Classification and prediction method, system, electronic device and storage medium for driver's global speed evolution pattern under progressive variable speed limit

Abstract

The present invention discloses a method, system, electronic device, and storage medium for classifying and predicting the evolution pattern of a driver's global speed under a progressively variable speed limit. The method comprises: deploying lane-level variable speed limit information release equipment; deploying a vehicle trajectory data acquisition device; utilizing a variable information system to release and control corresponding lane-level speed limit control instructions according to a preset control strategy, and adjusting the speed limit value of the lane-level variable speed limit plate; clustering the driver's global vehicle speed trajectory patterns; constructing a model for the evolution of the driver's global speed under progressively variable speed limit control; and constructing a prediction model for the evolution of the driver's global speed under lane-level variable speed limit control. The present invention can predict the driver's global speed by utilizing the driver's speed evolution under progressively variable speed limit conditions, providing a customized and personalized driving behavior warning solution. The method is low-cost, replicable, and robust.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent traffic control and relates to a pattern classification and prediction method, and in particular to a method, system, electronic device and storage medium for classifying and predicting a driver's global speed evolution pattern under a progressively variable speed limit. Background Art

[0002] With the advancement of sensing technology, artificial intelligence, 5G, and the Internet of Things, precise and personalized vehicle command delivery technology is rapidly gaining ground, and intelligent connected vehicle technology is undergoing continuous updates and iterations. Intelligent connected vehicles will reshape the relationship between people, vehicles, and roads, taking vehicle perception and control to a new level.

[0003] With the increasing prevalence of motor vehicles and the growing demand for transportation, the static speed control methods and information dissemination via variable message signs (VMS) currently used in active highway safety management technologies are increasingly failing to meet the needs of the intelligent connected environment. First, static speed limits cannot provide safe and reasonable speed limits for different operating conditions and specific circumstances in a dynamic road traffic environment. Second, VMS-based information dissemination cannot provide personalized speed limit guidance for specific vehicles, resulting in significant differences in management effectiveness between individuals and negatively impacting highway traffic safety. Third, the varying spacing and speed values ​​of VMSs placed continuously on highways lead to different driving strategies under the influence of progressive lane-level variable speed limits, necessitating targeted speed limit guidance tailored to each driver's driving behavior chain.

[0004] In view of this, there is an urgent need to design a new road safety control method to overcome at least some of the above-mentioned shortcomings of the existing road safety control method. Summary of the Invention

[0005] The present invention provides a method, system, electronic device and storage medium for classifying and predicting the evolution pattern of a driver's global speed under a progressively variable speed limit. The method can use the driver's speed evolution under progressively variable speed limits to predict the driver's global speed and provide a customized and personalized driving behavior warning solution. The method does not rely on vehicle-side equipment, has low cost, and is replicable and robust.

[0006] In order to solve the above technical problems, according to one aspect of the present invention, the following technical solution is adopted:

[0007] A method for classifying and predicting a driver's global speed evolution pattern under a progressively variable speed limit, the method comprising:

[0008] Step S1: deploying a series of lane-level variable speed limit information release devices at set intervals on a free-flow highway section;

[0009] Step S2: deploying vehicle trajectory data acquisition devices at set intervals on the road area to collect and analyze vehicle time-series trajectory data;

[0010] Step S3: According to the preset control strategy, the variable information system is used to issue corresponding lane-level speed limit control instructions, adjust the speed limit value of the lane-level variable speed limit plate, and export the vehicle trajectory data and lane-level speed limit information release data in real time;

[0011] Step S4: clustering the driver's global speed trajectory pattern, classifying the driver's vehicle trajectory according to different time series distribution characteristics, thereby classifying the driver under the progressive variable speed limit;

[0012] Step S5: constructing a global speed evolution model for the driver under progressive lane-level variable speed limit control, segmenting the global speed of the driver and vehicle, and obtaining state transition probabilities between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control;

[0013] Step S6: Construct a prediction model for the global speed evolution of the driver under lane-level variable speed limit control. Based on the global speed distribution characteristics of the driver under progressive lane-level variable speed limit control, predict the speed evolution trend, thereby predicting the driver's driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0014] As an embodiment of the present invention, in step S1, a lane-level variable speed limit information issuing device is deployed on the expressway section;

[0015] In step S2, vehicle trajectory data collection devices such as radars and cameras are deployed at set intervals on the road area to obtain continuous and uninterrupted sub-meter-level time-series vehicle trajectories with a speed collection accuracy of no less than 1 km / h and a collection frequency of 10 Hz, providing a data basis for the collection and analysis of the driver's real-time time-series driving behavior characteristics;

[0016] In step S3, the vehicle time series trajectory data and lane-level speed limit information release data are all collected in real time and use UTC time, and the location of data release uses a unified stake coordinate;

[0017] In step S6, based on the classification of the driver's global speed evolution pattern, the driver's global speed evolution pattern architecture is constructed based on the graph neural network, and the graph neural network is combined with CNN-LSTM to further construct the driver's global speed evolution prediction model, that is, the MTK-GNN-CNN-LSTM global speed evolution prediction model, to predict the driver's speed evolution trend, thereby predicting the driver's driving behavior, and actively predicting the occurrence of abnormal driving behavior of the driver under progressive lane-level variable speed limits.

[0018] As an embodiment of the present invention, in step S4, a K-Shape clustering algorithm is used to eliminate the amplitude influence of the time series through normalization, achieve local alignment by shifting the time series, and introduce a fast Fourier transform algorithm in calculating similarity to reduce the computational cost of high-dimensional time series. The K-Shape algorithm combines the concepts of DTW distance and shape features and can effectively capture the intrinsic structure of the time series. Step S4 includes the following steps:

[0019] Step S41: Unify the length of vehicle time series trajectory data and the distribution of acquisition point coordinates. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time series data. To enable matching based on vehicle position during the clustering of different time series, each vehicle's speed time series data is processed into equal-length time series data using longitudinal position as the "time series" and interpolated at 1m intervals.

[0020] Step S42, initialize cluster centers: randomly select K time series from the data set as initial cluster centers. These series will become the representative shapes of the clusters; set the number of clusters to 3;

[0021] Step S43, shape distance measurement: using DTW or other shape distance measurement to calculate the distance between each time series and each cluster center;

[0022] Step S44, assigning data points: assigning each time series to the cluster with the closest cluster center; in this way, each series will be classified into the cluster with the most similar shape to it;

[0023] Step S45, update cluster centers: update cluster centers using the average shape of the time series assigned to each cluster. In this way, cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster;

[0024] Step S46, repeat iteration: Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached; eventually, the obtained cluster will contain all time series in the data set, and each cluster represents a group of sequences with similar shapes.

[0025] As one embodiment of the present invention, in step S5, a global speed evolution model of the driver under progressive lane-level variable speed limit control is constructed, the global speed of the driver and vehicle is segmented, and the state transition probability between different trajectory segments is obtained, thereby effectively judging the driver's abnormal driving behavior and actively sensing the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control. Step S5 includes the following steps:

[0026] Step S51: Perform data discretization and quantile discretization processing on the vehicle speed time series to obtain vehicle speed discretization partitions.

[0027] Step S52: Create a Markov transition matrix for the partitioned time series data to obtain the matrix M1

[0028]

[0029] Step S53: Normalize the values ​​and calculate the transition probability matrix M′1:

[0030]

[0031] Step S54, calculating the Markov transition field;

[0032] Step S55: perform graph neural network encoding and mapping on the time-series segmented subsequences processed by the Markov transition field, and further construct a graph neural network to describe different speed fluctuation states and the relationship between them;

[0033] Step S56: Use the module partitioning algorithm in the ts ia graph neural network analysis tool to perform community partitioning on the obtained graph neural network; the driver's global speed state is divided into different state intervals, and the edges between neural vertices represent the transition probability between different speed state intervals; the process of the driver making decisions in the face of speed limits is divided into several state intervals as needed, so as to more finely describe the driver's global speed evolution process.

[0034] According to another aspect of the present invention, the following technical solution is adopted: a system for classifying and predicting the evolution pattern of a driver's global speed under a progressively variable speed limit, the system comprising:

[0035] A number of information dissemination devices are arranged on a free-flow highway section, and are arranged sequentially according to a set spacing rule;

[0036] Several vehicle trajectory data collection devices are arranged in sequence on the road area according to a set spacing rule to realize the collection and analysis of vehicle time series trajectory data;

[0037] The speed limit adjustment module is used to use the variable information system to issue corresponding lane-level speed limit control instructions according to the preset control strategy, adjust the speed limit value of the lane-level variable speed limit plate, and export vehicle trajectory data and lane-level speed limit information release data in real time;

[0038] A classification module is used to cluster the driver's global speed trajectory pattern and classify the driver's vehicle trajectory according to different temporal distribution characteristics, thereby classifying the driver under progressive variable speed limit;

[0039] The speed evolution model construction module is used to construct a global speed evolution model for the driver under progressive lane-level variable speed limit control. This module segments the driver's vehicle's global speed and obtains the state transition probability between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0040] The speed evolution prediction model construction module is used to construct a driver's global speed evolution prediction model under lane-level variable speed limit control. Based on the driver's global speed distribution characteristics under progressive lane-level variable speed limit control, its speed evolution trend is predicted, thereby predicting its driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0041] As an embodiment of the present invention, the information release device is a lane-level variable speed limit information release device;

[0042] The vehicle trajectory data acquisition device includes radar and / or camera to obtain continuous and uninterrupted sub-meter-level time-series vehicle trajectory, with a speed acquisition accuracy of no less than 1km / h and an acquisition frequency of 10Hz, providing a data basis for the collection and analysis of the driver's real-time time-series driving behavior characteristics;

[0043] The vehicle time series trajectory data and lane-level speed limit information release data are all collected in real time and use UTC time, and the locations where the data is released use unified stake coordinates;

[0044] Based on the classification of the driver's global speed evolution pattern, the speed evolution prediction model construction module is based on the driver's global speed evolution pattern architecture constructed by the graph neural network. The graph neural network is combined with CNN-LSTM to further construct the driver's global speed evolution prediction model, namely the MTK-GNN-CNN-LSTM global speed evolution prediction model, to predict the driver's speed evolution trend, thereby predicting the driver's driving behavior and actively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limits.

[0045] As an embodiment of the present invention, the classification module uses the K-Shape clustering algorithm to eliminate the amplitude influence of the time series through normalization, achieve local alignment by shifting the time series, and introduce the fast Fourier algorithm in calculating similarity to reduce the computational cost of high-dimensional time series. The K-Shape algorithm combines the concepts of DTW distance and shape features and can effectively capture the intrinsic structure of the time series. The classification process of the classification module includes the following steps:

[0046] Step S41: Unify the length of vehicle time series trajectory data and the distribution of acquisition point coordinates. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time series data. To enable matching based on vehicle position during the clustering of different time series, each vehicle's speed time series data is processed into equal-length time series data using longitudinal position as the "time series" and interpolated at 1m intervals.

[0047] Step S42, initialize cluster centers: randomly select K time series from the data set as initial cluster centers. These series will become the representative shapes of the clusters; set the number of clusters to 3;

[0048] Step S43, shape distance measurement: using DTW or other shape distance measurement to calculate the distance between each time series and each cluster center;

[0049] Step S44, assigning data points: assigning each time series to the cluster with the closest cluster center; in this way, each series will be classified into the cluster with the most similar shape to it;

[0050] Step S45, update cluster centers: update cluster centers using the average shape of the time series assigned to each cluster. In this way, cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster;

[0051] Step S46, repeat iteration: Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached; eventually, the obtained cluster will contain all time series in the data set, and each cluster represents a group of sequences with similar shapes.

[0052] As an embodiment of the present invention, the model construction process of the velocity evolution model construction module includes the following steps:

[0053] Step S51: Perform data discretization and quantile discretization processing on the vehicle speed time series to obtain vehicle speed discretization partitions.

[0054] Step S52: Create a Markov transition matrix for the partitioned time series data to obtain the matrix M1

[0055]

[0056] Step S53: Normalize the values ​​and calculate the transition probability matrix M′1:

[0057]

[0058] Step S54, calculating the Markov transition field;

[0059] Step S55: perform graph neural network encoding and mapping on the time-series segmented subsequences processed by the Markov transition field, and further construct a graph neural network to describe different speed fluctuation states and the relationship between them;

[0060] Step S56: Use the module partitioning algorithm in the ts ia graph neural network analysis tool to perform community partitioning on the obtained graph neural network; the driver's global speed state is divided into different state intervals, and the edges between neural vertices represent the transition probability between different speed state intervals; the process of the driver making decisions in the face of speed limits is divided into several state intervals as needed, so as to more finely describe the driver's global speed evolution process.

[0061] According to another aspect of the present invention, the following technical solution is adopted: an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0062] According to another aspect of the present invention, the following technical solution is adopted: a storage medium stores computer program instructions, and the computer program instructions implement the steps of the above method when executed by a processor.

[0063] The present invention finds an expressway with free-flow periods and continuous lane-level variable speed limit plates; arranges vehicle trajectory data acquisition devices at regular intervals on the roadside; uses a variable information system to issue corresponding lane-level speed limit control instructions according to a preset control strategy, and adjusts the speed limit value of the lane-level variable speed limit plates; uses the vehicle trajectory data acquisition device to collect and analyze vehicle trajectory characteristic parameters, extracts vehicle trajectory characteristic data of drivers under different speed limit conditions, and constructs a classification and prediction model for the driver's global speed evolution pattern under progressive variable speed limits based on vehicle driving trajectory data. The classification and prediction method for the driver's global speed evolution pattern under progressively variable speed limits provided by the present invention applies time-series vehicle trajectory feature data to realize the prediction of the driver's speed evolution under progressively variable speed limits. This solves the problems that continuous lane-level speed limit scenarios are ever-changing, the combination methods are endless, resulting in unclear mutual influence mechanisms between lane-separating speed limits and driving behaviors, making it impossible to predict the speed distribution characteristics of drivers under continuous speed limits, and the traditional traffic variable speed limit control is unable to perform targeted speed limits based on the driver's driving behavior characteristics. At the same time, it solves the problem that the current variable speed limit control on highways lacks an optimization mechanism for individual drivers, resulting in low driver compliance.

[0064] The beneficial effects of the present invention are as follows: the classification and prediction method, system, electronic device and storage medium of the driver's global speed evolution pattern under progressively variable speed limits proposed in the present invention can use the driver's speed evolution under progressively variable speed limits to predict the driver's global speed, and provide a customized and personalized driving behavior warning solution for the driver. It does not rely on vehicle-side equipment, has low cost, and is replicable and robust.

[0065] The method of the present invention utilizes a continuous vehicle trajectory acquisition device to collect and analyze the time series data of driving behavior under progressive lane-level variable speed limits, and utilizes the shape-based clustering method of K-Shape to realize the prediction of the driver's speed evolution under progressive variable speed limit conditions. This solves the problems that continuous lane-level speed limit scenarios are ever-changing, the combination methods are endless, resulting in unclear mutual influence mechanism between lane speed limit measures and driving behavior, making it impossible to predict the speed distribution characteristics of drivers under continuous speed limit conditions, and traditional traffic variable speed limit control is unable to perform targeted speed limit according to the driver's driving behavior characteristics.

[0066] The present invention utilizes a graph neural network model to discretize time series fluctuations into different subsequence fluctuations, thereby achieving accurate quantification of the global speed evolution law and state transition probability of individual drivers under continuous speed limit conditions, accurately extracting the driver's abnormal speed state, and finely describing the driver's speed state evolution under continuous speed limit. This provides a theoretical basis and analysis method for refined variable speed limit control, and can provide personalized speed limit guidance strategies based on driver behavior characteristics. It has the characteristics of low cost, replicability and strong robustness.

[0067] Based on the classification of the driver's global speed evolution pattern, the Markov transition field and graph neural network combined with CNN-LSTM are used to further construct the MTK-GNN-CNN-LSTM driver global speed evolution prediction model architecture. Compared with the conventional CNN-LSTM model, the global speed evolution pattern label obtained by the Markov transition field-graph neural network community partitioning algorithm is added to the CNN model input. The subsequence dependency and conversion probability of the time series can be input into the input layer to improve the accuracy and stability of the global speed prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 Flowchart of an evolution pattern classification and prediction method in one embodiment of the present invention.

[0069] Figure 2 Schematic diagram of the composition of an evolutionary pattern classification and prediction system in one embodiment of the present invention.

[0070] Figure 3 Schematic diagram of the principle of the driver's global speed evolution pattern classification and prediction method in one embodiment of the present invention.

[0071] Figure 4 Schematic diagram of a method for deploying variable speed limit information release equipment in one embodiment of the present invention.

[0072] Figure 5 Schematic diagram of a method for deploying traffic flow parameter collection equipment in one embodiment of the present invention.

[0073] Figure 6 Schematic diagram of vehicle speed distribution in three vehicle speed trajectory modes in one embodiment of the present invention.

[0074] Figure 7 Schematic diagram comparing vehicle speed distributions in three global vehicle speed trajectory modes under different speed limit scenarios in one embodiment of the present invention.

[0075] Figure 8 Schematic diagram of vehicle speed time series selected for the speed evolution pattern classification and prediction method in one embodiment of the present invention.

[0076] Figure 9Schematic diagram of the Markov transformation field generated by the vehicle speed time series in one embodiment of the present invention.

[0077] Figure 10 This is a schematic diagram of the neural network construction and parameter statistics in one embodiment of the present invention.

[0078] Figure 11 Schematic diagram of the relationship between the neural network community division and the mapping with time series data in one embodiment of the present invention.

[0079] Figure 12 Schematic diagram of a global velocity evolution prediction model based on MTK-GNN-CNN-LSTM in one embodiment of the present invention.

[0080] Figure 13 FIG. 1 is a schematic diagram of the composition of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0081] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0082] In order to further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0083] The description in this section is only for several typical embodiments, and the present invention is not limited to the scope of the embodiments described. The same or similar existing technical means and some technical features of the embodiments are mutually replaced within the scope of the description and protection of the present invention.

[0084] The description of the steps in each embodiment in the specification is only for the convenience of explanation, and the implementation method of this application is not limited by the order of implementation of the steps.

[0085] The term “connection” in the specification includes both direct connection and indirect connection.

[0086] The present invention discloses a classification and prediction method for the global speed evolution pattern of a driver under a progressive variable speed limit. Figure 1 is a flow chart of an evolution pattern classification and prediction method in one embodiment of the present invention, Figure 3 This is a schematic diagram of the principle of the driver's global speed evolution pattern classification and prediction method in one embodiment of the present invention; please refer to Figure 1 、 Figure 3 , the pattern classification and prediction method includes:

[0087] [Step S1] A series of lane-level variable speed limit information dissemination devices are deployed at predetermined intervals on a free-flow highway section;

[0088] [Step S2] Deploying vehicle trajectory data collection devices at set intervals on the road area to collect and analyze vehicle time-series trajectory data;

[0089] [Step S3] According to the preset control strategy, the variable information system is used to issue corresponding lane-level speed limit control instructions, adjust the speed limit value of the lane-level variable speed limit plate, and export the vehicle trajectory data and lane-level speed limit information release data in real time;

[0090] [Step S4] Based on the clustering of the driver's global speed trajectory pattern, the driver's vehicle trajectory is classified according to different time series distribution characteristics, thereby classifying the driver under the progressive variable speed limit;

[0091] [Step S5] Constructing a global speed evolution model for the driver under progressive lane-level variable speed limit control, segmenting the driver's vehicle global speed, and obtaining state transition probabilities between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control;

[0092] [Step S6] Construct a prediction model for the driver's global speed evolution under lane-level variable speed limit control. Based on the driver's global speed distribution characteristics under progressive lane-level variable speed limit control, predict their speed evolution trend, thereby predicting their driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0093] In one embodiment of the present invention, in step S1, a lane-level variable speed limit information issuing device is deployed on the expressway section.

[0094] In step S2, vehicle trajectory data collection devices such as radars and cameras are deployed at set intervals on the road to obtain continuous and uninterrupted sub-meter-level time-series vehicle trajectories. The speed collection accuracy is not less than 1km / h and the collection frequency reaches 10Hz, providing a data basis for the collection and analysis of the driver's real-time time-series driving behavior characteristics.

[0095] In step S3, the vehicle time series trajectory data and lane-level speed limit information release data are all collected in real time and use UTC time, and the locations where the data is released use unified stake coordinates.

[0096] In step S6, based on the classification of the driver's global speed evolution pattern, the driver's global speed evolution pattern architecture is constructed based on the graph neural network, and the graph neural network is combined with CNN-LSTM to further construct the driver's global speed evolution prediction model, that is, the MTK-GNN-CNN-LSTM global speed evolution prediction model, to predict the driver's speed evolution trend, thereby predicting the driver's driving behavior, and actively predicting the occurrence of abnormal driving behavior of the driver under progressive lane-level variable speed limits.

[0097] In one embodiment of the present invention, in step S4, the K-Shape clustering algorithm is used to eliminate the amplitude influence of the time series through normalization, achieve local alignment by shifting the time series, and introduce the fast Fourier transform algorithm into the similarity calculation to reduce the computational cost of high-dimensional time series. The K-Shape algorithm combines the concepts of DTW distance and shape features and can effectively capture the intrinsic structure of the time series. Step S4 includes the following steps:

[0098] Step S41: Unify the length of vehicle time series trajectory data and the distribution of acquisition point coordinates. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time series data. To enable matching based on vehicle position during the clustering of different time series, each vehicle's speed time series data is processed into equal-length time series data using longitudinal position as the "time series" and interpolated at 1m intervals.

[0099] Step S42, initialize cluster centers: randomly select K time series from the data set as initial cluster centers. These series will become the representative shapes of the clusters; set the number of clusters to 3;

[0100] Step S43, shape distance measurement: using DTW or other shape distance measurement to calculate the distance between each time series and each cluster center;

[0101] Step S44, assigning data points: assigning each time series to the cluster with the closest cluster center; in this way, each series will be classified into the cluster with the most similar shape to it;

[0102] Step S45, update cluster centers: update cluster centers using the average shape of the time series assigned to each cluster. In this way, cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster;

[0103] Step S46, repeat iteration: Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached; eventually, the obtained cluster will contain all time series in the data set, and each cluster represents a group of sequences with similar shapes.

[0104] In one embodiment of the present invention, in step S5, a global speed evolution model of the driver under progressive lane-level variable speed limit control is constructed, the global speed of the driver and vehicle is segmented, and the state transition probabilities between different trajectory segments are obtained, thereby effectively judging the driver's abnormal driving behavior and actively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control. Step S5 includes the following steps:

[0105] Step S51: Perform data discretization and quantile discretization processing on the vehicle speed time series to obtain vehicle speed discretization partitions.

[0106] Step S52: Create a Markov transition matrix for the partitioned time series data to obtain the matrix M1

[0107]

[0108] Step S53: Normalize the values ​​and calculate the transition probability matrix M′1:

[0109]

[0110] Step S54, calculating the Markov transition field;

[0111] Step S55: perform graph neural network encoding and mapping on the time-series segmented subsequences processed by the Markov transition field, and further construct a graph neural network to describe different speed fluctuation states and the relationship between them;

[0112] In step S56, the module partitioning algorithm in the TSIA graph neural network analysis tool developed by the Massachusetts Institute of Technology can be used to perform community partitioning on the obtained graph neural network; the driver's global speed state is divided into different state intervals, and the edges between neural vertices represent the transition probability between different speed state intervals; the process of the driver making decisions in the face of speed limits is divided into several state intervals as needed, so as to more finely describe the driver's global speed evolution process.

[0113] The present invention further discloses a classification and prediction system for the global speed evolution pattern of a driver under a progressively variable speed limit. Figure 2 This is a schematic diagram of the composition of the evolution pattern classification and prediction system in one embodiment of the present invention; please refer to Figure 2 The pattern classification and prediction system includes: several information release devices 1, several vehicle trajectory data acquisition devices 2, a speed limit value adjustment module 3, a classification module 4, a speed evolution model construction module 5 and a speed evolution prediction model construction module 6.

[0114] The information release device 1 is arranged on a free-flow highway section and arranged in sequence according to a set spacing rule; in one embodiment of the present invention, the information release device is a lane-level variable speed limit information release device;

[0115] The vehicle trajectory data acquisition devices 2 are deployed sequentially along the road at predetermined intervals to collect and analyze vehicle time-series trajectory data. These devices, which include radar and / or cameras, capture continuous, sub-meter-level vehicle trajectories with a speed accuracy of at least 1 km / h and a frequency of 10 Hz, providing a data foundation for the collection and analysis of real-time driving behavior characteristics.

[0116] The speed limit adjustment module 3 is used to use the variable information system to issue corresponding lane-level speed limit control instructions according to a preset control strategy, adjust the speed limit value of the lane-level variable speed limit plate, and export vehicle trajectory data and lane-level speed limit information release data in real time. In one embodiment, the vehicle time-series trajectory data and lane-level speed limit information release data are all collected in real time and use UTC time. The data release location uses unified stake coordinates.

[0117] The classification module 4 is used to cluster the driver's global vehicle speed trajectory pattern and classify the driver's vehicle trajectory according to different time series distribution characteristics, thereby classifying the driver under the progressive variable speed limit.

[0118] The speed evolution model construction module 5 is used to construct a global speed evolution model of the driver under progressive lane-level variable speed limit control, segment the global speed of the driver and vehicle, and obtain the state transition probability between different trajectory segments, so as to effectively judge the driver's abnormal driving behavior and actively perceive the occurrence of abnormal driving behavior under progressive lane-level variable speed limit.

[0119] The speed evolution prediction model construction module 6 is used to construct a driver's global speed evolution prediction model under lane-level variable speed limit control. According to the driver's global speed distribution characteristics under progressive lane-level variable speed limit control, the speed evolution trend is predicted, thereby predicting the driver's driving behavior and actively predicting the occurrence of abnormal driving behavior of the driver under progressive lane-level variable speed limit control.

[0120] The speed evolution prediction model construction module 6, based on the classification of the driver's global speed evolution pattern, constructs a global speed evolution pattern architecture of the driver based on the graph neural network, and uses the graph neural network combined with CNN-LSTM to further construct a global speed evolution prediction model for the driver, namely, the MTK-GNN-CNN-LSTM global speed evolution prediction model, to predict the driver's speed evolution trend, thereby predicting the driver's driving behavior, and actively predicting the occurrence of abnormal driving behavior of the driver under progressive lane-level variable speed limits.

[0121] In one embodiment of the present invention, the classification module 4 uses the K-Shape clustering algorithm to eliminate the amplitude influence of the time series through normalization, achieve local alignment by shifting the time series, and introduce the fast Fourier algorithm in calculating similarity to reduce the computational cost of high-dimensional time series. The K-Shape algorithm combines the concepts of DTW distance and shape features and can effectively capture the intrinsic structure of the time series. The classification process of the classification module 4 includes the following steps:

[0122] Step S41: Unify the length of vehicle time series trajectory data and the distribution of acquisition point coordinates. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time series data. To enable matching based on vehicle position during the clustering of different time series, each vehicle's speed time series data is processed into equal-length time series data using longitudinal position as the "time series" and interpolated at 1m intervals.

[0123] Step S42, initialize cluster centers: randomly select K time series from the data set as initial cluster centers. These series will become the representative shapes of the clusters; set the number of clusters to 3;

[0124] Step S43, shape distance measurement: using DTW or other shape distance measurement to calculate the distance between each time series and each cluster center;

[0125] Step S44, assigning data points: assigning each time series to the cluster with the closest cluster center; in this way, each series will be classified into the cluster with the most similar shape to it;

[0126] Step S45, update cluster centers: update cluster centers using the average shape of the time series assigned to each cluster. In this way, cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster;

[0127] Step S46, repeat iteration: Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached; eventually, the obtained cluster will contain all time series in the data set, and each cluster represents a group of sequences with similar shapes.

[0128] In one embodiment of the present invention, the model building process of the velocity evolution model building module 5 includes the following steps:

[0129] Step S51: Perform data discretization and quantile discretization processing on the vehicle speed time series to obtain vehicle speed discretization partitions.

[0130] Step S52: Create a Markov transition matrix for the partitioned time series data to obtain the matrix M1

[0131]

[0132] Step S53: Normalize the values ​​and calculate the transition probability matrix M′1:

[0133]

[0134] Step S54, calculating the Markov transition field;

[0135] Step S55: perform graph neural network encoding and mapping on the time-series segmented subsequences processed by the Markov transition field, and further construct a graph neural network to describe different speed fluctuation states and the relationship between them;

[0136] In step S56, the module partitioning algorithm in the ts ia graph neural network analysis tool developed by the Massachusetts Institute of Technology (of course, other tools can also be used) can be used to perform community partitioning processing on the obtained graph neural network; the driver's global speed state is divided into different state intervals, and the edges between the neural vertices represent the conversion probability between different speed state intervals; the process of the driver making decisions in the face of speed limits is divided into several state intervals as needed, so as to more finely describe the driver's global speed evolution process.

[0137] In one embodiment of the present invention, the method for classifying and predicting a driver's global speed evolution pattern under a progressively variable speed limit comprises the following steps:

[0138] Step 1) A series of lane-level variable speed limit information release devices are deployed at a certain interval on a highway section with free flow status.

[0139] The road alignment must comply with the requirements of the Highway Alignment Design Specifications (JTG D20-2017); the setting of signs and markings in the scene must comply with the requirements of the Road Traffic Signs and Markings (GB 5768-2009) and the Highway Speed ​​Limit Sign Design Specifications (JTG / T3381-02-2020). Figure 4 shown.

[0140] Step 2) Deploy vehicle trajectory data acquisition devices at regular intervals on the road to achieve continuous and uninterrupted sub-meter-level time-series vehicle trajectory acquisition and analysis.

[0141] The speed acquisition accuracy is not less than 1km / h, the acquisition frequency reaches 10Hz, and the acquisition equipment is arranged as follows Figure 5 shown.

[0142] Step 3) According to the preset control strategy, the variable information system is used to issue the corresponding lane-level speed limit control instructions, adjust the speed limit value of the lane-level variable speed limit plate, and export the traffic flow parameters and lane-level speed limit information release data in real time.

[0143] Lane-level variable speed limit signs are the information and instruction distribution devices for the variable speed limit control system. They are arranged on the gantry at intervals of 750 meters. Each lane-level variable speed limit sign issues variable speed limit information for a lane. Lane-level speed limit instructions can have different speed limits for different lanes and upstream and downstream lanes. By adjusting the speed limit value of the variable speed limit sign in real time, the operating status of road traffic flow can be changed in real time.

[0144] Step 4) Based on the K-Shape-based clustering of the driver's global speed trajectory patterns, the driver's vehicle trajectory is divided into three categories according to different temporal distribution characteristics, thereby dividing the driver under the progressive variable speed limit into three categories:

[0145] Specifically, the length of vehicle time-series trajectory data and the distribution of acquisition point coordinates were standardized. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time-series data. To enable position-based matching during the clustering of different time series, each vehicle's speed time-series data is processed into equal-length time-series data using its longitudinal position as the "time series" and interpolated at 1m intervals.

[0146] Initialize cluster centers: Randomly select K time series from the dataset as initial cluster centers. These series will become the representative shapes of the clusters; set the number of clusters to 3.

[0147] Shape distance metric: Calculate the distance between each time series and each cluster center using DTW or other shape distance metrics.

[0148] Assign data points: Assign each time series to the cluster with the closest cluster center. This way, each series will be classified into the cluster with the most similar shape to it.

[0149] Update cluster centers: Update the cluster centers using the average shape of the time series assigned to each cluster. In this way, the cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster.

[0150] Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached. Eventually, the resulting cluster will contain all time series in the dataset, and each cluster represents a group of series with similar shapes.

[0151] Finally, we get the speed distribution diagrams of the three speed trajectory modes, as shown in Figure 6 As shown in the figure, the speed distribution comparison of the three global speed trajectory modes under different speed limit scenarios is shown in the figure. Figure 7 shown.

[0152] Step 5) Construct a global speed evolution model for the driver under progressive lane-level variable speed limit control, segment the driver's vehicle global speed, and obtain the state transition probability between different trajectory segments, so as to effectively judge the driver's abnormal driving behavior and actively detect the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control;

[0153] The vehicle speed time series is discretized and processed into discrete quantiles to obtain the speed discretization partitions, such as Figure 8 As shown;

[0154] The Markov transition matrix is ​​established for the partitioned time series data to obtain the matrix M1, as shown in Figure 9 As shown;

[0155]

[0156] Specifically, normalize the values ​​and calculate the transition probability matrix M′1;

[0157]

[0158] Computes a Markov transition field.

[0159] The time-series segmented subsequences processed by the Markov transition field are encoded and mapped by the graph neural network, and the graph neural network is further constructed to describe the different speed fluctuation states and their relationships, such as Figure 10 shown.

[0160] The resulting graph neural network was subjected to community partitioning using the module partitioning algorithm from the TSIA graph neural network analysis tool developed by MIT. The driver's global speed state is divided into different state intervals, and the edges between neural vertices represent the transition probability between different speed state intervals. The driver's decision-making process facing speed limits can be divided into several state intervals as needed, thereby more precisely describing the driver's global speed evolution process, such as Figure 11 shown.

[0161] Step 6) Construct a prediction model for the global speed evolution of the driver under lane-level variable speed limit control. Based on the global speed distribution characteristics of the driver under progressive lane-level variable speed limit control, predict their speed evolution trend, thereby predicting their driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0162] Specifically, on the basis of completing the classification of the driver's global speed evolution pattern, the driver's global speed evolution pattern architecture is constructed based on the graph neural network, and the graph neural network is combined with CNN-LSTM to further construct the driver's global speed evolution prediction model, that is, the MTK-GNN-CNN-LSTM global speed evolution prediction model, which predicts the speed evolution trend and thus predicts the driver's driving behavior. It actively predicts the occurrence of abnormal driving behavior of the driver under progressive lane-level variable speed limits. Its prediction accuracy is compared with the other two models. Figure 12 shown

[0163] The lane-level variable speed limit group compliance prediction method for cross-sectional traffic flow parameters provided by the present invention applies cross-sectional traffic flow characteristic data to realize the prediction of downstream driver group compliance, solving the problem that traditional traffic variable speed limit control cannot optimize the speed limit according to driver compliance. At the same time, it solves the problem that most current roads do not have the function of vehicle trajectory collection but only have the function of cross-sectional traffic flow data collection. Limited cross-sectional traffic flow characteristic parameters can be used to predict compliance, which is relatively low in cost.

[0164] Table 1 Model error and performance table

[0165]

[0166] The present invention also discloses an electronic device, Figure 13 This is a schematic diagram of the composition of an electronic device according to an embodiment of the present invention; Figure 13 At the hardware level, the electronic device includes a memory, a processor, and at least one network interface. The processor may be a microprocessor, and the memory may include internal memory, such as random access memory (RAM) or non-volatile memory. Of course, the electronic device may also include other hardware as needed.

[0167] The processor, network interface, and memory may be interconnected via an internal bus, such as an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industrial Standard Architecture) bus. The bus may include an address bus, a data bus, a control bus, and the like. The memory is used to store programs (which may include operating system programs and application programs). The programs may include program code, which may include computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0168] In one embodiment, the processor can read the corresponding program from the non-volatile memory into the memory and then run it; the processor can execute the program stored in the memory and is specifically used to perform the following operations (such as Figure 1 shown):

[0169] [Step S1] A series of lane-level variable speed limit information dissemination devices are deployed at predetermined intervals on a free-flow highway section;

[0170] [Step S2] Deploying vehicle trajectory data collection devices at set intervals on the road area to collect and analyze vehicle time-series trajectory data;

[0171] [Step S3] According to the preset control strategy, the variable information system is used to issue corresponding lane-level speed limit control instructions, adjust the speed limit value of the lane-level variable speed limit plate, and export the vehicle trajectory data and lane-level speed limit information release data in real time;

[0172] [Step S4] Based on the clustering of the driver's global speed trajectory pattern, the driver's vehicle trajectory is classified according to different time series distribution characteristics, thereby classifying the driver under the progressive variable speed limit;

[0173] [Step S5] Constructing a global speed evolution model for the driver under progressive lane-level variable speed limit control, segmenting the driver's vehicle global speed, and obtaining state transition probabilities between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control;

[0174] [Step S6] Construct a prediction model for the driver's global speed evolution under lane-level variable speed limit control. Based on the driver's global speed distribution characteristics under progressive lane-level variable speed limit control, predict their speed evolution trend, thereby predicting their driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0175] The present invention further discloses a storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the following steps of the method of the present invention (eg Figure 1 shown):

[0176] [Step S1] A series of lane-level variable speed limit information dissemination devices are deployed at predetermined intervals on a free-flow highway section;

[0177] [Step S2] Deploying vehicle trajectory data collection devices at set intervals on the road area to collect and analyze vehicle time-series trajectory data;

[0178] [Step S3] According to the preset control strategy, the variable information system is used to issue corresponding lane-level speed limit control instructions, adjust the speed limit value of the lane-level variable speed limit plate, and export the vehicle trajectory data and lane-level speed limit information release data in real time;

[0179] [Step S4] Based on the clustering of the driver's global speed trajectory pattern, the driver's vehicle trajectory is classified according to different time series distribution characteristics, thereby classifying the driver under the progressive variable speed limit;

[0180] [Step S5] Constructing a global speed evolution model for the driver under progressive lane-level variable speed limit control, segmenting the driver's vehicle global speed, and obtaining state transition probabilities between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control;

[0181] [Step S6] Construct a prediction model for the driver's global speed evolution under lane-level variable speed limit control. Based on the driver's global speed distribution characteristics under progressive lane-level variable speed limit control, predict their speed evolution trend, thereby predicting their driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

[0182] In summary, the classification and prediction method of the driver's global speed evolution pattern under progressively variable speed limits proposed in the present invention can use the driver's speed evolution under progressively variable speed limits to predict the driver's global speed, and provide a customized and personalized driving behavior warning solution. It does not rely on vehicle-side equipment, has low cost, and is replicable and robust.

[0183] The method of the present invention utilizes a continuous vehicle trajectory acquisition device to collect and analyze the time series data of driving behavior under progressive lane-level variable speed limits, and utilizes the shape-based clustering method of K-Shape to realize the prediction of the driver's speed evolution under progressive variable speed limit conditions. This solves the problems that continuous lane-level speed limit scenarios are ever-changing, the combination methods are endless, resulting in unclear mutual influence mechanism between lane speed limit measures and driving behavior, making it impossible to predict the speed distribution characteristics of drivers under continuous speed limit conditions, and traditional traffic variable speed limit control is unable to perform targeted speed limit according to the driver's driving behavior characteristics.

[0184] The present invention utilizes a graph neural network model to discretize time series fluctuations into different subsequence fluctuations, thereby achieving accurate quantification of the global speed evolution law and state transition probability of individual drivers under continuous speed limit conditions, accurately extracting the driver's abnormal speed state, and finely describing the driver's speed state evolution under continuous speed limit. This provides a theoretical basis and analysis method for refined variable speed limit control, and can provide personalized speed limit guidance strategies based on driver behavior characteristics. It has the characteristics of low cost, replicability and strong robustness.

[0185] Based on the classification of the driver's global speed evolution pattern, the Markov transition field and graph neural network combined with CNN-LSTM are used to further construct the MTK-GNN-CNN-LSTM driver global speed evolution prediction model architecture. Compared with the conventional CNN-LSTM model, the global speed evolution pattern label obtained by the Markov transition field-graph neural network community partitioning algorithm is added to the CNN model input. The subsequence dependency and conversion probability of the time series can be input into the input layer to improve the accuracy and stability of the global speed prediction.

[0186] It should be noted that the present application can be implemented in software and / or a combination of software and hardware; for example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium; for example, a RAM memory, a magnetic or optical drive, or a floppy disk and the like. In addition, some steps or functions of the present application can be implemented in hardware; for example, as a circuit that cooperates with a processor to perform various steps or functions.

[0187] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The description and application of the present invention here are illustrative and are not intended to limit the scope of the present invention to the above-described embodiments. The effects or advantages involved in the embodiments may not be embodied in the embodiments due to interference from various factors, and the description of the effects or advantages is not used to limit the embodiments. Variations and changes to the embodiments disclosed here are possible, and the replacement of the embodiments and various equivalent components are well known to those of ordinary skill in the art. It should be clear to those skilled in the art that the present invention can be implemented in other forms, structures, arrangements, proportions, and with other components, materials, and parts without departing from the spirit or essential characteristics of the present invention. Other variations and changes can be made to the embodiments disclosed here without departing from the scope and spirit of the present invention.

Claims

1. A method for classifying and predicting the global speed evolution pattern of a driver under a progressively variable speed limit, characterized by: The pattern classification and prediction method includes: Step S1: deploying a series of lane-level variable speed limit information release devices at set intervals on a free-flow highway section; Step S2: deploying vehicle trajectory data acquisition devices at set intervals on the road area to collect and analyze vehicle time-series trajectory data; Step S3: According to the preset control strategy, the variable information system is used to issue corresponding lane-level speed limit control instructions, adjust the speed limit value of the lane-level variable speed limit plate, and export the vehicle trajectory data and lane-level speed limit information release data in real time; Step S4: clustering the driver's global speed trajectory pattern, classifying the driver's vehicle trajectory according to different time series distribution characteristics, thereby classifying the driver under the progressive variable speed limit; Step S5: constructing a global speed evolution model for the driver under progressive lane-level variable speed limit control, segmenting the global speed of the driver and vehicle, and obtaining state transition probabilities between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control; Step S6: Construct a prediction model for the global speed evolution of the driver under lane-level variable speed limit control. Based on the global speed distribution characteristics of the driver under progressive lane-level variable speed limit control, predict the speed evolution trend, thereby predicting the driver's driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

2. The method for classifying and predicting a driver's global speed evolution pattern under a progressively variable speed limit according to claim 1 is characterized by: In step S1, lane-level variable speed limit information release equipment is deployed on the expressway section; In step S2, radar and camera vehicle trajectory data acquisition devices are deployed at set intervals on the road area to obtain continuous and uninterrupted sub-meter-level time-series vehicle trajectories with a speed acquisition accuracy of no less than 1 km / h and an acquisition frequency of 10 Hz, providing a data basis for the collection and analysis of the driver's real-time time-series driving behavior characteristics; In step S3, the vehicle time series trajectory data and lane-level speed limit information release data are all collected in real time and use UTC time, and the location of data release uses a unified stake coordinate; In step S6, based on the classification of the driver's global speed evolution pattern, the driver's global speed evolution pattern architecture is constructed based on the graph neural network, and the graph neural network is combined with CNN-LSTM to further construct the driver's global speed evolution prediction model, that is, the MTK-GNN-CNN-LSTM global speed evolution prediction model, to predict the driver's speed evolution trend, thereby predicting the driver's driving behavior, and actively predicting the occurrence of abnormal driving behavior of the driver under progressive lane-level variable speed limits.

3. The method for classifying and predicting a driver's global speed evolution pattern under a progressively variable speed limit according to claim 1 is characterized by: In step S4, the K-Shape clustering algorithm is used to eliminate the amplitude effect of the time series through normalization, achieve local alignment by shifting the time series, and introduce the fast Fourier transform algorithm into the similarity calculation to reduce the computational cost of high-dimensional time series. The K-Shape algorithm combines the concepts of DTW distance and shape features and can effectively capture the intrinsic structure of the time series. Step S4 includes the following steps: Step S41: Unify the length of vehicle time series trajectory data and the distribution of acquisition point coordinates. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time series data. To enable vehicle position-based matching during the clustering of different time series, each vehicle's speed time series data is processed into time series data of equal length using longitudinal position as the "time series" and interpolated at 1m intervals. Step S42, initializing cluster centers: randomly selecting K time series from the data set as initial cluster centers; these series will become the representative shapes of the clusters; Set the number of clusters to 3; Step S43, shape distance measurement: using DTW or other shape distance measurement to calculate the distance between each time series and each cluster center; Step S44, assigning data points: assigning each time series to the cluster with the closest cluster center; in this way, each series will be classified into the cluster with the most similar shape to it; Step S45, updating the cluster centers: using the average shape of the time series assigned to each cluster to update the cluster centers; in this way, the cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster; Step S46, repeat iteration: Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached; eventually, the obtained cluster will contain all time series in the data set, and each cluster represents a group of sequences with similar shapes.

4. The method for classifying and predicting a driver's global speed evolution pattern under a progressively variable speed limit according to claim 1 is characterized by: In step S5, a global speed evolution model of the driver under progressive lane-level variable speed limit control is constructed, and the global speed of the driver and vehicle is segmented to obtain the state transition probability between different trajectory segments, thereby effectively judging the driver's abnormal driving behavior and actively sensing the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control. Step S5 includes the following steps: Step S51: performing data discretization and quantile discretization processing on the vehicle speed time series to obtain vehicle speed discretization partitions; Step S52: Create a Markov transition matrix for the partitioned time series data to obtain the matrix M1 Step S53: Normalize the values ​​and calculate the transition probability matrix M′1: Step S54, calculating the Markov transition field; Step S55: perform graph neural network encoding and mapping on the time-series segmented subsequences processed by the Markov transition field, and further construct a graph neural network to describe different speed fluctuation states and the relationship between them; Step S56: Use the module partitioning algorithm in the tsia graph neural network analysis tool to perform community partitioning on the obtained graph neural network; the driver's global speed state is divided into different state intervals, and the edges between the neural vertices represent the conversion probability between different speed state intervals; the process of the driver making decisions in the face of speed limits is divided into several state intervals as needed, so as to more finely describe the driver's global speed evolution process.

5. A classification and prediction system for the global speed evolution pattern of a driver under a progressively variable speed limit, characterized by: The pattern classification and prediction system includes: A number of information dissemination devices are arranged on a free-flow highway section, and are arranged sequentially according to a set spacing rule; Several vehicle trajectory data collection devices are arranged in sequence on the road area according to a set spacing rule to realize the collection and analysis of vehicle time series trajectory data; The speed limit adjustment module is used to use the variable information system to issue corresponding lane-level speed limit control instructions according to the preset control strategy, adjust the speed limit value of the lane-level variable speed limit plate, and export vehicle trajectory data and lane-level speed limit information release data in real time; A classification module is used to cluster the driver's global speed trajectory pattern and classify the driver's vehicle trajectory according to different temporal distribution characteristics, thereby classifying the driver under progressive variable speed limit; The speed evolution model construction module is used to construct a global speed evolution model for the driver under progressive lane-level variable speed limit control. This module segments the driver's vehicle's global speed and obtains the state transition probability between different trajectory segments, thereby effectively judging abnormal driving behavior and proactively detecting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control. The speed evolution prediction model construction module is used to construct a driver's global speed evolution prediction model under lane-level variable speed limit control. Based on the driver's global speed distribution characteristics under progressive lane-level variable speed limit control, its speed evolution trend is predicted, thereby predicting its driving behavior and proactively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limit control.

6. The system for classifying and predicting driver's global speed evolution patterns under progressively variable speed limits according to claim 5 is characterized by: The information release device is a lane-level variable speed limit information release device; The vehicle trajectory data acquisition device includes radar and / or camera to obtain continuous and uninterrupted sub-meter-level time-series vehicle trajectory, with a speed acquisition accuracy of no less than 1km / h and an acquisition frequency of 10Hz, providing a data basis for the collection and analysis of the driver's real-time time-series driving behavior characteristics; The vehicle time series trajectory data and lane-level speed limit information release data are all collected in real time and use UTC time, and the locations where the data is released use unified stake coordinates; Based on the classification of the driver's global speed evolution pattern, the speed evolution prediction model construction module is based on the driver's global speed evolution pattern architecture constructed by the graph neural network. The graph neural network is combined with CNN-LSTM to further construct the driver's global speed evolution prediction model, namely the MTK-GNN-CNN-LSTM global speed evolution prediction model, to predict the driver's speed evolution trend, thereby predicting the driver's driving behavior and actively predicting the occurrence of abnormal driving behavior under progressive lane-level variable speed limits.

7. The system for classifying and predicting driver's global speed evolution patterns under progressively variable speed limits according to claim 5 is characterized by: The classification module uses the K-Shape clustering algorithm to eliminate the amplitude effect of the time series through normalization, achieve local alignment by shifting the time series, and introduce the Fast Fourier Transform algorithm in calculating similarity to reduce the computational cost of high-dimensional time series. The K-Shape algorithm combines the concepts of DTW distance and shape features to effectively capture the intrinsic structure of the time series. The classification process of the classification module includes the following steps: Step S41: Unify the length of vehicle time series trajectory data and the distribution of acquisition point coordinates. Since the radar acquisition frequency is fixed, vehicles of different speeds take different amounts of time to pass through the same road section, resulting in different lengths of speed time series data. To enable vehicle position-based matching during the clustering of different time series, each vehicle's speed time series data is processed into time series data of equal length using longitudinal position as the "time series" and interpolated at 1m intervals. Step S42, initializing cluster centers: randomly selecting K time series from the data set as initial cluster centers; these series will become the representative shapes of the clusters; Set the number of clusters to 3; Step S43, shape distance measurement: using DTW or other shape distance measurement to calculate the distance between each time series and each cluster center; Step S44, assigning data points: assigning each time series to the cluster with the closest cluster center; in this way, each series will be classified into the cluster with the most similar shape to it; Step S45, updating the cluster centers: using the average shape of the time series assigned to each cluster to update the cluster centers; in this way, the cluster centers will be continuously adjusted to better represent the shape characteristics of the cluster; Step S46, repeat iteration: Repeat the above steps until the cluster center no longer changes and the predetermined number of iterations is reached; eventually, the obtained cluster will contain all time series in the data set, and each cluster represents a group of sequences with similar shapes.

8. The system for classifying and predicting driver's global speed evolution patterns under progressively variable speed limits according to claim 5 is characterized by: The model building process of the velocity evolution model building module includes the following steps: Step S51: performing data discretization and quantile discretization processing on the vehicle speed time series to obtain vehicle speed discretization partitions; Step S52: Create a Markov transition matrix for the partitioned time series data to obtain matrix M1: Step S53: Normalize the values ​​and calculate the transition probability matrix M′1: Step S54, calculating the Markov transition field; Step S55: perform graph neural network encoding and mapping on the time-series segmented subsequences processed by the Markov transition field, and further construct a graph neural network to describe different speed fluctuation states and the relationship between them; Step S56: Use the module partitioning algorithm in the tsia graph neural network analysis tool to perform community partitioning on the obtained graph neural network; the driver's global speed state is divided into different state intervals, and the edges between the neural vertices represent the conversion probability between different speed state intervals; the process of the driver making decisions in the face of speed limits is divided into several state intervals as needed, so as to more finely describe the driver's global speed evolution process.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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