Method, device, equipment and computer storage medium for determining road trajectory

Abstract

The application discloses a road track determination method, device and equipment and a computer storage medium, and relates to the technical field of intelligent transportation. The method obtains user positioning point coordinates, positioning time, preset road segment coordinates and topological relations; determines the matching probability of each positioning point and road segment according to the relationship among the positioning point coordinates, the road segment coordinates, the road segment topological relations and the matching probability; obtains a predicted track according to the relationship among the matching probability of the positioning point and the road segment, the road segment topological relations and a conditional probability; inputs the road segment coordinates of the predicted track and the user positioning point coordinates into a track correction model as input parameters of a characteristic function, and determines a target track according to the relationship among the pre-trained characteristic weight in the track correction model, the input parameters of the characteristic function and a target probability. The embodiment of the application can realize high-precision prediction of the user track and improve the accuracy of the generated road track.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent transportation, and particularly relates to a road trajectory determination method, device, equipment and computer storage medium. BACKGROUND

[0002] In the technical field of intelligent transportation, an operator can obtain a measurement report of a user equipment containing user positioning data through a base station, generate a trajectory using the user positioning data obtained by the operator network, and analyze the trajectory, which has become an important means of user behavior insight.

[0003] The existing trajectory generation method often matches the positioning data to the nearest road segment through the user positioning data when generating the trajectory, and splices the road segments into a continuous trajectory according to the time sequence, but when the road network topology is relatively complex, the accuracy of the generated road trajectory is low. SUMMARY

[0004] The embodiments of the present application provide a road trajectory determination method, device, equipment and computer storage medium to solve the problem of low accuracy of the road trajectory generated by the prior art.

[0005] In a first aspect, the embodiments of the present application provide a road trajectory determination method, which comprises:

[0006] Obtaining user positioning data and road network road data, the user positioning data comprising coordinates of a plurality of user positioning points and positioning times of the user positioning points, and the road network road data comprising coordinates of a plurality of preset road segments and preset road segment topological relations;

[0007] Determining a target matching probability of each user positioning point and road segment based on the coordinates of the user positioning points, the coordinates of the road segments and the preset road segment topological relations according to a relationship of the coordinates of the positioning points, the coordinates of the road segments, the road segment topological relations and the matching probability;

[0008] Based on the target matching probability and the preset road segment topological relations, determining a predicted conditional probability that a positioning point matches to each road segment according to a relationship of the matching probability of the positioning points and the road segments, the road segment topological relations and the conditional probability in a case that the positioning point at a first positioning time matches to each road segment in a sequence of the positioning times of the positioning points, obtaining a predicted trajectory composed of a road segment with the maximum predicted conditional probability, the first positioning time being a positioning time of any user positioning point, and the second positioning time being a next positioning time of the first positioning time;

[0009] The coordinates of the road segments of the predicted trajectory and the coordinates of the user positioning points are input into a trajectory correction model as input parameters of a feature function, and according to a relationship between pre-trained feature weights in the trajectory correction model, the input parameters of the feature function and target probabilities, target probabilities of the positioning points corresponding to the second positioning time matching to each target road segment under the condition that the positioning points corresponding to the first positioning time match to each road segment are sequentially determined according to a positioning time sequence of the positioning points, a target trajectory composed of road segments with maximum cumulative target probabilities is obtained, and the target road segments are the road segments in the predicted trajectory.

[0010] In a second aspect, an embodiment of the present application provides a device for determining a road trajectory, which comprises:

[0011] The acquisition module is configured to acquire user positioning data and road network road data, the user positioning data comprising coordinates of a plurality of user positioning points and positioning times of the user positioning points, and the road network road data comprising coordinates of a plurality of preset road segments and a preset road segment topological relationship;

[0012] The determination module is configured to determine, based on the coordinates of the user positioning points, the coordinates of the preset road segments and the preset road segment topological relationship, a target matching probability of each user positioning point and a road segment according to a relationship among the coordinates of the positioning points, the coordinates of the road segments, the road segment topological relationship and the matching probability;

[0013] The prediction module is configured to determine, based on the target matching probability and the preset road segment topological relationship, a predicted conditional probability of the positioning points corresponding to the second positioning time matching to each road segment under the condition that the positioning points corresponding to the first positioning time match to each road segment according to a relationship among the matching probability of the positioning points and the road segments, the road segment topological relationship and the conditional probability, sequentially determine the predicted conditional probability of the positioning points corresponding to the second positioning time matching to each road segment under the condition that the positioning points corresponding to the first positioning time match to each road segment according to a positioning time sequence of the positioning points, obtain a predicted trajectory composed of road segments with maximum predicted conditional probabilities, and the first positioning time is a positioning time of any user positioning point, and the second positioning time is a next positioning time of the first positioning time.

[0014] The correction module is configured to input the coordinates of the road segments of the predicted trajectory and the coordinates of the user positioning points into a trajectory correction model as input parameters of a feature function, and according to a relationship between pre-trained feature weights in the trajectory correction model, the input parameters of the feature function and target probabilities, target probabilities of the positioning points corresponding to the second positioning time matching to each target road segment under the condition that the positioning points corresponding to the first positioning time match to each target road segment are sequentially determined according to a positioning time sequence of the positioning points, a target trajectory composed of road segments with maximum cumulative target probabilities is obtained, and the target road segments are the road segments in the predicted trajectory.

[0015] In a third aspect, an embodiment of the present application provides a terminal device comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for determining a road trajectory as in the first aspect is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the method for determining a road trajectory according to the first aspect is implemented.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for determining a road trajectory as described in the first aspect.

[0018] The embodiments of the present application provide a method, device, equipment and computer storage medium for determining a road trajectory. The method first obtains the coordinates of multiple user positioning points, the positioning time of the user positioning point, the coordinates of multiple preset road segments and the preset road segment topological relationship. The target matching probability of each user positioning point and the road segment is determined based on the relationship between the coordinates of the positioning point, the coordinates of the road segment, the topological relationship of the road segment and the matching probability; the degree of matching between each positioning point and the road segment can be quantified, providing a confidence estimate of the road where the positioning point is located. Based on the matching probability between the positioning point and the road segment, the relationship between the topological relationship of the road segment and the conditional probability, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment is determined when the positioning point corresponding to the first positioning time matches each road segment, and the predicted trajectory consisting of the road segment with the largest predicted conditional probability is obtained; combined with the topological relationship of the road segment, the road segment with the largest predicted conditional probability is selected to form the predicted trajectory, which effectively improves the accuracy of the prediction. The coordinates of the road segments of the predicted trajectory and the coordinates of the user's positioning points are input into the trajectory correction model as input parameters of the feature function. According to the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function, and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment is determined in sequence according to the positioning time of the positioning point. The target trajectory consisting of the road segment with the largest cumulative target probability is obtained, and the target road segment is the road segment in the predicted trajectory. Through the pre-trained feature weights, the model can assign corresponding feature weights to different features and perform weighted summation, thereby adjusting the importance of different features, reducing the cumulative error caused by initial positioning error or prediction error, and improving prediction accuracy. The embodiment of the present application predicts the matching of the positioning point corresponding to the next positioning time based on the matching of the positioning point corresponding to the current positioning time, which can achieve high-precision prediction of the user trajectory and improve the accuracy of the generated road trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 is a flowchart of a method for determining a road trajectory provided in an embodiment of the present application;

[0021] Figure 2 This is a flowchart of an implementation method of data preprocessing provided in an embodiment of the present application;

[0022] Figure 3 This is a flowchart of an implementation method for determining target matching probability provided by an embodiment of the present application;

[0023] Figure 4 This is a flowchart of an implementation method for determining a predicted trajectory provided by an embodiment of the present application;

[0024] Figure 5 This is a flowchart of an implementation method for establishing a linear reference system event sequence provided by an embodiment of the present application;

[0025] Figure 6 is a schematic diagram of a linear referencing system event sequence provided by an embodiment of the present application;

[0026] Figure 7 is a schematic structural diagram of a device for determining a road trajectory provided in an embodiment of the present application;

[0027] Figure 8 It is a structural diagram of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0029] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0030] In the existing technology, operators can obtain the measurement report (MR) data of user equipment through base stations, which contains the user's location information. First, the roads are rasterized and interpolated, and the road, building and other attributes of the grid where the MR point is located are identified. Then, the positioning points during the commuting period are filtered out according to the user's residence and workplace, and the frequently visited grids are extracted. Finally, the missing sections are filled in along the road topology to obtain a complete commuting trajectory. However, the generated motion trajectory fragments are discrete, which makes it difficult to meet the requirements of full-time, continuous and complete trajectory generation. In addition, when generating the trajectory, the positioning data is often matched to the nearest road segment through the user's positioning data, and the road segments are spliced ​​into a continuous trajectory according to the time sequence. However, when the road network topology is more complex, it is difficult to effectively handle the problem of complex road network topology, and the generated road trajectory has low accuracy.

[0031] In order to solve the existing technology, the embodiments of the present application provide a method, device, equipment and computer storage medium for determining a road trajectory. The method first obtains the coordinates of multiple user positioning points, the positioning time of the user positioning point, the coordinates of multiple preset road segments and the preset road segment topological relationship. The target matching probability of each user positioning point and the road segment is determined based on the relationship between the coordinates of the positioning point, the coordinates of the road segment, the topological relationship of the road segment and the matching probability; the degree of matching between each positioning point and the road segment can be quantified, providing a confidence estimate of the road where the positioning point is located. Based on the matching probability between the positioning point and the road segment, the relationship between the topological relationship of the road segment and the conditional probability, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment is determined when the positioning point corresponding to the first positioning time matches each road segment, and the predicted trajectory consisting of the road segment with the largest predicted conditional probability is obtained; combined with the topological relationship of the road segment, the road segment with the largest predicted conditional probability is selected to form the predicted trajectory, which effectively improves the accuracy of the prediction. The coordinates of the road segments of the predicted trajectory and the coordinates of the user's positioning points are input into the trajectory correction model as input parameters of the feature function. According to the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function, and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment is determined in sequence according to the positioning time of the positioning point. The target trajectory consisting of the road segment with the largest cumulative target probability is obtained, and the target road segment is the road segment in the predicted trajectory. Through the pre-trained feature weights, the model can assign corresponding feature weights to different features and perform weighted summation, thereby adjusting the importance of different features, reducing the cumulative error caused by initial positioning error or prediction error, and improving prediction accuracy. The embodiment of the present application predicts the matching of the positioning point corresponding to the next positioning time based on the matching of the positioning point corresponding to the current positioning time, which can achieve high-precision prediction of the user trajectory and improve the accuracy of the generated road trajectory.

[0032] The following first introduces the method for determining the road trajectory provided in the embodiment of the present application.

[0033] Figure 1 FIG. 1 is a flow chart showing a method for determining a road trajectory according to an embodiment of the present application. Figure 1 As shown, the method may include the following steps: S101 to S104.

[0034] S101, obtaining user positioning data and road network data, wherein the user positioning data includes coordinates of multiple user positioning points and positioning times of the user positioning points, and the road network data includes coordinates of multiple preset road segments and preset road segment topological relationships.

[0035] The user positioning data is related data for indicating the user's location, the coordinates of the user positioning point are the coordinate values ​​of the user's specific location in the geographic space, and the positioning time of the user positioning point is the specific time when the user positioning point is acquired.

[0036] Road network road data is related data used to indicate road network information. The coordinates of the preset road segments may include the coordinates of key points such as the starting point, end point and connection point of the road segment. The preset road segment topological relationship describes the connection relationship between road segments.

[0037] In some embodiments, user positioning data can be obtained by obtaining measurement report (MR) data of the user equipment through the base station. The coordinates of the user positioning point obtained through the MR data are longitude and latitude coordinates, and the road network data can be obtained from a digital map service provider.

[0038] S102, based on the coordinates of the user's positioning point, the coordinates of the preset road segments, and the preset road segment topological relationship, determine the target matching probability between each user's positioning point and the road segment according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, the road segment topological relationship, and the matching probability.

[0039] The target matching probability is used to indicate the degree of matching between each user location point and each road segment. The greater the target matching probability, the greater the possibility of matching between the user location point and the corresponding road segment.

[0040] In some embodiments, the road segment topology relationship represents the connectivity on the road segment, where 1 represents connectivity and 0 represents disconnection.

[0041] In some embodiments, determining the target matching probability between each user's location point and the road segment based on the coordinates of the location point, the coordinates of the road segment, the topological relationship of the road segment, and the relationship between the matching probabilities may include:

[0042] Calculate the vertical distance from the positioning point to the road segment using the coordinates of the positioning point and the coordinates of the road segment;

[0043] The vertical distance and the sum of the topological relationships between the road segment and the road segments that match the positioning points corresponding to other adjacent positioning times are multiplied and normalized to obtain the target matching probability.

[0044] The closer the positioning point is to the road segment, the greater the probability that the user point is matched to the candidate matching road. At the same time, the topological relationship between the matching roads of the positioning points corresponding to adjacent positioning times is introduced. If the adjacent matching roads are not connected, the matching probability is reduced accordingly.

[0045] When calculating the matching probability, by introducing the topological relationship between the matching roads of the positioning points corresponding to adjacent positioning times, that is, checking whether these road segments are interconnected in the actual road network, it is possible to avoid mistakenly matching the user's positioning points to these disconnected roads, thereby improving the accuracy of the matching probability calculation.

[0046] S103, based on the target matching probability and the preset road segment topological relationship, according to the matching probability between the positioning point and the road segment, and the relationship between the road segment topological relationship and the conditional probability, in order of the positioning time of the positioning points, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment is determined, when the positioning point corresponding to the first positioning time matches each road segment. A predicted trajectory consisting of the road segments with the maximum predicted conditional probability is obtained, where the first positioning time is the positioning time of any user positioning point, and the second positioning time is the positioning time next to the first positioning time.

[0047] In some embodiments, the relationship between the matching probability of the positioning point and the road segment, the topological relationship of the road segment, and the conditional probability can be: the conditional probability that the positioning point corresponding to the second positioning time matches the specific road segment is equal to the probability that the positioning point corresponding to the first positioning time matches the specific road segment, the product of the matching probability of the second positioning point and the specific road segment and the value corresponding to the topological relationship of the road segment.

[0048] In some embodiments, when determining the predicted trajectory, a hidden Markov model (HMM) can be used to model the matching probability between the positioning point and the road segment, the relationship between the road segment topology and the conditional probability, to obtain a state transition matrix, an observation probability matrix and an initial state probability vector, and the Viterbi algorithm is used to calculate the probability based on the state transition matrix, the observation probability matrix and the initial state probability vector, thereby inferring the optimal predicted trajectory.

[0049] The state transition probability matrix is ​​defined based on the topological relationships of road segments. Each element represents the connection between the road segments represented by the row and column elements. Only interconnected road segments have a non-zero probability. Interconnected road segments can take a value of 1, while non-interconnected road segments take a value of 0. Each element in the observation probability matrix represents the matching probability between a positioning point and a road segment. The initial state probability vector is a preset value and represents the matching probability between the initial positioning point and the road segment.

[0050] In one example, the process of obtaining the predicted trajectory consisting of the road segments with the maximum prediction conditional probability may specifically be:

[0051] Based on the probability of the positioning point corresponding to the initial positioning time matching each road segment, the probability of the positioning point matching each road segment is multiplied by the probability of the positioning point corresponding to the second positioning time matching each road segment, and the road segment topological relationship of the road segments matched by the two positioning points. The conditional probability of the positioning point corresponding to the second positioning time matching each road segment is obtained when the positioning point corresponding to the initial positioning time matches each road segment;

[0052] Based on the probabilities that the positioning points corresponding to the initial positioning time and the second positioning time match the corresponding road segments, they are multiplied by the probabilities that the positioning points corresponding to the third positioning time match each road segment, and multiplied by the road segment topological relationship of the road segments matched by the second and third positioning points. The conditional probability that the positioning points corresponding to the third positioning time match each road segment is obtained when the positioning points corresponding to the initial positioning time and the second positioning time match each road segment.

[0053] The same process is repeated until the conditional probability that the positioning point corresponding to the last positioning time matches each road segment is obtained, assuming that all positioning points corresponding to the positioning times before the last positioning time match each road segment.

[0054] The road segment with the highest conditional probability of matching the positioning point corresponding to the last positioning time to each road segment is selected, and the road segments matched with the preceding matching condition of the conditional probability, that is, the positioning points corresponding to each positioning time before the last positioning time, are combined to form the predicted trajectory.

[0055] By considering the target matching probability and the road topology, the predicted trajectory consisting of the road segments with the highest probability is selected. This can improve the accuracy of the predicted trajectory generation while ensuring that the predicted trajectory is coherent.

[0056] S104: Input the coordinates of the road segments of the predicted trajectory and the coordinates of the user's positioning point into the trajectory correction model as input parameters of the feature function. Based on the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function, and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment is determined in sequence according to the positioning time of the positioning points, when the positioning point corresponding to the first positioning time matches each target road segment. The target trajectory consisting of the road segment with the largest cumulative target probability is obtained. The target road segment is the road segment in the predicted trajectory.

[0057] The trajectory correction model is used to correct the input predicted trajectory to determine the corrected target trajectory.

[0058] In some embodiments, the relationship between the pre-trained feature weights, the input parameters of the feature function, and the target probability can be that the target probability is equal to the result of inputting the input parameters into the feature function and performing weighted summation using the feature weights.

[0059] In some embodiments, the pre-trained feature data is obtained by training the trajectory correction model with the training data and selecting the parameter value that maximizes the probability that the predicted result will appear as the labeled result of the training data. It is used to evaluate the importance of the feature function to the trajectory correction and reflects the importance of different features to the trajectory correction.

[0060] In some embodiments, the trajectory correction model can be obtained by performing maximum likelihood estimation training on a conditional random field (CRF) model based on training data, a conditional random field (CRF) model, and a memory-constrained BFGS optimization algorithm.

[0061] In some embodiments, the feature function may include a transition feature and a state feature. The transition feature is a position feature between positioning points at adjacent positioning times, and the state feature is a position feature between a positioning point and a road segment.

[0062] In some embodiments, the trajectory correction model corrects the predicted trajectory through the characteristic function and characteristic weights in the trajectory correction model. The trajectory correction model inputs the input parameters into the characteristic function, and then calculates the weighted sum of the characteristic function values ​​according to the characteristic weights of the characteristic function and normalizes it. The sum is used as the probability value of the positioning point at the positioning time matching each target road segment, and the trajectory consisting of the road segments corresponding to the cumulative maximum probability value of each positioning point matching each target road segment is selected as the target trajectory.

[0063] By correcting the matching probability between the positioning points and the matching road segments according to the features and weight functions between the positioning points and the positioning points, the prediction trajectory can be precisely adjusted to improve the prediction accuracy.

[0064] The target matching probability between each user's positioning point and road segment is determined based on the relationship between the positioning point's coordinates, the road segment's coordinates, the road segment's topological relationship, and the matching probability. The degree of matching between each positioning point and road segment can be quantified, providing a confidence estimate for the road on which the positioning point is located. Based on the matching probability between the positioning point and road segment, the relationship between the road segment's topological relationship, and the conditional probability, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment, given that the positioning point corresponding to the first positioning time matches each road segment, is determined. The predicted trajectory is composed of the road segment with the highest predicted conditional probability. Combined with the topological relationship between the road segments, the road segment with the highest predicted conditional probability is selected to form the predicted trajectory, effectively improving prediction accuracy. The coordinates of the road segments of the predicted trajectory and the coordinates of the user's positioning points are input into the trajectory correction model as input parameters of the feature function. Based on the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function, and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment is determined in sequence according to the positioning time of the positioning points, when the positioning point corresponding to the first positioning time matches each target road segment. The target trajectory is obtained, which is composed of the road segment with the largest cumulative target probability. The target road segment is the road segment in the predicted trajectory. Through the pre-trained feature weights, the model can assign corresponding feature weights to different features and perform weighted summation, thereby adjusting the importance of different features, reducing the cumulative error caused by initial positioning error or prediction error, and improving prediction accuracy.

[0065] In some embodiments, before determining the target matching probability between each user location point and the road segment, the method may further include:

[0066] Obtain preset road segments whose coordinates are within the preset range of the user positioning point coordinates, calculate the vertical distance from each user positioning point coordinate to each road segment within the preset range, and obtain road segments whose distance does not exceed the set threshold as candidate matching roads for each user positioning point.

[0067] By selecting only road segments whose distance does not exceed a set threshold as candidate matching roads, the candidate range is effectively narrowed, the calculation process and amount are simplified, and the prediction efficiency is improved.

[0068] In some embodiments, as Figure 2 As shown, before determining the target matching probability of each positioning point and the road segment according to the relationship between the coordinates of the positioning point, the coordinates of the road segment and the matching probability, the method may further include: S201 to S202.

[0069] S201, cleaning the user location data to remove missing data and duplicate data in the user location data;

[0070] S202: Clean the road network road data to remove duplicate coordinates of preset road segments and topological errors in the topological relationships of preset road segments.

[0071] In some embodiments, the field missing data may include user identification ID, positioning time, longitude and latitude, etc., and the topology error may include illegal hanging points and self-intersections of road nodes in the road network.

[0072] By cleaning up duplicate data, unnecessary calculations can be reduced, computing resources can be saved, processing speed can be improved, and missing data and topological errors can be removed to prevent these erroneous data from misleading the matching algorithm, thereby improving the reliability of the matching results.

[0073] In some embodiments, the coordinates of the user's location point or the coordinates of the preset road segment are longitude and latitude coordinates. After obtaining the user's location data and the road network data, the method may further include:

[0074] The coordinates of the user positioning point and the coordinates of the preset road segment are converted into a plane rectangular coordinate system based on the road centerline as the new coordinates of the user positioning point and the coordinates of the preset road segment.

[0075] The conversion formula is:

[0076]

[0077]

[0078] Among them, x and y are the horizontal and vertical coordinates of the coordinate in the plane rectangular coordinate system, respectively. and λ are the latitude and longitude of the original longitude and latitude data respectively, and R is the radius of the earth.

[0079] Unifying the coordinates of the user's positioning point and the coordinates of the preset road segment into the same plane rectangular coordinate system can ensure data consistency, eliminate errors caused by different coordinate systems, and improve calculation efficiency.

[0080] In some embodiments, as Figure 3 As shown, based on the coordinates of the user's positioning point, the coordinates of the preset road segment and the preset road segment topological relationship, the target matching probability of each positioning point and the road segment is determined according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, the road segment topological relationship and the matching probability, which can include: S301 to S302.

[0081] S301, based on the coordinates of the user's positioning point and the coordinates of the candidate road segments, determining the vertical distance between the user's positioning point and the candidate road segments according to the relationship between the coordinates of the positioning point, the coordinates of the road segments, and the vertical distance between the positioning point and the road segments, where the candidate road segments are road segments whose coordinates are within a preset range of the coordinates of the user's positioning point;

[0082] S302: Based on the vertical distance between the user's positioning point and the candidate road segments and the preset road segment topological relationship, the target matching probability between each user's positioning point and the road segment is determined according to the relationship between the vertical distance between the positioning point and the road segment, the road segment topological relationship, and the matching probability.

[0083] By considering factors such as the coordinates of the user's positioning point and the road segment, the topological relationship of the road segment and the vertical distance, the matching probability between the user's positioning point and the road segment can be determined more accurately, thereby improving the matching accuracy.

[0084] In some embodiments, the matching probability is calculated as follows:

[0085]

[0086] Among them, f is the mapping function of Sigmoid form:

[0087]

[0088] Among them, λ is the balance factor, c i,j Represents the i-th positioning point p i Its adjacent point p j The topological connectivity of the matching road segments, 1 is connected, and 0 is disconnected. i For p i The collection of adjacent points of .

[0089] In some embodiments, as Figure 4 As shown, based on the target matching probability and the preset road segment topological relationship, according to the matching probability between the positioning point and the road segment, the relationship between the road segment topological relationship and the conditional probability, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment when the positioning point corresponding to the first positioning time matches each road segment is determined in sequence according to the positioning time of the positioning point, and the predicted trajectory consisting of the road segment with the largest predicted conditional probability is obtained, including: S401 to S403.

[0090] S401, using the target matching probability between the initial user positioning point and the road segment corresponding to the initial positioning time as the initial predicted conditional probability of matching the initial user positioning point to each road segment;

[0091] S402: Based on the target matching probability between each user's positioning point and the road segment, the preset road segment topological relationship, and the initial prediction conditional probability, the prediction conditional probability of the positioning point corresponding to the second positioning time matching each road segment is determined in sequence according to the positioning time sequence of the positioning points, if the positioning point corresponding to the first positioning time matches each road segment.

[0092] S403 : Selecting a trajectory consisting of a road segment with the highest prediction condition probability matched by the positioning point corresponding to the second positioning time and all road segments matched by the positioning points corresponding to the first positioning time as a predicted trajectory.

[0093] Matches are determined sequentially by location time, and predictions are made based on conditional probabilities. This ensures the continuity and temporal consistency of trajectory predictions by following the location time sequence of the positioning points. The most likely trajectory is obtained by selecting the predicted trajectory consisting of the road segment with the highest conditional probability. This enables time series prediction of user trajectories.

[0094] In some embodiments, the user location data further includes the speed of the user location point, and the road network data further includes the direction of a preset road segment and a speed threshold for the preset road segment. After obtaining the predicted trajectory consisting of the road segment with the highest prediction condition probability, the method may further include:

[0095] Determine the distance, average speed, and first change angle of adjacent user positioning points corresponding to adjacent positioning times according to the coordinates, positioning time, and speed of the user positioning points;

[0096] Determine the average speed difference between the user positioning point and the corresponding road segment and the second change angle according to the average speed of the user positioning point, the first change angle, the direction of the road segment and the speed threshold of the road segment;

[0097] The distance between adjacent user positioning points, the average speed, the first change angle, the average speed difference between the positioning point and the corresponding road segment, and the second change angle are used as input parameters of the feature function. Based on the input parameters of the feature function and the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment is determined in sequence according to the positioning time sequence of the positioning points, when the positioning point corresponding to the first positioning time matches each target road segment, and the target trajectory consisting of the road segment with the largest cumulative target probability is obtained.

[0098] A feature function is introduced as an input parameter. Based on the pre-trained feature weights and the input parameters of the feature function, the importance of different features can be adjusted, the relationship between the user's positioning point and the road segment can be more flexibly represented, and the accuracy and robustness of trajectory correction can be improved.

[0099] In some embodiments, the characteristic function includes a distance transfer feature, a velocity transfer feature, an angle transfer feature, a distance state feature, a topological state feature, a velocity state feature, and an angle state feature.

[0100] In some embodiments, the characteristic functions of the distance transfer feature, the speed transfer feature, and the angle transfer feature are:

[0101] t dis (y i ,y i-1 )=|y i -y i-1 |

[0102]

[0103] Among them, |·| represents the Euclidean distance, y i represents the i-th positioning point, t i is the positioning time of the i-th positioning point.

[0104] The characteristic functions of distance state characteristics, topological state characteristics, velocity state characteristics and angle state characteristics are:

[0105] s dist (y i ,q i )=d(y i ,q i )

[0106] s topo (y i ,q i ,q i-1 ,q i+1 )=c(q i ,q i-1 )+c(q i ,q i+1 )

[0107] s spd (y i ,q i )=|v(y i ,y i-1 )-v limit (q i )|

[0108]

[0109] Where d(·) is the vertical distance from the positioning point to the road, c(·) is the topological connectivity of the adjacent roads, and its value is 0 or 1. v(·) is the speed of the positioning point, v limit (·) is to match the road speed limit. The coordinates of the start and end nodes of the matching road.

[0110] In some embodiments, as Figure 5 As shown, after obtaining the target trajectory consisting of the road segments with the largest cumulative target probability, the method may further include: S501 to S503.

[0111] S501, determining a road segment that matches each positioning point according to the target trajectory, and determining the starting and ending mileage of the target trajectory in the road segment according to the coordinates of the positioning point and the coordinates of the road segment;

[0112] S502, determining the topological relationship of road segments matching positioning points corresponding to adjacent positioning times;

[0113] S503: Establish a linear referencing system event sequence for representing the target trajectory. The linear referencing system event sequence includes multiple linear referencing system events. Each linear referencing system event includes a preset identification ID of the road segment that matches the event's corresponding positioning point, the start and end mileage of the target trajectory segment on the road segment, and the topological relationship between the road segments.

[0114] A Linear Referencing System (LRS) event sequence includes the road segment identifiers, origin and destination mileage, and topological relationships for each LRS event. By establishing a LRS event sequence to represent a target trajectory, the trajectory can be better described, facilitating subsequent route analysis, simulation, and planning.

[0115] In some embodiments, specifically, the method may further include:

[0116] For each point y in the optimization trajectory i , calculate the ID r of the matching road id , relative position x on the road i and the accumulated mileage from the starting point i , get the projection coordinates (r id ,x i ,l i ):

[0117]

[0118] Where d(·) is the distance from the positioning point to the road, r start ,r endare the coordinates of the starting and ending nodes of the road, and |r| is the length of the road. i It is calculated by accumulating the distance between the previous positioning points.

[0119] Reconstruct the topological connectivity between the positioning points and check the adjacent positioning points y i and y j The matching road r i and r j , record its topological relationship c i,i+1 :

[0120]

[0121] c i,i+1 =1 means r i and r j Connected end to end, forward connected; c i,i+1 =0 means no connection or reverse connection.

[0122] In some embodiments, a trajectory is represented as a sequence of LRS events e1,…,e k , each event is:

[0123]

[0124] in is the road ID matched by the k-th trajectory, is the starting and ending mileage of the trajectory on the current road, Identifies the direction from the current road segment to the next road segment, 1 means forward, 0 means backward or not connected.

[0125] In one example, if Figure 6 As shown in the figure, event 1 indicates that the trajectory starts at 0m on Road 1 and ends at 100m. Event 2 indicates that the trajectory starts at 100m on Road 1 and ends at 200m. Event 3 indicates that the trajectory starts at 0m on Road 2 and ends at 150m, and then enters the next road segment in the forward direction. Event 4 indicates that the trajectory starts at 0m on Road 3 and ends at 120m, and then enters the next road segment in the forward direction.

[0126] Figure 7 FIG. 7 shows a schematic diagram of a road trajectory determination device 700 provided in an embodiment of the present application. Figure 7 As shown, the device may include: an acquisition module 701 , a determination module 702 , a prediction module 703 and a correction module 704 .

[0127] Acquisition module 701, for acquiring user positioning data and road network data, the user positioning data including the coordinates of multiple user positioning points and the positioning time of the user positioning points, and the road network data including the coordinates of multiple preset road segments and the preset road segment topological relationships;

[0128] Determining module 702, for determining a target matching probability between each user positioning point and a road segment based on the coordinates of the user positioning point, the coordinates of the preset road segment, and the preset road segment topological relationship, according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, the road segment topological relationship, and the matching probability;

[0129] Prediction module 703 is configured to determine, based on the target matching probability and the preset road segment topological relationship, the matching probability between the positioning point and the road segment, the relationship between the road segment topological relationship and the conditional probability, and sequentially according to the positioning time sequence of the positioning points, the predicted conditional probability that the positioning point corresponding to the second positioning time will match each road segment when the positioning point corresponding to the first positioning time matches each road segment, thereby obtaining a predicted trajectory consisting of the road segment with the maximum predicted conditional probability. The first positioning time is the positioning time of any user positioning point, and the second positioning time is the positioning time next to the first positioning time.

[0130] Correction module 704 is configured to input the coordinates of the road segments of the predicted trajectory and the coordinates of the user's positioning point into a trajectory correction model as input parameters of the feature function. Based on the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function, and the target probability, target probabilities are determined in sequence according to the positioning time sequence of the positioning points, when the positioning point corresponding to the first positioning time matches each target road segment. A target trajectory is obtained, which is composed of road segments with the largest cumulative target probability. The target road segment is the road segment in the predicted trajectory.

[0131] In some embodiments, the road trajectory determination apparatus 700 may further include a cleaning module configured to clean the user positioning data to remove missing data and duplicate data in the user positioning data before determining the target matching probability between each positioning point and the road segment based on the relationship between the coordinates of the positioning point, the coordinates of the road segment, and the matching probability;

[0132] Correspondingly, the cleaning module is also used to clean the road network road data to remove duplicate coordinates of preset road segments and topological errors in the topological relationships of preset road segments.

[0133] In some embodiments, the determination module 702 is further configured to determine, based on the coordinates of the user's location point and the coordinates of the candidate road segment, a vertical distance between the user's location point and the candidate road segment according to a relationship between the coordinates of the location point, the coordinates of the road segment, and the vertical distance between the location point and the road segment, wherein the candidate road segment is a road segment having coordinates within a preset range of the coordinates of the user's location point;

[0134] Correspondingly, the determination module 702 is further configured to determine the target matching probability between each user positioning point and the road segment based on the vertical distance between the user positioning point and the candidate road segment, the preset road segment topological relationship, and the relationship between the vertical distance between the positioning point and the road segment, the road segment topological relationship, and the matching probability.

[0135] In some embodiments, the prediction module 703 is further configured to use the target matching probability between the initial user positioning point and the road segment corresponding to the initial positioning time as the initial predicted conditional probability of matching the initial user positioning point to each road segment;

[0136] Accordingly, the determination module 702 is further configured to determine, based on the target matching probability between each user's positioning point and the road segment, the preset road segment topological relationship, and the initial prediction conditional probability, the predicted conditional probability that the positioning point corresponding to the second positioning time matches each road segment, if the positioning point corresponding to the first positioning time matches each road segment, according to the relationship between the matching probability between the positioning point and the road segment, the road segment topological relationship, and the prediction probability, in the order of the positioning time of the positioning points;

[0137] Correspondingly, the prediction module 703 is further configured to select a trajectory consisting of the road segment with the highest prediction condition probability matched by the positioning point corresponding to the second positioning time and all road segments matched by the positioning points corresponding to the first positioning time as the predicted trajectory.

[0138] In some embodiments, the user positioning data further includes the speed of the user positioning point, and the road network road data further includes the direction of a preset road segment and a speed threshold of the preset road segment. The road trajectory determination device 700 may further include a calculation module for determining the distance, average speed, and first change angle of adjacent user positioning points corresponding to adjacent positioning times based on the coordinates, positioning time, and speed of the user positioning point.

[0139] Correspondingly, the calculation module is further configured to determine the average speed difference between the user positioning point and the corresponding road segment and the second change angle according to the average speed of the user positioning point, the first change angle, the direction of the road segment, and the speed threshold of the road segment;

[0140] The correction module 704 is further configured to take the distance between adjacent user positioning points, the average speed, the first change angle and the positioning point, and the average speed difference, the second change angle of the corresponding road segment as input parameters of a feature function, and determine, according to a pre-trained feature weight in a trajectory correction model, the input parameters of the feature function and the relationship of the target probability, in a sequence of the positioning time of the positioning point, the target probability of the positioning point corresponding to the second positioning time matching to each target road segment under the condition that the positioning point corresponding to the first positioning time matches to each target road segment, to obtain a target trajectory composed of a road segment with the maximum cumulative target probability.

[0141] In some embodiments, the device 700 for determining a road trajectory can further include:

[0142] The determination module 702 is further configured to determine the road segment to which each positioning point matches according to the target trajectory, and determine the start and end mileages of the target trajectory on the road segment according to the coordinates of the positioning point and the coordinates of the road segment.

[0143] The determination module 702 is further configured to determine the topological relationship of the road segments to which the positioning points corresponding to adjacent positioning times match.

[0144] The establishment module is configured to establish a linear reference system event sequence for representing the target trajectory, the linear reference system event sequence including a plurality of linear reference system events, each linear reference system event including a preset identification ID of the road segment to which the event corresponding positioning point matches, the start and end mileages of the target trajectory segment on the road segment, and the topological relationship of the road segment.

[0145] Figure 7 Each module in the device shown can implement Figure 1 each step in the method and achieve the corresponding technical effects, which will not be described herein again for brevity.

[0146] Figure 8 A hardware structure schematic diagram of a terminal device provided by an embodiment of the present application is shown.

[0147] The terminal device can include a processor 801 and a memory 802 having computer program instructions stored therein.

[0148] Specifically, the processor 801 described above can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured as one or more integrated circuits that implement one or more embodiments of the present application.

[0149] The memory 802 can include mass storage for data or instructions. As an example and not by way of limitation, the memory 802 can include a Hard Disk Drive (HDD), a floppy disk drive, a flash memory, a Compact Disc Read Only Memory (CD-ROM), a Digital Versatile Disc (DVD), a Blu-Ray, a magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. In one example, the memory 802 can include removable or non-removable (or fixed) media, where the memory 802 is nonvolatile solid-state memory. The memory 802 can be internal or external to the integrated gateway disaster recovery device.

[0150] In one example, the memory 802 can include read-only memory (ROM), random access memory (RAM), magnetic disk storage mediums, optical storage mediums, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to

[0151] The processor 801 implements functions by reading and executing computer program instructions stored in the memory 802. Figure 1 the method of road trajectory determination in the illustrated embodiments.

[0152] In one example, the terminal device also includes a communication interface 803 and a bus 804. Wherein, as shown, the processor 801, the memory 802, the communication interface 803 are connected through the bus 804 and complete the communication between each other. Figure 8

[0153] The communication interface 803 is mainly used to realize the communication between each module, device, unit and / or equipment in the embodiments of the application.

[0154] ​Bus 804 includes hardware, software or both, couples the parts of terminal equipment to each other.For example, but not limitation, bus can include accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics buses, enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) bus, front side bus (Front Side Bus, FSB), hyper transport (Hyper Transport, HT) interconnection, industry standard architecture (Industry Standard Architecture, ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 804 can include one or more buses. Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0155] In addition, in conjunction with the road trajectory determination method in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the road trajectory determination methods in the above embodiments is implemented.

[0156] An embodiment of the present application further provides a computer program product, including a computer program, which, when executed, implements any one of the road trajectory determination methods in the above embodiments.

[0157] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0158] The functions indicated in the structural block diagrams described above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, and the like. When implemented in software, the elements of the present application are program or text segments used to perform the required tasks. The program or text segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of the machine-readable medium include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact discs (CD-ROM), optical disks, hard disks, optical fiber media, radio frequency (RF) links, and the like. The text segments can be downloaded via a computer network such as the Internet, an intranet, and the like.

[0159] It is also necessary to note that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0160] The above-described aspects and implementations of the present disclosure can be embodied in a variety of other forms, including as a method, an apparatus, a system, a computer program product, and / or any combination thereof. To clearly illustrate this interchangeability, various aspects of the present disclosure have been described above by reference to example methods, apparatus, and computer program products. It will be understood that, in some alternative implementations, the acts or event of the disclosed methods can occur in a different order. Additionally, various aspects described herein can be modified or combined, such as through the use of signals to keep track of an implemented function or action. Furthermore, each separate aspect or implementation described herein can be implemented with respect to either computer type or hardware type based on the desired implementation.

[0161] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method for determining a road trajectory, characterized in that: include: Acquire user positioning data and road network data, wherein the user positioning data includes coordinates of multiple user positioning points and positioning times of the user positioning points, and the road network data includes coordinates of multiple preset road segments and preset road segment topological relationships; Based on the coordinates of the user's positioning point, the coordinates of the preset road segments, and the preset road segment topological relationship, determining the target matching probability between each user's positioning point and the road segment according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, the road segment topological relationship, and the matching probability; Based on the target matching probability and the preset road segment topological relationship, according to the matching probability between the positioning point and the road segment, the relationship between the road segment topological relationship and the conditional probability, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment when the positioning point corresponding to the first positioning time matches each road segment is determined in sequence according to the positioning time of the positioning points, and a predicted trajectory consisting of the road segment with the maximum predicted conditional probability is obtained, where the first positioning time is the positioning time of any user positioning point, and the second positioning time is the positioning time next to the first positioning time. The coordinates of the road segments of the predicted trajectory and the coordinates of the user's positioning points are input into a trajectory correction model as input parameters of the feature function. Based on the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function, and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment is determined in sequence according to the positioning time of the positioning points, when the positioning point corresponding to the first positioning time matches each target road segment. The target trajectory consisting of the road segment with the largest cumulative target probability is obtained. The target road segment is the road segment in the predicted trajectory.

2. The method for determining a road trajectory according to claim 1, wherein: Before determining the target matching probability of each positioning point and the road segment according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, and the matching probability, the method further includes: Clean the user location data to remove missing and duplicate data in the fields; Clean the road network data to remove duplicate coordinates of preset road segments and topological errors in the topological relationships of preset road segments.

3. The method for determining a road trajectory according to any one of claims 1 to 2, characterized in that: The method of determining a target matching probability between each positioning point and the road segment based on the coordinates of the user positioning point, the coordinates of the preset road segment, and the preset road segment topological relationship, according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, the road segment topological relationship, and the matching probability, includes: determining, based on the coordinates of the user's location point and the coordinates of the candidate road segments, a vertical distance between the user's location point and the candidate road segments according to a relationship between the coordinates of the location point, the coordinates of the road segments, and the vertical distance between the location point and the road segments, the candidate road segments being road segments whose coordinates are within a preset range of the coordinates of the user's location point; Based on the vertical distance between the user's positioning point and the candidate road segments, the preset road segment topological relationship, and the relationship between the vertical distance between the positioning point and the road segment, the road segment topological relationship, and the matching probability, the target matching probability between each user's positioning point and the road segment is determined.

4. The method for determining a road trajectory according to claim 1, wherein: The method of determining, based on the target matching probability and the preset road segment topological relationship, the predicted conditional probability of the positioning point corresponding to the second positioning time matching each road segment when the positioning point corresponding to the first positioning time matches each road segment and the matching probability of the road segment topological relationship and the conditional probability is sequentially determined according to the positioning time sequence of the positioning points, and obtaining a predicted trajectory consisting of the road segment with the maximum predicted conditional probability, including: The target matching probability between the initial user positioning point and the road segment corresponding to the initial positioning time is used as the initial predicted conditional probability of matching the initial user positioning point to each road segment; Based on the target matching probability between each user's positioning point and the road segment, the preset road segment topological relationship, and the initial prediction conditional probability, the prediction conditional probability of the positioning point corresponding to the second positioning time matching each road segment is determined in sequence according to the positioning time sequence of the positioning points, if the positioning point corresponding to the first positioning time matches each road segment; The trajectory consisting of the road segment with the largest prediction condition probability matched by the positioning point corresponding to the second positioning time and all the road segments matched by the positioning points corresponding to the first positioning time is selected as the predicted trajectory.

5. The method for determining a road trajectory according to claim 1, wherein: The user positioning data further includes the speed of the user positioning point, and the road network road data further includes the direction of a preset road segment and a preset speed threshold of the road segment. After obtaining a predicted trajectory consisting of a road segment with a maximum prediction condition probability, the method further includes: Determine the distance, average speed, and first change angle of adjacent user positioning points corresponding to adjacent positioning times according to the coordinates, positioning time, and speed of the user positioning points; Determine the average speed difference between the user positioning point and the corresponding road segment and the second change angle according to the average speed of the user positioning point, the first change angle, the direction of the road segment and the speed threshold of the road segment; The distance between the adjacent user positioning points, the average speed, the first change angle, the average speed difference between the positioning point and the corresponding road segment, and the second change angle are used as input parameters of the feature function. Based on the input parameters of the feature function and the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function and the target probability, the target probability of the positioning point corresponding to the second positioning time matching each target road segment when the positioning point corresponding to the first positioning time matches each target road segment is determined in sequence according to the positioning time of the positioning points, and the target trajectory consisting of the road segment with the largest cumulative target probability is obtained.

6. The method for determining a road trajectory according to claim 1, wherein: After obtaining the target trajectory consisting of the road segments with the maximum cumulative target probability, the method further includes: Determine the road segment that matches each positioning point according to the target trajectory, and determine the starting and ending mileage of the target trajectory in the road segment according to the coordinates of the positioning point and the coordinates of the road segment; Determine the topological relationship of the road segments that match the positioning points corresponding to adjacent positioning times; A linear referencing system event sequence is established for representing a target trajectory. The linear referencing system event sequence includes multiple linear referencing system events. Each linear referencing system event includes a preset identification ID of a road segment that matches a corresponding positioning point of the event, the starting and ending mileages of the target trajectory segment on the road segment, and a topological relationship between the road segments.

7. A device for determining a road trajectory, characterized in that: The device comprises: An acquisition module, configured to acquire user positioning data and road network data, wherein the user positioning data includes coordinates of multiple user positioning points and positioning times of the user positioning points, and the road network data includes coordinates of multiple preset road segments and preset road segment topological relationships; a determination module for determining a target matching probability between each user positioning point and a road segment based on the coordinates of the user positioning point, the coordinates of the preset road segment, and the preset road segment topological relationship, according to the relationship between the coordinates of the positioning point, the coordinates of the road segment, the road segment topological relationship, and the matching probability; A prediction module is configured to determine, based on the target matching probability and the preset road segment topological relationship, the matching probability between the positioning point and the road segment, the relationship between the road segment topological relationship and the conditional probability, and in order of the positioning time of the positioning points, the predicted conditional probability that the positioning point corresponding to the second positioning time matches each road segment when the positioning point corresponding to the first positioning time matches each road segment, thereby obtaining a predicted trajectory consisting of road segments with the maximum predicted conditional probability, wherein the first positioning time is the positioning time of any user positioning point, and the second positioning time is the positioning time next to the first positioning time; The correction module is used to input the coordinates of the road segments of the predicted trajectory and the coordinates of the user positioning points into the trajectory correction model as input parameters of the feature function, and determine, based on the relationship between the pre-trained feature weights in the trajectory correction model, the input parameters of the feature function and the target probability, in sequence according to the positioning time of the positioning points, the target probability that the positioning point corresponding to the second positioning time matches each target road segment when the positioning point corresponding to the first positioning time matches each target road segment, so as to obtain a target trajectory consisting of the road segment with the largest cumulative target probability, wherein the target road segment is the road segment in the predicted trajectory.

8. A terminal device, characterized in that: The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for determining a road trajectory according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the method for determining a road trajectory according to any one of claims 1 to 6.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is caused to perform the method for determining a road trajectory as described in any one of claims 1 to 6.

Images