A method, apparatus, and medium for trajectory profiling of highway travelers

Abstract

The embodiment of the application discloses a highway in-transit personnel trajectory portrait method. The method comprises the following steps: distinguishing in-transit users from non-in-transit users according to mobile phone signaling data of highway users; clustering trajectory points of each in-transit user by using a DBSCAN clustering algorithm; constructing a user traffic behavior characteristic portrait according to categories of trajectory points included by each user; clustering user trajectory similarity by using a K-means clustering algorithm to construct a user traffic behavior relationship portrait; and clustering traffic behavior risk indexes of each user by using a K-means clustering method to construct a user traffic behavior risk portrait. The application can accurately depict traffic behavior characteristics of highway in-transit personnel from multiple angles.

Description

Technical Field

[0001] The embodiments of the present invention relate to trajectory profiling research in the field of transportation, and in particular to a trajectory profiling method for people on the road on a highway. Background Art

[0002] As a new technology, trajectory profiling can provide accurate data support for traffic control by monitoring and analyzing traffic conditions, effectively alleviating traffic problems. However, current user trajectory profiling systems fail to fully reflect the behavioral characteristics of people traveling on highways, and the accuracy of the information provided needs to be improved. Summary of the Invention

[0003] The present invention proposes a trajectory profiling method for people on the road on a highway to solve the above technical problems.

[0004] In a first aspect, an embodiment of the present invention provides a method for profiling the trajectory of people on a highway, comprising:

[0005] Based on the mobile phone signaling data of highway users, distinguish between users on the way and those not on the way;

[0006] Using geometric matching method, the trajectory of users on the way is matched to the highway network;

[0007] The DBSCAN clustering algorithm is used to cluster the matched trajectory points. Different categories correspond to different traffic behaviors. Based on the trajectory point categories of each user, a user traffic behavior feature profile is constructed.

[0008] A trajectory similarity model is established based on the speed and temporal and spatial information of the trajectory. The similarity of the trajectories of different users after matching is calculated using the trajectory similarity model. The similarities are clustered using the K-means clustering algorithm. Different categories correspond to different user relationships, thereby constructing a profile of the user's traffic behavior relationship.

[0009] Based on the matched trajectory points, the traffic behavior risk index of each user is calculated; and the traffic behavior risk index of each user is clustered using the K-means clustering method, with different categories corresponding to different risk levels. The SVM model is trained based on the K-means classification results, and the trained model is used to identify the traffic behavior risk level, thereby constructing a user traffic behavior risk profile.

[0010] In a second aspect, an embodiment of the present invention further provides an electronic device, comprising:

[0011] one or more processors;

[0012] a memory for storing one or more programs,

[0013] When the one or more programs are executed by the one or more processors, the one or more processors implement the trajectory profiling method for people on the highway as described in any embodiment.

[0014] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for profiling trajectories of people on the highway as described in any embodiment.

[0015] In summary, an embodiment of the present invention provides a trajectory profiling method for people on the highway. Based on mobile phone signaling data, the trajectory profiling method constructs a trajectory profiling method for people on the highway from three perspectives: traffic behavior characteristics, traffic behavior relationships, and traffic behavior risks. This method provides comprehensive and accurate data support for real-time monitoring of road conditions, analysis of vehicle behavior, detection of abnormal trajectories, and automatic driving path planning and decision-making. Specifically, in the traffic behavior characteristic profiling method, the method uses the DBSCAN clustering algorithm to change clustering parameters, and gradually identifies the user's speeding, normal driving, slow driving, and stopping traffic behaviors, which can more accurately analyze user behavior. In the traffic behavior relationship profiling method, a trajectory similarity model based on time, location, and speed is proposed to calculate the trajectory similarity of different users from both dynamic and static perspectives, which can more accurately identify user relationships. In the traffic behavior risk profiling method, reasonable risk indicators can be used to more accurately assess the user's risk status, thereby improving the information richness and accuracy of the entire user trajectory profiling method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart of a method for profiling the trajectory of people on a highway provided by an example of the present invention;

[0018] Figure 2 The result graph of the trajectory points of a user after the first behavior type clustering in the user traffic behavior feature portrait provided by the example of the present invention;

[0019] Figure 3 for Figure 2 The result diagram of the user's trajectory points after the second behavior type clustering;

[0020] Figure 4 for Figure 2The result diagram of the user's trajectory points after the third behavior type clustering;

[0021] Figure 5 The result graph of the K-means clustering algorithm used in the risk profile of user traffic behavior provided by the example of the present invention;

[0022] Figure 6 A schematic diagram of the confusion matrix of the classification results of the SVM linear kernel function model for identifying user traffic behavior risks, obtained by training based on the K-means clustering results provided in an example of the present invention;

[0023] Figure 7 A schematic diagram of the confusion matrix of the classification results of the SVM Gaussian kernel function model for identifying user traffic behavior risks obtained by training the K-means clustering results provided in an example of the present invention;

[0024] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0026] Figure 1 This is a flow chart of a method for profiling the trajectory of people on the highway based on mobile phone signaling data provided by an embodiment of the present invention. Figure 1 The method is executed by an electronic device and specifically includes the following steps:

[0027] S110 : Differentiate between on-the-way users and off-the-way users based on mobile phone signaling data of highway users.

[0028] This embodiment uses the mobile phone signaling data of highway users as a data source. Mobile phone signaling data relies on the mobile communication network, and its coverage covers almost all cities and major regions, which can comprehensively record the user's movement trajectory. In contrast, traditional trajectory data (such as GPS data) may be limited by factors such as device penetration rate and signal stability, making it difficult to achieve the same coverage level. Mobile phone signaling data is a passively collected data that is automatically recorded by the base station during the communication process with the mobile terminal, so data collection can be carried out uninterruptedly around the clock. This continuous data collection method helps to capture the user's complete travel trajectory, avoiding the problems of large sampling intervals and data discontinuity that may exist in traditional trajectory data. Mobile phone signaling data not only contains the user's location information, but also contains multi-dimensional information such as time, user identification ID, and communication event type. This information provides a rich data source for the method of this application, which helps to deeply explore the travel behavior characteristics and social activity patterns of users. At the same time, the temporal and spatial resolution of mobile phone signaling data is high, and it can accurately determine the specific time and location of the user's communication with the base station, providing a more refined data analysis basis for this application.

[0029] In one specific embodiment, after acquiring mobile phone signaling data, this step first identifies and filters out redundant data during the continuous recording. Next, the user's azimuth angle from the previous location to the next location, as well as the user's speed from the previous location to the next location, are calculated. Based on the numerical ranges of the azimuth angle and speed, the data is determined to be abnormal. If abnormal, the data is deleted. Finally, the user's actual distance from the starting and ending points is calculated, and the average of all non-zero speeds is calculated. Based on the actual distance from the starting and ending points and the average of the non-zero speeds, the user's en route status is determined.

[0030] For example, if the longitude and latitude of adjacent recorded moments are the same, it is determined to be redundant data, and the intermediate records are deleted, retaining only the time records of the first and last consecutive occurrences of the same location point. If the difference in the moving azimuth angle is greater than 100°, it is determined to be a turning point. If the trajectory shows continuous turning back, it is determined to be ping-pong data and is eliminated. If the moving speed exceeds 180km / h, it is determined to be drift data and is eliminated. If the actual distance between the user's starting and ending points is greater than 5km, and the average non-zero speed is greater than 20km / h, the user is determined to be a user in transit, otherwise it is a user not in transit.

[0031] Among them, the azimuth angle α i The calculation formula is as follows:

[0032] α i =2arctan{sin(long i -lon i+1 )×cos(lat i ),cos(lat i)×sin(lat i+1 )-sin(lat i )×cos(lat i+1 )×cos(lon i -lon i+1 )} (1)

[0033] Moving azimuth Δα i The calculation formula is as follows:

[0034] Δα i =|α i+1 -α i | (2)

[0035] Actual distance between starting and ending points S max The calculation formula is as follows:

[0036]

[0037] Among them, lon i ,lat i are the latitude and longitude of the user's i-th location, lon1 and lat1 are the latitude and longitude of the user's starting point, lon N ,lat N is the latitude and longitude of the user's destination. i and α i+1 are the user's i-th and i+1-th positions, Δα i Indicated by α i and α i+1 The calculated movement azimuth.

[0038] S120: Use a geometric matching method to match the trajectory of the en route user to the highway network.

[0039] Since the trajectory points in the mobile phone signaling data may not completely overlap with the highway, this step matches the user trajectory in the mobile phone signaling data to the highway road network to obtain a more accurate user trajectory, which will be used as the processing object for subsequent operations.

[0040] In a specific implementation, first, the road network data can be downloaded from the OpenStreetMap official website, and the latitude and longitude of the road network points can be obtained through the arcGIS software layer selection, screening, and construction point operations in the editor. The road network points should be on the road network within the user's distribution range, and the distance of the road network points along the road network should not exceed 0.1km.

[0041] Then, the actual straight-line distance between each two adjacent road network points is calculated, and the westernmost road network point is used as the origin of the route. The cumulative distance from each road network point to the origin of the route is calculated, and the cumulative distance is used as the mileage of the road network point; and the road network points whose longitude differs from the trajectory point by no more than 0.01 are selected, and the actual straight-line distance between the two is calculated.

[0042] Finally, the road network point with the shortest actual straight-line distance to the trajectory point is selected as the matching point.

[0043] Match all the trajectory points of the same user, interpolate the matching trajectory with the road network points according to the mileage of the matching points, and restore the user trajectory to obtain the user trajectory matched on the road network.

[0044] S130. Cluster the matched trajectory points using the DBSCAN clustering algorithm, with different categories corresponding to different traffic behaviors; and construct a user traffic behavior feature profile based on the trajectory point categories of each user.

[0045] This step analyzes the motion characteristics of the trajectory points by clustering, and constructs a characteristic profile of the user's traffic behavior based on the motion characteristics of the trajectory points.

[0046] In a specific implementation, first, the road network mileage positions of the matching points can be supplemented with a 1-minute time interval using a linear interpolation method to unify the time density.

[0047] Then, the DBSCAN clustering algorithm is used to screen out points from the supplemented matching points one by one in chronological order, whose temporal difference does not exceed 60 seconds and whose spatial difference does not exceed 1 km. If the number of points meeting the above two conditions is greater than or equal to 2, they are classified into a cluster, called slow points, and the other points are noise points, called moving points.

[0048] Then, the DBSCAN clustering algorithm is used again to screen out the slow points obtained from the first clustering one by one in chronological order, with a time difference of no more than 60 seconds and a spatial difference of no more than 0.17 km. If the number of points that meet the above two conditions is greater than or equal to 10, they are grouped into a cluster, called a stop point, and the other points are noise points, called slow points.

[0049] Finally, the DBSCAN clustering algorithm is used again to screen out points whose time difference is no more than 60 seconds and whose spatial difference is no more than 2 km from the moving points obtained in the first clustering in chronological order. If the number of points that meet the above two conditions is greater than or equal to 2, they are classified into a cluster, called normal points, and the other points are noise points, called speeding points.

[0050] After clustering is completed, the traffic behavior indicators of each user are calculated based on the temporal and spatial distribution of the stop points, slow-moving points, normal points and speeding points covered by each user, including travel time, number of trips, speeding time, number of speeding times, slow-moving time, number of slow-moving times, travel distance, etc., and a frequency distribution histogram is drawn. The indicator calculation results and the histogram are used together as a traffic behavior characteristic portrait of the user group. Figure 2 、 Figure 3 and Figure 4 The distribution diagram of each trajectory point type after the first, second, and third behavior type clustering of a certain user is shown. It can be seen that this method can vividly reflect the distribution of user behavior types.

[0051] S140: Establish a trajectory similarity model based on the speed and temporal and spatial information of the trajectory.

[0052] This step provides a new trajectory similarity calculation method for more accurately calculating the trajectory similarity of users.

[0053] In one specific implementation, first, the speed difference and the consistency of the speed direction can be used to evaluate the similarity of the dynamic characteristics of traffic behavior, namely, speed similarity. At the same time, the difference in road network mileage of matching points in each time interval is used to evaluate the similarity of the static characteristics of traffic behavior, namely, spatial similarity. Finally, the above speed similarity and spatial similarity are combined to establish a trajectory similarity model.

[0054] Optionally, the trajectory similarity model calculation formula is as follows:

[0055] DS j1,j2,i =|P j1,i -P j2,i | (4)

[0056]

[0057] K j1,j2 =KS j1,j2 ×KV j1,j2 (10)

[0058] Among them, DS j1,j2,i KS is the mileage difference between the matching points of user j1 and user j2 during the i-th time unit, j1,j2,i KS is the spatial similarity between user j1 and user j2 during the i-th time unit, j1,j2 is the spatial similarity between user j1 and user j2 during the study period, DV j1,j2,i KV is the speed difference between user j1 and user j2 during the i-th time unit, j1,j2,i is the speed similarity between user j1 and user j2 during the i-th time unit, KV j1,j2is the speed similarity between user j1 and user j2 during the study period, K j1,j2 is the comprehensive similarity between user j1 and user j2 during the study period.

[0059] S150. Calculate the similarities of the matched trajectories of different users using the trajectory similarity model; and cluster each similarity using a K-means clustering algorithm, where different categories correspond to different user relationships, thereby constructing a user traffic behavior relationship portrait.

[0060] This step analyzes the relationship between users by clustering the trajectory similarity and constructs a relational portrait of user traffic behavior based on the user relationship.

[0061] In a specific implementation, first, a time-position sequence and a time-speed sequence can be created for each user in units of 5 minutes; then, the spatial difference and speed difference between all users in each time unit are obtained through matrix operations, and the spatial similarity and speed similarity are calculated based on the differences; finally, the comprehensive similarity is obtained based on the spatial similarity and speed similarity.

[0062] Then, using non-zero spatial similarity as input, the initial cluster center point is randomly selected, and the K-means clustering algorithm is used to divide these user relationships into four categories. The classification result when the silhouette coefficient is optimal is taken as the final classification result.

[0063] It is important to note that when calculating similarity, to eliminate invalid similarity values ​​due to inconsistent movement directions, the sign function Sgn() is used to identify the movement direction. Furthermore, to avoid invalid similarity values ​​due to long periods of dwell time, the position and velocity of dwell points are assigned a value of 9999 when establishing the time-position and time-velocity sequences, and these values ​​are used to set filtering conditions when calculating similarity.

[0064] S160. Calculate the traffic behavior risk index of each user based on the matched trajectory points; cluster the traffic behavior risk index of each user using the K-means clustering method, with different categories corresponding to different risk levels; train the SVM model based on the K-means classification results, and use the trained model to identify the traffic behavior risk level, thereby constructing a user traffic behavior risk profile.

[0065] This step constructs a traffic behavior risk index and analyzes the risk level of user behavior through clustering, thereby constructing a characteristic profile of user traffic behavior.

[0066] In one specific embodiment, first, a risk indicator for user traffic behavior is constructed. Optionally, speed stability and acceleration stability mean that the vehicle can maintain a relatively constant speed, avoiding sudden acceleration or deceleration, reducing the driver's driving burden and fatigue, and significantly reducing the risk of traffic accidents such as rear-end collisions and rollovers. Therefore, the user traffic behavior risk indicator should include speed stability and acceleration stability. In addition, this embodiment primarily evaluates the risk of user traffic behavior during movement, so values ​​with zero speed should be eliminated. In terms of speed stability, the evaluation indicators include the average speed, speed standard deviation, speed overspeed time percentage, and the difference between the 85th and 15th percentile speeds. In terms of acceleration stability, the evaluation indicators include the average positive acceleration, the average negative acceleration, the positive acceleration standard deviation, the negative acceleration standard deviation, the acceleration standard deviation, the percentage of sudden acceleration time, and the percentage of sudden deceleration time.

[0067] Then, each risk index value of each user is calculated, and the risk index data is dimensionlessly processed using the Z-Score standardization method. The dimensionlessly processed risk index data is used as input, and the K-means clustering algorithm is used to classify the risks of people in transit into four risk levels. Figure 5 The clustering result of one risk level is shown as an example.

[0068] After clustering, the K-means classification results can be used to train an SVM model to identify traffic behavior risk levels. Alternatively, the K-means clustering dataset can be divided into a training set and a test set. Next, an appropriate kernel function and penalty coefficient are selected to construct the SVM model. The model is then trained using the training set, finding the optimal hyperplane by maximizing the margin. Finally, the trained model is used to predict the test set and analyze its accuracy. Figure 6 and Figure 7 The SVM models of the two kernel functions and the confusion matrices of the classification results of the two models are shown respectively.

[0069] Finally, the above risk levels, or the above risk level set SVM model, are used as the risk profile of the user's traffic behavior.

[0070] In summary, this embodiment provides a method for profiling the trajectories of people on highways. Based on mobile phone signaling data, this method constructs a trajectory profile of people on highways from three perspectives: traffic behavior characteristics, traffic behavior relationships, and traffic behavior risks. This provides comprehensive and accurate data support for real-time monitoring of road conditions, analysis of vehicle behavior, detection of abnormal trajectories, and autonomous driving path planning and decision-making. Specifically, in the traffic behavior characteristic profile, this method utilizes the DBSCAN clustering algorithm to change clustering parameters, gradually identifying user traffic behaviors such as speeding, normal driving, slowing down, and stopping, enabling more accurate analysis of user behavior. In the traffic behavior relationship profile, a trajectory similarity model based on time, location, and speed is proposed to calculate the trajectory similarity of different users from both dynamic and static perspectives, enabling more accurate identification of user relationships. In the traffic behavior risk profile, reasonable risk indicators are used to more accurately assess the user's risk status, thereby improving the information richness and accuracy of the entire user trajectory profile.

[0071] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 8 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 8 In the embodiment, a processor 60 is used as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 8 The bus connection is taken as an example.

[0072] Memory 61, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the highway passenger trajectory profiling method in the embodiments of the present invention. Processor 60 executes the software programs, instructions, and modules stored in memory 61 to execute various functional applications and data processing of the device, thereby implementing the aforementioned highway passenger trajectory profiling method.

[0073] The memory 61 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal. Furthermore, the memory 61 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the memory 61 may further include memory remotely located relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0074] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0075] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for profiling trajectories of people on the highway of any embodiment.

[0076] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0077] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0078] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0079] Computer program code for performing the operations of the present invention can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A trajectory profiling method for people on the highway, characterized by: include: Based on the mobile phone signaling data of highway users, distinguish between users on the way and those not on the way; Using geometric matching method, the trajectory of users on the way is matched to the highway network; Use linear interpolation to unify the time intervals of all matching points; use the matching point data after the unified time interval as input, and use the DBSCAN clustering algorithm to distinguish slow points from moving points; use the slow point data as input, and again use the DBSCAN clustering algorithm to distinguish stop points from slow-moving points; use the moving point data as input, and again use the DBSCAN clustering algorithm to distinguish normal points from speeding points; determine the temporal and spatial distribution of each user's stop points, slow-moving points, normal points, and speeding points, and calculate each user's traffic behavior indicators to construct a user traffic behavior feature profile; The similarity of dynamic characteristics of traffic behavior is calculated by using the speed difference and the consistency of speed direction; The similarity of static features of traffic behavior is calculated using the difference in road network mileage between matching points within each time interval. A trajectory similarity model is established by combining the similarity of dynamic features and the similarity of static features. The similarity between different users is classified into four categories using the K-means clustering algorithm. The classification result with the optimal silhouette coefficient is selected to represent the relationships between each type of user, thereby constructing a traffic behavior relationship profile. Specifically, the trajectory similarity model calculation formula is as follows: （4） （5） （6） （7） （8） （9） （10） in, is the mileage difference between the matching points of user j1 and user j2 during the i-th time unit, is the spatial similarity between user j1 and user j2 during the i-th time unit, is the spatial similarity between user j1 and user j2 during the study period, is the speed difference between user j1 and user j2 during the i-th time unit, is the speed similarity between user j1 and user j2 during the i-th time unit, is the speed similarity between user j1 and user j2 during the study period, is the comprehensive similarity between user j1 and user j2 during the study period; Based on the matched trajectory points, the traffic behavior risk index of each user is calculated; and the traffic behavior risk index of each user is clustered using the K-means clustering method, with different categories corresponding to different risk levels. The SVM model is trained based on the K-means classification results, and the trained model is used to identify the traffic behavior risk level, thereby constructing a user traffic behavior risk profile.

2. The method according to claim 1, characterized in that The method of distinguishing between on-the-go users and off-the-go users based on the mobile phone signaling data of highway users specifically includes: From the mobile phone signaling data of highway users, redundant data is filtered based on user location and recording time; Calculate the user movement azimuth between adjacent trajectory points of the same user, and eliminate ping-pong data based on the user movement azimuth and the change in movement azimuth; Calculate the movement speed of adjacent trajectory points of the same user and eliminate drift data based on the user's movement speed; The moving distance and non-zero speed average of each user are calculated, and users in transit and out of transit are distinguished based on the moving distance and non-zero speed average.

3. The method according to claim 1, characterized in that The geometric matching method is used to match the trajectory of the en route user to the highway network, specifically including: Increase the density of road network points through arcGIS and extract the longitude and latitude data of road network points; Calculate the actual cumulative distance from each road network point to a certain end of the road network as the road network point mileage; For each user's trajectory point, calculate the actual distance from each road network point to the current trajectory point, and select the road network point with the shortest distance as the matching point of the current trajectory point; According to the road network point mileage of all matching points of the same user, the matching trajectory is interpolated with the road network points to obtain the matched user trajectory.

4. The method according to claim 1, wherein The traffic behavior risk indicators of each user include: Speed ​​stability evaluation indicators, including speed average, speed standard deviation, speeding time ratio, and the speed difference between the 85th and 15th percentiles; Acceleration stability evaluation indicators include the average positive acceleration, the average negative acceleration, the standard deviation of positive acceleration, the standard deviation of negative acceleration, the standard deviation of acceleration, the proportion of rapid acceleration time, and the proportion of rapid deceleration time.

5. The method according to claim 1, wherein The K-means clustering method is used to cluster the traffic behavior risk indicators of each user, with different categories corresponding to different risk levels. Specifically, the method includes: performing dimensionless processing on the risk indicator data of each user; using the dimensionless risk indicator data as input, and clustering the in-transit users into four risk levels using the K-means clustering algorithm; Correspondingly, the SVM model is trained according to the K-means grading results, and the trained model is used to identify the risk level of traffic behavior, specifically including: according to the K-means grading results, statistics are collected on the distribution of traffic behavior risks of people on the way; the K-means grading results are used as label input, and the risk indicator data are used as feature input to train the SVM model to identify the risk level of people on the way.

6. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the trajectory profiling method for people on the highway as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the method for profiling the trajectory of people on the highway as described in any one of claims 1-5 is implemented.

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