Method for calculating carbon emissions of motor vehicles based on trajectory data

Abstract

The application discloses a kind of motor vehicle carbon emission estimation method based on trajectory data, the method includes: according to the driving speed of motor vehicle trajectory and residence time, the journey division of motor vehicle trip is carried out;Precise trajectory information is obtained by using public road network data to do road network matching to trajectory data;Microscopic carbon emission estimation model is applied to the carbon emission of vehicle journey section is estimated;The carbon emission of journey trajectory is attributed to road section, and the carbon emission amount at road section level is obtained.The application fully excavates the trip characteristics of motor vehicle trajectory data, uses road network matching to obtain high-precision trajectory information data, and calculates carbon emission, provides a solution for massive trajectory data information processing, accurate estimation of carbon emission carbon emission.The motor vehicle journey trajectory data required by the application is simple to obtain, and the data scale is large;Estimation efficiency is high, suitable for large quantities of trajectory data estimation.

Description

Technical Field

[0001] This invention belongs to the field of motor vehicle carbon emission measurement technology, specifically relating to a method for measuring motor vehicle carbon emissions based on trajectory data. Background Technology

[0002] Currently, the key research areas include carbon emission estimation methods for motor vehicles, the heterogeneity of carbon emission levels, and carbon emission management strategies. Among these, the estimation of carbon emissions from motor vehicles has become a research hotspot for socio-economic and environmental sustainable development.

[0003] Research on vehicle carbon emission measurement methods based on trajectory data plays an important role in achieving low-carbon cities and contributing to carbon peaking and carbon neutrality.

[0004] This method can be applied to the field of urban health checks, effectively identifying roads and areas with high carbon emissions, enabling timely and effective adjustments to planning strategies, and is of great significance for urban managers in urban governance under the goal of low carbon emissions.

[0005] Carbon emissions from road areas are closely related to urban structures. Research in this area can help urban planners and government agencies understand the impact of transportation carbon emissions on urban structures, and contribute to achieving carbon peaking and carbon neutrality goals in urban planning.

[0006] Existing methods for calculating motor vehicle carbon emissions primarily rely on macroscopic data. These studies suffer from low data accuracy and representativeness. In estimating transportation carbon emissions, researchers often use large-scale overall fuel use data and local field survey data. For example, statistical data on vehicles and roads from relevant statistical yearbooks are used to estimate vehicle emissions; some researchers have collected two-week video road survey data and parking data. These studies not only lack broad representativeness due to limited data volume, but also suffer from low accuracy due to difficulties in obtaining vehicle technical information and driving parameters; furthermore, the time-consuming and labor-intensive data collection results in poor timeliness. In research on vehicle trajectory information, GPS data is widely used for vehicle travel trajectory prediction and reconstruction, but further information extraction and effective application are lacking.

[0007] Current research lacks a systematic methodology that combines GPS trajectory data, road network matching, and carbon emission calculation. The following problems exist regarding GPS trajectory data mining and vehicle carbon emission calculation:

[0008] 1) The depth of GPS trajectory data mining is insufficient, and there is a lack of effective applications;

[0009] 2) The calculation of carbon emissions from motor vehicles relies on macro-level CO2 emission data or carbon emission data from gas stations, etc., which have small sample sizes and insufficient representativeness, resulting in rough estimates.

[0010] There is currently no technical method that combines trajectory data with carbon emission calculation. Summary of the Invention

[0011] The purpose of this invention is to effectively utilize massive and easily obtainable GPS trajectory data to accurately calculate the carbon emissions of motor vehicles.

[0012] To achieve the above objectives, this invention proposes a method for calculating motor vehicle carbon emissions based on trajectory data: First, the characteristics of motor vehicle GPS trajectory data are analyzed to divide the journey; second, public road network data and the ST-match algorithm are used to perform road network matching on the journey segment trajectory to obtain refined motor vehicle journey segment trajectory information; finally, the carbon emissions of the motor vehicle journey segment are calculated using the micro carbon emission calculation model VT-micro, and the carbon emissions of the journey segment are summarized to the road segment to obtain detailed motor vehicle carbon emission information.

[0013] The present invention is implemented using the following technical solutions:

[0014] A method for calculating motor vehicle carbon emissions based on trajectory data is provided, including:

[0015] The process involves acquiring GPS trajectory data of the vehicle's journey and regional road network data; preprocessing the GPS trajectory data to remove abnormal data; constructing a graph for the regional road network data, extracting edge information to form a road data table, and finally constructing and storing the corresponding road data table.

[0016] The spatiotemporal features of the trajectory points are extracted, the sampling frequency of the trajectory points is analyzed, the velocity and sampling interval of the trajectory are calculated by differential calculation, the stationary segment and the moving segment in the journey are distinguished according to the velocity and sampling interval, the journey is divided, and the journey segments with stationary points are filtered out are obtained and extracted into a specific journey data table.

[0017] Using the ST-Match road network matching algorithm, the travel data table for each travel segment is corrected and supplemented, and the road data table is merged to obtain a refined travel trajectory table;

[0018] Using the refined travel trajectory table, specific information about the vehicle's driving process is obtained through data processing. Based on the micro carbon emission calculation model VT-micro, the carbon emissions of the travel segment are estimated, and the carbon emissions of the travel trajectory are summarized to the road segment to obtain the carbon emissions at the road segment level.

[0019] As a further explanation of the present invention, the GPS trajectory data information includes vehicle ID, latitude and longitude coordinates, and sampling time information.

[0020] As a further explanation of the present invention, the preprocessing of the trip GPS trajectory data to remove abnormal data includes:

[0021] The GPS trajectory data of the trip is cleaned by removing outliers and speeding points from the trajectory point data.

[0022] As a further explanation of the present invention, the trip data table includes the trip start point, trip duration, trip trajectory line, and trip time point.

[0023] As a further explanation of the present invention, the step of using the road network matching algorithm ST-Match to correct and supplement the travel data table for each travel segment, and merging the road data table to obtain a refined travel trajectory table includes:

[0024] ST-Match is based on the HMM model, which includes two state sets and three probability matrices.

[0025] Implicit state S: corresponds to multiple candidate matching points c1, c2, ... of the current GPS point P to be matched in the road network matching;

[0026] Observable state O: corresponds to the point to be matched in the road network matching;

[0027] State transition probability matrix A: The relationship between the previous candidate matching point and the next candidate matching point, expressed in probabilistic form;

[0028] Observation state probability matrix B: represents the probability of the observed state under the hidden state condition at time t;

[0029] Initial state probability matrix π: represents the probability matrix of the hidden state at the initial time t=1;

[0030] For ST-Match, the observed state probability matrix is ​​as follows:

[0031]

[0032] p(o t |r i ) : The observation state matrix of the point to be matched at time t under road segment i, measured by a Gaussian distribution; σ o : A measure of noise level, the reliability of GPS points, and stable estimates calculated from real data; t The point to be matched at time t; Candidate points that are correctly matched at time t;

[0033] Its state transition probability matrix is ​​determined as follows:

[0034] Spatial analysis functions:

[0035]

[0036]

[0037] d i-1→i : Euclidean distance of GPS points; w (i-1，t)→(i，s) : Navigation distance of candidate points; Speed ​​exponent; Hidden state c i Gaussian distribution; Spatial similarity of candidate matching points.

[0038] Time analysis function: For spatially adjacent roads, speed is introduced as a time factor; the overall calculation uses cosine similarity, using the speed vector of the road segment [e′1·v,...,e′] on the shortest path. k ·v] and average velocity vector Measured by the difference;

[0039]

[0040]

[0041] l: Road length; Average velocity vector; e′ u • v: Road segment velocity vector; Temporal similarity of candidate matching points;

[0042] Finally, a candidate graph is constructed based on the candidate point set of each step, and the score of each transition edge is calculated.

[0043]

[0044] In path finding, the probabilities of candidate matching points F(P) are directly summed at each time step. c ),Right now:

[0045]

[0046] The point with the largest result among the candidate nodes is selected as the matching point;

[0047] By inputting the trip data table and the road data table, road network matching is performed on the road trip segments in sequence to obtain a refined trip trajectory table with a matching rate of over 90%.

[0048] As a further explanation of the present invention, the specific information during the driving process of the motor vehicle includes driving time, driving location, motor vehicle speed, and motor vehicle acceleration.

[0049] As a further explanation of the present invention, the detailed travel trajectory table is used to obtain specific information about the vehicle's driving process through data processing methods. Based on the micro-carbon emission calculation model VT-micro, the carbon emissions of the travel segment are estimated, and the travel trajectory carbon emissions are summarized to the road segment level to obtain the carbon emissions at the road segment level. This includes:

[0050] Carbon emission calculation for the journey segment:

[0051] The VT-Micro model is as follows:

[0052]

[0053] f_MOE: Vehicle carbon emissions or emissions, L / h or mg / s; i: speed power exponent; j: speed power exponent; v i and a j The regression coefficients; v: vehicle speed (m / s); a: vehicle acceleration (m / s²) 2 ;

[0054] Calculate the average velocity between discrete trajectory points in the travel segment. The velocity information v and travel correction coefficient ε at each point are obtained using a quadratic interpolation algorithm. f The acceleration information 'a' is substituted into the carbon emission calculation model formula to obtain the theoretical carbon emission value f_MOE at each point. Then, a second interpolation is performed on the discrete carbon emission point f_MOE series to obtain the corresponding interpolation function f. extrapolate Integrating over the time of the journey segment yields the calculated carbon emission value V for that segment. trip :

[0055] V trip =∫ fextrapolate dt

[0056] Based on the proportion r of motor vehicle travel on each road segment, the carbon emission value V for each travel segment is allocated. trip This yields the carbon emission calculation result V for any vehicle on any road during any trip. road ;

[0057]

[0058] V road =V trip ×r

[0059] S: Distance traveled in the trip segment; T: Travel time of the trip segment; S road: The distance traveled on the road segment; T road Travel time on the road segment: The time taken for a particular segment of the journey.

[0060] As a further explanation of the present invention, the regression coefficient It was calibrated using field data collected by Oak Ridge National Laboratory.

[0061] Compared with the prior art, the present invention has the following advantages:

[0062] This invention fully explores the travel characteristics of motor vehicle trajectory data, uses road network matching to obtain high-precision trajectory information data, and calculates carbon emissions, providing a solution for the information processing of massive trajectory data and the accurate measurement of carbon emissions.

[0063] The vehicle trajectory data required by this invention is simple to acquire and has a large data scale; it has high calculation efficiency and is suitable for the calculation of large batches of trajectory data; the generated road-level vehicle trajectory information is more accurate; and the use of the micro-carbon emission model makes the calculation of carbon emissions more accurate. Attached Figure Description

[0064] Figure 1 This is a data processing diagram of the vehicle carbon emission calculation method based on trajectory data provided by the present invention;

[0065] Figure 2 , Figure 3 , Figure 4 The flowchart of the method for calculating motor vehicle carbon emissions based on trajectory data provided by the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] like Figure 1-4 As shown, this invention provides a method for calculating the carbon emissions of motor vehicles based on trajectory data, comprising the following steps:

[0068] Step 1: Obtain the vehicle's GPS trajectory data (including vehicle ID, latitude and longitude coordinates, and sampling time information) and regional road network data (derived from OSM road network data).

[0069] The GPS trajectory data of the trip is cleaned. For the trajectory point data, outliers (deviating from the previous and next trajectory paths) and speeding points (deviating too much from the previous and next trajectory points) are removed. For the regional road network data information (graphml, an XML format), a graph is constructed, and then the edge information is extracted to form a road data table (node_id, edge_id, geometry). Finally, the corresponding road data table is constructed and stored.

[0070] Step 2: Extract the spatiotemporal features of the trajectory points, analyze the sampling frequency of the trajectory points, calculate the velocity and sampling interval of the trajectory using differential calculation, distinguish the stationary segments and moving segments in the journey based on features such as velocity and sampling interval, divide the journey, and obtain the journey segments after filtering out stationary points, extract them into a specific journey data table (journey start point, journey time, journey trajectory line, journey time point, etc.).

[0071] Step 3: Using the ST-Match road network matching algorithm, the travel data table for each travel segment is corrected and supplemented, and the road data table is merged to obtain a refined travel trajectory table (the trajectory table includes the travel start point, travel start time, and trajectory line fused with road network data).

[0072] The specific road network matching process is as follows:

[0073] Step 3-1, Road Network Matching Algorithm Flow:

[0074] ST-Match is based on a Hidden Markov Model (HMM): a statistical model used to describe a Markov process with hidden unknown parameters. It contains five elements: two sets of states and three probability matrices.

[0075] (a) Implicit state S: cannot be directly observed and corresponds to multiple candidate matching points c1, c2, ... of the current GPS point P to be matched in the road network matching.

[0076] (b) Observable state O: Associated with the hidden state, it can be obtained through direct observation. It corresponds to the point to be matched in the road network matching.

[0077] (c) State transition probability matrix A: describes the transition probabilities between states in the Hidden Markov Model. That is, the relationship between the previous candidate matching point and the next candidate matching point, which can be expressed in probabilistic form.

[0078] (d) Observation state probability matrix B: represents the probability of the observed state under the hidden state condition at time t.

[0079] (e) Initial state probability matrix π: represents the probability matrix of the hidden state at the initial time t=1.

[0080] For ST-Match, its state transition probability matrix is ​​determined as follows:

[0081] Spatial distance: Measured by the difference between the distance between GPS points and the distance between candidate points. The numerator is the Euclidean distance between GPS points, and the denominator is the navigation distance (shortest path distance) between candidate points.

[0082]

[0083]

[0084] Time: For spatially adjacent roads, spatial features alone are insufficient for differentiation; therefore, speed is introduced as a temporal factor. Overall, cosine similarity is used for calculation, employing the speed vectors of the shortest road segments [e′1·v,...,e′] k ·v] and average velocity vector The difference is used to measure it.

[0085]

[0086]

[0087] Finally, a candidate graph is constructed based on the candidate point set of each step, and the score of each transition edge is calculated.

[0088]

[0089] In path solving, each time step is directly summed, that is:

[0090]

[0091] The point with the largest result among the candidate nodes is selected as the matching point.

[0092] Step 3-2, Matching Process:

[0093] Input the trip data table and the road data table, and perform road network matching on the road trip segments in turn to obtain a refined trip trajectory table with a matching rate of over 90% (including original trajectory points, matched trajectory points, matched trajectory lines, road IDs, errors, and other information).

[0094] Step 4: Using a refined travel trajectory table, data processing methods are used to obtain specific information (time, location, speed, acceleration, etc.) during the vehicle's journey. Based on the micro carbon emission calculation model VT-micro, carbon emissions for the travel segment are estimated, and the carbon emissions of the travel trajectory are summarized to the road segment to obtain the carbon emissions at the road segment level.

[0095] Specifically:

[0096] Step 4-1, Calculation of carbon emissions during the journey:

[0097] The VT-Micro model can calculate vehicle carbon emissions and emissions based on the vehicle's instantaneous speed and acceleration, both of which can be calculated from the vehicle's trajectory. The model is as follows:

[0098]

[0099] f_MOE: Vehicle carbon emissions or emissions, L / h or mg / s; i: speed power exponent; j: speed power exponent; v i and a j The regression coefficients; v: vehicle speed (m / s); a: vehicle acceleration (m / s²) 2 .

[0100] Regression coefficient These values ​​were obtained using field data collected by Oak Ridge National Laboratory. The values ​​are shown in the table below:

[0101] Positive a <![CDATA[a 0 ]]> -7.73452 0.02799 -0.0002228 1.09E-06 <![CDATA[a 1 ]]> 0.22946 0.0068 -4.40E-05 4.80E-08 <![CDATA[a 2 ]]> -0.00561 -0.00077221 7.90E-07 3.27E-08 <![CDATA[a 3 ]]> 9.77E-05 8.38E-06 8.17E-07 -7.79E-09 Negative a <![CDATA[a 0 ]]> -7.73452 0.02804 -0.00021988 1.08E-06 <![CDATA[a 1 ]]> -0.01799 0.00772 -0.00005219 2.47E-07 <![CDATA[a 2 ]]> -0.00427 0.00083744 -7.44E-06 4.87E-08 <![CDATA[a 3 ]]> 0.00018829 -0.00003387 2.77E-07 3.79E-10

[0102] Calculate the average velocity between discrete trajectory points in the travel segment. The velocity information v and travel correction coefficient ε at each point are obtained using a quadratic interpolation algorithm. f The acceleration information 'a' is substituted into the carbon emission calculation model formula to obtain the theoretical carbon emission value f_MOE at each point. Then, a second interpolation is performed on the discrete carbon emission point f_MOE series to obtain the corresponding interpolation function f. extrapolate Integrating over the time of the journey segment yields the calculated carbon emission value V for that segment. trip :

[0103] V trip =∫f extrapolate dt

[0104] Step 4-2: Allocate carbon emission values ​​V for each road segment based on the proportion r of the vehicle's travel status on each road segment. trip This yields the carbon emission calculation result V for any vehicle on any road during any trip. road .

[0105]

[0106] V road =V trip ×r

[0107] S: Distance traveled in the trip segment; T: Travel time of the trip segment; S road : The distance traveled on the road segment; T road Travel time on the road segment: The time taken for a particular segment of the journey.

[0108] This invention fully explores the travel characteristics of motor vehicle trajectory data, uses road network matching to obtain high-precision trajectory information data, and calculates carbon emissions, providing a solution for the information processing of massive trajectory data and the accurate measurement of carbon emissions.

[0109] The vehicle trajectory data required by this invention is simple to acquire and has a large data scale; it has high calculation efficiency and is suitable for the calculation of large batches of trajectory data; the generated road-level vehicle trajectory information is more accurate; and the use of the micro-carbon emission model makes the calculation of carbon emissions more accurate.

[0110] It should be noted that, in this document, terms such as “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0111] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for calculating motor vehicle carbon emissions based on trajectory data, characterized in that, include: Obtain GPS trajectory data of motor vehicles and regional road network data; The GPS trajectory data of the trip is preprocessed to remove abnormal data; For the regional road network data information, a graph is constructed, then the edge information is extracted to form a road data table, and finally the corresponding road data table is constructed and stored. The spatiotemporal features of the trajectory points are extracted, the sampling frequency of the trajectory points is analyzed, the velocity and sampling interval of the trajectory are calculated by differential calculation, the stationary segment and the moving segment in the journey are distinguished according to the velocity and sampling interval, the journey is divided, and the journey segments with stationary points are filtered out are obtained and extracted into a specific journey data table. Using the ST-Match road network matching algorithm, the travel data table for each travel segment is corrected and supplemented, and the road data table is merged to obtain a refined travel trajectory table; Based on the refined travel trajectory table, the average speed between discrete trajectory points in the travel segment is calculated, and the speed information, travel correction coefficient and acceleration information of each trajectory point are obtained by a quadratic interpolation algorithm. The velocity and acceleration information are substituted into the VT-Micro microscopic carbon emission calculation model to obtain the theoretical carbon emission values ​​at each trajectory point. The discrete carbon emission point sequence is interpolated twice to obtain the emission interpolation function. The emission interpolation function is integrated over the time range of the travel segment to obtain the carbon emission calculation value of the travel segment. Based on the proportion of mileage and time traveled by each road segment, the carbon emission calculation value of the travel segment is allocated to the corresponding road segment to obtain the carbon emission at the road segment level.

2. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 1, characterized in that, The GPS trajectory data includes vehicle ID, latitude and longitude coordinates, and sampling time information.

3. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 1, characterized in that, The preprocessing of the GPS trajectory data to remove abnormal data includes: The GPS trajectory data of the trip is cleaned by removing outliers and speeding points from the trajectory point data.

4. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 1, characterized in that, The trip data table includes the trip start point, trip duration, trip trajectory line, and trip time points.

5. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 1, characterized in that, The method utilizes the ST-Match road network matching algorithm to correct and supplement the travel data table for each travel segment, and merges the road data table to obtain a refined travel trajectory table, including: ST-Match is based on the HMM model, which includes two state sets and three probability matrices. Implicit state S: corresponds to multiple candidate matching points c1, c2, ... of the current GPS point P to be matched in the road network matching; Observable state O: corresponds to the point to be matched in the road network matching; State transition probability matrix A: The relationship between the previous candidate matching point and the next candidate matching point, expressed in probabilistic form; Observation state probability matrix B: represents the probability of the observed state under the hidden state condition at time t; Initial state probability matrix π: represents the probability matrix of the hidden state at the initial time t=1; For ST-Match, the observed state probability matrix is ​​as follows: The observation state matrix of the point to be matched at time t under road segment i is measured by a Gaussian distribution. : A measure of noise level, the reliability of GPS points, and a stable estimate calculated from real data; The point to be matched at time t; Candidate points that are correctly matched at time t; Its state transition probability matrix is ​​determined as follows: Spatial analysis functions: Euclidean spacing of GPS points; : Navigation distance of candidate points; Speed ​​exponent; Implicit state Gaussian distribution; Spatial similarity of candidate matching points. Time analysis function: For spatially adjacent roads, speed is introduced as a time factor; the overall calculation uses cosine similarity, using the speed vector of the road segment on the shortest path. and average velocity vector Measured by the difference; Road length; : Average velocity vector; : Road segment velocity vector; Temporal similarity of candidate matching points; Finally, a candidate graph is constructed based on the candidate point set of each step, and the score of each transition edge is calculated. In path finding, the probabilities of candidate matching points are directly summed at each time step. ,Right now: The point with the largest result among the candidate nodes is selected as the matching point; By inputting the trip data table and the road data table, road network matching is performed on the road trip segments in sequence to obtain a refined trip trajectory table with a matching rate of over 90%.

6. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 1, characterized in that, The specific information during the vehicle's operation includes travel time, location, speed, and acceleration.

7. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 1, characterized in that, Based on the refined travel trajectory table, the average speed between discrete trajectory points in the travel segment is calculated. A quadratic interpolation algorithm is used to obtain the speed information, travel correction coefficient, and acceleration information for each trajectory point. The speed and acceleration information are then substituted into the VT-Micro micro-carbon emission calculation model to obtain the theoretical carbon emission value at each trajectory point. A quadratic interpolation is performed on the discrete carbon emission point sequence to obtain the emission interpolation function. The emission interpolation function is integrated over the time range of the travel segment to obtain the calculated carbon emission value for the travel segment. Based on the proportion of travel mileage and travel time on each road segment, the calculated carbon emission value for the travel segment is allocated to the corresponding road segment to obtain the carbon emission amount at the road segment level, including: Carbon emission calculation for the journey segment: The VT-Micro model is as follows: Carbon emissions or emissions from vehicles or ; Speed ​​exponent; Speed ​​exponent; : and The regression coefficients; Vehicle speed ; Vehicle acceleration ; Calculate the average velocity between discrete trajectory points in the travel segment. The velocity information of each point is obtained through a quadratic interpolation algorithm. Travel correction factor and acceleration information Substituting these values ​​into the carbon emission calculation model formula yields the theoretical carbon emission values ​​at each point. Again, for discrete carbon emission points The series of quadratic interpolations yields the corresponding interpolation function. Integrating over the time of the journey segment yields the calculated carbon emissions for that segment. : Based on the proportion of motor vehicle travel patterns on each road segment Carbon emission values ​​for allocating travel segments This allows us to obtain the carbon emission calculation results for any vehicle on any road during any trip. ; : The distance traveled during the trip; Travel time for the specified segment of the journey; : The distance traveled on the road segment; Travel time on the road segment: The time taken for a particular segment of the journey.

8. The method for calculating motor vehicle carbon emissions based on trajectory data according to claim 7, characterized in that, The regression coefficients were obtained by calibrating using field data collected by Oak Ridge National Laboratory.

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