Method and system for verifying carbon emission reduction of low-carbon travel based on travel chain big data

Abstract

The application provides a low-carbon travel carbon emission reduction amount verification method and system based on travel chain big data, which comprises the following steps: step 1) obtaining sample data of travel data; step 2) when the sample data is walking data, going to step 3); when the sample data is bicycle data, going to step 4); when the sample data is electric bicycle data, going to step 5); step 3) verifying the sample data by using a walking travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of walking according to the final effective travel distance; step 4) verifying the sample data by using a bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of the bicycle according to the final effective travel distance; step 5) verifying the sample data by using an electric bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; and calculating the carbon emission of the electric bicycle.

Description

A Method and System for Verifying Carbon Emission Reductions of Low-Carbon Travel Based on Big Data from the Travel Chain

[0001] This application is a divisional application of the application filed on September 9, 2020, with application number 202010940108.4, entitled "Verification Method and System for Low-Carbon Travel Carbon Emission Reduction Based on Travel Chain Big Data". Technical Field

[0002] This invention relates to the field of carbon emission reduction accounting, specifically to a method and system for verifying low-carbon travel carbon emission reductions based on big data from the travel chain. Background Technology

[0003] Firstly, regarding low carbon, the "Path of Developing a Low-Carbon Economy in China" provides a relevant definition: A new economic and social entity that, compared with a traditional economic and energy entity, can save energy and reduce harmful gas emissions in the process of production and consumption, while maintaining stable economic growth and a trend of sustainable social development, can be called a low-carbon trend.

[0004] Reducing carbon dioxide emissions is a fundamental consensus for humanity in addressing global climate change. Cities, as centers of human economic activity, are also hubs of energy consumption and carbon emissions. Statistics show that in developed countries, carbon emissions from urban residents' total energy consumption account for 30%-60% of total urban emissions. China, especially its more developed regions, is in a critical period of rapid urbanization, and high energy consumption and high carbon emissions are core characteristics of this stage of development. Therefore, carbon emissions from residential areas deserve serious attention.

[0005] With the convening of the 2015 Paris Climate Conference, global attention to addressing climate change has reached unprecedented levels. As the world's largest emitter of greenhouse gases, China has taken numerous measures in recent years to fulfill its carbon dioxide emission reduction commitments, with carbon trading being one of the most effective. The "Administrative Measures for Voluntary Greenhouse Gas Emission Reduction Trading" issued by the National Development and Reform Commission standardizes the approval procedures and steps for voluntary emission reduction projects, clarifying the accuracy of emission reduction calculations. These measures provide a channel for enterprises with voluntary carbon emission reduction intentions to enter the carbon trading market. Looking at domestic and international carbon trading markets, the current implementation of carbon trading mainly focuses on the industrial production sector, with little involvement in low-carbon living among residents. As the main consumers of industrial products and services, controlling carbon emissions from residents' daily lives is crucial for controlling the greenhouse effect and mitigating global climate change. China is also actively exploring low-carbon development models amidst rapid urbanization and industrialization, and has comprehensively launched low-carbon pilot projects nationwide. To reasonably control the rapid growth of carbon emissions in the residential sector and accelerate the formation of a new pattern of low-carbon society construction with full public participation, these measures are being implemented. Guangdong Province in China was the first in the world to propose carbon inclusion innovation, applying the core concept of carbon trading to residents' daily lives and quantifying and incentivizing low-carbon behaviors. The carbon inclusion system extends the core concept of existing carbon trading from the production sector to the living sector, allowing users to apply for emission reduction projects and participate in market-based carbon trading in accordance with standardized procedures and voluntary emission reduction trading management regulations.

[0006] Firstly, regarding low carbon, the "Path of Developing a Low-Carbon Economy in China" provides a relevant definition: A new economic and social entity that, compared with a traditional economic and energy entity, can save energy and reduce harmful gas emissions in the process of production and consumption, while maintaining stable economic growth and a trend of sustainable social development, can be called a low-carbon trend.

[0007] Reducing carbon dioxide emissions is a fundamental consensus for humanity in addressing global climate change. Cities, as centers of human economic activity, are also hubs of energy consumption and carbon emissions. Statistics show that in developed countries, carbon emissions from urban residents' total energy consumption account for 30%-60% of total urban emissions. China, especially its more developed regions, is in a critical period of rapid urbanization, and high energy consumption and high carbon emissions are core characteristics of this stage of development. Therefore, carbon emissions from residential areas deserve serious attention.

[0008] Looking at domestic and international carbon trading markets, the current implementation of carbon trading mainly focuses on the industrial production sector, with little involvement in low-carbon living for residents. As the main consumers of industrial products and services, controlling carbon emissions from residents' daily lives is crucial for controlling the greenhouse effect and mitigating global climate change. China is actively exploring low-carbon development models amidst rapid urbanization and industrialization, and has comprehensively launched low-carbon pilot programs nationwide. To reasonably control the rapid growth of carbon emissions in the residential sector and accelerate the formation of a new pattern of low-carbon society construction with public participation, Guangdong Province in China has pioneered the concept of carbon inclusion innovation, applying the core concept of carbon trading to residents' daily lives and quantifying and incentivizing low-carbon public behavior. The carbon inclusion system extends the core content of existing carbon trading from the production sector to the residential sector, allowing users to apply for emission reduction projects and participate in market-based carbon trading according to standardized procedures and regulations. The established carbon trading and low-carbon incentive model is as follows: local government agencies or enterprises apply for and register carbon reduction projects. These projects must first monitor and record individual travel records. Through the quantified carbon emission reductions generated by individual users in the project, and after the carbon emission reductions are recalculated and verified by a third-party verification agency, the government agencies or enterprises can apply to trade the cumulative carbon emission reductions of the project on the carbon trading market. The benefits obtained from selling carbon emission reductions are combined with commercial mechanisms to reward individuals and the public who generate carbon emission reductions directly or indirectly.

[0009] Promoting carbon credits is beneficial for raising public awareness of low-carbon practices, mobilizing the whole society to actively engage in green and low-carbon behaviors, reducing carbon emissions in daily life, and meeting the inherent needs of sustainable urban development. The core of carbon credits lies in assigning value to individuals' energy-saving and carbon-reduction behaviors, with the calculation of individual carbon emission reductions from low-carbon behaviors being a prerequisite and data foundation for the implementation of carbon credits.

[0010] The basic calculation formula used in domestic and international research on emission reductions of public self-propelled systems can be expressed as:

[0011] E R =EF R ×N×D AVE

[0012] Among them, E R Emission reduction (t) for the project; EF R N represents the amount of carbon dioxide emitted per kilometer by motor vehicles (g CO2 / km); D represents the number of times the public bicycle system is used; AVEThe average trip distance per person per trip (km) is used. This study focuses on the entire public bicycle project, calculating the overall carbon reduction based on the average trip distance. After registering on the Guangzhou Carbon Benefit Platform and linking their public bicycle account, users are assessed by the platform based on their public bicycle usage records. The carbon reduction is calculated using the formula above, and then returned to the user's Carbon Benefit account in the form of "carbon coins." Users can use these "carbon coins" to redeem other goods or obtain carbon reduction benefits in other ways.

[0013] Current carbon emission reduction verification methods lack sufficient coverage of low-carbon travel modes: the "Calculation of Individual Carbon Emission Reductions Based on Urban Public Bicycles under the Carbon Credit System" only covers trips using public bicycles as a mode of transportation. The coverage of low-carbon travel modes is insufficient. Current carbon emission reduction verification methods do not verify the accuracy and reasonableness of travel data: the verification of travel mileage in the "Calculation of Individual Carbon Emission Reductions Based on Urban Public Bicycles under the Carbon Credit System" is based on the latitude and longitude of stations provided by the bicycle management company, integrated with Gaode Maps, and assessed using a walking mode to obtain the shortest cycling distance between stations. The bicycle company provides the bicycle rental and return time and station information, corresponding to the specific mileage data. The mileage verification data obtained by assessing the shortest bicycle travel distance using a walking mode is inaccurate. Furthermore, this verification method does not consider the possibility of falsified travel records or the function of correcting verification mileage discrepancies. Summary of the Invention

[0014] The purpose of this invention is to overcome the above-mentioned technical deficiencies and propose a method for verifying low-carbon travel carbon emission reductions based on big data from the travel chain. The method includes:

[0015] Step 1) Obtain sample data of travel data;

[0016] Step 2) If the sample data is walking data, proceed to step 3); if the sample data is bicycle data, proceed to step 4); if the sample data is electric bicycle data, proceed to step 5.

[0017] Step 3) Verify the sample data using the walking distance verification method to obtain the calculable travel distance; superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; calculate the upper limit of the effective travel distance to obtain the final effective travel distance; calculate the baseline carbon emission reduction and carbon emission of a single walking trip based on the final effective travel distance.

[0018] Step 4) Verify the sample data using the bicycle travel distance verification method to obtain the calculable travel distance; superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; calculate the upper limit of the effective travel distance to obtain the final effective travel distance; and calculate the baseline carbon emission reduction of a bicycle trip and the carbon emission of a bicycle trip based on the final effective travel distance.

[0019] Step 5) Verify the sample data using the verification method for electric bicycle travel distance; obtain the calculable travel distance; superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; calculate the upper limit of the effective travel distance to obtain the final effective travel distance; calculate the baseline carbon emission reduction and carbon emission of one electric bicycle trip based on the final effective travel distance.

[0020] As an improvement to the above method, step 3) specifically includes:

[0021] Step 3-1) Verify the sample data using the walking distance verification method to obtain the calculable travel distance;

[0022] Step 3-2) Superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance;

[0023] Step 3-3) Arrange the walking trip data of the same user in chronological order, sum up the effective total trip distance of all walking trips of the same user, and retain only the effective total trip distance of less than or equal to 9km as the final effective trip distance;

[0024] Steps 3-4) Baseline carbon emissions for a single walking trip = final effective travel distance * corresponding car emission coefficient * conversion coefficient;

[0025] Steps 3-5) Carbon emissions from a single walking trip = final effective travel distance * walking carbon emission coefficient.

[0026] As an improvement to the above method, step 3-1) specifically includes:

[0027] Extract GPS trajectory point data from each data point in the pedestrian travel data: T i Lon i and Lat i ;T i Lon represents the time corresponding to the coordinates of the i-th GPS track point. i Let Lat be the longitude coordinates of the i-th GPS track point. i Let be the latitude coordinates of the i-th GPS track point;

[0028] For each data point, the GPS track point data of each travel record are sorted in chronological order. If there are duplicate GPS track point records, only the last one in chronological order among the duplicate GPS track points is retained.

[0029] Calculate the distance between two adjacent trajectory points:

[0030] Δ Lon =Lon i+1 -Lon i

[0031] Δ Lat =Lat i+1 -Lat i

[0032]

[0033] Where: Δ Lon Δ is the difference in longitude between the (i+1)th and ith GPS track point coordinates. Lat Δ is the latitude difference between the (i+1)th and ith GPS track point coordinates. dist The distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point;

[0034] For walking trip data, if Δ dist >3, Δ in the calculation formula Lon =Lon i+2 -Lon i Δ Lat =Lat i+2 -Lat i And recalculate Δ dist Furthermore, the calculation results no longer undergo a reasonable judgment on the distance between two adjacent trajectory points;

[0035] By summing the distances between all GPS track points for walking, we obtain the verification trip distance D. 核验出行距离 :

[0036] D 核验出行距离 =∑Δ dist

[0037] Compare the verified travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance:

[0038]

[0039] Among them, the travel distance is the original travel distance of the walking travel data;

[0040] When all "absolute deviations of travel distance" are arranged from smallest to largest, and the 90th percentile of "absolute deviations of travel distance" is <= 20%, then it is considered "passing" and the travel distance can be calculated as the travel distance of the sample data.

[0041] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is greater than 20%, then it is considered "failed"; the travel distance can be calculated as the verification travel distance D. 核验出行距离 .

[0042] As an improvement to the above method, step 3-2) specifically includes:

[0043] When the start and end times of walking and public transportation (bus or subway) overlap:

[0044] If the start and end times are in an inclusive relationship, only the calculable travel distance of public transport and rail travel data is retained, and the calculable travel distance of walking travel is deleted, which is taken as the final valid travel distance.

[0045] If the start and end times are intersecting, the effective travel distance is obtained by subtracting the calculable travel distance of the intersecting part from the calculable travel distance of the walking part.

[0046] When the start and end times of walking and cycling or electric bicycle trips overlap:

[0047] If the start and end times are in an inclusive relationship, only the calculable travel distance corresponding to the travel mode with a larger start and end time span is considered as the effective travel distance;

[0048] If the start and end times are in an inclusive relationship, the effective travel distance is calculated by subtracting the calculated travel distance of the intersecting part from the calculated travel distance of the walking part.

[0049] As an improvement to the above method, step 4) specifically includes:

[0050] Step 4-1) Verify the sample data using the bicycle travel distance verification method to obtain the calculable travel distance;

[0051] Step 4-2) Superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; specifically including:

[0052] When the start and end times of cycling and public transportation (bus or subway) overlap:

[0053] If the start and end times are in an inclusive relationship, only the computable travel distance of public transport and rail travel data is retained, and the computable travel distance of bicycle travel is deleted, which is taken as the final valid travel distance.

[0054] If the start and end times are intersecting, the effective travel distance is obtained by subtracting the intersecting part's calculable travel distance from the calculable travel distance of the bicycle.

[0055] Step 4-3) Arrange the bicycle trip data of the same user in chronological order, sum up the effective total trip distance of all bicycle trips of the same user, and retain only the effective total trip distance of less than or equal to 21km as the final effective trip distance;

[0056] Step 4-4) Baseline carbon emissions for a single bicycle trip = final effective trip distance * corresponding car emission coefficient * conversion coefficient;

[0057] Steps 4-5) Carbon emissions per bicycle trip = final effective trip distance * bicycle carbon emission coefficient.

[0058] As an improvement to the above method, step 4-1) specifically includes:

[0059] Extracting GPS track point data from each data point in bicycle trip data: T i Lon i and Lat i ;T i Lon represents the time corresponding to the coordinates of the i-th GPS track point. i Let Lat be the longitude coordinates of the i-th GPS track point. i Let be the latitude coordinates of the i-th GPS track point;

[0060] For each data point, the GPS track point data of each travel record are sorted in chronological order. If there are duplicate GPS track point records, only the last one in chronological order among the duplicate GPS track points is retained.

[0061] Calculate the distance between two adjacent trajectory points:

[0062] Δ Lon =Lon i+1 -Lon i

[0063] Δ Lat =Lat i+1 -Lat i

[0064]

[0065] The longitude difference of the coordinates; Δ Lat Δ is the latitude difference between the (i+1)th and ith GPS track point coordinates. distThe distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point;

[0066] For bicycle travel data, if Δ dist >7, Δ in the calculation formula Lon =Lon i+2 -Lon i Δ Lat =Lat i+2 -Lat i And recalculate Δ dist Furthermore, the calculation results no longer undergo a reasonable judgment on the distance between two adjacent trajectory points;

[0067] By summing the distances between GPS track points of all bicycles, the verification trip distance D can be obtained. 核验出行距离 :

[0068] D 核验出行距离 =∑Δ dist

[0069] Compare the verified travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance:

[0070]

[0071] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is <= 15%, then it is considered "passing"; the travel distance can be calculated based on the sample data.

[0072] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is greater than 15%, then it is considered "failed"; the travel distance can be calculated as the verification travel distance D. 核验出行距离 .

[0073] As an improvement to the above method, step 5) specifically includes:

[0074] Step 5-1) Verify the sample data using the verification method for electric bicycle travel distance to obtain the calculable travel distance;

[0075] Step 5-2) Superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; specifically including:

[0076] When the start and end times of electric bicycle and public transportation / subway trips overlap:

[0077] If the start and end times are in an inclusive relationship, only the calculable travel distance of public transport and rail travel data is retained, while the calculable travel distance of electric bicycle travel is deleted, and this is taken as the final valid travel distance.

[0078] If the start and end times are intersecting, the effective travel distance is obtained by subtracting the intersecting part from the calculable travel distance of the electric bicycle.

[0079] Step 5-3) Arrange the electric bicycle travel data of the same user in chronological order, sum up the effective total travel distance of all electric bicycle trips of the same user, and retain only the effective total travel distance of less than or equal to 21km as the final effective travel distance.

[0080] Step 5-4) Carbon emissions of a single trip by an electric bicycle = final effective travel distance * corresponding car emission coefficient * conversion coefficient;

[0081] Step 5-5) Carbon emissions per trip by electric bicycle = final effective travel distance * carbon emission coefficient of electric bicycle.

[0082] As an improvement to the above method, step 5-1) specifically includes:

[0083] Extracting GPS track point data from each data point in electric bicycle travel data: T i Lon i and Lat i ;T i Lon represents the time corresponding to the coordinates of the i-th GPS track point. i Let Lat be the longitude coordinates of the i-th GPS track point. i Let be the latitude coordinates of the i-th GPS track point;

[0084] For each data point, the GPS track point data of each travel record are sorted in chronological order. If there are duplicate GPS track point records, only the last one in chronological order among the duplicate GPS track points is retained.

[0085] Based on the above array, calculate the distance between two adjacent trajectory points:

[0086] Δ Lon =Lon i+1 -Lon i

[0087] Δ Lat =Lat i+1 -Lat i

[0088] The longitude difference of the coordinates; Δ Lat Δ is the latitude difference between the (i+1)th and ith GPS track point coordinates. distThe distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point;

[0089] For electric bicycle travel data, if Δ dist >10, Δ in the calculation formula Lon =Lon i+2 -Lon i Δ Lat =Lat i+2 -Lat i And recalculate Δ dist Furthermore, the calculation results no longer undergo a reasonable judgment on the distance between two adjacent trajectory points;

[0090] By summing the distances between GPS track points of the electric bicycle, the verification trip distance D can be obtained. 核验出行距离 :

[0091] D 核验出行距离 =∑Δ dist

[0092] Compare the verified travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance:

[0093]

[0094] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is <= 15%, then it is considered "passing"; the travel distance can be calculated based on the sample data.

[0095] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is greater than 15%, then it is considered "failed"; the travel distance can be calculated as the verification travel distance D. 核验出行距离 .

[0096] This invention also proposes a low-carbon travel carbon emission reduction verification system based on big data of travel chains. The system includes: a data acquisition module, a judgment module, a walking data verification module, a bicycle data verification module, and an electric bicycle data verification module.

[0097] The data acquisition module is used to acquire sample data of travel data;

[0098] The judgment module is used to activate the walking data verification module when the sample data is walking data; the walking data verification module is activated when the sample data is bicycle data; and the electric bicycle data verification module is activated when the sample data is electric bicycle data.

[0099] The walking data verification module is used to verify the sample data using the walking travel distance verification method to obtain the calculable travel distance; to superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; to calculate the upper limit of the effective travel distance to obtain the final effective travel distance; and to calculate the baseline carbon emission reduction and carbon emission of a single walking trip based on the final effective travel distance.

[0100] The bicycle data verification module is used to verify sample data using a bicycle travel distance verification method to obtain the calculable travel distance; it then superimposes and removes duplicates from the calculable travel distance to obtain the effective travel distance; it calculates the upper limit of the effective travel distance to obtain the final effective travel distance; and based on the final effective travel distance, it calculates the baseline carbon emission reduction for a single bicycle trip and the carbon emission for a single bicycle trip.

[0101] The electric bicycle data verification module is used to verify sample data using the verification method of electric bicycle travel distance; to obtain the calculable travel distance; to superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; to calculate the upper limit of the effective travel distance to obtain the final effective travel distance; and to calculate the baseline carbon emission reduction and carbon emission of a single electric bicycle trip based on the final effective travel distance.

[0102] The advantages of this invention are:

[0103] 1. The method of this invention provides a reasonable and quantifiable method for calculating and verifying carbon emission reductions for walking, cycling, electric bicycles, public transportation, and rail travel. It can effectively record the carbon emission reductions of individuals' low-carbon travel (travel chain carbon emission reductions), effectively ensuring the scientificity and accuracy of carbon emission reduction verification and certification of carbon reduction projects. It also provides an effective guarantee for incentivizing urban residents to choose low-carbon travel methods and public transportation, thereby alleviating urban traffic congestion and traffic pollution.

[0104] 2. The method of this invention establishes a collaborative mechanism with established enterprises or companies in the market that are capable of providing travel data, to quantify and calculate the carbon emission reduction of residents' travel and interact directly with enterprises or companies. The carbon emission reduction benefits are directly linked to the carbon reduction project verification algorithm and process of individual travel records.

[0105] 3. The method of the present invention provides data processing and technical solutions to the technical difficulties in verifying carbon emission reductions in low-carbon travel carbon reduction projects, such as: verifying the accuracy of travel records, accurately calculating travel carbon emission reductions, processing large-scale travel data, processing multi-source travel data, standardizing travel data from different sources, and avoiding invalid travel data. Attached Figure Description

[0106] Figure 1 is a flowchart of the travel distance verification method of the present invention;

[0107] Figure 2 is a schematic diagram showing the overlap of public transportation travel time and walking travel time for a certain user in a time sequence;

[0108] Figure 3 is a schematic diagram showing the overlap between cycling and walking travel times for a certain user in a time sequence. Detailed Implementation

[0109] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0110] Embodiment 1 of the present invention proposes a method for verifying carbon emission reductions of low-carbon travel based on big data of the travel chain, the method comprising:

[0111] Step 1: Identify mode of transportation

[0112] Reasonable thresholds were set for four fields in walking, cycling, and electric bicycle travel data: average speed, maximum speed, travel distance (original travel distance in the travel data), and travel time. Initial cleaning of the walking, cycling, and electric bicycle data was performed, retaining data within the threshold range. Specific threshold ranges are shown in the table below:

[0113]

[0114]

[0115] Step 2: Fixed Sample Size Sampling

[0116] The acquired data includes all travel data; data from Gaode and Baidu Maps on walking, cycling, and electric bicycles, and data from public transport and rail systems. Sampling is used to improve computational efficiency. The process involves verifying the sample data to determine if all data is correct (the original data's travel mileage is close to the actual value). If correct, carbon emission reductions are calculated directly using the original data's travel distance. If incorrect, a travel distance verification calculation is performed for each data point (this calculation is computationally intensive and time-consuming; sampling aims to minimize this calculation. If the original data fails the sampling verification, this calculation must be performed).

[0117] A fixed sample of 10,000 travel data points was randomly sampled for walking, cycling, and electric bicycle travel data. Then, steps three through seven were performed on the sample data for each mode of travel.

[0118] Step 3: GPS Track Point Timing Processing

[0119] Extract GPS trajectory point data for each data point from walking, cycling, and e-bike travel data, forming an array [T1, Lon1, Lat2|T2, Lon2, Lat2|…|T i Lon i Lat i |…];

[0120] in:

[0121] T i : Represents the time corresponding to the coordinates of the i-th GPS track point, with the time format being yyyy-MM-ddHH:mm:ss;

[0122] Lon i : Represents the longitude coordinates of the i-th GPS track point;

[0123] Lat i : Represents the latitude coordinates of the i-th GPS track point;

[0124] For each data point, the GPS track point data of each travel record are sorted in chronological order. If there are duplicate GPS track point records, only the last one in the time sequence is retained.

[0125] Step 4: Calculate the distance between GPS track points

[0126] Based on the above array, calculate the distance between two adjacent trajectory points and determine if it is reasonable:

[0127] Δ Lon =Lon i+1 -Lon i

[0128] Δ Lat =Lat i+1 -Lat i

[0129]

[0130] in:

[0131] Δ Lon : The difference in longitude between the (i+1)th GPS track point's longitude coordinates and the ith GPS track point's longitude coordinates;

[0132] Δ Lat The difference in latitude between the (i+1)th GPS track point and the ith GPS track point.

[0133] Δ dist: The distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point;

[0134] For walking trip data, if Δ dist >3, Δ in the calculation formula Lon =Lon i+2 -Lon i Δ Lat =Lat i+2 -Lat i And recalculate Δ dist Furthermore, the calculation results no longer include a judgment on the reasonableness of the distance between two adjacent trajectory points.

[0135] For bicycle travel data, if Δ dist >7, Δ in the calculation formula Lon =Lon i+2 -Lon i Δ Lat =Lat i+2 -Lat i And recalculate Δ dist Furthermore, the calculation results no longer include a judgment on the reasonableness of the distance between two adjacent trajectory points.

[0136] For electric bicycle travel data, if Δ dist >10, Δ in the calculation formula Lon =Lon i+2 -Lon i Δ Lat =Lat i+2 -Lat i And recalculate Δ dist Furthermore, the calculation results no longer include a judgment on the reasonableness of the distance between two adjacent trajectory points.

[0137] Step 5: Accumulate the distances between GPS track points

[0138] D 核验出行距离 =∑Δ dist

[0139] (Note: The total distance between all GPS track points during a single trip)

[0140] Step Six: Compare the verified travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance:

[0141]

[0142] (Note: Calculate the travel distance deviation for each travel data point in the sample data)

[0143] Among them, 'verified travel distance' is the travel distance corresponding to each travel data point calculated in this calculation method; 'travel distance' is the original travel distance of the travel data.

[0144] Step 7: Determine whether the sample passes verification.

[0145] (1) For walking:

[0146] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is <= 20%, then it is considered "passing".

[0147] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is greater than 20%, then it is "not passed".

[0148] (2) For travel by bicycle and electric vehicle:

[0149] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is <= 15%, then it is considered "passing".

[0150] When all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th percentile of the "absolute deviation of travel distance" is greater than 15%, then it is "not passed".

[0151] If the sample data passes the above verification, the "calculable travel distance" value for each trip used for subsequent calculations will be the original travel distance value from the travel data.

[0152] If the sample data does not pass the above verification, then the "verification travel distance" calculation in steps four and five is performed on the full sample data of all travel modes. The "calculable travel distance" value for each trip used in subsequent calculations is the value of the "verification travel distance".

[0153] The above steps are shown in Figure 1.

[0154] Step 8: Effective travel distance superposition and deduplication

[0155] 1. If the start and end times of the same user's bus and subway trips (card-swiping travel data refers to travel data using Beijing Yikatong for buses or subways, including card-swiping bus travel data and card-swiping subway travel data; QR code-swiping travel data refers to travel data using the Beijing Bus APP QR code for buses or subways, including QR code-swiping bus travel data and card-swiping subway travel data) overlap, the "calculable travel distance" of both trips will be retained as the final "effective travel distance".

[0156] 2. The start and end times of the same user's walking / cycling (walking, cycling, e-bike) and public transportation trips overlap:

[0157] The start and end times are inclusive and contained in each other. Only the "calculable travel distance" of public transport and rail travel data is retained, while the "calculable travel distance" of walking and cycling travel is deleted, and the final "effective travel distance" is used.

[0158] The start and end times are intersecting. The "effective travel distance" is calculated by subtracting the "calculable travel distance" of the intersecting part from the "calculable travel distance" of the corresponding step cycling.

[0159] As shown in Figure 2: the time sequence of two consecutive trips for a certain user, then:

[0160] V 步 = Walking trip's distance can be calculated / (|Walking trip start time - Walking trip end time|)

[0161] dt = Bus stop end time - Walking start time

[0162] Effective walking distance = Calculated walking distance - V 步 *dt

[0163] 3. The start and end times of the same user's walking and cycling (bicycle, electric bicycle) trips overlap.

[0164] The start and end times are inclusive and contained in each other. Only the 'calculable travel distance' corresponding to the travel mode with a large start and end time span is considered as the 'effective travel distance'.

[0165] The start and end times are inclusive and contained relationships. The corresponding walking 'calculable travel distance' minus the 'calculable travel distance' of the intersecting part is used as the 'effective travel distance'.

[0166] As shown in Figure 3, given the time sequence of two consecutive trips by a certain user, then:

[0167] V 步 = Walking trip's distance can be calculated / (|Walking trip start time - Walking trip end time|)

[0168] dt = Cycling end time - Walking start time

[0169] Effective walking distance = Calculated walking distance - V 步 *dt

[0170] Step 9: Determine the date and time range

[0171] Based on the start time field in the travel data, find the carbon emission reduction coefficients for different travel modes at different times throughout the year for the entire road network on the corresponding date and time.

[0172] Among them, the emission reduction coefficients for different travel modes at different times throughout the year for the entire road network have been calculated based on the "Beijing Low-Carbon Travel Carbon Emission Reduction Methodology" (Trial Version).

[0173] Step 10: Calculation of the upper limit of 'effective travel distance' for walking, cycling, and electric bicycles

[0174] For each user ID, set an upper limit on the 'effective travel distance' for all walking, cycling, and electric vehicle travel records.

[0175] Among them: the principle for setting the upper limit:

[0176] 1. For the same user ID, if the total effective travel distance exceeds 9km, it will be counted as 9km;

[0177] 2. For bicycles under the same user ID, if the total effective travel distance exceeds 21km, it will be counted as 21km.

[0178] 3. For electric vehicles under the same user ID, if the total effective travel distance exceeds 21km, it will be counted as 21km.

[0179] Calculation method:

[0180] 1. Arrange the walking data of the same user in chronological order, sum up the 'total effective travel distance' of all walking trips of the same user, and only retain the 'total effective travel distance' of less than or equal to 9km as the 'final effective travel distance';

[0181] 2. Arrange the bicycle trip data of the same user in chronological order, sum up the 'total effective trip distance' of all bicycle trips of the same user, and only retain the 'total effective trip distance' of less than or equal to 21km as the 'final effective trip distance';

[0182] 3. Arrange the electric bicycle travel data of the same user in chronological order, sum up the 'total effective travel distance' of all electric bicycle trips of the same user, and retain only the 'total effective travel distance' less than or equal to 21km as the 'final effective travel distance'.

[0183] Step 11: Calculate the baseline carbon emission reduction for a single trip

[0184] Walking: Baseline carbon emissions for a single trip = 'final effective trip distance' * corresponding car emission coefficient * conversion coefficient (walking);

[0185] Bicycle: Baseline carbon emissions for a single trip = 'final effective trip distance' * corresponding car emission coefficient * conversion coefficient (bicycle);

[0186] Electric bicycles: Baseline carbon emissions per trip = 'final effective trip distance' * corresponding car emission coefficient * conversion coefficient (bicycle);

[0187] Public transport: Carbon emissions per trip = 'final effective trip distance' * corresponding car emission coefficient * conversion coefficient (public transport);

[0188] Subway: Baseline carbon emissions for a single trip = 'final effective trip distance' * corresponding car emission coefficient * conversion coefficient (subway);

[0189] The corresponding passenger car emission coefficient is based on the start time field in the travel data, and corresponds to the passenger car emission coefficient of the entire road network at different times throughout the year for the corresponding date and time; the passenger car emission coefficient of the entire road network at different times throughout the year has been calculated according to the "Beijing Low-Carbon Travel Carbon Emission Reduction Methodology" (Trial Version).

[0190] The conversion coefficients for different modes of transportation (walking, bicycle, electric bicycle, bus, subway) have been calculated based on the "Beijing Low-Carbon Travel Carbon Emission Reduction Methodology" (Trial Version).

[0191] Step 12: Calculate the carbon emissions of a single trip

[0192] Walking: Carbon emissions per trip = 'final effective trip distance' * corresponding carbon emission coefficient (walking);

[0193] Bicycle: Carbon emissions per trip = 'final effective trip distance' * corresponding carbon emission coefficient (bicycle);

[0194] Electric bikes and bicycles: Carbon emissions per trip = 'final effective trip distance' * corresponding carbon emission coefficient (bicycle);

[0195] Public transport: Carbon emissions per trip = 'final effective trip distance' * corresponding carbon emission coefficient (public transport);

[0196] Subway: Carbon emissions per trip = 'final effective trip distance' * corresponding carbon emission coefficient (subway);

[0197] The carbon emission coefficients for different modes of transportation (walking, bicycle, electric bicycle, bus, and subway) have been calculated based on the "Beijing Low-Carbon Travel Carbon Emission Reduction Methodology" (Trial Version).

[0198] Embodiment 2 of the present invention proposes a low-carbon travel carbon emission reduction verification system based on big data of travel chain. The system includes: a data acquisition module, a judgment module, a walking data verification module, a bicycle data verification module, and an electric bicycle data verification module.

[0199] The data acquisition module is used to acquire sample data of travel data;

[0200] The judgment module is used to activate the walking data verification module when the sample data is walking data; the walking data verification module is activated when the sample data is bicycle data; and the electric bicycle data verification module is activated when the sample data is electric bicycle data.

[0201] The walking data verification module is used to verify the sample data using the walking travel distance verification method to obtain the calculable travel distance; to superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; to calculate the upper limit of the effective travel distance to obtain the final effective travel distance; and to calculate the baseline carbon emission reduction and carbon emission of a single walking trip based on the final effective travel distance.

[0202] The bicycle data verification module is used to verify sample data using a bicycle travel distance verification method to obtain the calculable travel distance; it then superimposes and removes duplicates from the calculable travel distance to obtain the effective travel distance; it calculates the upper limit of the effective travel distance to obtain the final effective travel distance; and based on the final effective travel distance, it calculates the baseline carbon emission reduction for a single bicycle trip and the carbon emission for a single bicycle trip.

[0203] The electric bicycle data verification module is used to verify sample data using the verification method of electric bicycle travel distance; to obtain the calculable travel distance; to superimpose and deduplicate the effective travel distances of the calculable travel distances to obtain the effective travel distance; to calculate the upper limit of the effective travel distance to obtain the final effective travel distance; and to calculate the baseline carbon emission reduction and carbon emission of a single electric bicycle trip based on the final effective travel distance.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for verifying carbon emission reductions from low-carbon travel based on big data from the travel chain, the method comprising: Acquire travel data, which includes at least one of walking data, bicycle data, and electric bicycle data; The verification and calculation steps include: verifying travel data to obtain a calculable travel distance, specifically including: extracting GPS trajectory point data for each data point from walking data, bicycle data, and electric bicycle data; calculating the distance between GPS trajectory points; accumulating the distance between GPS trajectory points to obtain the verified travel distance; comparing the verified travel distance with the travel distance in the travel data; calculating the absolute deviation of the travel distance; determining whether the sample passes verification based on the absolute deviation of the travel distance; identifying the time overlap relationship between different travel data records under the same user; and retaining, removing, or deducting the calculable travel distance of each travel data record according to the combination type of the travel data and the nature of the time overlap relationship to determine the effective travel distance corresponding to each travel data; calculating the upper limit limit of the effective travel distance based on the preset upper limit threshold corresponding to each travel data to obtain the final effective travel distance; and calculating the baseline carbon emission reduction and the carbon emission of a single trip based on the final effective travel distance, combined with the corresponding time period car emission coefficient determined by the travel start time, the preset conversion coefficient corresponding to each travel data, and the carbon emission coefficient.

2. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 1 further includes: Obtain sample data of travel data, including walking data, bicycle data, electric bicycle data and / or public transport data.

3. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 2 further includes: Clean the sample data, retaining only the data within the threshold range; Random sampling was performed on walking data, bicycle data, and electric bicycle data. Verify the sampling data obtained from the sampling.

4. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 1, wherein, The travel data includes at least walking data, and the verification and calculation steps specifically include step 3) verifying the walking data using a walking distance verification method to obtain a calculable travel distance; superimposing and deduplicating the effective travel distances of the calculable travel distances to obtain an effective travel distance; calculating the upper limit of the effective travel distance to obtain the final effective travel distance; and calculating the baseline carbon emission reduction and carbon emission of a single walking trip based on the final effective travel distance.

5. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 4, wherein, Step 3) specifically includes: Step 3-1) Verifying walking data using a walking distance verification method to obtain calculable travel distance; Step 3-2) Superimposing and deduplicating the effective travel distances of the calculable travel distances to obtain the effective travel distance; Step 3-3) Arranging the walking travel data of the same user in chronological order, accumulating the total effective travel distance of all walking trips of the same user, and retaining only the portion of the total effective travel distance less than or equal to 9km as the final effective travel distance; Step 3-4) Carbon emissions of a single walking trip = final effective travel distance * corresponding car emission coefficient * conversion coefficient; Step 3-5) Carbon emissions of a single walking trip = final effective travel distance * walking carbon emission coefficient.

6. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 5, wherein, Step 3-1) specifically includes: extracting GPS trajectory point data for each data point from the walking travel data: ， and ； The time corresponding to the coordinates of the i-th GPS track point. Let be the longitude coordinates of the i-th GPS track point; Let be the latitude coordinate of the i-th GPS track point; for each data point, sort the GPS track point data of each trip record in chronological order. If there are duplicate GPS track point records, only retain the last one in chronological order among the duplicate GPS track points; calculate the distance between two adjacent track points: in: It is the difference in longitude between the (i+1)th GPS track point coordinates and the ith GPS track point coordinates. The difference in latitude between the (i+1)th GPS track point's latitude coordinates and the ith GPS track point's latitude coordinates; Let be the distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point; for walking travel data, if >3, in the calculation formula ， And recalculate Furthermore, the calculation results no longer undergo a reasonable judgment on the distance between adjacent track points; the distances between all walking GPS track points are summed to obtain the verified travel distance. ： Compare and verify the travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance: The travel distance refers to the original travel distance of the walking travel data. If all "absolute travel distance deviations" are arranged from smallest to largest, and the 90th quantile of the "absolute travel distance deviation" is <= 20%, then the result is "passed," and the travel distance can be calculated as the walking travel distance. If all "absolute travel distance deviations" are arranged from smallest to largest, and the 90th quantile of the "absolute travel distance deviation" is > 20%, then the result is "failed," and the travel distance can be calculated as the verification travel distance. 。 7. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 5, characterized in that, Step 3-2) specifically includes: When the start and end times of walking and public transportation / subway travel overlap: If the start and end times are inclusive, only the calculable travel distance of public transportation / subway travel data is retained, and the calculable travel distance of walking is deleted, which is taken as the final effective travel distance; if the start and end times are intersecting, the calculable travel distance of walking is subtracted from the calculable travel distance of the intersecting part to obtain the effective travel distance; When the start and end times of walking and bicycle or electric bicycle travel overlap: If the start and end times are inclusive, only the calculable travel distance corresponding to the corresponding travel mode with a larger start and end time span is considered, which is taken as the effective travel distance; if the start and end times are inclusive, the calculable travel distance of walking is subtracted from the calculable travel distance of the intersecting part to obtain the effective travel distance.

8. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 1, wherein, The travel data includes at least bicycle data, and the verification and calculation steps specifically include step 4) verifying the bicycle data using a bicycle travel distance verification method to obtain a calculable travel distance; superimposing and deduplicating the effective travel distances of the calculable travel distances to obtain an effective travel distance; calculating the upper limit of the effective travel distance to obtain the final effective travel distance; and calculating the baseline carbon emission reduction for a single bicycle trip and the carbon emission for a single bicycle trip based on the final effective travel distance.

9. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 8, wherein, Step 4) specifically includes: Step 4-1) Verifying the sample data using the bicycle travel distance verification method to obtain the calculable travel distance; Step 4-2) Superimposing and deduplicating the effective travel distances of the calculable travel distances to obtain the effective travel distance; specifically including: when the start and end times of bicycle and public transport / subway travel overlap: if the start and end times are inclusive, only the calculable travel distance of public transport / subway travel data is retained, and the calculable travel distance of bicycle travel is deleted as the final effective travel distance; if the start and end times are intersecting, the calculable travel distance of bicycle is subtracted from the calculable travel distance of the intersecting part as the effective travel distance; Step 4-3) Arranging the bicycle travel data of the same user in chronological order, accumulating the total effective travel distance of all bicycle trips of the same user, and retaining only the portion of the total effective travel distance less than or equal to 21km as the final effective travel distance; Step 4-4) The baseline carbon emission of a single bicycle trip = final effective travel distance * corresponding car emission coefficient * conversion coefficient; Step 4-5) The carbon emission of a single bicycle trip = final effective travel distance * bicycle carbon emission coefficient.

10. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 9, characterized in that, Step 4-1) specifically includes: extracting GPS trajectory point data for each data point from the bicycle travel data. ， and ； The time corresponding to the coordinates of the i-th GPS track point. Let be the longitude coordinates of the i-th GPS track point; Let be the latitude coordinate of the i-th GPS track point; for each data point, sort the GPS track point data of each trip record in chronological order. If there are duplicate GPS track point records, only retain the last one in chronological order among the duplicate GPS track points; calculate the distance between two adjacent track points: in: It is the difference in longitude between the (i+1)th GPS track point coordinates and the ith GPS track point coordinates. The difference in latitude between the (i+1)th GPS track point's latitude coordinates and the ith GPS track point's latitude coordinates; Let be the distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point; for bicycle travel data, if >7, in the calculation formula ， and recalculate Furthermore, the calculation results no longer undergo a reasonable judgment on the distance between two adjacent track points; the distances between all bicycle GPS track points are summed to obtain the verified travel distance. ： Compare and verify the travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance: The travel distance is the original travel distance of the bicycle travel data. If all "absolute travel distance deviations" are arranged from smallest to largest, and the 90th quantile of the "absolute travel distance deviation" is <= 15%, then it is considered "passed"; the calculated travel distance is the travel distance of the bicycle data. If all "absolute travel distance deviations" are arranged from smallest to largest, and the 90th quantile of the "absolute travel distance deviation" is > 15%, then it is considered "failed"; the calculated travel distance is the verification travel distance. 。 11. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 1, wherein, The travel data includes at least electric bicycle data, and the verification and calculation steps specifically include step 5) verifying the electric bicycle data using the electric bicycle travel distance verification method to obtain a calculable travel distance; superimposing and deduplicating the effective travel distances of the calculable travel distances to obtain an effective travel distance; calculating the upper limit of the effective travel distance to obtain the final effective travel distance; and calculating the baseline carbon emission reduction and carbon emission of one electric bicycle trip based on the final effective travel distance.

12. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 11, wherein, Step 5) specifically includes: Step 5-1) Verifying the sample data using the verification method for electric bicycle travel distance to obtain the calculable travel distance; Step 5-2) Superimposing and deduplicating the effective travel distances of the calculable travel distances to obtain the effective travel distance; specifically including: when the start and end times of electric bicycle and public transport / subway travel overlap: if the start and end times are inclusive, only the calculable travel distance of public transport / subway travel data is retained, and the calculable travel distance of electric bicycle travel is deleted as the final effective travel distance; if the start and end times are intersecting, the calculable travel distance of electric bicycle is subtracted from the calculable travel distance of the intersecting part as the effective travel distance; Step 5-3) Arranging the electric bicycle travel data of the same user in chronological order, accumulating the total effective travel distance of all electric bicycle trips of the same user, and retaining only the portion of the total effective travel distance less than or equal to 21km as the final effective travel distance; Step 5-4) Carbon emissions of a single electric bicycle trip = final effective travel distance * corresponding car emission coefficient * conversion coefficient; Step 5-5) Carbon emissions of a single electric bicycle trip = Ultimate effective travel distance * carbon emission coefficient of e-bike.

13. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 12, characterized in that, Step 5-1) specifically includes: extracting GPS trajectory point data for each data point from the electric bicycle travel data. ， and ； The time corresponding to the coordinates of the i-th GPS track point. Let be the longitude coordinates of the i-th GPS track point; Let be the latitude coordinate of the i-th GPS track point; for each data point, sort the GPS track point data of each trip record in chronological order. If there are duplicate GPS track point records, only retain the last one in chronological order among the duplicate GPS track points; calculate the distance between two adjacent track points: in: It is the difference in longitude between the (i+1)th GPS track point coordinates and the ith GPS track point coordinates. The difference in latitude between the (i+1)th GPS track point's latitude coordinates and the ith GPS track point's latitude coordinates; Let be the distance between the coordinates of the (i+1)th GPS track point and the coordinates of the ith GPS track point; for electric bicycle travel data, if >10, in the calculation formula ， And recalculate Furthermore, the calculation results no longer undergo a reasonable judgment on the distance between two adjacent trajectory points; the distances between the GPS trajectory points of the electric bicycle are summed to obtain the verified travel distance. ： Compare and verify the travel distance with the travel distance in the travel data, and calculate the absolute deviation of the travel distance: Among them, the travel distance is the original travel distance of the electric bicycle travel data; when all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th quantile of the "absolute deviation of travel distance" is <=15%, it is considered "passed"; the calculable travel distance is the travel distance of the sample data; when all "absolute deviations of travel distance" are arranged from smallest to largest, if the 90th quantile of the "absolute deviation of travel distance" is >15%, it is considered "failed"; the calculable travel distance is the verification travel distance. 。 14. The method for verifying low-carbon travel carbon emission reductions based on big data of the travel chain according to claim 2, 7, 9 or 12, wherein, The public transport and rail travel data includes card-swipe travel data and / or QR code-swipe travel data, wherein the card-swipe travel data includes card-swipe bus travel data and / or card-swipe subway travel data, and the QR code-swipe travel data includes QR code-swipe bus travel data and / or QR code-swipe subway travel data.

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