ ij ×d′ ij
in the formula, PK ij Is the travel turnover, P 'of the ith space unit and the jth traffic mode' ij Is the travel number of the ith space unit and the jth traffic mode, d' ij Is the average travel correction distance of the ith space unit and the jth traffic mode.
Calculating the travel turnover corresponding to the urban rail transit in each space unit through the following steps:
and multiplying the average travel correction distance of the urban rail transit by the travel quantity of the urban rail transit to obtain the travel turnover quantity corresponding to the urban rail transit in each space unit.
It will be appreciated that in this example, since the energy consumption coefficient of the urban rail transit is calculated in kilometers, the urban rail transit travel need not be converted to the number of cars in this step.
And calculating the travel turnover quantity according to the travel quantity and the average travel correction distance in all traffic modes. The travel amount corrected according to the annual report is obtained according to the mobile phone signaling data, and the travel amount corrected according to the annual report is acquired by the mobile phone signaling data. The urban rail transit directly uses the travel quantity (mankind) to calculate the average travel correction distance to obtain the travel turnover quantity of the urban rail transit, wherein the unit is mankind kilometers; the method comprises the steps of firstly converting a travel volume unit from a person to a vehicle number according to a vehicle conversion coefficient, and then calculating the travel turnover volume taking a vehicle kilometer as a unit by using an average travel correction distance of the travel volume (vehicle number) in a traffic mode except urban rail traffic.
Specifically, in step S5, the energy consumption corresponding to each traffic mode in each space unit is calculated according to each travel turnover:
determining the main fuel type corresponding to each traffic mode according to the characteristics of the traffic modes;
it is understood that the primary fuel type for a type of transportation is the most efficient fuel type for that transportation.
According to the main fuel type, obtaining an energy consumption coefficient corresponding to each traffic mode;
it can be understood that the energy consumption coefficient of the main fuel type of each traffic pattern is obtained through the related literature research data and is used as the corresponding energy consumption coefficient of each traffic pattern. In order to facilitate comparison of energy consumption of different traffic modes, density of main fuel and conversion coefficient of standard coal of different traffic modes can be obtained, and energy consumption is uniformly converted into statistical value taking standard coal consumption as unit, as shown in table 3:
TABLE 3 energy consumption coefficient and conversion coefficient of standard coal for main fuel of each traffic mode
| Traffic pattern | Primary fuel type | Coefficient of energy consumption | Density of fuel | Conversion coefficient of standard coal |
| Traditional bus | Diesel oil | 38.7L/hundred kilometers | 0.84kg/L | 1.4571 ton of standard coal/ton of oil |
| New energy bus | Electric power | 80 Kwh/hundred kilometers | 3.06 ton standard coal/kilowatt hour | |
| Traditional car | Gasoline | 9L/hundred kilometers | 0.725kg/L | 1.4714 ton standard coal/ton oil |
| New energy car | Electric power | 20 Kwh/hundred kilometers | 3.06 ton standard coal/kilowatt hour | |
| Urban rail transit | Electric power | 6.5 Kwh/hundred kilometers | 3.06 ton standard coal/kilowatt hour | |
| Motorcycle | Gasoline | 1L/hundred kilometers | 0.725kg/L | 1.4714 ton standard coal/ton oil |
Multiplying each energy consumption coefficient by the corresponding travel turnover to obtain the energy consumption corresponding to each traffic mode in each space unit.
Specifically, the energy consumption corresponding to each traffic pattern in each space unit is calculated according to the following formula:
E ij ＝PK ij ×e j
wherein E is ij Is the energy consumption and PK of the j-th traffic mode with the travel starting point positioned in the i-th space unit ij Is the travel turnover quantity of the j-th traffic mode with the travel starting point positioned in the i-th space unit, e j Is the energy consumption coefficient of the j-th traffic mode.
Specifically, the calculating, in step S6, the carbon emission amount corresponding to each traffic mode in each space unit according to each energy consumption amount includes:
according to the main fuel type, acquiring an emission factor of each traffic mode;
for traffic with electricity as the primary fuel type:
multiplying each emission factor by the corresponding energy consumption to obtain the carbon emission corresponding to each traffic mode in each space unit;
for traffic modes with fuel other than electricity as the primary fuel type:
and converting the energy consumption of each traffic mode into standard coal consumption, and multiplying the obtained standard coal consumption by a corresponding emission factor to obtain the carbon emission corresponding to each traffic mode in each space unit.
It can be understood that, for the traffic mode using electricity as the main fuel type, the energy consumption is calculated and then multiplied by the emission factor of the electricity; for the traffic mode which uses other fuel types except electric power as main fuel types, after the energy consumption is calculated, the energy consumption is converted into a statistic value which takes the standard coal consumption as a unit by multiplying the corresponding fuel density and the standard coal conversion coefficient, and then the emission factor of the standard coal is multiplied.
The embodiment of the invention obtains the emission factor of the standard coal and the emission factor of the electric power through the related literature research data, and particularly, the emission factor in the embodiment of the invention takes 1.73 tons of CO 2 Per ton of standard coal, 4.508 tons of CO 2 /kilowatt-hour.
Further, summarizing each carbon emission amount to obtain the total carbon emission amount; wherein the total amount of carbon emissions includes at least one of: and the total carbon emission amount corresponding to all the traffic modes in each space unit and the total carbon emission amount corresponding to all the traffic modes in the area to be calculated.
For traffic with electricity as the primary fuel type: obtaining the total carbon emission corresponding to the transportation mode taking electricity as the main fuel type in each space unit according to the following formula:
wherein,is the total carbon emission amount of the electric power-based transportation mode in the ith space unit, E ij For the energy consumption of the travel starting point in the ith space unit and the jth type of traffic mode using electricity as the main fuel type, ef j Is the emission factor of the j-th class of transportation mode using electricity as the main fuel type. It can be appreciated that ef j Is the emission factor of the main fuel corresponding to the j-th type of traffic mode using electric power as the main fuel type, namely the emission factor of the electric power.
For traffic modes with fuel other than electricity as the primary fuel type: obtaining the total carbon emission amount corresponding to the transportation mode taking the fuel except the electric power as the main fuel type in each space unit according to the following formula:
wherein,is the total carbon emission amount of the traffic mode with the fuel except the electric power as the main fuel type in the ith space unit, G ij For the standard coal consumption amount gf of the transportation mode with the fuel except electric power as the main fuel type, the travel starting point is located in the ith space unit and the jth class j Is the emission factor of the j-th class of transportation mode in which fuel other than electric power is the main fuel type. It can be appreciated that gf j The emission factor of the j-th type of the main fuel corresponding to the transportation mode taking the fuel except the electric power as the main fuel type, namely the emission factor of the standard coal.
The total amount of carbon emissions for all traffic patterns in each space cell is obtained according to the following equation:
wherein C is i For the total amount of carbon emissions for all modes of transportation in the ith space cell,is the total amount of carbon emissions in the ith space element for electric-based transportation, the +.>Is the total amount of carbon emissions in the ith space element for transportation modes with fuel other than electricity as the main fuel type.
In the embodiment of the present invention, as shown in fig. 5, 500m×500m grids are used as space units, and the total carbon emission of all traffic modes in each space unit in guangzhou city is summarized to obtain the total carbon emission and the spatial distribution of urban traffic.
Further, the total carbon emission amount corresponding to all traffic modes in the area to be calculated is obtained according to the following formula:
wherein C is the total carbon emission of all traffic modes of the area to be calculated, C i Is the total amount of carbon emissions for all modes of transportation within the ith space cell.
The embodiment of the invention also provides a carbon emission measuring and calculating device for urban traffic travel, which comprises a controller, wherein the controller is configured to:
dividing space units of an area to be calculated, and acquiring travel total of all traffic modes in each space unit based on mobile phone signaling data of the area to be calculated; wherein, the transportation mode at least comprises: automobiles, buses, urban rail transit and motorcycles;
obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating to obtain a travel amount corresponding to each traffic mode in each space unit according to the travel total amount and each sharing rate;
Calculating the average travel straight line distance of each space unit, and carrying out linear regression analysis by using the average travel straight line distance and the pre-acquired actual travel length of the public transportation travel mode and the average travel straight line distance and the pre-acquired actual travel length of the self-driving travel mode to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode;
correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode;
calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance, and calculating the energy consumption corresponding to each traffic mode in each space unit according to each travel turnover;
and calculating the carbon emission corresponding to each traffic mode in each space unit according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission.
Embodiments of the present invention also provide a computer-readable storage medium including a stored computer program; wherein the computer program, when run, controls the apparatus in which the computer readable storage medium resides to perform the carbon emission measurement method for urban traffic travel as in the above-described embodiments.
Referring to fig. 6, fig. 6 is a block diagram of a structure of a terminal device 20 according to an embodiment of the present invention, where the terminal device 20 includes: a processor 21, a memory 22 and a computer program stored in said memory 22 and executable on said processor 21. The processor 21, when executing the computer program, implements the steps of the embodiment of the method for measuring and calculating carbon emissions for urban traffic. Alternatively, the processor 21 may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device 20.
The terminal device 20 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The terminal device 20 may include, but is not limited to, a processor 21, a memory 22. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the terminal device 20 and does not constitute a limitation of the terminal device 20, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device 20 may further include input and output devices, network access devices, buses, etc.
The processor 21 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 21 is a control center of the terminal device 20, and connects various parts of the entire terminal device 20 using various interfaces and lines.
The memory 22 may be used to store the computer program and/or module, and the processor 21 may implement various functions of the terminal device 20 by running or executing the computer program and/or module stored in the memory 22 and invoking data stored in the memory 22. The memory 22 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure digital (SecureDigital, SD) Card, flash Card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
Wherein the integrated modules/units of the terminal device 20 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the method embodiments described above when executed by the processor 21. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Compared with the prior art, the carbon emission measuring and calculating method, device and equipment for urban traffic travel and the medium provided by the embodiment of the invention have the following beneficial effects:
(1) The invention takes the mobile phone signaling data as the main data base, and can be effectively applied to the scene of the refined traffic data loss. Based on mobile phone signaling data and in combination with supplementary data such as urban traffic running annual report, carbon emission measurement and calculation of urban multi-mode traffic can be achieved, the requirement on a data source is relatively low while accurate measurement is achieved, and the defect that the prior art excessively depends on fine traffic flow data is overcome.
(2) The measurement result of the invention can embody the spatial distribution characteristic of carbon emission and is applicable to spatial units with different scales. In the carbon emission measurement of urban traffic, the spatial distribution characteristics of carbon emissions are also important consideration factors in low-carbon traffic planning besides the total amount of carbon emissions. The measuring and calculating method provided by the invention can not only obtain the total carbon emission of urban traffic travel, but also grasp the spatial distribution characteristics of the urban traffic travel due to the high degree of spatialization of the mobile phone signaling data. Meanwhile, the method is flexible in space statistics, and is applicable to measurement and calculation of urban traffic travel carbon emission of different scales such as grids, traffic cells, towns and the like.
(3) The method and the device can be used for evaluating the carbon reduction effect and the space difference of the traffic mode under different policy situations. The traditional calculation method based on the statistical data can only calculate the total carbon reduction amount of the whole area, but the method can refine the specific spatial distribution of the carbon reduction amount, and the calculation parameters related to the method are various, comprise travel distance, total travel amount, new energy vehicle sharing rate, vehicle energy types and the like, and are beneficial to developing effect evaluation of different types of carbon reduction policies.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (8)
1. The method for measuring and calculating the carbon emission of urban traffic travel is characterized by comprising the following steps of:
dividing space units of an area to be calculated, and acquiring travel total of all traffic modes in each space unit based on mobile phone signaling data of the area to be calculated; wherein, the transportation mode at least comprises: automobiles, buses, urban rail transit and motorcycles;
obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating to obtain a travel amount corresponding to each traffic mode in each space unit according to the travel total amount and each sharing rate;
calculating the average travel straight line distance of each space unit, and carrying out linear regression analysis by using the average travel straight line distance and the pre-acquired actual travel length of the public transportation travel mode and the average travel straight line distance and the pre-acquired actual travel length of the self-driving travel mode to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode;
correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode;
Calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance, and calculating the energy consumption corresponding to each traffic mode in each space unit according to each travel turnover;
calculating the carbon emission corresponding to each traffic mode in each space unit according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission;
the method comprises the following steps of calculating the average travel straight line distance of each space unit, wherein the steps comprise:
calculating a centroid linear distance between the centroid of the starting point space unit i and the centroid of the end point space unit k based on the OD table;
calculating the average travel straight line distance of each space unit according to the centroid straight line distance and the initial travel quantity;
wherein the OD table is obtained by:
acquiring mobile phone signaling data of a user in an area to be calculated;
identifying the preprocessed mobile phone signaling data to obtain a starting point position and an ending point position of each trip of a user;
dividing the space units of the region to be calculated, summarizing the travel quantity obtained based on the starting point position and the end point position of each travel of the user into corresponding space units, and constructing an OD table; wherein the OD table comprises: the initial travel amounts of all traffic modes between the starting point space unit i, the end point space unit k and the starting point space unit i to the end point space unit k; wherein i is more than or equal to 1, i is an integer, k is more than or equal to 1, and k is an integer;
The calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance comprises the following steps:
the corresponding travel turnover of the automobile, the bus and the motorcycle in each space unit is calculated through the following steps:
setting a vehicle conversion coefficient corresponding to each traffic mode according to the passenger capacity of each traffic mode;
dividing each travel amount by a corresponding vehicle conversion coefficient to obtain travel times corresponding to each traffic mode in each space unit;
multiplying each travel number by the corresponding average travel correction distance to obtain travel turnover corresponding to each traffic mode in each space unit;
calculating the travel turnover corresponding to the urban rail transit in each space unit through the following steps:
multiplying the average travel correction distance of the urban rail transit by the travel quantity of the urban rail transit to obtain the travel turnover quantity corresponding to the urban rail transit in each space unit;
the energy consumption corresponding to each traffic mode in each space unit is calculated according to each travel turnover:
Determining the main fuel type corresponding to each traffic mode according to the characteristics of the traffic modes;
according to the main fuel type, obtaining an energy consumption coefficient corresponding to each traffic mode;
multiplying each energy consumption coefficient by the corresponding travel turnover to obtain the energy consumption corresponding to each traffic mode in each space unit.
2. The method for measuring and calculating carbon emission of urban traffic travel according to claim 1, wherein the dividing the space unit into the areas to be measured and obtaining the travel total amount of all traffic modes in each space unit based on the mobile phone signaling data of the areas to be measured comprises:
obtaining initial travel total of all traffic modes in each space unit by using the OD table;
acquiring the actual travel total amount of all traffic modes in the area to be calculated;
and according to the OD table, taking the actual travel total amount as a reference, carrying out equal-ratio sample expansion on the initial travel total amount of all the traffic modes in each space unit, and obtaining the travel total amount of all the traffic modes in each space unit.
3. The method for measuring and calculating carbon emission of urban traffic according to claim 1, wherein the step of obtaining the sharing rate corresponding to each traffic mode in each space unit, and calculating the travel amount corresponding to each traffic mode in each space unit according to the total travel amount and each sharing rate comprises the steps of:
Acquiring the motorized traffic mode travel amount in the actual travel total amount of all traffic modes in the area to be calculated;
acquiring the travel volume ratio of buses, the travel volume ratio of automobiles, the travel volume ratio of urban rail transit and the travel volume ratio of motorcycles in the travel volume of the motorized traffic mode;
calculating to obtain the travel amount duty ratio of the new energy automobile and the travel amount duty ratio of the traditional fuel automobile according to the travel amount duty ratio of the automobile, the duty ratio of the new energy automobile in the area to be calculated and the duty ratio of the traditional fuel automobile in the area to be calculated;
calculating to obtain the travel amount ratio of the new energy bus and the travel amount ratio of the traditional energy bus according to the travel amount ratio of the bus, the new energy bus retention ratio in the to-be-calculated area and the traditional energy bus retention ratio in the to-be-calculated area;
and multiplying the travel amount duty ratio by the new energy automobile sharing rate, the traditional fuel automobile sharing rate, the new energy bus sharing rate, the traditional energy bus sharing rate, the urban rail transit sharing rate and the motorcycle sharing rate to obtain the travel amount corresponding to each traffic mode in each space unit.
4. The method for measuring and calculating carbon emission of urban traffic travel according to claim 2, wherein the calculating the average travel straight line distance of each space unit, and performing linear regression analysis using the average travel straight line distance and the actual travel length of the public traffic travel mode, and the average travel straight line distance and the actual travel length of the self-driving travel mode, to obtain the correction coefficient of the public traffic travel mode and the correction coefficient of the self-driving travel mode comprises:
calculating the average travel straight line distance of each space unit;
acquiring the actual travel length of a public transportation travel mode and the actual travel length of a self-driving travel mode;
taking the average travel straight line distance as an independent variable, and performing linear regression analysis by taking the actual travel length of the public transportation travel mode as a dependent variable to obtain a correction coefficient of the public transportation travel mode;
and taking the average travel straight line distance as an independent variable, and performing linear regression analysis by taking the actual travel length of the self-driving travel mode as a dependent variable to obtain a correction coefficient of the self-driving travel mode.
5. The method for measuring and calculating the carbon emission of the urban traffic according to claim 1, wherein the calculating the carbon emission corresponding to each traffic pattern in each space unit according to each energy consumption comprises:
According to the main fuel type, acquiring an emission factor of each traffic mode;
for traffic with electricity as the primary fuel type:
multiplying each emission factor by the corresponding energy consumption to obtain the carbon emission corresponding to each traffic mode in each space unit;
for traffic modes with fuel other than electricity as the primary fuel type:
and converting the energy consumption of each traffic mode into standard coal consumption, and multiplying the obtained standard coal consumption by a corresponding emission factor to obtain the carbon emission corresponding to each traffic mode in each space unit.
6. A carbon emission measurement device for urban traffic travel, comprising a controller configured to:
dividing space units of an area to be calculated, and acquiring travel total of all traffic modes in each space unit based on mobile phone signaling data of the area to be calculated; wherein, the transportation mode at least comprises: automobiles, buses, urban rail transit and motorcycles;
obtaining a sharing rate corresponding to each traffic mode in each space unit, and calculating to obtain a travel amount corresponding to each traffic mode in each space unit according to the travel total amount and each sharing rate;
Calculating the average travel straight line distance of each space unit, and carrying out linear regression analysis by using the average travel straight line distance and the pre-acquired actual travel length of the public transportation travel mode and the average travel straight line distance and the pre-acquired actual travel length of the self-driving travel mode to obtain a correction coefficient of the public transportation travel mode and a correction coefficient of the self-driving travel mode;
correcting the average travel straight line distance by using each correction coefficient to obtain an average travel correction distance of a public transportation travel mode and an average travel correction distance of a self-driving travel mode;
calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance, and calculating the energy consumption corresponding to each traffic mode in each space unit according to each travel turnover;
calculating the carbon emission corresponding to each traffic mode in each space unit according to each energy consumption, and summarizing each carbon emission to obtain the total carbon emission;
the method comprises the following steps of calculating the average travel straight line distance of each space unit, wherein the steps comprise:
Calculating a centroid linear distance between the centroid of the starting point space unit i and the centroid of the end point space unit k based on the OD table;
calculating the average travel straight line distance of each space unit according to the centroid straight line distance and the initial travel quantity;
wherein the OD table is obtained by:
acquiring mobile phone signaling data of a user in an area to be calculated;
identifying the preprocessed mobile phone signaling data to obtain a starting point position and an ending point position of each trip of a user;
dividing the space units of the region to be calculated, summarizing the travel quantity obtained based on the starting point position and the end point position of each travel of the user into corresponding space units, and constructing an OD table; wherein the OD table comprises: the initial travel amounts of all traffic modes between the starting point space unit i, the end point space unit k and the starting point space unit i to the end point space unit k; wherein i is more than or equal to 1, i is an integer, k is more than or equal to 1, and k is an integer;
the calculating the travel turnover corresponding to each traffic mode in each space unit according to each average travel correction distance comprises the following steps:
the corresponding travel turnover of the automobile, the bus and the motorcycle in each space unit is calculated through the following steps:
Setting a vehicle conversion coefficient corresponding to each traffic mode according to the passenger capacity of each traffic mode;
dividing each travel amount by a corresponding vehicle conversion coefficient to obtain travel times corresponding to each traffic mode in each space unit;
multiplying each travel number by the corresponding average travel correction distance to obtain travel turnover corresponding to each traffic mode in each space unit;
calculating the travel turnover corresponding to the urban rail transit in each space unit through the following steps:
multiplying the average travel correction distance of the urban rail transit by the travel quantity of the urban rail transit to obtain the travel turnover quantity corresponding to the urban rail transit in each space unit;
the energy consumption corresponding to each traffic mode in each space unit is calculated according to each travel turnover:
determining the main fuel type corresponding to each traffic mode according to the characteristics of the traffic modes;
according to the main fuel type, obtaining an energy consumption coefficient corresponding to each traffic mode;
multiplying each energy consumption coefficient by the corresponding travel turnover to obtain the energy consumption corresponding to each traffic mode in each space unit.
7. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the carbon emission measurement method of an urban traffic trip according to any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, wherein the computer readable storage medium comprises a stored computer program; wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the carbon emission measurement method for urban traffic travel according to any one of claims 1 to 5.
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