A traffic commuting carbon emission analysis method based on big data

Abstract

The present application 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, data providing; step 2, selecting a research range; step 3, calculating an average trip sharing rate; step 4, threshold screening; step 5, calculating an average trip distance; and step 6, carbon emission calculation. In the present application, the method can accurately obtain the average trip sharing rate and the average trip distance of each type of traffic commuting mode by using Baidu LBS data, greatly improving the data precision required for traffic carbon emission calculation. The Baidu LBS data has high adaptability to the age structure of the commuting population, greatly improving the stability, uniformity and accuracy of the required data. Through research range selection and threshold judgment, the flexibility and convenience of commuting carbon emission calculation for different scales and different modes are greatly improved, and the human and material costs caused by traditional investigation are effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of carbon emission analysis, and particularly relates to a commuting carbon emission analysis method. BACKGROUND

[0002] Under the prior art, the travel data required for carbon emission calculation is mainly obtained through traditional traffic surveys or relevant urban traffic data reports, and it is impossible to accurately and quantitatively analyze the commuting carbon emission of different scale research ranges flexibly. For example, for small-scale research ranges, there are disadvantages such as insufficient sample size; for large-scale research ranges, there are problems such as uneven sample data distribution. These will result in certain errors in the calculated commuting carbon emission.

[0003] And the prior art cannot use big data to standardize and analyze and calculate the travel share ratio and travel distance of different commuting modes, and cannot provide effective support for multi-scale traffic commuting carbon emission calculation. SUMMARY

[0004] In order to solve the above problems, the primary purpose of the present application is to provide a traffic commuting carbon emission analysis method based on big data, which can accurately obtain the average travel share ratio and average travel distance of each type of traffic commuting mode by using Baidu LBS data, greatly improving the stability, uniformity and accuracy of the required data.

[0005] In order to achieve the above purpose, the technical solution of the present application is as follows.

[0006] A traffic commuting carbon emission analysis method based on big data, characterized in that the method comprises the following steps:

[0007] Step 1, data provision: obtaining Baidu LBS data to form a plurality of basic data sets; the basic data set comprises departure point position coordinates, target point position coordinates, commuting distance, and a plurality of commuting mode proportions;

[0008] Step 2, selecting a research range: selecting a spatial position as the research range and screening out the basic data sets whose departure point position coordinates / target point position coordinates are located within the research range;

[0009] Step 3, average travel share ratio calculation: calculating the average value of each commuting mode proportion of the plurality of basic data sets (the basic data sets whose departure point position coordinates / target point position coordinates are located within the research range) in the research range; the average travel share ratio of any commuting mode is the average value of the commuting mode proportion in the plurality of basic data sets in the research range;

[0010] Step 4, threshold screening: set a threshold, and screen out the basic data sets in the research range (the basic data sets whose starting point position coordinates / target point position coordinates are in the research range) that have a commuting mode proportion greater than the threshold; and the basic data sets screened out have a commuting mode proportion greater than the threshold and a commuting distance match, to form a plurality of "commuting mode-commuting distance" index pairs;

[0011] Step 5, average travel distance calculation: in the plurality of "commuting mode-commuting distance" index pairs, the average value of the commuting distance of the index pairs of the same type of commuting mode is calculated as the average travel distance of the commuting mode;

[0012] Step 6, carbon emission calculation: select any commuting mode, and substitute the average travel share rate and the average travel distance of the commuting mode to calculate the total carbon emission, and the calculation formula is

[0013] M = ∑D i *C i *P*S i

[0014] M—total carbon emission (g);

[0015] D i —average travel distance (km);

[0016] C i —carbon emission coefficient (g / person.km);

[0017] P—travel person times (person times);

[0018] S i —average travel share rate.

[0019] The method can accurately obtain the average travel share rate and the average travel distance of each type of traffic commuting mode by using Baidu LBS data, greatly improving the data accuracy required for traffic carbon emission calculation. The Baidu LBS data has high adaptability to the age structure of the commuting population, greatly improving the stability, uniformity and accuracy of the required data. 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 human and material costs caused by traditional investigation are effectively reduced.

[0020] Further, in step 1, when the Baidu LBS data is acquired, data 1 and data 2 are acquired; data 1 includes a plurality of samples 1, and the sample 1 includes a starting point position coordinate, a target point position coordinate and a commuting distance; data 2 includes a plurality of samples 2, and the sample 2 includes a starting point position coordinate, a target point position coordinate and a plurality of commuting mode proportions; after the data 1 and the data 2 are cleaned and selected, the data 1 and the data 2 are matched to form a plurality of basic data sets.

[0021] Further, the cleaning of the data 1 and the data 2: all samples 1 in which the data 1 has a non-unique case of “starting point position coordinate & target point position coordinate” are removed, and all samples 2 in which the data 2 has a non-unique case of “starting point position coordinate & target point position coordinate” are removed. Through cleaning, the risk of mismatch in the subsequent matching process of the data 1 and the data 2 is prevented.

[0022] Further, the sample 1 further includes a commuting quantity, and the selection of the data 1 and the data 2 is to select the sample 1 with the commuting quantity of 1 in the data 1. The selection can ensure that the sample commuting data corresponds to the individual user one by one, avoid the case that the same commuting information corresponds to multiple residents, and prevent the influence on the reliability of information matching.

[0023] Further, the matching of the data 1 and the data 2 is that the sample 1 and the sample 2 with the same starting point position coordinate and target point position coordinate are fused to form a basic data set.

[0024] Further, before the data 1 and the data 2 are cleaned and selected, the coordinate accuracy of the data 1 and the data 2 is unified.

[0025] Further, the commuting distance is the Haversine distance from the starting point position coordinate to the target point position coordinate.

[0026] Further, the threshold value is 0.5. The commuting mode proportion exceeding 0.5 means that the probability of the resident using the commuting mode is greater than the sum of the probabilities of using other commuting modes, and the commuting mode can be regarded as the “regular / leading commuting mode” of the resident.

[0027] Further, the plurality of commuting mode proportions include the proportions of private car, subway, bus, cycling and walking travel modes.

[0028] The beneficial effects of the present application are that in the present application, the average travel share rate and the average travel distance of each type of traffic commuting mode can be accurately obtained by using Baidu LBS data, greatly improving the data accuracy required for traffic carbon emission calculation. The Baidu LBS data has high adaptability to the age structure of the commuting population, greatly improving the stability, uniformity and accuracy of the required data. And through the 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 human and material costs brought by traditional investigation are effectively reduced. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of the present application.

[0030] Figure 2 is a schematic table of a plurality of basic data sets.

[0031] Figure 3 is a schematic table of data 1.

[0032] Figure 4 is a schematic table of data 2. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0034] A traffic commuting carbon emission analysis method based on big data, characterized in that the method comprises the following steps:

[0035] Step 1, data providing: obtaining Baidu LBS data to form a plurality of basic data sets; the basic data sets include departure point position coordinates (residential grid center coordinates), target point position coordinates (work grid center coordinates), commuting distance (Harvard distance from the departure point position coordinates to the target point position coordinates), and multiple commuting modes (private car, subway, bus, cycling, walking, etc. travel mode) proportion;

[0036] Step 2, selecting a research range: selecting a spatial position as the research range, and screening out the basic data sets whose departure point position coordinates are located within the research range;

[0037] Step 3, average travel sharing rate calculation: calculate the average of the proportion of each commuting mode in multiple basic data sets within the study range; the average travel sharing rate of any commuting mode is the average of the proportion of that commuting mode in multiple basic data sets within the study range; for example, if there are two basic data sets within the study range, the proportions of A commuting mode in the two basic data sets are 0.2 and 0.4 respectively, and the proportions of B commuting mode are 0.8 and 0.6 respectively; then the average of A commuting mode is 0.3, and the average of B commuting mode is 0.7; the average travel sharing rate of A commuting mode is 0.3, and the average travel sharing rate of B commuting mode is 0.7.

[0038] Step 4, threshold screening: set a threshold (0.5) to screen out basic data sets within the study range that have a commuting mode proportion greater than the threshold; and among the multiple screened basic data sets (basic data sets with a commuting mode proportion greater than the threshold 0.5), the proportion of the commuting mode and the commuting distance match to form multiple "commuting mode-commuting distance" index pairs.

[0039] Step 5, average travel distance calculation: in the multiple "commuting mode-commuting distance" index pairs, the average commuting distance of the index pairs of the same type of commuting mode is calculated as the average travel distance of that type of commuting mode.

[0040] Step 6, carbon emission calculation: select any commuting mode, and substitute the average travel sharing rate and average travel distance of the commuting mode to calculate the total carbon emission, the calculation formula is

[0041] M = ∑D i *C i *P*S i

[0042] M - total carbon emission (g);

[0043] D i - average travel distance (km);

[0044] C i - carbon emission coefficient (g / person.km);

[0045] P - number of trips (person);

[0046] S i - average travel sharing rate.

[0047] Wherein the trip person-time is calculated from the population data (population data of the selected spatial location, directly obtained from the selected spatial location, which is known data in the calculation formula), trip intensity (directly obtained from the urban traffic trip report of the selected spatial location, which is known data in the calculation formula), the average trip distance and the average trip share rate come from the data analysis in the previous step; the carbon emission coefficients corresponding to different travel modes are mainly from the greenhouse gas emission factor database published by IPCC and related literature, which are known data in the calculation formula.

[0048] Specifically, in step 1, when obtaining the Baidu LBS data, data 1 and data 2 are obtained, data 1 and data 2 are provided by a Baidu supplier, and the data accuracy is 250m grid; data 1 includes a plurality of samples 1, the sample 1 includes a departure point position coordinate, a target point position coordinate, and a commuting distance, and the coordinate accuracy is 10 decimal places; data 2 includes a plurality of samples 2, the sample 2 includes a departure point position coordinate, a target point position coordinate, and a plurality of commuting mode proportions, and the coordinate accuracy is up to 6 decimal places; after cleaning and screening data 1 and data 2, matching is performed to form a plurality of basic data sets.

[0049] Cleaning of data 1 and data 2: all samples 1 with non-unique "departure point position coordinate & target point position coordinate" in data 1 are removed, and all samples 2 with non-unique "departure point position coordinate & target point position coordinate" (i.e., there are two or more samples 1 / sample 2 with the same "departure point position coordinate & target point position coordinate") in data 2 are removed.

[0050] The sample 1 further includes the number of commutes, and the screening of data 1 and data 2 is to screen the sample 1 with the number of commutes being 1 in data 1.

[0051] The matching of data 1 and data 2 is to fuse the sample 1 and the sample 2 with the same departure point position coordinate and target point position coordinate to form a basic data set.

[0052] Before cleaning and screening data 1 and data 2, the coordinate accuracy of data 1 and data 2 is unified. The coordinate accuracy of data 1 and data 2 is unified to take 5 decimal places.

[0053] The above is only a preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing carbon emissions from transportation commuting based on big data, characterized in that, The method includes the following steps: Step 1, Data Provision: Obtain Baidu LBS data to form multiple basic datasets; the basic datasets include the coordinates of the starting point, the coordinates of the destination point, the commuting distance, and the proportion of multiple commuting modes; Step 2: Select the research scope: Select a spatial location as the research scope, and filter out the basic datasets whose starting point coordinates / target point coordinates are located within this research scope; Step 3: Calculate the average modal share: Calculate the average proportion of each commuting mode in multiple basic datasets within the research scope; the average modal share of any commuting mode is the average proportion of that commuting mode in multiple basic datasets within the research scope. Step 4, Threshold Filtering: Set a threshold to filter out basic datasets within the research scope that have a commuting mode ratio greater than the threshold; and match the commuting modes and commuting distances in the multiple filtered basic datasets that have a commuting mode ratio greater than the threshold to form multiple "commuting mode-commuting distance" indicator pairs. Step 5: Calculate the average travel distance: For each pair of "commuting mode - commuting distance" indicators, calculate the average commuting distance of the pair of indicators for the same commuting mode, and use it as the average travel distance of that commuting mode. Step 6: Carbon Emission Calculation: Select any commuting mode, substitute its average modal share and average travel distance to calculate its total carbon emissions. The calculation formula is as follows: M=∑D i *C i *P*S i M – Total carbon emissions (g); D i —Average travel distance (km); C i —Carbon emission coefficient (g / person-time.km); P – Number of trips (person-times); S i —Average modal share; In step 1, when acquiring Baidu LBS data, data 1 and data 2 are obtained. Data 1 includes multiple samples 1, which include the coordinates of the starting point, the coordinates of the destination point, and the commuting distance. Data 2 includes multiple samples 2, which include the coordinates of the starting point, the coordinates of the destination point, and the proportion of multiple commuting modes. Data 1 and data 2 are cleaned, filtered, and then matched to form multiple basic datasets. Cleaning of Data 1 and Data 2: Remove all samples 1 of Data 1 that have non-unique "starting point coordinates & target point coordinates"; remove all samples 2 of Data 2 that have non-unique "starting point coordinates & target point coordinates". Sample 1 also includes the number of commutes. The filtering of data 1 and data 2 is to filter data 1 and select sample 1 with a commuting number of 1. The matching of data 1 and data 2 is as follows: samples 1 and 2 with the same starting point coordinates and target point coordinates are merged to form the basic dataset; Before cleaning and filtering data 1 and data 2, the coordinate precision of data 1 and data 2 should be standardized.

2. The method for analyzing carbon emissions from transportation commuting based on big data according to claim 1, characterized in that, The commuting distance is the Hatton distance from the starting point coordinates to the destination point coordinates.

3. The method for analyzing carbon emissions from transportation commuting based on big data according to claim 1, characterized in that, The threshold is 0.

5.

4. The method for analyzing carbon emissions from transportation commuting based on big data according to claim 1, characterized in that, The percentages of various commuting modes include private car, subway, bus, cycling, and walking.

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