A Method and System for Analyzing the Travel Characteristics of New Energy Vehicles on Expressways

Abstract

The present invention discloses a method and system for analyzing the travel characteristics of new energy vehicles on expressways, including: collecting and preprocessing expressway data to obtain images, traffic flow, vehicle speed, road and facility location information, partitioning according to facility locations, extracting acceleration wave and deceleration wave characteristics and vehicle type images, defining a dynamic area, using the vehicle type images in the dynamic area to obtain short-term motion characteristics, identifying the first and second vehicle types, calculating the charging demand index, combining the charging demand index with the traffic flow, fitting the motion data of the short-term motion characteristics of the two vehicle types, and then calculating the conflict index. Then, using the cellular transmission model, traffic flow change information is obtained based on the above data. With the help of graph neural network analysis, by inputting its motion data, conflict index, traffic flow change data and expressway topology structure, the travel characteristics of the first vehicle type are output. This method realizes the analysis of the travel characteristics of new energy vehicles in the expressway scenario through the quantification of the charging demand index, and can provide auxiliary decision-making support for the construction of charging facilities on expressways.

Description

Technical Field

[0001] The present invention relates to the field of analysis of the travel characteristics of motor vehicles, and particularly to a method and system for analyzing the travel characteristics of new energy vehicles on expressways. Background Art

[0002] As the proportion of new energy vehicles in expressway travel gradually increases, traditional methods for analyzing expressway traffic characteristics are no longer sufficient to accurately analyze the travel characteristics of new energy vehicles, such as charging requirements and special driving behaviors. In existing expressway traffic analysis, a fixed zoning method is mostly used to divide the sections where vehicles enter and exit facilities, which cannot respond in real time to the fluctuating characteristics such as acceleration waves and deceleration waves in the traffic flow, resulting in a mismatch between the zoning range and the actual traffic impact area, and reducing the accuracy of traffic characteristic analysis.

[0003] An increasing number of vehicle models with different power combinations appear in the relatively closed environment of expressways, which has a greater impact on the movement characteristics of vehicles in heterogeneous traffic flows and the traffic flow conditions on different sections. These are all problems that need to be solved urgently at present. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for analyzing the travel characteristics of new energy vehicles on expressways.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The first aspect of the present invention provides a method for analyzing the travel characteristics of new energy vehicles on expressways, including:

[0007] Collecting and preprocessing the data of the expressway to be processed to obtain image data, traffic flow, vehicle speed, road data, and facility points;

[0008] Dividing the sections where vehicles enter and exit facilities based on the facility points, obtaining the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the divided areas according to the image data, traffic flow, and vehicle speed of the divided areas, and using the acceleration wave characteristics and deceleration wave characteristics to correct the zoning range to obtain a dynamic area;

[0009] Obtaining short-term movement characteristics using the vehicle type images of the dynamic area, obtaining the first vehicle type in the intervals before and after the charging facilities according to the vehicle type images, taking the remaining vehicle types as the second vehicle type, and calculating the charging demand index according to the first vehicle type that enters and does not enter the charging facilities;

[0010] According to the charging demand index and traffic flow in the dynamic area, the short-term movement characteristics of the first vehicle type and the second vehicle type are fitted to obtain the movement data of the first vehicle type and the movement data of the second vehicle type. The conflict index of the first vehicle type and the conflict index of the second vehicle type are calculated using the movement data of the first vehicle type, the movement data of the second vehicle type, and the partition road data. Based on the conflict index of the first vehicle type, the conflict index of the second vehicle type, the movement data of the first vehicle type, the movement data of the second vehicle type, and the partition road data, the traffic flow change data is obtained through the cellular transmission model;

[0011] Based on the movement data of the first vehicle type, the conflict index of the first vehicle type, the traffic flow change data, and the highway topology structure, the travel characteristics of the first vehicle type on the highway to be processed are analyzed through a graph neural network.

[0012] As a further method, the method for collecting the data of the highway to be processed and performing preprocessing to obtain image data, traffic flow, vehicle speed, and facility points includes:

[0013] Collect the image data, traffic flow, and vehicle speed data of the highway to be processed, obtain the original coordinates of charging facilities, fueling facilities, and service area facilities, and the highway topology structure. Filter and enhance the image data in sequence, use the interpolation method to repair abnormal data for the traffic flow and vehicle speed data, and match and correct the original coordinates of the facility points with the high-precision map to obtain the preprocessed image data, traffic flow, vehicle speed, road data, and facility points.

[0014] As a further method, the method for partitioning the sections where vehicles enter and exit the facilities based on the facility points, and obtaining the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the partitions according to the image data, traffic flow, and vehicle speed of the partitions, and using the acceleration wave characteristics and deceleration wave characteristics to correct the partition range to obtain the dynamic area includes:

[0015] Based on the transition area design standard of the highway facilities points, divide the direction along the highway extension into the pre-order influence area and the subsequent influence area. Align the image data, traffic flow, and vehicle speed in time stamps according to the partitions, slice the image data, extract the vehicle type images and vehicle position changes in the slices, calculate the inter-vehicle distance using the vehicle position changes and vehicle speed, and use the inter-vehicle distance, traffic flow, and vehicle speed to analyze the traffic flow speed sequence using the car-following model to obtain the acceleration wave characteristics and deceleration wave characteristics. If three wave times of the acceleration wave and the deceleration wave are superimposed, expand the pre-order influence area and the subsequent influence area according to the multiple.

[0016] As a further method, the method for obtaining the short-term movement characteristics using the vehicle type images in the dynamic area includes:

[0017] Extract the vehicle positions and vehicle speeds of different vehicles at different times in the video data slices based on the vehicle type images in the dynamic area and the vehicle speed as short-term motion data. Use the short-term motion data to construct a spatio-temporal propagation matrix, and calculate the fluctuation energy density of multi-vehicle motion at different positions and times. The formula for the spatio-temporal propagation matrix is:

[0018]

[0019] Where is the spatio-temporal propagation matrix, n is the total number of vehicles, representing the fluctuation energy density at position and time ; is the speed of the th vehicle at time ; is the speed of the i-th vehicle at time ; The time interval is determined by the time stamp scale, is the position coordinate of the i-th vehicle, is the Dirac delta function, which takes an infinite value only when , and is 0 at other times, is the wave propagation speed, which is the average wave speed of highway traffic flow, is the wave propagates from to the target position The required time delay;

[0020] According to the short-term motion data, use the car-following model to analyze the vehicle motion characteristics. Input the fluctuation energy density into the car-following model as an additional stimulus term to correct the acceleration in the vehicle motion characteristics. Calculate the jerk according to the time change rate of the acceleration, perform an integral operation on the acceleration to obtain the speed, connect the vehicle positions at different times in chronological order to form a motion trajectory, and obtain the lateral offset and heading angle based on the lane markings and the vehicle position; Calculate the relative speed and distance of the adjacent vehicle using the vehicle position and vehicle speed of the vehicle and the adjacent vehicle to obtain the short-term motion characteristics. The short-term motion characteristics include jerk, acceleration, speed, motion trajectory, heading angle, lateral offset, relative speed of the adjacent vehicle, and relative distance of the adjacent vehicle.

[0021] As a further method, the method for obtaining the first vehicle type in the front and rear intervals of the charging facility according to the vehicle type image, and using the remaining vehicle types as the second vehicle type, and calculating the charging demand index according to the first vehicle type that enters and does not enter the charging facility, includes:

[0022] Based on the vehicle type image in the dynamic area of the charging facility, the license plate color is extracted through the license plate color recognition algorithm as the vehicle type information. Using the vehicle type information, license plates with green backgrounds and white characters or gradient green backgrounds are classified as the first vehicle type, and other vehicles are classified as the second vehicle type. The first vehicle type is divided into two categories: those that have entered and those that have not entered the charging facility. For the first vehicle type that has entered and not entered the charging facility, lane change selection data is extracted through the movement trajectory. Based on the lane change selection data, queuing selection data for the first vehicle type is obtained according to the two categories of those that have entered and those that have not entered the charging facility. The charging demand index is calculated based on the first vehicle type that has entered and not entered the charging facility. The formula for the charging demand index is:

[0023]

[0024] Where is the number of charging vehicles in the first vehicle type, is the number of the first vehicle type in the dynamic area of the charging facility, is the vehicle 's charging identifier, is the charging vehicle in the first vehicle type 's fluctuating energy density, is the fluctuating energy density of the first vehicle type in the dynamic area of the charging facility, is the charging vehicle in the first vehicle type 's heading angle change amount, is the heading angle change amount of the first vehicle type in the dynamic area of the charging facility, is the average value of the heading angle change amount of the first vehicle type in the dynamic area of the charging facility, is the charging vehicle in the first vehicle type 's lane change selection, is the lane change selection of the first vehicle type in the dynamic area of the charging facility, is the lane change mean of the first vehicle type, expressed as the lane change ratio, is the charging vehicle in the first vehicle type 's speed change amount, is the speed change amount of the first vehicle type in the dynamic area of the charging facility, is the average value of the speed change amount of the first vehicle type in the dynamic area of the charging facility, is the charging vehicle in the first vehicle type 's queuing selection, is the queuing selection of the first vehicle type in the dynamic area of the charging facility, is the average value of the queuing selection of the first vehicle type in the dynamic area of the charging facility, expressed as the queuing ratio.

[0025] As a further method, the method of obtaining the short-term motion data of the first vehicle type and the second vehicle type by fitting the charging demand index and traffic flow of the dynamic area, and calculating the conflict index of the first vehicle type and the conflict index of the second vehicle type using the short-term motion data of the first vehicle type, the short-term motion data of the second vehicle type, and the partition road data, includes:

[0026] Align the time stamps of the charging demand index, traffic flow, and the short-term motion characteristics of the first vehicle type and the second vehicle type in the dynamic area. Use the input charging demand index and traffic flow as input features, and use the short-term motion characteristics of the first vehicle type and the second vehicle type as output labels. Perform non-linear fitting through a random forest based on the input features and output labels to obtain the short-term motion data of the first vehicle type and the short-term motion data of the second vehicle type. The short-term motion data of the first vehicle type and the short-term motion data of the second vehicle type include the lane change advance amount and the deceleration curve slope. Obtain the partition road data of the dynamic area according to the road data. The partition road data includes the lane width and the curve radius. Calculate the conflict index of the first vehicle type and the conflict index of the second vehicle type using the short-term motion data of the first vehicle type, the short-term motion data of the second vehicle type, and the corresponding short-term motion characteristics in combination with the partition road data. The conflict index formula is:

[0027]

[0028] Where is the relative distance between adjacent vehicles, is the time conflict point, indicating the time for the two vehicles to maintain their current motion states until collision, is the jerk is the absolute value of is the heading angle gradient, is the vehicle speed change amount, CDI is the charging demand index, is the lane change advance amount, is the deceleration curve slope.

[0029] As a further method, the method of obtaining traffic flow change data through a cellular transmission model based on the conflict index of the first vehicle type, the conflict index of the second vehicle type, the short-term motion data of the first vehicle type, the short-term motion data of the second vehicle type, and the partition road data, includes:

[0030] ​The cell units are obtained by evenly dividing based on the length of the dynamic area. The sections of the starting point of the acceleration wave and the ending point of the deceleration wave are used for the cell boundaries. The lane width, curve radius, and facility point type are used as cell attributes. The traffic flow density is obtained based on the divisor of the number of vehicles and the area of the cell section. The vehicle position and speed are obtained according to the short-term motion data. The vehicle type ratio is obtained using the proportion of the first vehicle type. The conflict index of the first vehicle type and the conflict index of the second vehicle type are used as the cell risk coefficients. The charging demand index is used as the cell behavior driving parameter. The inflow is determined based on the lane width, conflict index, and lane-changing advance of the upstream cell exit. The outflow is determined according to the lane width and deceleration curve slope of the downstream cell. For the first vehicle type, the charging demand index is used to trigger the charging induction behavior. According to the vehicle acceleration, jerk, and heading angle gradient in the short-term motion characteristics as the initial car-following parameters, a cell transmission model is obtained;

[0031] Based on the cell transmission model, with the traffic flow fluctuation time scale as the time step, the states of each cell are updated in the order of the upstream cell, the current cell, and the downstream cell. The inflow, outflow, change in the internal vehicle type ratio, and speed decay are calculated in sequence. According to the cell transmission model, the traffic flow density, proportion of the first vehicle type, average speed, and queue length of each cell at different times are output, and the traffic flow change data is obtained.

[0032] As a further method, the method for obtaining the travel characteristics of the first vehicle type on the highway to be processed through graph neural network analysis based on the first vehicle type motion data, the first vehicle type conflict index, the traffic flow change data, and the highway topology structure includes:

[0033] The highway topology structure is extracted according to the road data and facility points. Based on the highway topology structure, a graph structure is obtained. The dynamic area is mapped to the nodes in the graph structure. The first motion data, the first conflict index, the traffic flow change data, and the position information of the highway topology structure are used to endow the nodes. According to the connection relationship of the road data as the edges of the nodes, the graph structure data is obtained, and it is divided into a training set, a validation set, and a test set

[0034] The training set is input into the graph neural network. The model aggregates the node features through at least two graph convolutional layers, uses the attention mechanism layer to aggregate the value vectors of adjacent nodes through weighted coefficients, and uses the time series graph convolutional layer to process the temporal evolution of the node states. During training, the difference between the prediction result and the image data, traffic flow, and vehicle speed is calculated through the cross-entropy loss function. The validation set is input into the model, and the overfitting state is verified according to the loss function value and the change in accuracy. If the accuracy improvement of the validation set is less than 0.7% for 5 consecutive rounds, the training ends. The difference is fed back to each layer of the model using the backpropagation algorithm to adjust the parameters until the maximum number of iterations is reached and the training is completed. The test set is input into the trained graph neural network, and the model performance is evaluated using accuracy and recall. Based on the trained model, the travel characteristics of the first vehicle type are output.

[0035] In the second aspect of the present invention, a system for analyzing the travel characteristics of new energy vehicles on expressways is provided, including:

[0036] Data acquisition module: used to collect the expressway data to be processed for preprocessing, and obtain image data, traffic flow, vehicle speed, road data, and facility points;

[0037] Data processing module: based on the facility points, divide the sections where vehicles enter and exit the facilities, obtain the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the divided areas according to the image data, traffic flow, and vehicle speed of the divided areas, and use the acceleration wave characteristics and deceleration wave characteristics to correct the divided area range to obtain a dynamic area;

[0038] Traffic flow change simulation module: used to obtain short-term motion characteristics using the vehicle type images in the dynamic area, obtain the first vehicle type in the intervals before and after the charging facilities according to the vehicle type images, regard the remaining vehicle types as the second vehicle type, calculate the charging demand index according to the first vehicle types that enter and do not enter the charging facilities, fit the short-term motion characteristics of the first vehicle type and the second vehicle type according to the charging demand index and traffic flow in the dynamic area to obtain the first vehicle type motion data and the second vehicle type motion data, calculate the first vehicle type conflict index and the second vehicle type conflict index using the first vehicle type motion data, the second vehicle type motion data, and the divided area road data, and obtain the traffic flow change data through the cell transmission model based on the first vehicle type conflict index, the second vehicle type conflict index, the first vehicle type motion data, the second vehicle type motion data, and the divided area road data;

[0039] Travel characteristic output module: based on the first vehicle type motion data, the first vehicle type conflict index, the traffic flow change data, and the highway topology structure, analyze through a graph neural network to obtain the travel characteristics of the first vehicle type on the expressway to be processed.

[0040] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0041] (1) The present invention corrects the divided area range through acceleration wave and deceleration wave characteristics to obtain a dynamic area, can respond to traffic flow fluctuations in real time, is more in line with actual traffic changes compared with fixed partitions, accurately captures the impact of vehicle entry and exit from facilities on traffic flow, lays a more reliable foundation for subsequent traffic characteristic analysis, and improves the accuracy of overall traffic analysis.

[0042] (2) The present invention combines the car-following model, the cell transmission model, and the graph neural network to analyze the expressway traffic conditions from the microscopic vehicle motion acceleration, jerk, etc., the macroscopic traffic flow simulation of traffic flow density, vehicle type ratio, etc., to the comprehensive feature extraction, and improves the accuracy and comprehensiveness of the analysis of traffic flow changes and the travel characteristics of new energy vehicles.

[0043] (3) By using license plate color recognition to distinguish new energy vehicles from other vehicle types, calculating the charging demand index and analyzing their travel characteristics, the present invention takes into account the charging demand characteristics of new energy vehicles under heterogeneous traffic flow, provides data support for the layout of highway charging facilities, the formulation of traffic guidance strategies, etc., promotes the convenience of new energy vehicles traveling on highways, and optimizes traffic management and resource allocation. Description of the Drawings

[0044] Figure 1 It is a flowchart of the steps of a method for analyzing the travel characteristics of new energy vehicles on highways in an embodiment of the present invention. Detailed Embodiment

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0046] Refer to Figure 1 As shown, the present invention provides a method for analyzing the travel characteristics of new energy vehicles on highways, including:

[0047] Collect the data of the highway to be processed for preprocessing to obtain image data, traffic flow, vehicle speed, road data and facility points;

[0048] In actual evaluation, select a section of the highway to be processed, collect image data with a resolution of 1920×1080 and a frame rate of 25fps through road monitoring cameras, use median filtering to remove noise, and then enhance the image contrast through histogram equalization. The traffic flow and vehicle speed data are collected by microwave sensors along the road section and recorded every 5 minutes. For missing or abnormal data, cubic spline interpolation is used for repair. Obtain the GPS coordinates (30.2° north latitude, 120.5° east longitude) of gas stations, service areas, ramps and charging facilities, and combine with high-precision maps to obtain road data such as lane width (3.75 meters) and bend radius.

[0049] Based on the facility points, divide the sections where vehicles enter and exit the facilities, and obtain the acceleration wave characteristics, deceleration wave characteristics and vehicle type images of the divided areas according to the image data, traffic flow and vehicle speed of the divided areas. Use the acceleration wave characteristics and deceleration wave characteristics to correct the divided range to obtain the dynamic area;

[0050] In the actual evaluation, according to the design standard of the transition area of the charging facility points, the 500 meters upstream along the extension direction of the highway is divided into the pre - influence area and the subsequent influence area of 300 meters downstream. The image data of the partition, the average traffic flow of 200 vehicles per hour, and the average vehicle speed of 80 km / h are time - stamped at 5 - minute intervals. The image data is sliced at 10 - second intervals per frame. The vehicle type images are extracted through the YOLOv5 model. The vehicle - to - vehicle distance is calculated by using the change in the vehicle displacement position between adjacent frames of the video edge and the vehicle speed, and the average vehicle - to - vehicle distance is obtained as 30 meters. The GM car - following model is used to analyze the vehicle flow speed sequence. At a certain moment, three wave superpositions of acceleration waves and deceleration waves occur, and the pre - influence area and the subsequent influence area are each extended by 100 meters to form a dynamic area.

[0051] The short - term motion characteristics are obtained using the vehicle type images in the dynamic area. The first vehicle type in the front and rear sections of the charging facility is obtained according to the vehicle type images, and the remaining vehicle types are used as the second vehicle type. The charging demand index is calculated according to the first vehicle type that enters and does not enter the charging facility.

[0052] It should be explained that the wave energy density is input into the car - following model as an additional stimulus item to correct the acceleration in the vehicle motion characteristics. The core logic is that the wave energy density reflects the degree of dynamic disturbance of the traffic flow around the vehicle, and this disturbance will affect the adjustment of the vehicle's acceleration. For example, when the traffic flow around the vehicle fluctuates greatly, the vehicle needs to adjust its acceleration more frequently to maintain safe car - following. By incorporating the wave energy density into the car - following model, it becomes a factor to correct the acceleration, so as to more accurately simulate the acceleration change of the vehicle in a complex traffic environment.

[0053] In the actual evaluation, according to the vehicle type images and vehicle speeds in a dynamic area, the positions of different vehicles at different times in the sliced image data are extracted through the YOLOv5 model. The speed is calculated by dividing the difference in positions between two adjacent frames by the time interval. One position synthesizes 50 vehicles. The wave propagation speed is 60 km / h, and the time interval is 2 seconds. The wave energy density of the multi - vehicle motion is calculated as 28.37, and it is input into the GM model together with the short - term motion data to correct the vehicle acceleration. For example, a certain vehicle accelerates from 105 km / h to 124 km / h, and the acceleration is 0.72 m / s². The jerk is calculated as 0.1 m / s³ according to the acceleration change rate. The speed is obtained by integrating the acceleration. The vehicle positions are connected in chronological order to form a motion trajectory. Based on the lane markings and vehicle positions, the average lateral offset of 0.2 meters and the heading angle of 5° with the lane line are obtained, and the relative speed of adjacent vehicles is calculated. For example, the speed difference between two adjacent vehicles is 5 km / h and the distance is 25 meters, and the short - term motion characteristics are obtained, including jerk, acceleration, speed, motion trajectory, heading angle, lateral offset, relative speed of adjacent vehicles, and distance.

[0054] In the actual evaluation, based on the vehicle type images and vehicle speeds in a dynamic area, the license plate color is identified through the YOLOv5 model. New energy vehicles with green license plates and white characters are the first vehicle type, and other vehicles are the second vehicle type. The first vehicle type is divided into two categories: those entering and not entering the charging facilities. Through identification, =1 indicates entering the charging facility, =0 indicates not entering the charging facility. For the first vehicle type that enters and does not enter the charging facility, lane-changing selection data is extracted from the movement trajectory. Based on the lane-changing selection data, queuing selection data for the first vehicle type is obtained for the two categories of entering and not entering the charging facility. The lane-changing selection data and queuing selection data are normalized and the values are set to 0 and 1. 1 0 Lane change, 1 Queue 0 Queue There are a total of 50 first vehicle types (new energy vehicles) in the dynamic area, among which 20 actual charging vehicles enter the charging facility. For the 20 vehicles that enter the charging facility, 15 are judged to queue through the trajectory, and the 30 vehicles that do not enter the charging facility are defaulted not to queue. Among them, the fluctuating energy density = 32.5, and for all first vehicle types the fluctuating energy density = 28.37. Substituting into the charging demand index formula, the calculated charging demand index CDI is 0.53.

[0055] According to the charging demand index and traffic flow in the dynamic area, the short-term movement characteristics of the first vehicle type and the second vehicle type are fitted to obtain the movement data of the first vehicle type and the movement data of the second vehicle type. Using the movement data of the first vehicle type, the movement data of the second vehicle type, and the partition road data, the conflict index of the first vehicle type and the conflict index of the second vehicle type are calculated. Based on the conflict index of the first vehicle type, the conflict index of the second vehicle type, the movement data of the first vehicle type, the movement data of the second vehicle type, and the partition road data, traffic flow change data is obtained through the cellular transmission model;

[0056] In actual evaluation, the charging demand index, traffic flow data of the dynamic area, and short-term motion characteristics of the first vehicle model and the second vehicle model are collected. These data are time-stamped and aligned at a time interval of every 5 minutes, and a total of 288 time-stamped data are collected. The charging demand index and traffic flow data are used as input features to form a matrix, and the short-term motion characteristics of the first and second vehicle models are used as output labels. The input features are divided into a training set and a test set at a ratio of 7:3. Using the scikit-learn library, a random forest regression model is initialized, with the number of estimators set to 100, the maximum depth set to 10, and the random state set to 42. The data of the first and second vehicle models are trained separately. During the model training, each decision tree in the forest learns the input features to predict the output labels. After training, the mean squared error (MSE) is calculated using the test set to evaluate the performance. For example, the MSE of the first vehicle model is 0.03, and that of the second vehicle model is 0.04, indicating that the model performs well. Finally, the trained model is used to fit the data. For example, for new data with a CDI of 0.6 and a traffic flow of 1500 vehicles per hour, the fitted values of the lane change advance amount and deceleration curve slope of the first vehicle model are obtained as 0.6 seconds and -2.8 m / s² respectively, and those of the second vehicle model are 0.5 seconds and -2.5 m / s² respectively. These fitted lane change advance amounts and deceleration curve slopes are the first and second motion data, providing key inputs for subsequent analysis. For example, with a lane change advance amount of 0.5 seconds and a deceleration curve slope of -2 m / s², according to a lane width of 3.75 meters and substituting into the conflict index formula, where = 25 meters, divided by is 5 seconds, = 5 km / h, the lane change advance amount is 0.5 seconds, the jerk is 0.1 m / s³, the yaw rate of change is 0.05 rad / m, and the deceleration curve slope is -2 m / s². The calculated first conflict index CTI is 0.85.

[0057] It should be noted that when constructing the cellular transmission model, the above short-term motion characteristics are used as the initial car-following parameters. Specifically, if a vehicle has a large acceleration, it indicates strong power performance, and a relatively large initial car-following distance is set within the cell to avoid frequent adjustment of the distance due to strong acceleration ability. For a vehicle with a high jerk, it means that its acceleration changes violently, and the initial car-following distance is appropriately increased to ensure driving safety and comfort. If the yaw angle gradient is large, it means that the vehicle has a significant tendency to change direction, and the initial car-following parameters are adjusted, such as reducing the threshold of car-following distance change, so that it can adjust its position more smoothly during direction change and reduce the conflict risk with adjacent vehicles. By integrating the short-term motion characteristics into the initial car-following parameters, the cellular transmission model can better fit the actual vehicle motion characteristics, thus more accurately simulating the transmission and change of traffic flow between cells.

[0058] In the actual evaluation, the dynamic area with a length of 200 meters is evenly divided into 4 cell units with a length of 50 meters. The starting point of the acceleration wave and the ending point of the deceleration wave define the cell boundaries. The lane width and the type of facility points are used as cell attributes. At this time, the traffic flow density is equal to the number of vehicles divided by the cell area. 60 / (50 × 3.75) is 0.3 vehicles per meter. The vehicle positions and speeds are obtained according to the short-term movement data. The proportion of the first vehicle type is 30%. The conflict index of 0.85 is used as the cell risk coefficient, and the charging demand index of 0.65 is used as the cell behavior driving parameter. The inflow is determined based on the lane width, conflict index, and lane-changing advance of the upstream cell exit. , where is the lane width, and the outflow . The charging demand index of 0.65 is used to trigger the charging induction behavior for the first vehicle type, shortening the vehicle following distance within a 5-meter cell. The cell status is updated with a 5-minute step length, and the final output shows the traffic flow change data such as the traffic flow density (0.32 vehicles per meter), the proportion of the first vehicle type of 30%, the average speed of 78 km / h, and the queue length of 54.8 meters at different times for each cell, realizing the dynamic simulation and analysis of the congested traffic flow state on the highway.

[0059] Based on the movement data of the first vehicle type, the conflict index of the first vehicle type, the traffic flow change data, and the highway topology structure, the travel characteristics of the first vehicle type on the highway to be processed are obtained through graph neural network analysis.

[0060] In the actual evaluation, a 15-kilometer expressway section is selected, which includes 5 dynamic areas, 2 service areas, and 1 charging station. The highway topology structure and graph structure data are extracted. One node is divided per kilometer, resulting in a total of 15 nodes. The dynamic areas are respectively mapped to nodes 3 - 5 (dynamic area 1), nodes 6 - 8 (dynamic area 2), and nodes 10 - 12 (dynamic area). The first motion data, the first conflict index, the traffic flow change data, and the position information of the highway topology structure are assigned to the nodes. For example, the lane-changing lead time of node 4 in dynamic area 1 is 0.5 seconds, the deceleration curve slope is -2.5 m / s², the first conflict index is 0.7, the traffic flow change data is the traffic flow density of 0.8 vehicles / meter, the average speed is 85 km / h, and the position information is the longitude and latitude of node 4 (116.87, 39.92). Edges and data partitioning are determined according to the road connection relationship. For example, node 1 is connected to node 2, and node 2 is connected to node 3, etc., to construct the edges. The graph structure data is divided into a training set of 10 nodes, a validation set of 3 nodes, and a test set of 2 nodes according to the ratio of 7:2:1. Using the PyTorch Geometric library, a model with two graph convolutional layers is constructed. The first graph convolutional layer GCN fuses features such as lane-changing lead time and conflict index, and uses multi-head attention to weighted aggregate the adjacent node value vectors. For example, 4-head attention is used to assign a weight of 0.6 to the traffic flow density of adjacent nodes and a weight of 0.4 to the speed. The time series graph convolutional layer collects data every hour to analyze the change in traffic flow density of node 4 at different time periods, and a graph neural network model is constructed using a convolutional layer with a one-dimensional convolutional kernel of 3;

[0061] During training, through the cross-entropy loss function , where represents the value of the cross-entropy loss function, which is used to measure the difference between the model output and the true label, represents the number of samples, that is, how many data points are used to calculate the loss, represents the true label (actual value) of the th sample, represents the predicted label (predicted value) of the th sample, which is the probability prediction of the model for the sample i belonging to a certain category. Calculate the differences between the prediction and the actual image, traffic flow, and vehicle speed. During training, the validation set is input into the model. If the accuracy improvement is less than 0.7% for 5 consecutive rounds, the training ends. The model parameters are adjusted for each layer through the backpropagation algorithm, and the maximum number of iterations is set to 300 times. After the model training is completed, the test set is input into the model to calculate the model performance, where the accuracy reaches 88% and the recall rate reaches 83%. Finally, the first vehicle type travel characteristics are output through the trained graph neural network model. The first vehicle type travel characteristics include that at nodes 10 - 12 with a relatively high charging demand index, the average lane-changing lead time of the first vehicle type is shortened by 0.1 second; at node 4 with a relatively large conflict index, the average speed of the first vehicle type decreases by 6 km / h.

[0062] In this embodiment, the method for collecting the to-be-processed highway data for preprocessing to obtain image data, traffic flow, vehicle speed, and facility points includes:

[0063] Collect the image data, traffic flow, and vehicle speed data of the to-be-processed highway, obtain the original coordinates of charging facilities, fueling facilities, and service area facilities, and the highway topology structure. Filter and enhance the image data in sequence, use the interpolation method to repair abnormal data in the traffic flow and vehicle speed data, and match and correct the original coordinates of the facility points with the high-precision map to obtain the preprocessed image data, traffic flow, vehicle speed, road data, and facility points.

[0064] In this embodiment, the method for partitioning the sections where vehicles enter and exit the facilities based on the facility points, and obtaining the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the partitions according to the image data, traffic flow, and vehicle speed of the partitions, and using the acceleration wave characteristics and deceleration wave characteristics to correct the partition range to obtain the dynamic area includes:

[0065] Based on the transition area design standard of the highway facility points, divide the direction along the highway extension into a pre-order influence area and a subsequent influence area. Align the time stamps of the image data, traffic flow, and vehicle speed according to the partitions, slice the image data, extract the vehicle type images and vehicle position changes in the slices, calculate the vehicle spacing using the vehicle position changes and vehicle speed, and use the vehicle spacing, traffic flow, and vehicle speed to analyze the traffic flow speed sequence using the car-following model to obtain the acceleration wave characteristics and deceleration wave characteristics. If three wave times of the acceleration wave and deceleration wave are superimposed, expand the pre-order influence area and the subsequent influence area according to the multiple.

[0066] In this embodiment, the method for obtaining the short-term motion characteristics using the vehicle type images of the dynamic area includes:

[0067] Extract the vehicle positions and vehicle speeds of different vehicles at different times in the image data slices based on the vehicle type images and vehicle speed of the dynamic area as short-term motion data, use the short-term motion data to construct a spatio-temporal propagation matrix, and calculate the wave energy density of the multi-vehicle motion at different positions and times. The formula for the spatio-temporal propagation matrix is:

[0068]

[0069] Where is the spatio-temporal propagation matrix, n is the total number of vehicles, representing the wave energy density at position and time , is the rd vehicle's speed at time , is the i-th vehicle's speed at time , The time interval is determined by the timestamp scale, is the position coordinate of the i-th vehicle, is the Dirac delta function, which takes an infinite value only at and is 0 at other times, is the wave propagation speed, which is the average wave speed of highway traffic flow, The wave propagates from to the target position The required time delay;

[0070] According to the short-term motion data, the car-following model is used to analyze the vehicle motion characteristics. The wave energy density is input into the car-following model as an additional stimulus term to correct the acceleration in the vehicle motion characteristics. The jerk is calculated according to the time change rate of the acceleration. The speed is obtained by integrating the acceleration. The vehicle positions at different times are connected in chronological order to form a motion trajectory. The lateral offset and heading angle are obtained based on the lane markings and vehicle positions; the relative speed and distance of adjacent vehicles are calculated using the vehicle positions and vehicle speeds of the vehicle and adjacent vehicles to obtain short-term motion characteristics, and the short-term motion characteristics include jerk, acceleration, speed, motion trajectory, heading angle, lateral offset, relative speed of adjacent vehicles, and relative distance of adjacent vehicles.

[0071] In this embodiment, the method for obtaining the first vehicle type in the front and rear intervals of the charging facility according to the vehicle type image, taking the remaining vehicle types as the second vehicle type, and calculating the charging demand index according to the first vehicle type entering and not entering the charging facility includes:

[0072] Based on the vehicle type image in the dynamic area of the charging facility, the license plate color is extracted as vehicle type information through the license plate color recognition algorithm. The green background with white characters or gradually changing green background license plates are classified as the first vehicle type using the vehicle type information, and other vehicles are classified as the second vehicle type; the first vehicle type is divided into two categories: entering and not entering the charging facility. The lane-changing selection data is extracted from the first vehicle type entering and not entering the charging facility through the motion trajectory. Based on the lane-changing selection data, the queuing selection data of the first vehicle type is obtained for the two categories of entering and not entering the charging facility. The charging demand index is calculated according to the first vehicle type entering and not entering the charging facility. The charging demand index calculation formula is:

[0073]

[0074] Where is the number of charging vehicles in the first vehicle type, is the number of the first vehicle type in the dynamic area of the charging facility, is the vehicle 's charging identification, is the charging vehicle in the first vehicle type 's wave energy density, is the fluctuating energy density of the first vehicle type in the dynamic area of the charging facility, is the charging vehicle in the first vehicle type variation in the heading angle, is the variation in the heading angle of the first vehicle type in the dynamic area of the charging facility, is the average value of the variation in the heading angle of the first vehicle type in the dynamic area of the charging facility, is the charging vehicle in the first vehicle type lane change selection, is the lane change selection of the first vehicle type in the dynamic area of the charging facility, is the lane change mean value of the first vehicle type, expressed as a lane change ratio, is the charging vehicle in the first vehicle type variation in speed, is the variation in speed of the first vehicle type in the dynamic area of the charging facility, is the average value of the variation in speed of the first vehicle type in the dynamic area of the charging facility, is the charging vehicle in the first vehicle type queueing selection, is the queueing selection of the first vehicle type in the dynamic area of the charging facility, is the average value of the queueing selection of the first vehicle type in the dynamic area of the charging facility, expressed as a queueing ratio.

[0075] In this embodiment, the method of obtaining the motion data of the first vehicle type and the second vehicle type by fitting the short-term motion characteristics of the first vehicle type and the second vehicle type according to the charging demand index and traffic flow in the dynamic area, and calculating the conflict index of the first vehicle type and the conflict index of the second vehicle type using the motion data of the first vehicle type, the motion data of the second vehicle type and the partition road data, includes:

[0076] Align the time stamps of the charging demand index, traffic flow, and the short-term motion characteristics of the first vehicle type and the second vehicle type in the dynamic area. Use the input charging demand index and traffic flow as input features, and use the short-term motion characteristics of the first vehicle type and the second vehicle type as output labels. Perform non-linear fitting through a random forest based on the input features and output labels to obtain the motion data of the first vehicle type and the motion data of the second vehicle type. The motion data of the first vehicle type and the motion data of the second vehicle type include the lane change lead and the deceleration curve slope. Obtain the partition road data of the dynamic area according to the road data. The partition road data includes the lane width and the curve radius. Calculate the conflict index of the first vehicle type and the conflict index of the second vehicle type using the motion data of the first vehicle type, the motion data of the second vehicle type, the corresponding short-term motion characteristics, and the partition road data through the conflict index formula. The conflict index formula is:

[0077]

[0078] where is the relative distance between adjacent vehicles, is the time conflict point, representing the time for the two vehicles to maintain their current motion states until collision, is the jerk absolute value of, is the course angle gradient, is the vehicle speed change amount, CDI is the charging demand index, is the lane change advance amount, is the deceleration curve slope.

[0079] In this embodiment, the method for obtaining traffic flow change data through a cellular transmission model based on the first vehicle type conflict index, the second vehicle type conflict index, the first vehicle type motion data, the second vehicle type motion data, and the sectional road data includes:

[0080] Based on the dynamic zone length, uniformly divide to obtain cellular units, use the sections of the acceleration wave starting point and the deceleration wave ending point as the cellular boundaries, use the lane width, the curve radius, and the facility point type as cellular attributes, obtain the traffic flow density based on the divisor of the number of vehicles and the cellular section area, obtain the vehicle position and vehicle speed according to the short-term motion data, obtain the vehicle type ratio using the proportion of the first vehicle type, use the first vehicle type conflict index and the second vehicle type conflict index as the cellular risk coefficients, use the charging demand index as the cellular behavior driving parameter, determine the inflow based on the lane width, conflict index, and lane change advance amount of the upstream cellular exit, determine the outflow according to the lane width and deceleration curve slope of the downstream cell, trigger the charging induction behavior for the first vehicle type using the charging demand index, and use the vehicle acceleration, jerk, and course angle gradient in the short-term motion characteristics as the initial car-following parameters to obtain the cellular transmission model;

[0081] Based on the cellular transmission model, use the traffic flow fluctuation time scale as the time step, update the states of each cell in the order of the upstream cell, the current cell, and the downstream cell, calculate the inflow, outflow, internal vehicle type ratio change, and speed decay in sequence, and obtain the traffic flow change data according to the output of the cellular transmission model of the traffic flow density, the proportion of the first vehicle type, the average speed, and the queue length of each cell at different times;

[0082] In this embodiment, the method for analyzing and obtaining the travel characteristics of the first vehicle type on the highway to be processed through a graph neural network based on the first vehicle type motion data, the first vehicle type conflict index, the traffic flow change data, and the highway topology structure includes:

[0083] Extract the highway topology based on road data and facility points, obtain a graph structure based on the highway topology, map the dynamic area to nodes in the graph structure, endow the nodes with the first motion data, the first conflict index, traffic flow change data, and the location information of the highway topology, use the connection relationship of the road data as the edges of the nodes to obtain graph structure data, and divide it into a training set, a validation set, and a test set

[0084] Input the training set into the graph neural network. The model aggregates node features through at least two graph convolutional layers, uses the attention mechanism layer to aggregate the value vectors of adjacent nodes through weighted coefficients, and uses the time series graph convolutional layer to process the temporal evolution of node states. During training, calculate the differences between the prediction results and the image data, traffic flow, and vehicle speed through the cross-entropy loss function. Input the validation set into the model, verify the overfitting state according to the loss function value and the change in accuracy. If the accuracy improvement of the validation set is less than 0.7% for 5 consecutive rounds, end the training. Use the backpropagation algorithm to feedback the differences to each layer of the model to adjust the parameters until the maximum number of iterations is reached to complete the training. Input the test set into the trained graph neural network, evaluate the model performance using accuracy and recall, and output the travel characteristics of the first vehicle type based on the trained model

[0085] The second aspect of the present invention also provides a highway new energy vehicle travel characteristic analysis system, including:

[0086] Data acquisition module: used to collect the highway data to be processed for preprocessing to obtain image data, traffic flow, vehicle speed, road data, and facility points

[0087] Data processing module: partition the sections where vehicles enter and exit the facilities based on the facility points, obtain the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the partitions according to the image data, traffic flow, and vehicle speed of the partitions, and use the acceleration wave characteristics and deceleration wave characteristics to correct the partition range to obtain the dynamic area

[0088] Traffic flow change simulation module: used to obtain short-term motion characteristics using the vehicle type images in the dynamic area, obtain the first vehicle type in the intervals before and after the charging facilities according to the vehicle type images, regard the remaining vehicle types as the second vehicle type, calculate the charging demand index according to the first vehicle type that enters and does not enter the charging facilities, fit the short-term motion characteristics of the first vehicle type and the second vehicle type according to the charging demand index and traffic flow in the dynamic area to obtain the first vehicle type motion data and the second vehicle type motion data, calculate the first vehicle type conflict index and the second vehicle type conflict index using the first vehicle type motion data, the second vehicle type motion data, and the partition road data, and obtain the traffic flow change data through the cellular transmission model based on the first vehicle type conflict index, the second vehicle type conflict index, the first vehicle type motion data, the second vehicle type motion data, and the partition road data

[0089] Travel feature output module: Based on the first vehicle type movement data, the first vehicle type conflict index, the traffic flow change data, and the highway topology structure, the travel features of the first vehicle type on the highway to be processed are obtained through graph neural network analysis.

[0090] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claims, they should fall within the protection scope of the present invention.

Claims

1. A method for analyzing the travel characteristics of new energy vehicles on expressways, characterized in that, Including the following steps: Collect the expressway data to be processed and perform preprocessing to obtain image data, traffic flow, vehicle speed, road data, and facility points; Based on the facility points, divide the sections where vehicles enter and exit the facilities. Obtain the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the divided sections according to the image data, traffic flow, and vehicle speed of the sections. Use the acceleration wave characteristics and deceleration wave characteristics to correct the division range to obtain the dynamic area; Obtain the short-term motion characteristics using the vehicle type images in the dynamic area. Obtain the first vehicle type in the intervals before and after the charging facility according to the vehicle type images, and regard the remaining vehicle types as the second vehicle type. Calculate the charging demand index according to the first vehicle type that enters and does not enter the charging facility; Fit the short-term motion characteristics of the first vehicle type and the second vehicle type according to the charging demand index and traffic flow in the dynamic area to obtain the motion data of the first vehicle type and the second vehicle type. Calculate the conflict index of the first vehicle type and the conflict index of the second vehicle type using the motion data of the first vehicle type, the motion data of the second vehicle type, and the section road data. Obtain the traffic flow change data through the cellular transmission model based on the conflict index of the first vehicle type, the conflict index of the second vehicle type, the motion data of the first vehicle type, the motion data of the second vehicle type, and the section road data; Analyze and obtain the travel characteristics of the first vehicle type on the expressway to be processed through a graph neural network based on the motion data of the first vehicle type, the conflict index of the first vehicle type, the traffic flow change data, and the expressway topology; 2. The method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1, wherein The method of collecting the expressway data to be processed and performing preprocessing to obtain image data, traffic flow, vehicle speed, and facility points includes: Collect the image data, traffic flow, and vehicle speed data of the expressway to be processed. Obtain the original coordinates of the charging facilities, fueling facilities, and service area facilities and the expressway topology. Filter and enhance the image data in sequence. Use the interpolation method to repair abnormal data in the traffic flow and vehicle speed data. Match and correct the original coordinates of the facility points with the high-precision map to obtain the preprocessed image data, traffic flow, vehicle speed, road data, and facility points; 3. A method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1, characterized in that The method of dividing the sections where vehicles enter and exit the facilities based on the facility points, obtaining the acceleration wave characteristics, deceleration wave characteristics, and vehicle type images of the divided sections according to the image data, traffic flow, and vehicle speed of the sections, and using the acceleration wave characteristics and deceleration wave characteristics to correct the division range to obtain the dynamic area includes: Based on the transition area design standard of the expressway facility points, divide it into the pre-sequence influence area and the subsequent influence area along the extension direction of the expressway. Align the time stamps of the image data, traffic flow, and vehicle speed according to the division. Slice the image data, extract the vehicle type images and vehicle position changes in the slices. Calculate the vehicle spacing using the vehicle position changes and vehicle speed. Analyze the traffic flow speed sequence using the follow-up model based on the vehicle spacing, traffic flow, and vehicle speed to obtain the acceleration wave characteristics and deceleration wave characteristics. If three wave superpositions occur in the acceleration wave and deceleration wave, expand the pre-sequence influence area and the subsequent influence area according to the multiple; 4. A method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1, characterized in that, The method of obtaining the short-term motion characteristics using the vehicle type images in the dynamic area includes: Based on the vehicle images of different models in the dynamic area and the vehicle speed, extract the vehicle positions and vehicle speeds of different vehicles at different times in the image data slices as short-term motion data. Use the short-term motion data to construct a spatio-temporal propagation matrix, and calculate the fluctuation energy density of multi-vehicle motion at different positions and times. The formula for the spatio-temporal propagation matrix is: where is the spatio-temporal propagation matrix, n is the total number of vehicles, representing the wave energy density at position and time ; is the speed of the -th vehicle at time ; is the speed of the i-th vehicle at time ; is the time interval determined by the time stamp scale, is the position coordinate of the i-th vehicle, is the Dirac delta function, which takes infinity only at and is 0 at other times, is the wave propagation speed, which is the average wave speed of highway traffic flow, is the wave from propagating to the target position required time delay; Analyze the vehicle motion characteristics using a car-following model based on the short-term motion data. Input the fluctuation energy density into the car-following model as an additional stimulus term to correct the acceleration in the vehicle motion characteristics. Calculate the jerk based on the time change rate of the acceleration, perform an integral operation on the acceleration to obtain the speed, connect the vehicle positions at different times in chronological order to form a motion trajectory, and obtain the lateral offset and heading angle based on the lane markings and vehicle positions; Calculate the relative speed and distance of adjacent vehicles using the vehicle positions and vehicle speeds of the vehicle and its adjacent vehicles to obtain short-term motion characteristics, where the short-term motion characteristics include jerk, acceleration, speed, motion trajectory, heading angle, lateral offset, relative speed of adjacent vehicles, and relative distance of adjacent vehicles.

5. The method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1 is characterized in that, The method for obtaining the first vehicle model in the front and rear intervals of the charging facility according to the vehicle model image and calculating the charging demand index for the remaining vehicle models includes: Extract the license plate color as vehicle model information based on the vehicle model image in the dynamic area of the charging facility through a license plate color recognition algorithm. Use the vehicle model information to classify license plates with green backgrounds and white letters or gradient green backgrounds as the first vehicle model, and classify other vehicles as the second vehicle model; Divide the first vehicle model into two categories: those entering and not entering the charging facility. Extract lane-changing selection data for the first vehicle model entering and not entering the charging facility through the motion trajectory, obtain the queuing selection data for the first vehicle model based on the lane-changing selection data for the two categories of entering and not entering the charging facility, and calculate the charging demand index according to the first vehicle model entering and not entering the charging facility. The formula for the charging demand index is: wherein is the number of charging vehicles in the first vehicle type, is the number of the first vehicle type in the dynamic area of the charging facilities, is the vehicle charging identifier, is the fluctuating energy density of the charging vehicles in the first vehicle type ; is the fluctuating energy density of the first vehicle type in the dynamic area of the charging facilities, is the change amount of the heading angle of the charging vehicles in the first vehicle type ; is the change amount of the heading angle of the first vehicle type in the dynamic area of the charging facilities, is the average value of the change amount of the heading angle of the first vehicle type in the dynamic area of the charging facilities, is the lane change selection of the charging vehicles in the first vehicle type ; is the lane change selection of the first vehicle type in the dynamic area of the charging facilities, is the lane change average value of the first vehicle type, expressed as a lane change ratio, is the speed change amount of the charging vehicles in the first vehicle type ; is the speed change amount of the first vehicle type in the dynamic area of the charging facilities, is the average value of the speed change amount of the first vehicle type in the dynamic area of the charging facilities, is the queuing selection of the charging vehicles in the first vehicle type ; is the queuing selection of the first vehicle type in the dynamic area of the charging facilities, is the average value of the queuing selection of the first vehicle type in the dynamic area of the charging facilities, expressed as a queuing ratio.

6. The method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1 is characterized in that, The method for calculating the conflict index of the first vehicle model and the conflict index of the second vehicle model by fitting the short-term motion characteristics of the first vehicle model and the second vehicle model based on the charging demand index and traffic flow in the dynamic area to obtain the motion data of the first vehicle model and the motion data of the second vehicle model includes: Align the time stamps of the charging demand index, traffic flow, and short-term motion characteristics of the first vehicle model and the second vehicle model in the dynamic area. Use the input charging demand index and traffic flow as input features, and use the short-term motion characteristics of the first vehicle model and the second vehicle model as output labels. Perform non-linear fitting through a random forest based on the input features and output labels to obtain the motion data of the first vehicle model and the motion data of the second vehicle model. The motion data of the first vehicle model and the motion data of the second vehicle model include the lane-changing advance amount and the deceleration curve slope; Obtain the partition road data of the dynamic area according to the road data, where the partition road data includes the lane width and the curve radius. Calculate the conflict index of the first vehicle model and the conflict index of the second vehicle model using the motion data of the first vehicle model and the motion data of the second vehicle model, the corresponding short-term motion characteristics, and the partition road data through the conflict index formula. The conflict index formula is: wherein is the relative distance between adjacent vehicles, is the time conflict point, representing the time for the two vehicles to maintain their current motion states until collision, is the absolute value of is the heading angle gradient, is the vehicle speed change amount, CDI is the charging demand index, is the lane change advance amount, is the deceleration curve slope.

7. The method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1, wherein, The method for obtaining traffic flow change data through a cellular transmission model based on the first vehicle type conflict index, the second vehicle type conflict index, the first vehicle type movement data, the second vehicle type movement data, and the sectional road data includes: Uniformly dividing based on the dynamic zone length to obtain cellular units, using the sections of the starting point of the acceleration wave and the ending point of the deceleration wave as the cellular boundaries, using the lane width, the curve radius, and the type of facility point as cellular attributes, obtaining the traffic flow density based on the divisor of the number of vehicles and the sectional area of the cell, obtaining the vehicle position and vehicle speed according to the short-term movement data, obtaining the vehicle type ratio using the proportion of the first vehicle type, taking the first vehicle type conflict index and the second vehicle type conflict index as the cellular risk coefficients, taking the charging demand index as the cellular behavior driving parameter, determining the inflow based on the lane width, conflict index, and lane-changing advance amount at the exit of the upstream cell, determining the outflow according to the lane width and deceleration curve slope of the downstream cell, triggering the charging induction behavior for the first vehicle type using the charging demand index, taking the vehicle acceleration, jerk, and heading angle gradient in the short-term movement characteristics as the initial car-following parameters, and obtaining the cellular transmission model; Based on the cellular transmission model, using the traffic flow fluctuation time scale as the time step, updating the states of each cell in the order of the upstream cell, the current cell, and the downstream cell, calculating the inflow, outflow, change in the internal vehicle type ratio, and speed decay in sequence, and obtaining the traffic flow change data according to the traffic flow density, proportion of the first vehicle type, average speed, and queue length of each cell output by the cellular transmission model at different times.

8. A method for analyzing the travel characteristics of new energy vehicles on expressways according to claim 1, characterized in that, The method for analyzing and obtaining the travel characteristics of the first vehicle type on the highway to be processed through a graph neural network based on the first vehicle type movement data, the first vehicle type conflict index, the traffic flow change data, and the highway topology structure includes: Extracting the highway topology structure according to the road data and the facility points, obtaining the graph structure based on the highway topology structure, mapping the dynamic zone to the nodes in the graph structure, endowing the nodes with the first movement data, the first conflict index, the traffic flow change data, and the position information of the highway topology structure, and obtaining the graph structure data according to the connection relationship of the road data as the edges of the nodes, and dividing it into a training set, a validation set, and a test set Inputting the training set into the graph neural network, the model aggregates the node features through at least two graph convolutional layers, uses the attention mechanism layer to aggregate the value vectors of adjacent nodes through weighted coefficients, uses the time series graph convolutional layer to process the temporal evolution of the node states, calculates the differences between the prediction results and the image data, traffic volume, and vehicle speed through the cross-entropy loss function during training, inputs the validation set into the model, verifies the overfitting state according to the loss function value and the change in accuracy, and ends the training if the accuracy improvement of the validation set is less than 0.7% for 5 consecutive rounds. Feedback the differences to each layer of the model using the backpropagation algorithm to adjust the parameters until the maximum number of iterations is reached to complete the training. Input the test set into the trained graph neural network, evaluate the model performance using accuracy and recall, and output the travel characteristics of the first vehicle type based on the trained model.

9. A new energy vehicle travel characteristic analysis system for highways, which is used to execute the new energy vehicle travel characteristic analysis method according to any one of claims 1 to 8, and is characterized in that, The system includes: Data acquisition module: It is used to collect the expressway data to be processed for preprocessing, and obtain image data, traffic flow, vehicle speed, road data and facility points; Data processing module: Based on the facility points, the sections where vehicles enter and leave the facilities are partitioned. According to the image data, traffic flow and vehicle speed of the partitions, the acceleration wave characteristics, deceleration wave characteristics and vehicle type images of the partitions are obtained. The acceleration wave characteristics and deceleration wave characteristics are used to correct the partition range to obtain the dynamic area; Traffic flow change simulation module: It is used to obtain short-term motion characteristics using the vehicle type images in the dynamic area, obtain the first vehicle type in the intervals before and after the charging facility according to the vehicle type images, regard the remaining vehicle types as the second vehicle type, calculate the charging demand index according to the first vehicle type that enters and does not enter the charging facility, fit the short-term motion characteristics of the first vehicle type and the second vehicle type according to the charging demand index and traffic flow in the dynamic area to obtain the first vehicle type motion data and the second vehicle type motion data, calculate the first vehicle type conflict index and the second vehicle type conflict index using the first vehicle type motion data, the second vehicle type motion data and the partition road data, and obtain the traffic flow change data through the cellular transmission model based on the first vehicle type conflict index, the second vehicle type conflict index, the first vehicle type motion data, the second vehicle type motion data and the partition road data; Travel characteristic output module: Based on the first vehicle type motion data, the first vehicle type conflict index, the traffic flow change data and the highway topology structure, the travel characteristics of the first vehicle type on the expressway to be processed are analyzed through a graph neural network.

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