CN-121563577-B - Track data-based truck formation carbon emission reduction evaluation method and system

CN121563577BCN 121563577 BCN121563577 BCN 121563577BCN-121563577-B

Abstract

The application relates to a track data-based truck formation carbon emission reduction evaluation method and a track data-based truck formation carbon emission reduction evaluation system, wherein the method comprises the steps of preprocessing track data to obtain preprocessed track data; the method comprises the steps of carrying out space position matching on track points in the preprocessed track data and candidate road segments in a road network to construct a continuous running path, carrying out track similarity calculation on the track data of different card workshops to identify cooperative running behaviors of the two workshops, constructing a multi-vehicle cooperative running map, connecting vehicle pairs meeting the track similarity and cooperative time length requirements with construction edges, extracting communication components, identifying a primary cooperative running vehicle set meeting set conditions, merging and judging the primary cooperative running vehicle set to obtain a final cooperative running vehicle set, and carrying out modeling analysis on the final cooperative running vehicle set based on vehicle dynamics and aerodynamic models to obtain a carbon emission reduction evaluation result. The application has the effect of improving the accuracy of the energy conservation and emission reduction evaluation of the road freight.

Inventors

Li Aoyong
PAN DENGHUI
REN YILONG
YU HAIYANG

Assignees

北京航空航天大学

Dates

Publication Date: 20260505
Application Date: 20260124

Claims (7)

1. The method for evaluating the carbon emission reduction of the truck formation based on the track data is characterized by comprising the following steps of: Acquiring truck running track data in a target area, and preprocessing the track data to obtain preprocessed track data; based on a road network map matching algorithm, performing spatial position matching on track points in the preprocessed track data and candidate road segments in a road network, and introducing transition probability to construct a continuous driving path; Based on an improved longest public subsequence algorithm, track similarity calculation is carried out on track data of different card workshops, and cooperative driving behaviors of the two workshops are identified; constructing a multi-vehicle cooperative driving diagram based on graph theory modeling, connecting vehicle pairs meeting the requirements of track similarity and cooperative time length, extracting communication components, identifying a preliminary cooperative driving vehicle set meeting set conditions, and combining and judging the preliminary cooperative driving vehicle set to obtain a final cooperative driving vehicle set; modeling and analyzing the final cooperative driving vehicle set based on a vehicle dynamics and aerodynamic model to obtain a carbon emission reduction evaluation result, wherein the carbon emission reduction evaluation result comprises fuel saving amount and carbon emission reduction amount; The modeling analysis is carried out on the final cooperative driving vehicle set based on the vehicle dynamics and aerodynamic model to obtain a carbon emission reduction evaluation result, which comprises the following steps: And calculating the traction force required by each vehicle in the final cooperative driving vehicle set according to the track data, wherein the calculation formula of the traction force required by the vehicle is as follows: , for the mass of the vehicle it is, In order for the acceleration to be a function of the acceleration, In order to provide a rolling resistance, Is pneumatic resistance; Taking the vehicle position relation and the vehicle speed characteristics of each formation in the final cooperative driving vehicle set as pneumatic correction factors, respectively generating air resistance reduction coefficients of the head vehicle and the following vehicle, and dynamically correcting the required traction according to the air resistance reduction coefficients; Estimating fuel consumption in unit time based on the required traction and the engine efficiency, and calculating a corresponding carbon emission by combining a carbon emission factor; counting fuel oil conservation and carbon emission reduction of each vehicle in the final cooperative driving vehicle set in a cooperative driving interval to obtain a carbon emission reduction evaluation result of each cooperative vehicle in the final cooperative driving vehicle set; And carrying out structural arrangement on the identification information of the final cooperative driving vehicle set and the carbon emission reduction evaluation result so as to be used for calling a multidimensional classification statistics and scheduling system.
2. The method for evaluating carbon emission reduction of truck formation based on track data according to claim 1, wherein the preprocessing of the track data to obtain preprocessed track data specifically comprises: cleaning the track data to obtain cleaned track data, wherein the cleaning operation comprises removing track points with abnormal speed, missing time stamps, repeated time stamps or invalid coordinates; In the cleaned track data, if the time interval between adjacent track points is larger than a preset time threshold value, dividing the cleaned track data into different travel sections to generate a structured track sequence; Numbering the structured track sequence, and reserving corresponding vehicle identification and time information to obtain the preprocessed track data.
3. The method for evaluating carbon emission reduction of truck formation based on track data according to claim 1, wherein the map matching algorithm based on the road network performs spatial position matching on track points in the preprocessed track data and candidate road segments in the road network, and introduces transition probability to construct a continuous driving path, and specifically comprises the following steps: Constructing a space buffer zone with a preset radius by taking the geographic coordinates of each track point in the preprocessed track data as a circle center, and searching a plurality of road segments falling into the space buffer zone as a candidate road segment set; For each candidate road section, calculating a matching probability based on the vertical distance from the track points to the candidate road section, and calculating a transition probability between the track points based on the geographic distance between the track points and the network shortest distance between the candidate road sections, wherein the calculation formula of the matching probability is as follows: , representing the perpendicular distance of the track point to the road segment, The calculation formula of the transition probability is as follows: , for the shortest distance of the road network, For the actual geographical distance to be a real geographical distance, For the attenuation coefficient, the actual geographical distance formula is: , For the radius of the earth, 、 Is the latitude and longitude difference between two points, wherein phi 1 is the latitude value of a first track point in an adjacent track point pair and phi 2 is the latitude value of a second track point in the adjacent track point pair; And determining an optimal path matching sequence according to the matching probability and the transition probability so as to generate a continuous running track aligned with the road network.
4. The method for evaluating carbon emission reduction of truck formation based on track data according to claim 1, wherein the improved longest common subsequence algorithm is used for calculating track similarity of track data of different card workshops, and identifying cooperative driving behaviors of the two workshops, and specifically comprises the following steps: uniformly sampling and time aligning tracks of any two vehicles to construct a track overlapping time interval; Identifying matching point pairs meeting a space distance and time difference threshold based on an improved longest public subsequence algorithm, and calculating a space matching rate according to the matching point pairs; calculating the direction difference of the matching point pairs, generating a direction similarity, and fusing the direction similarity with the space matching rate to obtain an overall track similarity score, wherein the calculation formula of the direction similarity is as follows: , For the pair-wise pair of points, , The calculation formulas of the scores of the similar points of the whole track are respectively course angles: , wherein, In order for the spatial matching rate to be the same, Is a weighting coefficient, wherein i is a vehicle i, j is a vehicle j, |M| is the total number of paired-point pairs; And when the track similarity score is higher than a preset similarity threshold value and the continuous time period covered by the matching point pair is not smaller than a preset cooperative time length threshold value, judging that the two vehicles have cooperative driving behaviors.
5. The method for evaluating carbon emission reduction of truck formation based on track data according to claim 1, wherein the constructing a multi-vehicle cooperative driving graph based on graph theory modeling includes connecting vehicle pairs meeting requirements of track similarity and cooperative time length, extracting a communication component, and identifying a preliminary cooperative driving vehicle set meeting a set condition, wherein the method specifically includes: all vehicle pairs which meet the requirement that the track similarity score is higher than a preset threshold value and the time period covered by the matching point pairs is not smaller than a cooperative time length threshold value are constructed as edge connection in a graph structure, and an undirected graph is constructed by taking corresponding vehicles as nodes; Extracting all connected components from the undirected graph through depth-first traversal, and calculating the connected density of each connected component, wherein the calculation formula of the connected density is as follows: , Is the actual number of edges in the component, The number of vehicles contained as a component, For density, represent the degree of vehicle coordination, where |E c | is the number of actual associations between vehicles; And identifying components with the communication density larger than a set density threshold as effective cooperative formation, and counting the obtained effective cooperative formation to obtain the preliminary cooperative driving vehicle set.
6. The method for evaluating carbon emission reduction of truck formation based on track data according to claim 1, wherein the step of merging and judging the preliminary cooperative driving vehicle set to obtain a final cooperative driving vehicle set specifically comprises the following steps: and respectively calculating the overlapping proportion of the vehicle members and the overlapping proportion of the time interval of any two components based on each cooperative formation in the preliminary cooperative driving vehicle set, wherein the overlapping proportion of the vehicle members is defined as: , for the number of member intersections, For a smaller aggregate vehicle number; And when the overlapping proportion of the vehicle members and the overlapping proportion of the time interval exceed the preset merging threshold, merging the corresponding collaborative formations into a group of collaborative traveling vehicle sets, performing numbering processing on all the collaborative formation sets meeting merging conditions, and determining and outputting a final collaborative traveling vehicle set.
7. A truck formation carbon emission reduction evaluation system based on track data is characterized in that, the truck formation carbon emission reduction evaluation system based on the track data comprises: the track acquisition and preprocessing module is used for acquiring truck running track data in the target area, preprocessing the track data and obtaining preprocessed track data; the track matching construction module is used for carrying out space position matching on track points in the preprocessed track data and candidate road segments in a road network based on a road network map matching algorithm, and introducing transition probability to construct a continuous driving path; The track similarity recognition module is used for carrying out track similarity calculation on track data of different card workshops based on an improved longest public subsequence algorithm and recognizing cooperative driving behaviors of the two workshops; The collaborative map modeling and merging module is used for constructing a multi-vehicle collaborative running map based on graph theory modeling, connecting vehicle pairs meeting the requirements of track similarity and collaborative duration, extracting connected components, identifying a preliminary collaborative running vehicle set meeting set conditions, merging and judging the preliminary collaborative running vehicle set, and obtaining a final collaborative running vehicle set; The carbon emission reduction modeling module is used for carrying out modeling analysis on the final collaborative traveling vehicle set based on a vehicle dynamics and aerodynamic model to obtain a carbon emission reduction evaluation result, wherein the carbon emission reduction evaluation result comprises fuel saving amount and carbon emission reduction amount; the result structuring module is used for carrying out structural arrangement on the identification information of the final cooperative driving vehicle set and the carbon emission reduction evaluation result so as to be used for calling a multi-dimensional classification statistics and scheduling system; The carbon emission reduction modeling module includes: the traction calculation sub-module is used for calculating the traction required by each vehicle in the final cooperative driving vehicle set according to the track data, wherein the calculation formula of the traction required by the vehicle is as follows: , for the mass of the vehicle it is, In order for the acceleration to be a function of the acceleration, In order to provide a rolling resistance, Is pneumatic resistance; the pneumatic correction sub-module is used for taking the vehicle position relation and the vehicle speed characteristics of each formation in the final cooperative driving vehicle set as pneumatic correction factors, respectively generating air resistance reduction coefficients of the head vehicle and the following vehicle, and dynamically correcting the required traction according to the air resistance reduction coefficients; The fuel consumption emission calculation sub-module is used for estimating fuel consumption in unit time based on the required traction and the engine efficiency and calculating the corresponding carbon emission by combining a carbon emission factor; And the evaluation result generation sub-module is used for counting the fuel oil conservation and carbon emission reduction of each vehicle in the final cooperative driving vehicle set in the cooperative driving interval to obtain the carbon emission reduction evaluation result of each cooperative vehicle in the final cooperative driving vehicle set.

Description

Track data-based truck formation carbon emission reduction evaluation method and system Technical Field The application relates to the technical field of vehicle driving carbon emission measuring and calculating methods, in particular to a truck formation carbon emission reduction assessment method based on track data. Background At present, continuous pushing of carbon emission reduction is realized, and control of the carbon emission of the road freight becomes a key link of urgent breakthrough. Heavy trucks are used as the main force of freight, and the high energy consumption and high emission characteristics of the heavy trucks are important in industry control. The truck formation reduces aerodynamic resistance through the cooperation of multiple vehicles, realizes fuel saving and carbon emission reduction, and is an important research and application direction of current intelligent network traffic and green freight transportation. However, most of the existing researches depend on small-range simulation or experimental scenes, and cannot truly reflect the spontaneous formation characteristics and the carbon reduction effect of the trucks under the condition of actual road network and multi-enterprise mixed traveling. Meanwhile, the lack of a comprehensive technical system capable of efficiently and accurately excavating spontaneous formation and energy-saving effects in large-scale track data causes the lack of scientific basis for evaluating carbon emission reduction effects, and restricts intelligent scheduling, policy excitation and industry popularization. The prior art scheme has the defect that the existing evaluation of carbon emission reduction often depends on a static model or an estimated value, and the actual running parameters, the dynamic equation and the aerodynamic characteristic factors of the vehicle cannot be introduced for accurate calculation, so that there is room for improvement. Disclosure of Invention In order to improve the accuracy of energy conservation and emission reduction evaluation of road freight, the application provides a truck formation carbon emission reduction evaluation method based on track data. The first object of the present application is achieved by the following technical solutions: A truck formation carbon emission reduction evaluation method based on track data, the truck formation carbon emission reduction evaluation method based on track data comprising: Acquiring truck running track data in a target area, and preprocessing the track data to obtain preprocessed track data; based on a road network map matching algorithm, performing spatial position matching on track points in the preprocessed track data and candidate road segments in a road network, and introducing transition probability to construct a continuous driving path; Based on an improved longest public subsequence algorithm, track similarity calculation is carried out on track data of different card workshops, and cooperative driving behaviors of the two workshops are identified; constructing a multi-vehicle cooperative driving diagram based on graph theory modeling, connecting vehicle pairs meeting the requirements of track similarity and cooperative time length, extracting communication components, identifying a preliminary cooperative driving vehicle set meeting set conditions, and combining and judging the preliminary cooperative driving vehicle set to obtain a final cooperative driving vehicle set; modeling and analyzing the final cooperative driving vehicle set based on a vehicle dynamics and aerodynamic model to obtain a carbon emission reduction evaluation result, wherein the carbon emission reduction evaluation result comprises fuel saving amount and carbon emission reduction amount; And carrying out structural arrangement on the identification information of the final cooperative driving vehicle set and the carbon emission reduction evaluation result so as to be used for calling a multidimensional classification statistics and scheduling system. By adopting the technical scheme, invalid or abnormal data can be removed by collecting track data of a plurality of trucks and performing preprocessing operation on the track data, the accuracy of subsequent analysis data is improved, thereby laying a high-quality data foundation for collaborative traveling behavior recognition, natural collaborative traveling behaviors can be accurately mined by recognizing collaborative formation with space-time consistency based on the preprocessed track data, energy-saving behavior discovery without additional communication control is realized, the problem that overlapping or partially consistent formations are split is solved by combining and judging a preliminary collaborative traveling vehicle set, the comprehensiveness and stability of recognition results are improved, and quantitative analysis on fuel-saving benefits brought by collaborative traveling can be realized by constructing a vehicle tra