CN114528501A - Traffic commuting carbon emission analysis method based on big data - Google Patents

Traffic commuting carbon emission analysis method based on big data Download PDF

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CN114528501A
CN114528501A CN202210082377.0A CN202210082377A CN114528501A CN 114528501 A CN114528501 A CN 114528501A CN 202210082377 A CN202210082377 A CN 202210082377A CN 114528501 A CN114528501 A CN 114528501A
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commuting
carbon emission
point position
average
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方煜
张越
程崴知
王成坤
王陶
郑琦
戴继锋
郝奇
孙文勇
刘永合
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Shenzhen Branch Of China Academy Of Urban Planning And Design
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Abstract

The invention belongs to the field of carbon emission analysis, and particularly relates to a traffic commuting carbon emission analysis method based on big data, which comprises the following steps: step 1, providing data; step 2, selecting a research range; step 3, calculating an average trip sharing rate; step 4, threshold value screening; step 5, calculating the average travel distance; and 6, calculating carbon emission. According to the method, the average trip sharing rate and the average trip distance of various traffic commuting modes can be accurately acquired by using Baidu LBS data, and the data precision required by traffic carbon emission calculation is greatly improved. The Baidu LBS data has high adaptability to the age structure of commuter population, and the stability, uniformity and accuracy of the required data are greatly improved. And through research range selection and threshold judgment, the flexibility and convenience of commuting carbon emission calculation of different scales and different modes are greatly improved, and the labor and material cost caused by the work of the traditional survey is effectively reduced.

Description

Traffic commuting carbon emission analysis method based on big data
Technical Field
The invention belongs to the field of carbon emission analysis, and particularly relates to a commuting carbon emission analysis method.
Background
Under the prior art, trip data required by carbon emission calculation is mainly acquired through traditional traffic surveys or relevant urban traffic data reports, and accurate quantitative analysis on commuting carbon emission in different scale research ranges cannot be flexibly carried out. For example, when the small-scale research range is researched, the defects of insufficient sample number and the like exist; the problem of uneven sample data distribution exists in large-scale research range. These all result in errors in the calculated commute carbon emissions.
In addition, in the prior art, standardized analysis and calculation of the trip sharing rate and the trip distance of different commuting modes cannot be performed by utilizing big data, and effective support cannot be provided for multi-scale carbon emission calculation of traffic commutes.
Disclosure of Invention
In order to solve the above problems, a primary object of the present invention is to provide a traffic commute carbon emission analysis method based on big data, which can accurately obtain average trip share rate and average trip distance of various types of traffic commute modes by using Baidu LBS data, and greatly improve stability, uniformity and accuracy of required data.
In order to achieve the above object, the present invention has the following technical means.
A traffic commuting carbon emission analysis method based on big data is characterized by comprising the following steps:
step 1, data providing: acquiring Baidu LBS data to form a plurality of basic data sets; the basic data set comprises a departure point position coordinate, a target point position coordinate, a commuting distance and a plurality of commuting mode ratios;
step 2, selecting a research range: selecting a space position as a research range, and screening out a basic data set of which the starting point position coordinates/target point position coordinates are located in the research range;
step 3, calculating the average trip sharing rate: calculating the average value of the duty ratio of each commuting mode of a plurality of basic data sets (basic data sets with departure point position coordinates/target point position coordinates in the research range) in the research range; the average trip sharing rate of any one commuting mode is the average value of the commuting mode in a plurality of basic data sets in the research range;
step 4, threshold value screening: setting a threshold value, and screening out basic data sets with a commuting mode ratio larger than the threshold value in a plurality of basic data sets (basic data sets with departure point position coordinates/target point position coordinates in the research range); and the ratio of the plurality of screened basic data sets is larger than the threshold value, the commuting mode and the commuting distance are matched to form a plurality of 'commuting mode-commuting distance' index pairs;
step 5, calculating the average travel distance: respectively calculating the average value of the commuting distances of the index pairs of the similar commuting modes in a plurality of index pairs of 'commuting modes-commuting distances', and taking the average value as the average trip distance of the commuting modes;
step 6, calculating carbon emission: selecting any one commuting mode, substituting the average trip sharing rate and the average trip distance of the commuting mode to calculate the total carbon emission of the commuting mode, wherein the calculation formula is
M＝∑Di*Ci*P*Si
M-total carbon emission (g);
Di-average trip distance (km);
Ci-carbon emission coefficient (g/man-times. km);
p-number of people going out (number of people);
Si-average trip share rate.
According to the method, the average trip sharing rate and the average trip distance of various traffic commuting modes can be accurately acquired by using Baidu LBS data, and the data precision required by traffic carbon emission calculation is greatly improved. The Baidu LBS data has high adaptability to the age structure of commuter population, and the stability, uniformity and accuracy of the required data are greatly improved. And through research range selection and threshold judgment, the flexibility and convenience of commuting carbon emission calculation of different scales and different modes are greatly improved, and the labor and material cost caused by the work of the traditional survey is effectively reduced.
Further, in step 1, when acquiring the Baidu LBS data, acquiring data 1 and data 2; the data 1 comprises a plurality of samples 1, and the samples 1 comprise departure point position coordinates, target point position coordinates and commuting distances; the data 2 comprises a plurality of samples 2, and the samples 2 comprise departure point position coordinates, target point position coordinates and a plurality of duty mode ratios; and cleaning and screening the data 1 and the data 2, and then matching to form a plurality of basic data sets.
Further, cleaning of data 1 and data 2: all samples 1 of which data 1 has a non-unique condition of 'departure point position coordinate & target point position coordinate' are rejected, and all samples 2 of which data 2 has a non-unique condition of 'departure point position coordinate & target point position coordinate' are rejected. Through cleaning, the risk of mismatching in the subsequent data 1 and data 2 matching process is prevented.
Further, the sample 1 also includes the commute number, the screening of the data 1 and the data 2 is to screen the data 1, and the sample 1 with the commute number of 1 is screened from the data 1. The screening can guarantee that sample commute data and individual one-to-one of user avoid the condition that same commute information corresponds a plurality of residents, prevent to influence the reliability of information matching.
Further, the matching of data 1 and data 2 is: sample 1 and sample 2, whose coordinates of the start point position and the target point position are the same, are fused to form a basic data set.
Further, before the data 1 and the data 2 are cleaned and screened, the coordinate accuracy of the data 1 and the data 2 is unified.
Further, the commute distance is a hattan distance from the departure point position coordinate to the target point position coordinate.
Further, the threshold value is 0.5. The duty ratio of the commuting manner exceeding 0.5 means that the probability of the resident adopting the commuting manner is larger than the sum of the probabilities of adopting other commuting manners, and the commuting manner is considered to be the "regular/dominant commuting manner" of the resident.
Furthermore, the duty ratios of the plurality of commuting modes comprise duty ratios of private cars, subways, buses, riding modes and walking travel modes.
The method has the advantages that the average trip sharing rate and the average trip distance of various traffic commuting modes can be accurately acquired by using Baidu LBS data, and the data precision required by traffic carbon emission calculation is greatly improved. The Baidu LBS data has high adaptability to the age structure of commuter population, and the stability, uniformity and accuracy of the required data are greatly improved. And through research range selection and threshold judgment, the flexibility and convenience of commuting carbon emission calculation of different scales and different modes are greatly improved, and the labor and material cost caused by the work of the traditional survey is effectively reduced.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic table of a plurality of underlying data sets.
Fig. 3 is a schematic table of data 1.
Fig. 4 is a schematic table of data 2.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A traffic commuting carbon emission analysis method based on big data is characterized by comprising the following steps:
step 1, data providing: acquiring Baidu LBS data to form a plurality of basic data sets; the basic data set comprises a departure point position coordinate (a residence grid center coordinate), a target point position coordinate (a working ground grid center coordinate), a commuting distance (a Hawden distance from the departure point position coordinate to the target point position coordinate), and a plurality of commuting modes (travel modes such as private cars, subways, buses, riding and walking);
step 2, selecting a research range: selecting a space position as a research range, and screening out a basic data set of which the starting point position coordinates are located in the research range;
step 3, calculating the average trip sharing rate: calculating the average value of the duty ratio of each commuting mode of a plurality of basic data sets in the research range; the average trip sharing rate of any one commuting mode is the average value of the commuting mode in a plurality of basic data sets in the research range; if two basic data sets exist in the research range, the duty ratio of the A commuting modes in the two basic data sets is 0.2 and 0.4 respectively, and the duty ratio of the B commuting modes is 0.8 and 0.6 respectively; then the average value of the commute mode a is 0.3 and the average value of the commute mode B is 0.7; wherein the average trip sharing rate of the A commuting mode is 0.3, and the average trip sharing rate of the B commuting mode is 0.7.
Step 4, threshold value screening: setting a threshold value (0.5), and screening out the basic data sets with the commuting mode ratio larger than the threshold value in a plurality of basic data sets in the research range; and the duty ratio of the plurality of screened basic data sets (basic data sets with the duty ratio of the commuting mode larger than the threshold value of 0.5 exist) is matched with the commuting distance to form a plurality of 'commuting mode-commuting distance' index pairs;
step 5, calculating the average travel distance: respectively calculating the average value of the commuting distances of the index pairs of the similar commuting modes in a plurality of index pairs of 'commuting modes-commuting distances', and taking the average value as the average trip distance of the commuting modes;
step 6, calculating carbon emission: selecting any one commuting mode, substituting the average trip sharing rate and the average trip distance of the commuting mode to calculate the total carbon emission of the commuting mode, wherein the calculation formula is
M＝∑Di*Ci*P*Si
M-total carbon emission (g);
Di-average trip distance (km);
Ci-carbon emission coefficient (g/man-times. km);
p-number of people going out (number of people);
Si-average trip share rate.
The trip times are calculated by population data (population data of the selected spatial position, which are directly obtained according to the selected spatial position and are known data in the calculation formula), trip intensity (which are directly obtained according to the urban traffic trip report of the selected spatial position and are known data in the calculation formula), and the average trip distance and the average trip sharing rate are obtained from the data analysis of the previous step; the carbon emission coefficients corresponding to different travel modes mainly come from a greenhouse gas emission factor database published by IPCC and relevant documents, and are known data in the calculation formula.
Specifically, in step 1, when acquiring the Baidu LBS data, acquiring data 1 and data 2, wherein the data 1 and the data 2 are provided by a Baidu supplier, and the data precision is 250m grids; the data 1 comprises a plurality of samples 1, the samples 1 comprise departure point position coordinates, target point position coordinates and commuting distances, and the coordinate precision is 10 decimal places behind a decimal point; the data 2 comprises a plurality of samples 2, the samples 2 comprise departure point position coordinates, target point position coordinates and a plurality of duty mode ratios, and the highest coordinate precision is 6 decimal places behind a decimal point; and cleaning and screening the data 1 and the data 2, and then matching to form a plurality of basic data sets.
Cleaning of data 1 and data 2: all samples 1 of which data 1 has a non-unique condition of 'departure point position coordinate & target point position coordinate' are rejected, and all samples 2 of which data 2 has a non-unique condition of 'departure point position coordinate & target point position coordinate' (that is, there are two or more samples 1/sample 2 having the same 'departure point position coordinate & target point position coordinate').
The sample 1 also comprises the commute number, the screening of the data 1 and the data 2 is to screen the data 1, and the sample 1 with the commute number of 1 is screened from the data 1.
The match of data 1 and data 2 is: sample 1 and sample 2, whose coordinates of the start point position and the target point position are the same, are fused to form a basic data set.
Before the data 1 and the data 2 are cleaned and screened, the coordinate accuracy of the data 1 and the data 2 is unified. The coordinate accuracies of the data 1 and the data 2 are unified to 5 bits after the decimal point is taken.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A traffic commuting carbon emission analysis method based on big data is characterized by comprising the following steps:
step 1, data providing: acquiring Baidu LBS data to form a plurality of basic data sets; the basic data set comprises a departure point position coordinate, a target point position coordinate, a commuting distance and a plurality of commuting mode ratios;
step 2, selecting a research range: selecting a space position as a research range, and screening out a basic data set of which the starting point position coordinates/target point position coordinates are located in the research range;
step 3, calculating the average trip sharing rate: calculating the average value of the duty ratio of each commuting mode of a plurality of basic data sets in the research range; the average trip sharing rate of any one commuting mode is the average value of the commuting mode in a plurality of basic data sets in the research range;
step 4, threshold value screening: setting a threshold value, and screening out the basic data sets with the commuting mode ratio larger than the threshold value in a plurality of basic data sets in the research range; and the ratio of the plurality of screened basic data sets is larger than the threshold value, the commuting mode and the commuting distance are matched to form a plurality of 'commuting mode-commuting distance' index pairs;
step 5, calculating the average travel distance: respectively calculating the average value of the commuting distances of the index pairs of the similar commuting modes in a plurality of index pairs of 'commuting modes-commuting distances' to be used as the average travel distance of the commuting mode;
step 6, calculating carbon emission: selecting any one commuting mode, substituting the average trip sharing rate and the average trip distance of the commuting mode to calculate the total carbon emission of the commuting mode, wherein the calculation formula is
M＝∑Di*Ci*P*Si
M-total carbon emission (g);
Di-average trip distance (km);
Ci-carbon emission coefficient (g/man-times. km);
p-number of people going out (number of people);
Si-average trip share rate.
2. The big-data-based traffic commuting carbon emission analysis method according to claim 1, wherein in the step 1, when the Baidu LBS data is acquired, data 1 and data 2 are acquired; the data 1 comprises a plurality of samples 1, and the samples 1 comprise departure point position coordinates, target point position coordinates and commuting distances; the data 2 comprises a plurality of samples 2, and the samples 2 comprise departure point position coordinates, target point position coordinates and a plurality of commuting mode duty ratios; and cleaning and screening the data 1 and the data 2, and then matching to form a plurality of basic data sets.
3. The big-data-based traffic commute carbon emission analysis method according to claim 2, wherein the cleaning of data 1 and data 2 is as follows: all samples 1 of which data 1 has a non-unique condition of 'departure point position coordinate & target point position coordinate' are rejected, and all samples 2 of which data 2 has a non-unique condition of 'departure point position coordinate & target point position coordinate' are rejected.
4. The big-data-based traffic commute carbon emission analysis method according to claim 2, wherein the sample 1 further includes the commute number, the screening of the data 1 and the data 2 is to screen the data 1, and the sample 1 with the commute number of 1 is screened in the data 1.
5. The big-data-based traffic commute carbon emission analysis method as claimed in claim 2, wherein the matching of data 1 and data 2 is: sample 1 and sample 2, whose coordinates of the start point position and the target point position are the same, are fused to form a basic data set.
6. The big-data-based traffic commuting carbon emission analysis method as claimed in claim 2, wherein before the data 1 and the data 2 are cleaned and screened, the coordinate accuracy of the data 1 and the data 2 is unified.
7. The big-data-based traffic commute carbon emission analysis method of claim 1, wherein the commute distance is a hattan distance from a departure point location coordinate to a target point location coordinate.
8. The big-data based traffic commute carbon emission analysis method of claim 1, wherein the threshold is 0.5.
9. The big-data-based carbon emission analysis method for transportation commutes according to claim 1, wherein the plurality of commuting mode ratios include ratios of private cars, subways, buses, riding and walking travel modes.
CN202210082377.0A 2022-01-24 2022-01-24 Traffic commuting carbon emission analysis method based on big data Pending CN114528501A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115186870A (en) * 2022-06-10 2022-10-14 北京工业大学 Big data-based residential trip carbon emission accounting method
CN115908071A (en) * 2022-10-13 2023-04-04 广州市城市规划勘测设计研究院 Method, device, equipment and medium for measuring and calculating carbon emission of urban transportation trips

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115186870A (en) * 2022-06-10 2022-10-14 北京工业大学 Big data-based residential trip carbon emission accounting method
CN115908071A (en) * 2022-10-13 2023-04-04 广州市城市规划勘测设计研究院 Method, device, equipment and medium for measuring and calculating carbon emission of urban transportation trips
CN115908071B (en) * 2022-10-13 2023-12-15 广州市城市规划勘测设计研究院 Method, device, equipment and medium for measuring and calculating carbon emission of urban traffic trip

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