CN112907985A - Method and device for dividing traffic control area - Google Patents
Method and device for dividing traffic control area Download PDFInfo
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- CN112907985A CN112907985A CN201911134140.7A CN201911134140A CN112907985A CN 112907985 A CN112907985 A CN 112907985A CN 201911134140 A CN201911134140 A CN 201911134140A CN 112907985 A CN112907985 A CN 112907985A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
Abstract
The invention provides a method and a device for dividing a traffic control area, which are characterized in that a road network topological relation and road network data in the traffic control area are obtained, and then; according to the topological relation of the road network, constructing a similarity matrix representing the connection relation between each intersection in the road network; constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data; determining a degree matrix of the road network according to the similarity adjacent matrix; determining a Laplace matrix of the road network according to the similarity adjacent matrix and the degree matrix; and determining the eigenvalue and the eigenvalue difference of the Laplace matrix of the road network, and finally dividing the traffic control area according to the eigenvalue difference. The rationality of the division of the traffic control area is improved, and the effect of carrying out zone control on the traffic control area is further improved.
Description
Technical Field
The invention relates to the technical field of traffic control, in particular to a method and a device for dividing a traffic control area.
Background
Traffic systems are an essential basis for national and social development. The intelligent traffic system applies the information communication technology to traffic infrastructure and vehicles, relieves traffic jam, improves traffic efficiency and safety, reduces energy consumption and exhaust emission, and increases economic benefit. The division of the effective traffic control area is the basis for efficient area coordination control. The basic idea is to divide a complex and huge road network into a plurality of independent sub-areas according to certain principle indexes, respectively execute appropriate control optimization strategies according to the characteristics of the sub-areas, and gradually release the control right, so that the system becomes more flexible and reliable. Therefore, finding a suitable division index and an algorithm thereof become a research hotspot of the current traffic region coordination control.
In the prior art, a plurality of clustering methods for dividing a traffic control area exist, a spectral clustering algorithm can solve the clustering problem of complex distribution data, and is an effective means for solving the division of the urban traffic control area, but the number of the partitions of the spectral clustering needs to be configured externally, and the partitioning cannot be performed by combining the characteristics of the spectral clustering.
However, in the prior art, the number of traffic control divided areas is set externally to divide the traffic control areas, so that the areas are unreasonable, and the area control effect is poor.
Disclosure of Invention
The invention provides a method and a device for dividing a traffic control area, which are used for realizing the division of the traffic control area, improving the rationality of the division of the traffic control area and further improving the effect of carrying out the divisional control on the traffic control area.
In a first aspect, an embodiment of the present invention provides a method for dividing a traffic control area, including:
acquiring road network topological relation and road network data in a traffic control area, wherein the road network data comprises intersection flow data, or the road network data comprises intersection flow data and intersection distance data;
according to the topological relation of the road network, constructing a similarity matrix representing the connection relation between each intersection in the road network;
constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data;
determining a degree matrix of the road network according to the similarity adjacent matrix;
determining a Laplace matrix of the road network according to the similarity adjacent matrix and the degree matrix;
determining a characteristic value and a characteristic value difference value of a Laplace matrix of a road network;
and dividing the traffic control area according to the characteristic value difference.
According to the embodiment of the application, the Laplace matrix of the road network is established according to the topological relation of the road network and the road network data, the traffic control area is divided according to the characteristic value difference value of the Laplace matrix of the road network, the rationality of the division of the traffic control area is improved, and the effect of carrying out zone control on the traffic control area is further improved. In addition, the traffic control area can be divided according to the road network topological relation and the intersection flow data, the intersection distance can be considered, the traffic control area can be divided according to the road network topological relation, the intersection flow data and the intersection distance data, and the rationality of dividing the traffic control area is further improved.
Optionally, before constructing a similarity matrix representing a connection relationship between each intersection in the road network according to the road network topological relationship, the method further includes:
and carrying out normalization processing on the road network data.
In the embodiment of the application, the reliability of the Laplace matrix of the road network is improved by performing normalization processing on road network data before constructing the similarity matrix representing the connection relation between each intersection in the road network according to the topological relation of the road network.
Optionally, determining the eigenvalue and the eigenvalue difference of the laplacian matrix of the road network includes:
a plurality of eigenvalues of a Laplace matrix of a road network are determined.
And sequencing the plurality of characteristic values to obtain a plurality of sequenced characteristic values.
And determining a plurality of target characteristic values of which the characteristic value numerical values are in a preset range in the sorted plurality of characteristic values.
Feature value differences between adjacent ones of the plurality of target feature values are calculated.
Optionally, determining a difference value of eigenvalues of a laplacian matrix of a road network includes:
a plurality of eigenvalues of a Laplace matrix of a road network are determined.
Among the plurality of feature values, a plurality of target feature values having feature value values within a preset range are determined.
The plurality of target feature values are ranked and a feature value difference between adjacent ones of the plurality of target feature values is determined.
Optionally, the dividing the traffic control area according to the feature value difference includes:
and determining the ranking N of the target characteristic value corresponding to the maximum value of the characteristic value difference in the plurality of target characteristic values, and dividing the traffic control area into N sub-areas, wherein N is an integer greater than 1.
And determining the feature vector corresponding to each of the first N feature values.
And determining the intersection contained by each sub-area according to the feature vector corresponding to each of the first N feature values.
The following describes an apparatus, a device, a storage medium, and a computer program product for dividing a traffic control area according to an embodiment of the present application, and contents and effects thereof may refer to the method for dividing a traffic control area according to the first aspect of the present application, and are not described again.
In a second aspect, an embodiment of the present application provides a device for dividing a traffic control area, including:
the system comprises an acquisition module, a traffic control module and a traffic control module, wherein the acquisition module is used for acquiring road network topological relation and road network data in a traffic control area, and the road network data comprises intersection flow data or the road network data comprises intersection flow data and intersection distance data;
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for establishing a similarity matrix representing the connection relation between each intersection in the road network according to the road network topological relation; constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data; determining a degree matrix of the road network according to the similarity adjacent matrix; determining a Laplace matrix of the road network according to the similarity adjacent matrix and the degree matrix;
the determining module is used for determining the eigenvalue and the eigenvalue difference of the Laplace matrix of the road network;
and the dividing module is used for dividing the traffic control area according to the characteristic value difference.
Optionally, the device for dividing a traffic control area provided in the embodiment of the present application further includes:
and the processing module is used for carrying out normalization processing on the road network data.
Optionally, the determining module is specifically configured to:
a plurality of eigenvalues of a Laplace matrix of a road network are determined.
And sequencing the plurality of characteristic values to obtain a plurality of sequenced characteristic values.
And determining a plurality of target characteristic values of which the characteristic value numerical values are in a preset range in the sorted plurality of characteristic values.
Feature value differences between adjacent ones of the plurality of target feature values are calculated.
Optionally, the determining module is specifically configured to:
determining a plurality of eigenvalues of a Laplace matrix of a road network;
determining a plurality of target characteristic values of which the characteristic value values are within a preset range in the plurality of characteristic values;
the plurality of target feature values are ranked and a feature value difference between adjacent ones of the plurality of target feature values is determined.
Optionally, the dividing module is specifically configured to:
and determining the ranking N of the target characteristic value corresponding to the maximum value of the characteristic value difference in the plurality of target characteristic values, and dividing the traffic control area into N sub-areas, wherein N is an integer greater than 1.
And determining the feature vector corresponding to each of the first N feature values.
And determining the intersection contained by each sub-area according to the feature vector corresponding to each of the first N feature values.
In a third aspect, an embodiment of the present invention provides a server, including:
a processor;
a memory; and
a computer program;
wherein a computer program is stored in the memory and configured to be executed by the processor, the computer program comprising instructions for performing the method of dividing a traffic control area as described in the first aspect and the alternatives of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and the computer program enables a server to execute the method for dividing a traffic control area according to the first aspect and the optional aspects of the first aspect.
In a fifth aspect, an embodiment of the present invention provides a computer program product, including: executable instructions for implementing the method of dividing a traffic control zone as described in the first aspect or the first aspect alternative.
According to the method and the device for dividing the traffic control area, the road network topological relation and the road network data in the traffic control area are obtained, wherein the road network data comprise intersection flow data, or the road network data comprise intersection flow data and intersection distance data; according to the topological relation of the road network, constructing a similarity matrix representing the connection relation between each intersection in the road network; constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data; determining a degree matrix of the road network according to the similarity adjacent matrix; determining a Laplace matrix of the road network according to the similarity adjacent matrix and the degree matrix; determining a characteristic value and a characteristic value difference value of a Laplace matrix of a road network; and dividing the traffic control area according to the characteristic value difference. The Laplace matrix of the road network is established according to the topological relation of the road network and the road network data, and the traffic control area is divided according to the eigenvalue difference of the Laplace matrix of the road network, so that the rationality of the division of the traffic control area is improved, and the effect of performing zone control on the traffic control area is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a diagram of an exemplary application scenario according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for dividing a traffic control area according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an exemplary topology relationship of a network provided by an embodiment of the present application;
fig. 4 is a schematic flow chart of a traffic control area dividing apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an apparatus provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Traffic systems are an essential basis for national and social development. The intelligent traffic system applies the information communication technology to traffic infrastructure and vehicles, relieves traffic jam, improves traffic efficiency and safety, reduces energy consumption and exhaust emission, and increases economic benefit. The division of the effective traffic control area is the basis for efficient area coordination control. The basic idea is to divide a complex and huge road network into a plurality of independent sub-areas according to certain principle indexes, respectively execute appropriate control optimization strategies according to the characteristics of the sub-areas, and gradually release the control right, so that the system becomes more flexible and reliable. Therefore, finding a suitable division index and an algorithm thereof become a research hotspot of the current traffic region coordination control. However, in the prior art, the number of traffic control divided areas is set externally to divide the traffic control areas, so that the areas are unreasonable, and the area control effect is poor. In order to solve the above technical problem, embodiments of the present application provide a method and an apparatus for dividing a traffic control area.
An exemplary application scenario of the embodiments of the present invention is described below.
In a traffic control system, in order to improve the control effect on a traffic control area, a complex and huge road network is generally divided into a plurality of independent sub-areas according to a certain principle index, and a hierarchical traffic control system can be adopted to control the road network, fig. 1 is an exemplary application scene diagram of the embodiment of the present application, as shown in fig. 1, the hierarchical traffic control system can include a central control machine, a plurality of area control machines and a plurality of intersection control machines, the intersection control machines are responsible for signal control of a local intersection, the area control machines are responsible for optimal coordination control of the plurality of intersection control machines in a smaller area, and the central control machine is responsible for coordination among the area control machines. The embodiment of the present application does not limit the form of the traffic control system. Based on this, the embodiment of the application provides a method and a device for dividing a traffic control area.
Fig. 2 is a flowchart of a method for dividing a traffic control area according to an embodiment of the present application, where the method may be executed by a device for dividing a traffic control area, and the device may be implemented by software and/or hardware, for example: the device may be a part or all of a server or a terminal device, the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, and the like, and the method for dividing the traffic control area is described below with the terminal device as an execution subject, as shown in fig. 2, the method in the embodiment of the present application may include:
step S101: and acquiring road network topological relation and road network data in the traffic control area.
The road network topological relation in the traffic control area can comprise intersections, intersection identifications and connection relations among the intersections in the traffic control area, and the specific type and content of the road network topological relation are not limited in the embodiment of the application. The road network topological relation can be represented in the form of a road network topological graph, or in the form of a table, a text and the like. Fig. 3 is a schematic diagram of an exemplary road network topology relationship provided in an embodiment of the present application, as shown in fig. 3, there are 25 intersections in the traffic control area, and a connection relationship exists between each intersection and some other intersections. It should be noted that fig. 3 is only an exemplary schematic diagram of a topology relationship of a network, and the embodiment of the present application is not limited thereto.
The embodiment of the present application does not limit the specific data type of the road network data, and in a possible implementation, the road network data may include intersection traffic data.
The intersection flow data is the number of vehicles or pedestrians passing through the intersection in a certain period of time, which is not limited in the embodiment of the present application, and in addition, the time period for obtaining the intersection flow data is not limited in the embodiment of the present application, for example, the time period for obtaining the intersection flow data may be obtained every 15 minutes, every half hour, every hour, or every hour, and the like, and the time period for obtaining the intersection flow data may be specifically set according to the user requirements.
In another possible embodiment, the road network data includes intersection traffic data and intersection distance data. The road network data may include intersection traffic data and intersection distance data, and the data type, form and the like of the intersection distance data are not limited in the embodiment of the application. For example, the distance between two connected intersections can be labeled by being between the two intersections in fig. 3.
Step S102: and according to the topological relation of the road network, constructing a similarity matrix representing the connection relation between each intersection in the road network.
Taking the road network topological relation shown in fig. 3 as an example, a similarity matrix representing the connection relation between each intersection in the road network is constructed, where the table is an exemplary intersection similarity relation in the embodiment of the present application, and is shown in table one:
table one is an exemplary intersection similarity relationship in the embodiments of the present application
The first row and the first column of the table respectively represent intersection numbers, as can be seen from table one, the intersection numbers coexist at 25 intersections, starting from row 2 and column 2 of the table to row 26 to column 26 of the table, and the connection relationship between the 25 intersections is represented, for example, in the row with the intersection number of 1, the column with the intersection number of 3 corresponds to a numerical value of 1, which indicates that the intersection 1 and the intersection 3 have the connection relationship. In the row of intersection number 1, the column of intersection number 2 has a value of 0, indicating that there is no connection between intersection 1 and intersection 2.
The similarity matrix may be formed by data from row 2, column 2 to row 26 to column 26 of the table in table i, and data from row i and column j of the similarity matrix represents a connection relationship between intersection i and intersection j, where i and j are integers greater than 0 and less than or equal to the total number of intersections. The similarity matrix can thus be expressed as:
wherein, aijData representing the ith row and the jth column, i is an integer greater than 0 and less than n, j is an integer greater than 0 and less than n and represents the total number of intersections in the road network, for example, n in the above table is 25.
In the acquired road network data, the traffic flow of each intersection may have a large difference, and optionally, before constructing a similarity matrix representing a connection relationship between each intersection in the road network according to a road network topological relationship, the method for dividing a traffic control area provided in the embodiment of the present application may further include: and carrying out normalization processing on the road network data. The embodiment of the present application does not limit the implementation manner of normalization processing on the road network data.
Step S103: and constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data.
Weighting the similarity matrix a according to the intersection traffic data or according to the intersection traffic data and the intersection distance data, for example, the greater the intersection traffic correlation degree of the intersection 1 and the intersection 3 is, the greater the weight between the intersection 1 and the intersection 3 is, and further, for example, the greater the intersection traffic correlation degree of the intersection 1 and the intersection 3 is, and the shorter the distance between the intersection 1 and the intersection 3 is, the greater the weight between the intersection 1 and the intersection 3 is, which is not limited in the embodiment of the present application. So as to construct a similarity adjacency matrix representing the circulation relationship between each intersection in the road network:
wherein, the road junctionThe flow data can be obtained by constructing a flow matrix Q with m rows and n columnsmnWhere m represents the number of intersections and n represents the number of time periods for the data. If the number of intersections is 25 and the intersection flow data is counted once per hour in a day, then m is 25 and n is 24, which is not limited in this embodiment of the present application. Flow matrix QmnThe traffic flow of any intersection in the m intersections in any time period in the n time periods is included. The flow of annex one is counted once per hour a day, and the number of intersections of the matrix is 25.
To the flow matrix QmnPerforming normalization, the embodiment of the present application does not limit the specific way of normalization, and in a possible implementation, the normalization may be performed by selecting a traffic matrix QmnAnd the interval [0,0.5 ] formed by the maximum value and the minimum value in (1)]Carrying out equal proportion mapping to obtain a flow matrix QmnAnd (5) normalizing the result. Then according to the traffic matrix QmnThe result of normalization is that the similarity is calculated through a Gaussian similarity function to obtain deltaijI.e. the similarity between the ith crossing and the jth crossing, WijRepresenting the elements of the similarity adjacency matrix W at the ith row and the jth column.
The Gaussian similarity function calculation method comprises the following steps:
wherein, for any time period, xiRepresenting a flow matrix QmnTraffic at the ith intersection, x, in the normalized resultjRepresenting a flow matrix QmnThe traffic of the jth intersection in the normalized result, wherein i is a positive integer less than or equal to m, and j is a positive integer less than or equal to n; deltaijRepresenting the similarity between the ith intersection and the jth intersection; sigma is an externally configured parameter and represents that the neighborhood width of the sample point is controlled, namely, the larger sigma represents that the similarity between the sample point and the sample point with a longer distance is larger, and vice versa; | xi-xj||2Indicates the ith wayThe Euclidean distance between the traffic of the intersection and the traffic of the jth intersection represents the similar situation between the ith intersection and the jth intersection; h isij1 means that the ith crossing is adjacent to the jth crossing, hij0 means that the ith intersection and the jth intersection are not adjacent.
Then delta will beijMapping the value of (a) to a corresponding position of the similarity matrix to obtain a similarity adjacency matrix W:
W＝(δij)n*n
step S104: and determining a degree matrix of the road network according to the similarity adjacent matrix.
After the similarity adjacent matrix W is determined, a degree matrix D of the road network is determined:
wherein, the degree matrix D is a diagonal matrix, the data in each row is the sum of the data of the corresponding row in the similarity adjacent matrix, Di＝∑iWij。
Step S105: and determining the Laplace matrix of the road network according to the similarity adjacent matrix and the degree matrix.
According to the definition of the laplace matrix:
L＝I-D1/2WD1/2
where I denotes an identity matrix and W denotes a similarity adjacency matrix. And respectively substituting the matrixes into a definition formula of the Laplace matrix to determine the Laplace matrix of the road network.
Step S106: and determining the eigenvalue and the eigenvalue difference of the Laplace matrix of the road network.
After the laplacian matrix of the road network is determined, the eigenvalues and the eigenvalue differences of the laplacian matrix of the road network may be determined by solving the laplacian matrix of the road network. The embodiment of the present application does not limit this.
In one possible embodiment, determining eigenvalues and eigenvalue differences of a laplacian matrix of a road network comprises:
determining a plurality of eigenvalues of a Laplace matrix of a road network; sequencing the plurality of characteristic values to obtain a plurality of sequenced characteristic values; determining a plurality of target characteristic values of which the characteristic value numerical values are within a preset range in the sorted plurality of characteristic values; feature value differences between adjacent ones of the plurality of target feature values are calculated.
For example, if ten eigenvalues of the laplacian matrix of the road network are determined, which are 1.21, 1.62, 0.89, 0.83, 0.54, 0.74, 0.45, 0.61, 1.03, and 1.34, respectively, the multiple eigenvalues are sorted, for example, in order from size to size: 1.62, 1.34, 1.21, 1.03, 0.89, 0.83, 0.74, 0.61, 0.54, 0.45; then, a plurality of target characteristic values within a preset range are determined, the specific numerical range of the preset range is not limited in the implementation of the present application, and the specific numerical range can be set by itself according to needs, optionally, the preset range is (0, 1), the plurality of target characteristic values with characteristic value numerical values within the range of (0, 1) are respectively 0.89, 0.83, 0.74, 0.61, 0.54 and 0.45, and then characteristic value differences between adjacent target characteristic values in the plurality of target characteristic values are respectively 0.06, 0.09, 0.13, 0.07 and 0.09.
In another possible implementation, determining eigenvalues and eigenvalue differences of a laplacian matrix of a road network comprises:
determining a plurality of eigenvalues of a Laplace matrix of a road network; determining a plurality of target characteristic values of which the characteristic value values are within a preset range in the plurality of characteristic values; the plurality of target feature values are ranked and a feature value difference between adjacent ones of the plurality of target feature values is determined.
For example, ten eigenvalues of the laplacian matrix of the road network are determined, which are 1.21, 1.62, 0.89, 0.83, 0.54, 0.74, 0.45, 0.61, 1.03, and 1.34, respectively, and then a plurality of target eigenvalues within a preset range are determined, which is not limited by the specific numerical range of the preset range, and optionally, the preset range is (0, 1), and then the plurality of target eigenvalues within the preset range are 0.89, 0.83, 0.54, 0.74, 0.45, and 0.61, then the plurality of target eigenvalues are sorted, which are 0.89, 0.83, 0.74, 0.61, 0.54, and 0.45, and then eigenvalue difference between adjacent target eigenvalues among the plurality of target eigenvalues is calculated, which is 0.06, 0.09, 0.13, 0.07, and 0.09, respectively.
Step S107: and dividing the traffic control area according to the characteristic value difference.
The embodiment of the present application does not limit the manner of dividing the traffic control area according to the eigenvalue difference, for example, the traffic control area may be divided according to a matrix perturbation theory, where the Matrix Perturbation Theory (MPT) is a method for rapidly solving and re-analyzing the eigenvalues. According to the perturbation theory of the matrix, when the difference between the kth and (k + 1) th eigenvalues is larger, the subspace formed by the selected k eigenvectors is more stable. Based on this, in one possible implementation, the dividing the traffic control area according to the feature value difference comprises:
determining ranking N of a target characteristic value corresponding to the maximum value of the characteristic value difference in a plurality of target characteristic values, and dividing a traffic control area into N sub-areas, wherein N is an integer greater than 1; determining feature vectors corresponding to the first N feature values respectively; and determining the intersection contained by each sub-area according to the feature vector corresponding to each of the first N feature values.
Taking the exemplary introduction in step S106 as an example, if the maximum value of the feature value difference is 0.13, and the rank of the target feature value corresponding to the maximum value of the feature value difference in the plurality of target feature values is 3, the traffic control area is divided into 3 sub-areas. The first 3 eigenvalues are then determined: and 0.89, 0.83 and 0.74 respectively corresponding feature vectors determine the intersection contained by each sub-area. The embodiment of the present application does not limit the specific implementation manner of determining the intersection included in each sub-region according to the feature vector corresponding to each of the first N feature values.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Fig. 4 is a schematic flow chart of a device for dividing a traffic control area according to an embodiment of the present application, where the device may be implemented by software and/or hardware, for example: the device may be a server or a part or all of a terminal device, and the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, as shown in fig. 4, the device for dividing a traffic control area provided in the embodiment of the present application may include:
the acquiring module 51 is configured to acquire road network topological relations and road network data in a traffic control area, where the road network data includes intersection traffic data, or the road network data includes intersection traffic data and intersection distance data.
The establishing module 52 is configured to establish a similarity matrix representing a connection relationship between each intersection in the road network according to the road network topological relation; constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data; determining a degree matrix of the road network according to the similarity adjacent matrix; and determining the Laplace matrix of the road network according to the similarity adjacent matrix and the degree matrix.
And a determining module 53, configured to determine eigenvalues and eigenvalue differences of the laplacian matrix of the road network.
Optionally, the determining module 53 is specifically configured to:
a plurality of eigenvalues of a Laplace matrix of a road network are determined. And sequencing the plurality of characteristic values to obtain a plurality of sequenced characteristic values. And determining a plurality of target characteristic values of which the characteristic value numerical values are in a preset range in the sorted plurality of characteristic values. Feature value differences between adjacent ones of the plurality of target feature values are calculated.
Optionally, the determining module 53 is specifically configured to:
determining a plurality of eigenvalues of a Laplace matrix of a road network; determining a plurality of target characteristic values of which the characteristic value values are within a preset range in the plurality of characteristic values; the plurality of target feature values are ranked and a feature value difference between adjacent ones of the plurality of target feature values is determined.
And a dividing module 54, configured to divide the traffic control area according to the feature value difference.
Optionally, the dividing module 54 is specifically configured to:
and determining the ranking N of the target characteristic value corresponding to the maximum value of the characteristic value difference in the plurality of target characteristic values, and dividing the traffic control area into N sub-areas, wherein N is an integer greater than 1. And determining the feature vector corresponding to each of the first N feature values. And determining the intersection contained by each sub-area according to the feature vector corresponding to each of the first N feature values.
Optionally, the device for dividing a traffic control area provided in the embodiment of the present application further includes:
and the processing module 55 is configured to perform normalization processing on the road network data.
Fig. 5 is a schematic structural diagram of an apparatus provided in an embodiment of the present application, and as shown in fig. 5, the apparatus includes:
a processor 61, a memory 62, a transceiver 63 and a computer program; wherein the transceiver 63 implements data transmission between the car radio and other devices, a computer program is stored in the memory 62 and configured to be executed by the processor 61, the computer program comprising instructions for executing the above-mentioned method for dividing a traffic control area, the contents and effects of which refer to the method embodiment.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for dividing a traffic control area, comprising:
acquiring road network topological relation and road network data in a traffic control area, wherein the road network data comprises intersection flow data, or the road network data comprises the intersection flow data and intersection distance data;
constructing a similarity matrix representing the connection relation between each intersection in the road network according to the road network topological relation;
constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data;
determining a degree matrix of the road network according to the similarity adjacent matrix;
determining a Laplace matrix of the road network according to the similarity adjacency matrix and the degree matrix;
determining a characteristic value and a characteristic value difference value of a Laplace matrix of the road network;
and dividing the traffic control area according to the characteristic value difference.
2. The method according to claim 1, before said constructing a similarity matrix representing a connection relationship between each intersection in said road network according to said road network topological relation, further comprising:
and carrying out normalization processing on the road network data.
3. The method according to claim 1 or 2, wherein determining eigenvalues and eigenvalue differences of the laplacian matrix of the road network comprises:
determining a plurality of eigenvalues of a Laplace matrix of the road network;
sequencing the plurality of characteristic values to obtain a plurality of sequenced characteristic values;
determining a plurality of target characteristic values of which the characteristic value numerical values are within a preset range in the sorted plurality of characteristic values;
and calculating a feature value difference value between adjacent target feature values in the plurality of target feature values.
4. The method according to claim 1 or 2, wherein determining eigenvalue differences of laplacian matrices of said road network comprises:
determining a plurality of eigenvalues of a Laplace matrix of the road network;
determining a plurality of target characteristic values of which the characteristic value numerical values are within a preset range in the plurality of characteristic values;
the target feature values are sorted, and a feature value difference between adjacent ones of the target feature values is determined.
5. The method of claim 4, wherein the dividing the traffic control area according to the eigenvalue difference comprises:
determining ranking N of a target characteristic value corresponding to the maximum value of the characteristic value difference in the plurality of target characteristic values, and dividing the traffic control area into N sub-areas, wherein N is an integer greater than 1;
determining feature vectors corresponding to the first N feature values respectively;
and determining the intersection contained by each sub-area according to the characteristic vector corresponding to each of the first N characteristic values.
6. A device for dividing a traffic control area, comprising:
the system comprises an acquisition module, a traffic control module and a traffic control module, wherein the acquisition module is used for acquiring road network topological relation and road network data in a traffic control area, and the road network data comprises intersection flow data or the road network data comprises the intersection flow data and intersection distance data;
the establishing module is used for establishing a similarity matrix representing the connection relation between each intersection in the road network according to the road network topological relation; constructing a similarity adjacent matrix representing the circulation relationship between each intersection in the road network according to the similarity matrix and the road network data; determining a degree matrix of the road network according to the similarity adjacent matrix; determining a Laplace matrix of the road network according to the similarity adjacency matrix and the degree matrix;
the determining module is used for determining the eigenvalue and the eigenvalue difference of the Laplace matrix of the road network;
and the dividing module is used for dividing the traffic control area according to the characteristic value difference.
7. The apparatus of claim 6, further comprising:
and the processing module is used for carrying out normalization processing on the road network data.
8. The apparatus according to claim 6 or 7, wherein the determining module is specifically configured to:
determining a plurality of eigenvalues of a Laplace matrix of the road network;
sequencing the plurality of characteristic values to obtain a plurality of sequenced characteristic values;
determining a plurality of target characteristic values of which the characteristic value numerical values are within a preset range in the sorted plurality of characteristic values;
and calculating a feature value difference value between adjacent target feature values in the plurality of target feature values.
9. The apparatus according to claim 6 or 7, wherein the determining module is specifically configured to:
determining a plurality of eigenvalues of a Laplace matrix of the road network;
determining a plurality of target characteristic values of which the characteristic value numerical values are within a preset range in the plurality of characteristic values;
the target feature values are sorted, and a feature value difference between adjacent ones of the target feature values is determined.
10. The apparatus according to claim 9, wherein the partitioning module is specifically configured to:
determining ranking N of a target characteristic value corresponding to the maximum value of the characteristic value difference in the plurality of target characteristic values, and dividing the traffic control area into N sub-areas, wherein N is an integer greater than 1;
determining feature vectors corresponding to the first N feature values respectively;
and determining the intersection contained by each sub-area according to the characteristic vector corresponding to each of the first N characteristic values.
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