CN114613124A - Traffic information processing method, device, terminal and computer readable storage medium - Google Patents
Traffic information processing method, device, terminal and computer readable storage medium Download PDFInfo
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Abstract
The invention is suitable for the field of intelligent traffic, and provides a traffic information processing method, a device, a terminal and a computer readable storage medium, wherein the method comprises the following steps: acquiring information of traffic points; converting the information of the traffic points into traffic track information; determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information; obtaining a high-dimensional map characteristic matrix of the traffic network by using a spectral clustering method based on the information of the traffic points, the adjacency relation of any two adjacent traffic points and the passing times; extracting the category information of the traffic points from the high-dimensional representation; and classifying and storing the traffic points based on the class information to form a database. In the technical scheme, the similarity between the traffic points is measured by using the track communication times, and the relation between the spatio-temporal data is represented from the angle of a graph network by using a spectral clustering algorithm, so that the clustering result of the traffic points is more consistent with the actual traffic network condition.
Description
Technical Field
The invention belongs to the field of intelligent transportation, and particularly relates to a traffic information processing method, a traffic information processing device, traffic information processing equipment and a computer-readable storage medium.
Background
In the fields of urban calculation and intelligent transportation, a plane clustering method is widely applied, such as traffic passenger flow trend analysis and prediction, public transportation area division and the like. By using the clustering method, the planes of the city can be divided into different classes, each class may represent different roads, cells and the like, and business circles, residences, congested road sections and the like can be further mined from the classes.
In recent years, with the development of urban big data and intelligent transportation technology, more and more information can be mined from the data, and support is further provided for different applications, such as business district mining, accurate advertisement, traffic capacity analysis, passenger flow prediction and the like. For convenience of analysis, the applications all need a basic premise, that is, a city plan is discretized, that is, the plane of the city is divided into different small blocks, so that each small block can be labeled with different labels to support further analysis.
Generally, the collected data are all GPS data reported by traffic participants such as buses, taxi appointments, taxis, subways and private cars, and the GPS data represent different roads which can pass through in a city, and how to divide the GPS points into different clusters is a fundamental and important problem.
For spatial plane clustering, there are currently many methods, such as K-Means, DBSCAN, and a large number of variants based on these two methods, and GPS points can be classified into different classes by calculating euclidean distances between GPS points, that is, the spatial planes are classified into different blocks by combining these two methods.
The main problem with the above prior art is that they use euclidean distance as a measure, i.e. the spatial distance relationship. In practical scenarios, however, objects (such as vehicles or people) typically move in a spatial network, i.e., in a network-structured manner, rather than in euclidean planar space. For example, vehicles are often moving along urban roads, and although two roads may be closely spaced, it is virtually impossible for a vehicle to move across a barrier from one road to the other. In K-Means or DBSCAN with euclidean distance measurement, the GPS reported by the vehicles on the two roads is considered as a category, but obviously, the category does not conform to the actual situation. That is, clustering using spatial distance will classify neighboring points that are unlikely to intersect at all into the same class.
Disclosure of Invention
In view of this, embodiments of the present invention provide a traffic information processing method, an apparatus, a terminal, and a computer-readable storage medium, so as to solve the problem that a complex urban traffic network cannot be represented due to the use of euclidean distance in a conventional clustering algorithm when traffic information processing is performed.
A first aspect of an embodiment of the present invention provides a method for processing traffic information, including:
acquiring information of traffic points;
converting the information of the traffic points into traffic track information;
determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information;
based on the information of the traffic points, the adjacency relation between any two adjacent traffic points and the passing times, obtaining a high-dimensional representation of a traffic network formed by the traffic points by using a spectral clustering method;
extracting the category information of the traffic points from the high-dimensional representation;
and classifying and storing the information of the traffic points based on the class information to form a database.
A second aspect of an embodiment of the present invention provides a traffic information processing apparatus, including:
an acquisition unit configured to acquire information of a traffic point;
the conversion unit is used for converting the information of the traffic points into traffic track information;
the determining unit is used for determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information;
the calculation unit is used for obtaining a high-dimensional representation of a traffic network formed by the traffic points by using a spectral clustering method based on the information of the traffic points, the adjacency relation of any two adjacent traffic points and the passing times;
the extraction unit is used for extracting the category information of the traffic points from the high-dimensional representation;
and the storage unit is used for classifying and storing the information of the traffic points based on the class information to form a database.
A third aspect of the embodiments of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and operable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method as described above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the technical scheme, the similarity between the traffic points is measured by using the track communication times, and the relation between the space-time data is represented from the angle of a graph network by using a spectral clustering algorithm, so that the clustering result of the traffic points is more accordant with the actual traffic network condition.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of a first embodiment of a traffic information processing method of the present invention;
fig. 2 is a detailed flowchart of S13 in the first embodiment of the traffic information processing method of the present invention;
fig. 3 is a detailed flowchart of S14 in the first embodiment of the traffic information processing method of the present invention;
fig. 4 is a schematic structural diagram of a distributed database system in the first embodiment of the traffic information processing method of the present invention;
FIG. 5 is a graph of the effects of different clustering algorithms;
FIG. 6 is a flow chart of a second embodiment of a traffic information processing method of the present invention;
FIG. 7 is a data query latency test chart;
fig. 8 is a schematic configuration diagram of a first embodiment of a traffic information processing apparatus of the present invention;
fig. 9 is a schematic structural diagram of a first embodiment of the terminal of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to illustrate the technical means of the present invention, the following description is given by way of specific examples.
In the embodiment of the traffic information processing method, the information of the traffic points is converted into the information of the traffic tracks by acquiring the information of the traffic points, the adjacency relation and the track passing times of any two adjacent traffic points in the traffic tracks are determined according to the information of the traffic tracks, and the high-dimensional map feature matrix of the traffic network formed by the traffic points is obtained by using a spectral clustering method based on the information of the traffic points, the adjacency relation and the passing times of any two adjacent traffic points. And classifying and storing the traffic points based on the class information of the traffic points extracted from the high-dimensional graph feature matrix to form a database.
In the embodiment of the invention, the similarity between the traffic points is measured by using the track communication times, and the relation between the spatio-temporal data is represented from the angle of a graph network by using a spectral clustering algorithm, so that the clustering result of the traffic points is more consistent with the actual traffic network condition.
Fig. 1 is a flowchart of a first embodiment of a traffic information processing method of the present invention, and as shown in fig. 1, the traffic information processing method includes the steps of:
and S11, acquiring the information of the traffic points.
The information of the traffic points includes GPS data reported by traffic participants (such as buses, taxi appointments, taxis, subways, private cars, and the like). Each piece of GPS data may be represented as (participant ID, reporting time, longitude longtitude, latitude). The acquisition mode can be direct reception, or can be reading historical GPS data stored in a local or other storage medium.
And S12, converting the information of the traffic points into traffic track information.
In this embodiment, the traffic track information includes a traffic track sequence, and specifically, the GPS data reported by the same participant is divided into a group according to the participant ID of the GPS data, and the group is sorted according to the reporting time of the GPS data, so as to obtain the traffic track sequence of each traffic participant.
In other embodiments of the method of the present invention, the GPS data may be classified according to other parameters in the GPS data to obtain corresponding traffic track information.
And S13, determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information.
In this implementation, as shown in fig. 2, step S13 specifically includes the following sub-steps:
s131, splitting the traffic track information into adjacent traffic point pairs;
s132, determining the adjacency relation of any two adjacent traffic points according to the adjacent traffic point pairs;
and S133, determining the track passing times of any two adjacent traffic points according to the adjacent traffic point pairs.
Specifically, in sub-step S131, the longitude and latitude of the GPS data may be encoded into a number lx by using a geohash method, i.e., lx represents a specific traffic point, i.e., a pair of longitude and latitude. Assuming that x1, x2, x3 and x4 are four adjacent traffic points one by one, the track sequence can be represented as (i, lx1, t1), (i, lx2, t2), (i, lx3, t3), (i, lx4, t4), wherein i is the ID of a traffic participant reporting corresponding to GPS data, lx1-lx4 are numbers obtained by geohash coding corresponding to the longitude and latitude of the traffic points x1-x4, and t1-t4 is the reporting time of the corresponding GPS data. The respective split adjacent traffic point pairs are denoted as (lx1, lx2), (lx2, lx3) and (lx3, lx 4). By the above method, each sequence of tracks is split into a set of pairs of adjacent traffic points (la, lb), where a and b represent any two adjacent traffic points.
In sub-step S132, each GPS point is considered as a vertex, and an adjacent edge exists between vertices la and lb, i.e., the adjacent relationship e (a, b).
In the sub-step S133, since it is an undirected graph, counting the number of (la, lb) and (lb, la) appearing in the set of adjacent traffic point pairs, the number of passing times w (a, b) of the track of any two adjacent traffic points can be obtained, where w (a, b) is expressed as:
w(a，b)＝w′(a，b)+w′(b，a)
and S14, obtaining a high-dimensional map feature matrix of the traffic network formed by the traffic points by using a spectral clustering method based on the information of the traffic points, the adjacency relation of any two adjacent traffic points and the passing times.
In this embodiment, as shown in fig. 3, step S14 specifically includes the following sub-steps:
s141, determining an adjacency matrix by taking the track passing times of any two adjacent traffic points as the weight values of the adjacency relation of the corresponding two adjacent traffic points;
s142, obtaining a weighted undirected adjacency graph of the traffic network formed by the traffic points according to the information of the traffic points, the adjacency relation and the adjacency matrix of any two adjacent traffic points;
and S143, calculating a high-dimensional map feature matrix of the traffic network formed by the traffic points according to the undirected adjacency graph with the weight.
Specifically, in the sub-step S141, the total weight of the adjacency e (a, b) is set to W (a, b), that is, the similarity between any two adjacent traffic points, so that an adjacency matrix W can be obtained.
In sub-step S142, a weighted undirected adjacency graph G (V, E, W) of the traffic network formed by any GPS points is obtained according to the adjacency relation E between any two adjacent traffic points and the adjacency matrix W (the adjacency matrix indicates whether or not there is a connection between all vertices, and if there is a connection, the weight of the connecting edge is the value on the corresponding matrix).
In sub-step S143, based on the weighted undirected adjacency graph G (V, E, W), the degree (degree) di corresponding to each traffic point can be calculated:
the degree corresponding to all the traffic points is calculated, and a degree matrix Deg is obtained.
Based on the degree matrix Deg and the adjacency matrix W, the non-regularized laplacian matrix Lap of the undirected adjacency graph G with weights is calculated by the following formula:
Lap＝Deg-W
then, the eigenvectors and eigenvalues of the laplace matrix Lap are solved. To reduce the amount of computation while avoiding the occurrence of a large number of meaningless clusters, the eigenvectors Λ will be addressednUsing the TopK algorithm:
Λn←solve|Lap-λI|＝0extract the eigenvalues(λ1，λ2，…，λn)；
Λk←TopK(Λn)；
specifically, the placian matrix Lap is n × n, and n eigenvalues (n is the number of vertices) are obtained by taking the eigenvalues in the foregoing, and each eigenvalue corresponds to an eigenvector with a length of n. Then, topk (the largest k eigenvalues) is taken according to the eigenvalues, and eigenvectors corresponding to the first k eigenvalues are arranged in sequence to obtain a new k × n matrix, namely the high-dimensional map eigenvalue matrix of the traffic network formed by the GPS points.
And S15, extracting the type information of the traffic points from the high-dimensional map feature matrix.
In this embodiment, by clustering on the high-dimensional map feature matrix, the category information of different traffic points can be extracted, and the extracted category information of the traffic points includes a classification table (longitude, latitude, category number) of GPS points.
And S16, classifying and storing the information of the traffic points based on the class information to form a database.
In this embodiment, after the classification table of the GPS point is obtained, the original GPS data is stored in the database according to the classification table, and then the corresponding GPS data can be searched by using the class number clusterID for further analysis.
In particular, a distributed database may be used to convert and store the raw GPS data. For example, data is stored in a manner of HBase + Phoenix (Phoenix is a plug-in of HBase, and can provide a query interface similar to SQL for HBase, through which SQL can be directly used to query data of HBase), and the implementation of the clustering algorithm is based on Spark-SQL framework, so that the architecture of the whole system is as shown in fig. 4.
The classification is realized by the GPS data RawData reported by the traffic participants and the algorithm operated in the Analysis Engine. The Analysis Engine is a big data framework and a program for program operation, and the algorithm can be realized by Spark-SQL. The Master is a server node used by spark to manage the cluster, and the program is submitted through the Master. The server node worker is a server node of a distributed operation algorithm, a program submitted to the master is divided and then respectively sent to different workers to operate, and a result is sent back to the master after the operation is finished.
The client Query can run on a client terminal device, such as a client computer, and the data stored in the HBASE can be queried by installing the SQL client.
The Spark-SQL realizes the clustering algorithm, stores the GPS data into the HBase according to the generated classification table, and the HBase uses a plurality of servers (namely, Region servers in the figure) as storage media, namely, the GPS data are dispersed on different servers. Specifically, different types of GPS data are placed in different servers by placing the ClusterID at the beginning of a row key Rowkey (HBase queries data through Rowkey), then placing the data with the ClusterID of c1 (the beginning of Rowkey is c1) in a first Server Region Server1, placing the data with the ClusterID of c2 in a second Server Region Server2, and so on.
In addition, in order to optimize the query efficiency, the RowKey of HBase can be designed by using an inverted index:
RowKey＝ClusterID+MaxValue-Timestamp+ID
the ClusterID is the classification ID of the cluster, the MaxValue is the maximum value of time, and the reverse index is realized by utilizing the time difference between the ClusterID and the maximum value of the time, namely, the latest data is placed on the upper layer of the database, so that the query efficiency is higher. In general, the data is not uniformly distributed in space, so that the hot spot problem can occur inevitably by using the ClusterID as the prefix of the RowKey. To solve this problem and to make the computations proceed in parallel, the RowKey is scattered as widely as possible. For example, a function sold Tables of Phoenix can be used to specify how many Servers (Region Servers) it will transparently add a Hash symbol in front of the Rowkey, and then put the Rowkey data starting with different symbols on different Servers according to the above, and put the data on different Servers uniformly. Since the second string of rowkey is ClusterID, it is possible to put data of the same ClusterID on a server.
For the taxi track of beijing, the GPS data is classified by using (a) the KMeans method, (b) the dbss method and (c) the method according to the first embodiment of the present invention (i.e. the spectrum in fig. 5), and the classification result is shown in fig. 5, it can be seen that clustering performed by using (a) the KMeans method and (b) the dbss method generally divides the space into more regular blocks, and these and actual situations do not always meet. And (c) the method of the first embodiment of the present invention is used for clustering, the space is represented in the form of graph network, and the divided different categories are more consistent with the distribution of real tracks or roads.
In the first embodiment of the traffic information processing method, the adjacency relation between different traffic points is represented in a graph mode, and then the adjacency graph is used for carrying out cluster analysis to divide an urban traffic space plane. Aiming at the problem of measuring the correlation between different traffic points, the track passing times between two adjacent traffic points are analyzed by utilizing the statistics of historical traffic data to represent.
Fig. 6 is a flowchart of a second embodiment of the traffic information processing method of the present invention, in this embodiment, the method of the present invention includes:
s61, acquiring information of traffic points;
s62, converting the information of the traffic points into traffic track information;
s63, determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information;
s64, based on the information of the traffic points and the adjacency relation and the passing times of any two adjacent traffic points, obtaining a high-dimensional map feature matrix of the traffic network formed by the traffic points by using a spectral clustering method;
s65, extracting the type information of the traffic points from the high-dimensional map feature matrix;
s66, classifying and storing the information of the traffic points based on the category information to form a database;
s67, receiving a request for acquiring information of the traffic points of the specific category;
and S68, acquiring the information of the traffic points of the corresponding category from the database based on the category information in the request for response.
In this embodiment, steps S61-S66 correspond to steps S11-S16 of the first embodiment of the method, and are not repeated herein.
Referring to fig. 4, in step S67, a request to acquire information of a traffic point is received from a client Query, the request including category information of the traffic point. In step S68, based on the category information, information of the traffic point of the corresponding category is acquired from the database to respond. For example, the information of the corresponding traffic point is directly sent to the corresponding client or sent to the corresponding client after being subjected to preset processing.
Due to the fact that the data are stored in the HBase + Phoenix mode, the Phoenix can provide a query interface similar to SQL for the HBase, the data of the HBase can be directly queried through the Phoenix, SQL sentences can be analyzed into parallel sentences by the Phoenix during query, the data are queried in parallel on a plurality of storage media, efficiency is improved, and the method can be applied to millisecond-level real-time query.
As shown in FIG. 7, for the test on the 43 hundred million GPS data sets, the framework of the invention increases the query delay in an approximately linear manner with the increase of the input scale of the query, and the delay within 4s can be achieved in most cases, so as to meet the requirement of real-time query.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In the embodiment of the present invention, a traffic information processing apparatus is further provided, where the traffic information processing apparatus includes units for executing the steps in the embodiment corresponding to fig. 1. Please refer to fig. 1-5 for related descriptions of embodiments. Fig. 8 shows a schematic configuration diagram of a first embodiment of a traffic information processing apparatus 800 of the present invention, including:
an acquisition unit 81 for acquiring information of a traffic point;
a conversion unit 82 for converting information of the traffic points into traffic track information;
the determining unit 83 is configured to determine an adjacency relation between any two adjacent traffic points in the traffic track and the track passing frequency according to the traffic track information;
a calculating unit 84, configured to obtain a high-dimensional map feature matrix of a traffic network formed by traffic points by using a spectral clustering method based on information of the traffic points, an adjacency relation between any two adjacent traffic points, and a passing frequency;
the extracting unit 85 is used for extracting the category information of the traffic points from the high-dimensional representation;
the storage unit 86 is configured to store the traffic point information in a classified manner based on the category information to form a database.
Wherein the determining unit 83 includes:
a first determining module 831, configured to split the traffic track information into adjacent traffic point pairs;
a second determining module 832, configured to determine an adjacency relation between any two adjacent traffic points according to the adjacent traffic point pairs;
and a third determining module 833, configured to determine the number of track passing times of any two adjacent traffic points according to the adjacent traffic point pairs.
Among them, the calculation unit 84 includes:
the adjacency matrix calculation module 841 is used for determining an adjacency matrix by taking the track passing times of any two adjacent traffic points as the weight values of the adjacency relation of the corresponding two adjacent traffic points;
an adjacency graph calculation module 842, configured to obtain a weighted undirected adjacency graph of a traffic network formed by traffic points according to information of the traffic points, an adjacency relation between any two adjacent traffic points, and the adjacency matrix;
and the high-dimensional representation calculation module 843 is used for calculating a high-dimensional map feature matrix of the traffic network formed by the traffic points according to the weighted undirected adjacency graph.
Further, the high-dimensional characterization calculation module 843 includes the following sub-modules (not shown in the figure):
the first sub-module is used for calculating the corresponding degrees of all traffic points according to the undirected adjacency graphs with weights so as to obtain a degree matrix;
the second submodule is used for calculating a Laplace matrix of the undirected adjacency graph with weights based on the degree matrix and the adjacency matrix;
the third submodule is used for solving eigenvectors and eigenvalues of the Laplace matrix;
and the fourth submodule is used for obtaining a high-dimensional map feature matrix of the traffic network formed by the traffic points by using a TopK algorithm according to the feature vector and the feature value of the Laplace matrix.
In a second embodiment of the traffic information processing apparatus according to the present invention, based on fig. 8, the apparatus further includes a receiving unit and a responding unit, configured to execute corresponding steps in the embodiment corresponding to fig. 6, and refer to the related description in the embodiment corresponding to fig. 6 specifically.
The receiving unit is used for receiving a request for acquiring information of traffic points in a specific category.
The response unit is used for acquiring the information of the traffic points of the corresponding category from the database based on the category information in the request so as to respond.
The present invention also provides a terminal, as shown in fig. 9, the terminal 100 includes: a processor 101, a memory 102, and a computer program 103 stored in the memory 102 and operable on the processor 101. The steps in the embodiments of the traffic information processing method described above are implemented when the processor 101 executes the computer program 103. Alternatively, the processor 101, when executing the computer program 103, implements the functions of each unit/module/submodule in each device embodiment described above.
Illustratively, the computer program 103 may be partitioned into one or more units/modules/sub-modules that are stored in the memory 102 and executed by the processor 101 to carry out the invention. The one or more units/modules/sub-modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 103 in the traffic information processing apparatus/terminal 100. For example, the computer program 62 may be divided into an acquisition module, an execution module, and a generation module (module in a virtual device), and the specific functions of each module are as follows:
acquiring information of traffic points; converting the information of the traffic points into traffic track information; determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information; based on the information of the traffic points, the adjacency relation between any two adjacent traffic points and the passing times, a high-dimensional map feature matrix of the traffic network formed by the traffic points is obtained by using a spectral clustering method; extracting the category information of the traffic points from the high-dimensional map feature matrix; and classifying and storing the traffic points based on the class information to form a database.
The terminal 100 may be a computing device such as a desktop computer, a notebook, a palm top computer, and a cloud server. The terminal 100 can include, but is not limited to, a processor 101, a memory 102. Those skilled in the art will appreciate that fig. 9 is merely an example of the terminal 100 and does not constitute a limitation of the terminal 100 and may include more or less components than those shown, or combine certain components, or different components, e.g., the terminal 100 may further include input-output devices, network access devices, buses, etc.
The Processor 101 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 102 may be an internal storage unit of the terminal 100, such as a hard disk or a memory of the terminal 100. The memory 102 may also be an external storage device of the terminal 100, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the terminal 100. Further, the memory 102 may also include both internal storage units of the terminal 100 and external storage devices. The memory 102 is used for storing the computer program 103 and other programs and data required by the terminal 100. The memory 102 may also be used to temporarily store data that has been output or is to be output.
The invention also provides a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of any of the embodiments of the traffic information processing method, for example.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for processing traffic information, the method comprising:
acquiring information of traffic points;
converting the information of the traffic points into traffic track information;
determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information;
based on the information of the traffic points, the adjacency relation between any two adjacent traffic points and the passing times, obtaining a high-dimensional representation of a traffic network formed by the traffic points by using a spectral clustering method;
extracting the category information of the traffic points from the high-dimensional representation;
and classifying and storing the information of the traffic points based on the class information to form a database.
2. The method of claim 1, wherein the information about the traffic point comprises GPS data, and wherein converting the information about the traffic point into traffic track information comprises:
and sequencing the GPS data reported by the same traffic participant according to the reporting time to obtain the traffic track information of the traffic participant.
3. The method of claim 1, wherein the determining the adjacency relation and the passing times of any two adjacent traffic points in the traffic track according to the traffic track information comprises:
splitting the traffic track information into adjacent traffic point pairs;
determining the adjacency relation of any two adjacent traffic points according to the adjacent traffic point pairs;
and determining the track passing times of any two adjacent traffic points according to the adjacent traffic point pairs.
4. The method of claim 1, wherein the obtaining of the high-dimensional representation of the traffic network formed by the traffic points by using a spectral clustering method based on the information of the traffic points, the adjacency relation between any two adjacent traffic points and the track passing times comprises:
determining an adjacency matrix by taking the track passing times of any two adjacent traffic points as the weight values of the adjacency relation of the corresponding two adjacent traffic points;
obtaining a weighted undirected adjacency graph of a traffic network formed by the traffic points according to the information of the traffic points, the adjacency relation of any two adjacent traffic points and the adjacency matrix;
and calculating a high-dimensional representation of a traffic network formed by the traffic points according to the weighted undirected adjacency graph.
5. The method of claim 4, wherein computing a high-dimensional representation of a traffic network of the traffic points from the weighted undirected adjacency graph comprises:
calculating the corresponding degrees of all traffic points according to the weighted undirected adjacency graphs to obtain a degree matrix;
calculating a Laplace matrix of the weighted undirected adjacency graph based on the degree matrix and the adjacency matrix;
solving eigenvectors and eigenvalues of the Laplace matrix;
and according to the eigenvectors and the eigenvalues of the Laplace matrix, using a TopK algorithm to obtain a high-dimensional representation of the traffic network formed by the traffic points.
6. The method of claim 1, further comprising:
receiving a request for acquiring information of traffic points of a specific category;
and acquiring information of the traffic points of the corresponding category from the database based on the category information in the request for response.
7. An apparatus for processing traffic information, the apparatus comprising:
an acquisition unit configured to acquire information of a traffic point;
the conversion unit is used for converting the information of the traffic points into traffic track information;
the determining unit is used for determining the adjacency relation of any two adjacent traffic points in the traffic track and the track passing times according to the traffic track information;
the calculation unit is used for obtaining a high-dimensional representation of a traffic network formed by the traffic points by using a spectral clustering method based on the information of the traffic points, the adjacency relation of any two adjacent traffic points and the passing times;
the extraction unit is used for extracting the category information of the traffic points from the high-dimensional representation;
and the storage unit is used for classifying and storing the information of the traffic points based on the class information to form a database.
8. The apparatus of claim 7, wherein the computing unit comprises:
the adjacency matrix calculation module is used for determining an adjacency matrix by taking the track passing times of any two adjacent traffic points as the weight values of the adjacency relation of the corresponding two adjacent traffic points;
the adjacency graph calculation module is used for obtaining a weighted undirected adjacency graph of a traffic network formed by the traffic points according to the information of the traffic points, the adjacency relation of any two adjacent traffic points and the adjacency matrix;
and the high-dimensional representation calculation module is used for calculating the high-dimensional representation of the traffic network formed by the traffic points according to the undirected adjacency graph with the weight.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN202011395101.5A CN114613124B (en) | 2020-12-03 | 2020-12-03 | Traffic information processing method, device, terminal and computer readable storage medium |
| PCT/CN2020/140134 WO2022116326A1 (en) | 2020-12-03 | 2020-12-28 | Transportation information processing method, device, terminal, and computer-readable storage medium |
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