CN111275963A - Method and device for mining hot spot area, electronic equipment and storage medium - Google Patents
Method and device for mining hot spot area, electronic equipment and storage medium Download PDFInfo
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- CN111275963A CN111275963A CN202010036291.5A CN202010036291A CN111275963A CN 111275963 A CN111275963 A CN 111275963A CN 202010036291 A CN202010036291 A CN 202010036291A CN 111275963 A CN111275963 A CN 111275963A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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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
Abstract
The application discloses a method and a device for mining a hotspot region, electronic equipment and a storage medium. The application can be applied to the field of automatic driving. The specific implementation scheme is as follows: gridding the target area to obtain each grid area in the target area; obtaining the number of vehicles passing through each grid area by using the running track of each vehicle; obtaining the grade of each grid area by using the number of vehicles passing through each grid area; and determining the hot spot area in the target area by utilizing the grade of each grid area. According to the embodiment of the application, the target area is gridded to obtain each grid area, the hot spot area is excavated by combining the driving track of the vehicle, and effective reference information can be provided for traffic management.
Description
Technical Field
The present application relates to the field of intelligent transportation, and in particular, to a method and an apparatus for mining a hot spot area, an electronic device, and a storage medium. The application can be applied to the field of automatic driving.
Background
With the acceleration of the urbanization process, the demand of people on own vehicles is continuously increased, the number of vehicles on roads is increased day by day, so that the traffic management is challenged, and the problem of traffic congestion generally exists in each city. At present, traffic management lacks effective information guidance.
Disclosure of Invention
The embodiment of the application provides a method for excavating a hot spot region, which comprises the following steps:
gridding the target area to obtain each grid area in the target area;
obtaining the number of vehicles passing through each grid area by using the running track of each vehicle;
obtaining the grade of each grid area by using the number of vehicles passing through each grid area;
and determining the hot spot area in the target area by utilizing the grade of each grid area.
According to the embodiment of the application, the target area is gridded to obtain each grid area, the hot spot area is excavated by combining the driving track of the vehicle, and effective reference information can be provided for traffic management.
In one embodiment, obtaining the rank of each grid area using the number of vehicles passing through each grid area comprises:
clustering by using the number of vehicles passing through each grid area to obtain a plurality of clustering results, wherein the plurality of clustering results respectively correspond to a plurality of levels;
and determining the grade of each grid region according to the clustering result to which each grid region belongs.
In the above embodiment, the level of each grid region is determined by clustering, so that the traffic heat of the grid regions of different levels has obvious difference, and the hot spot region is determined more reasonably and accurately.
In one embodiment, determining the hot spot area in the target area by using the level of each grid area comprises:
and under the condition that the grade of the grid area is higher than a preset threshold value, determining the grid area as a hot spot area.
In one embodiment, the method further comprises:
the level of each grid area is marked in the map of the target area.
In the embodiment, the level of each grid area is marked in the map, which is favorable for visually presenting the traffic heat of each grid area and provides convenience for applying the hot spot area in traffic management.
In one embodiment, marking the level of each grid area in the map of the target area comprises:
and marking the color corresponding to the level of each grid area in the map of the target area to obtain the traffic thermodynamic diagram of the target area.
In the above embodiment, the map is marked with the color corresponding to the level of each mesh area, and the traffic heat of each mesh area is visually presented.
In one embodiment, the method further comprises:
and acquiring the running track of each vehicle in a preset time period, so as to determine the hot spot area of the target area in the preset time period by using the running track of each vehicle in the preset time period.
In the above embodiment, the hot spot region corresponding to the predetermined time period is determined by using the travel track of the predetermined time period, and the hot spot region can be determined in time periods, so that the hot spot distribution condition of the target region in different time periods is reflected.
The embodiment of the present application further provides an excavating device for a hot spot area, including:
the gridding module is used for gridding the target area to obtain each grid area in the target area;
the quantity module is used for obtaining the quantity of the vehicles passing through each grid area by using the running track of each vehicle;
the level module is used for obtaining the level of each grid area by utilizing the number of vehicles passing through each grid area;
and the hot spot module is used for determining the hot spot area in the target area by utilizing the grade of each grid area.
In one embodiment, the level module includes:
the clustering submodule is used for clustering by using the number of vehicles passing through each grid area to obtain a plurality of clustering results, and the clustering results respectively correspond to a plurality of levels;
and the determining submodule is used for determining the grade of each grid region according to the clustering result to which each grid region belongs.
In one embodiment, the hotspot module is further configured to determine that the grid area is a hotspot area if the level of the grid area is higher than a preset threshold.
In one embodiment, the apparatus further comprises:
and the marking module is used for marking the level of each grid area in the map of the target area.
In one embodiment, the labeling module is further configured to label, in the map of the target area, a color corresponding to the level of each grid area to obtain a traffic thermodynamic diagram of the target area.
In one embodiment, the apparatus further comprises:
the acquisition module is used for acquiring the running track of each vehicle in a preset time period so as to determine the hot spot area of the target area in the preset time period by using the running track of each vehicle in the preset time period.
An embodiment of the present application further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the methods of mining a hotspot region of the present disclosure.
The present application further provides a non-transitory computer readable storage medium storing computer instructions, where the computer instructions are configured to enable a computer to execute any one of the mining methods for a hotspot area in the present application.
One embodiment in the above application has the following advantages or benefits: each mesh region is obtained by meshing the target region. By using the driving track of the vehicle, the number of vehicles passing through each grid area can be obtained, so that the grade of each grid area is obtained, and the hot spot area in the target area is determined. By mining the hot spot area, the traffic management can obtain effective reference information, and traffic congestion is avoided.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a flowchart of a method for mining a hotspot region according to an embodiment of the present application;
FIG. 2 is a schematic diagram of setting the spacing of grid lines according to an embodiment of the present application;
FIG. 3 is a schematic diagram of setting the spacing of grid lines according to another embodiment of the present application;
FIG. 4 is a flow chart of a method for mining a hotspot region according to another embodiment of the present application;
FIG. 5 is a block diagram of a mining device for hot spot areas according to an embodiment of the present application;
fig. 6 is a block diagram of a mining device for a hot spot area according to another embodiment of the present application.
Fig. 7 is a block diagram of an electronic device for implementing the mining method for hotspot areas according to the embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a mining method for hot spot areas according to an embodiment of the present application. As shown in fig. 1, the method may include:
step S11, gridding the target area to obtain each grid area in the target area;
step S12, obtaining the number of vehicles passing through each grid area by using the driving track of each vehicle;
step S13, obtaining the grade of each grid area by using the number of vehicles passing through each grid area;
step S14, determining a hot spot region in the target region using the level of each mesh region.
In the embodiment of the present application, the target area may include a plurality of determination manners. For example, the target area may include province, city, business district, etc., may also include areas where several intersections are located or areas where a section of road is located, and may also include areas having a certain shape centered on a certain geographical position, such as a circular area or a square area.
Gridding the target area may include: dividing the target area into a plurality of grids according to a certain standard, wherein each grid comprises an area which is a grid area. The target area in the map is divided into a plurality of grid areas, and information such as the position of each grid area in the map can be obtained.
In step S11, for example, the intervals of the grid lines in the grid may be set in advance, and the target area may be divided into a plurality of grid areas using the grid with the set intervals. There are various ways of setting the spacing of the grid lines, and two exemplary ways are shown below:
example one, the distance between grid lines is set according to the mining requirement of the hot spot area. For example, as shown in fig. 2, in order to excavate hot spot regions in two opposite traveling directions in one road, the spacing S of the grid lines may be set to be approximately equal to the half width W of the road so as to calculate the number of vehicles in the two traveling directions, respectively.
Example two, the ratio of the space between the mesh lines and the size information of the target region is set in advance, and the space between the mesh lines is calculated from the size information of the target region and the ratio. For example, the target area is a square area, and the size information of the target area may include a length, a width, or a diameter of the circular area of the square area. The ratio may be, for example, 1:50 or 1:200, etc. As shown in fig. 3, the space S of the grid lines is calculated at a ratio of 1:25 according to the length H of the target region. In the case where the target area is large, for example, in the case where the target area includes a city or a business district, the distance between the grid lines may be calculated based on the scale and the size information of the target area, and the distribution of the hot spots in the entire target area may be mined using the distance.
In step S12, the driving trace may include position information of the vehicle during driving. There are various embodiments for obtaining the travel trajectory. For example, electronic images can be taken with various image capturing devices arranged on a road, such as an electric police camera at an intersection, and the like. And then obtaining the running track of the vehicle through image recognition, target tracking and the like. For another example, the driving tracks of the vehicle are obtained from an internet of vehicles server or an electronic map server, and the driving tracks may be acquired by a GPS (Global Positioning System) installed inside the vehicle or a terminal device carried by a person in the vehicle, and then uploaded to the internet of vehicles server or the electronic map server.
Using the driving trajectory of a certain vehicle, it is possible to determine which grid areas the vehicle passes. If the trajectory of a vehicle includes the vehicle entering and/or exiting a grid area, it may be determined that the vehicle passes through the grid area. For each grid area, the number of vehicles passing through the grid area may be increased by one if there is a travel path that indicates that the vehicle passes through the grid area. The number of vehicles passing through the grid area during a period of time may be accumulated using the travel path of each vehicle over the period of time (e.g., during a day or several hours). As shown in fig. 3, the traveling track of the vehicle 30 traveling from the north side of the park to the north side of the mall is known, and the number of vehicles passing through the mesh area 31 can be increased by confirming that the vehicle 30 enters the mesh area 31 and then leaves the mesh area 31 using the traveling track.
In step S13, the level of the grid area may include a level of the number of vehicles passing through the grid area. For example, the value range of the number of vehicles may be divided into a plurality of value intervals, and each value interval corresponds to one level. For example, in two hours of an early peak, the number of vehicles passing through one grid area is in the range of 1 to 120, and 1 to 120 may be divided into 4 numerical intervals. The interval lengths of each numerical interval may be equal or different. Each value interval corresponds to a level. And, if the levels are set from small to large in the numerical intervals, it is possible to obtain: the value [1,30] corresponds to a level of 1, the value [31,60] corresponds to a level of 2, the value [61,90] corresponds to a level of 3, and the value [91,120] corresponds to a level of 4. The number of vehicles passing through a certain grid area, for example 70, falls within a certain numerical interval [61,90], and the level of the grid area is level 3 corresponding to the numerical interval.
In some embodiments, the classification and the obtaining of the classification of each grid region may also be based on actual data of the number of vehicles. In one exemplary embodiment, the actual data range of the number of vehicles is divided into a plurality of value ranges, each value range corresponding to a level. For example, the minimum value of the number of vehicles passing through each grid area in the target area is 3 and the maximum value is 100, which are obtained by using the driving track, 3 to 100 may be divided into a plurality of numerical value intervals, and the level of the grid interval may be determined according to the numerical value interval in which the number of vehicles passing through the grid area is located.
In another exemplary embodiment, the step S13 of obtaining the rank of each grid area by using the number of vehicles passing through each grid area may include:
clustering by using the number of vehicles passing through each grid area to obtain a plurality of clustering results, wherein the plurality of clustering results respectively correspond to a plurality of levels;
and determining the grade of each grid region according to the clustering result to which each grid region belongs.
In the above embodiment, the number of vehicles in each grid area is divided into a plurality of clustering results by clustering, and a corresponding relationship between each clustering result and a level is established. And if the number of vehicles passing through a certain grid area belongs to which clustering result, the grade of the grid area is the grade corresponding to the clustering result. Clustering can enable the numerical values in the same clustering result to be close, and the numerical values of different clustering results have obvious difference. Thus, the grade of each grid area determined by the number of vehicles passing through the grid area is more reasonable and accurate, and the traffic heat difference of each grid area corresponding to different numbers of vehicles is fully reflected.
In one example, the greater the number of vehicles passing through the grid area, the higher the ranking. For example, 1 to 120 are divided into 4 value intervals, the level corresponding to the value [1,30] is 1, the level corresponding to the value [31,60] is 2, the level corresponding to the value [61,90] is 3, and the level corresponding to the value [91,120] is 4. If the number of vehicles passing through the grid area is within 30, the level of the grid area is 1; if the number of vehicles passing through the grid area is within 90 or more, the grid area is ranked 4. As another example, the number of vehicles passing through seven grid regions is 2, 3, 11, 49, 51, 96 and 99, and clustering results are obtained, wherein the first clustering result comprises 2, 3 and 11, the second clustering result comprises 49 and 51, and the third clustering result comprises 96 and 99. The higher the mean or median of the values in the clustering result is set, the higher the level is, so that the more the number of vehicles in the clustering result is, the higher the level of the grid region belonging to the clustering result is. With such an arrangement, if the number of vehicles passing through the grid area is in the first clustering result, the level of the grid area is the lowest; if the number of vehicles passing through the grid area is in the third classification result, the grid area is ranked highest.
In one embodiment, the step S14 of determining the hot spot area in the target area by using the level of each grid area may include: and under the condition that the grade of the grid area is higher than a preset threshold value, determining the grid area as a hot spot area. If the level of the grid area is higher than the preset threshold, the number of vehicles passing through the grid area is large, the traffic heat is high in the area with the large number of vehicles, and the area can be determined as a hot spot area.
As an exemplary implementation manner, as shown in fig. 4, the mining method for a hotspot area provided in the embodiment of the present application may further include:
step S15 marks the level of each mesh area in the map including the target area.
The grade of each grid area of the target area is marked in the map, so that the traffic heat of each grid area can be visually presented, and convenience is provided for applying hot spot areas in traffic management. Illustratively, a grid may be superimposed on the map including the target area, with numbers being marked in the grid to indicate the level of each grid area. Different levels can be set to correspond to different colors, gray scales or other visual features, and the visual features corresponding to the levels of the grid regions are marked on the grids. For example, the map including the target area is marked with the color corresponding to the level of each grid area, the grid area with the highest level corresponds to red, the grid area with the lowest level corresponds to green, and the middle levels are further set to be orange, purple and the like. Traffic thermodynamic diagrams of various colors corresponding to the levels including the target area may be displayed on the screen. By marking the colors corresponding to the levels of the grid areas in the map, the traffic heat of each grid area can be visually presented, so that the traffic management department can reasonably schedule the traffic heat and the congestion can be relieved.
In the embodiment of the application, the number of vehicles passing through each grid area is obtained by using the driving track of the vehicle, so that the grade of each grid area is obtained, and the hot spot area in the target area is determined. The travel trajectory of the vehicle may include, among other things, a travel trajectory at a predetermined time period, such as an early peak time period or a late peak time period. By acquiring the running track of each vehicle in a preset time period, the number of vehicles passing through each grid area in the preset time period can be obtained by utilizing the running track of each vehicle in the preset time period, so that the grade of each grid area in the preset time period is obtained, and the hot spot area in the preset time period in the target area is determined.
For example, the travel track of each vehicle during the early peak period is used for determining the hot spot region of the target region during the early peak period; the driving track of each vehicle in the late peak period is used for determining the hot spot area of the target area in the late peak period. Illustratively, the early peak hours may be 8:00 to 9:00 per day and the late peak hours may be 18:00 to 19:00 per day. The hot spot areas are determined in different time periods, and the hot spot distribution condition of the target area in different time periods can be reflected.
For example, the travel track of each vehicle in a predetermined period of time every day may be extracted from the travel tracks of each vehicle in a period of time, for example, several days or one month, the number of vehicles passing through each grid area in a predetermined period of time every day may be averaged to obtain the corresponding rank, and the hot spot area of the target area in a predetermined period of time may be determined. The daily average data is obtained by collecting long-time data, and the accuracy of determining the hot spot area can be improved.
In the embodiment of the application, each grid area is obtained by gridding the target area. By using the driving track of the vehicle, the number of vehicles passing through each grid area can be obtained, so that the grade of each grid area is obtained, and the hot spot area in the target area is determined. By mining the hot spot area, the traffic management can obtain effective reference information, and traffic congestion is avoided.
Fig. 5 is a block diagram of a mining device for a hot spot area according to an embodiment of the present application. As shown in fig. 5, the apparatus includes:
a gridding module 51, configured to grid the target area to obtain each grid area in the target area;
a quantity module 52, configured to obtain the quantity of vehicles passing through each grid area by using the driving track of each vehicle;
a level module 53, configured to obtain a level of each grid area by using the number of vehicles passing through each grid area;
and a hot spot module 54, configured to determine a hot spot area in the target area by using the level of each grid area.
In one embodiment, as shown in FIG. 6, the level module 53 includes:
the clustering submodule 531 is configured to perform clustering by using the number of vehicles passing through each grid area to obtain a plurality of clustering results, where the plurality of clustering results respectively correspond to a plurality of levels;
the determining submodule 532 is configured to determine a level of each grid region according to a clustering result to which each grid region belongs.
In one embodiment, hotspot module 54 is further configured to determine the grid area as a hotspot area if the level of the grid area is higher than a preset threshold.
In one embodiment, as shown in fig. 6, the apparatus further comprises:
and a labeling module 55, configured to label a level of each grid area in the map of the target area.
In one embodiment, the labeling module 55 is further configured to label a color corresponding to the level of each grid area in the map of the target area to obtain a traffic thermodynamic diagram of the target area.
In one embodiment, as shown in fig. 6, the apparatus further comprises:
and the acquisition module 56 is used for acquiring the running track of each vehicle in a preset time period.
The quantity module 52 is further configured to determine a quantity of vehicles passing through each grid area in a predetermined period of time using a travel track of each vehicle in the predetermined period of time; the level module 53 is further configured to obtain a level of each grid area in the predetermined period by using the number of vehicles passing through each grid area in the predetermined period; hotspot module 54 is further configured to determine a hotspot zone in the target zone over the predetermined period of time using the rank of each grid zone over the predetermined period of time.
In this way, the apparatus of the embodiment of the present application may determine the hot spot area in the target area in the predetermined period by using the travel track of each vehicle in the predetermined period and the above-mentioned mesh area divided by the target area.
The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 7 is a block diagram of an electronic device of a mining method for a hotspot area according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 7, the electronic apparatus includes: one or more processors 901, memory 902, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 7 illustrates an example of a processor 901.
The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the electronic device of the mining method of the hotspot area, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 902 may optionally include memory located remotely from the processor 901, which may be connected to the electronics of the mining method of hot spot areas over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the mining method for the hotspot area may further include: an input device 903 and an output device 904. The processor 901, the memory 902, the input device 903 and the output device 904 may be connected by a bus or other means, and fig. 7 illustrates an example of a connection by a bus.
The input device 903 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device, such as an input device like a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, etc. The output devices 904 may include a display device, auxiliary lighting devices (e.g., LEDs), tactile feedback devices (e.g., vibrating motors), and the like. The Display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) Display, and a plasma Display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, Integrated circuitry, Application Specific Integrated Circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (Cathode Ray Tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the target area is gridded to obtain each grid area, the driving track of the vehicle is combined, the hot spot area is excavated, and effective reference information can be provided for traffic management. The level of each grid area is marked in the map, so that the traffic heat of each grid area can be visually presented, and convenience is provided for applying hot spot areas in traffic management. The grade of each grid area is determined through clustering, so that the traffic heat degrees of the grid areas with different grades have obvious difference, and the hot spot area is determined more reasonably and accurately. And marking the color corresponding to the level of each grid area in the map, and visually presenting the traffic heat of each grid area. The hot spot area corresponding to the preset time period is determined by utilizing the running track of the preset time period, the hot spot area can be determined in time periods, and the hot spot distribution condition of the target area in different time periods is reflected.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (14)
1. A method for mining a hot spot region is characterized by comprising the following steps:
gridding a target area to obtain each grid area in the target area;
obtaining the number of vehicles passing through each grid area by using the driving track of each vehicle;
obtaining the grade of each grid area by using the number of vehicles passing through each grid area;
and determining a hot spot area in the target area by using the grade of each grid area.
2. The method of claim 1, wherein obtaining the rank for each of the grid areas using the number of vehicles passing through each of the grid areas comprises:
clustering by using the number of vehicles passing through each grid area to obtain a plurality of clustering results, wherein the plurality of clustering results respectively correspond to a plurality of levels;
and determining the grade of each grid region according to the clustering result of each grid region.
3. The method of claim 1, wherein determining a hotspot region in a target region using the rank of each of the grid regions comprises:
and under the condition that the grade of the grid area is higher than a preset threshold value, determining that the grid area is a hot spot area.
4. The method of claim 1, further comprising:
the level of each of the grid areas is marked in the map of the target area.
5. The method of claim 4, wherein labeling the level of each of the grid regions in the map of the target region comprises:
and marking the color corresponding to the grade of each grid area in the map of the target area to obtain the traffic thermodynamic diagram of the target area.
6. The method of any of claims 1 to 4, further comprising: acquiring the running track of each vehicle in a preset time period;
obtaining the number of vehicles passing through each grid area by using the driving track of each vehicle, wherein the method comprises the following steps: determining the number of vehicles passing through each grid area in a preset time period by using the running track of each vehicle in the preset time period;
obtaining the grade of each grid area by using the number of vehicles passing through each grid area, wherein the grade comprises the following steps: obtaining the grade of each grid area in the preset time period by using the number of vehicles passing through each grid area in the preset time period;
determining a hotspot region in the target region using the rank of each of the grid regions, comprising: determining a hot spot region in the target region in the predetermined period of time using a level of each of the grid regions in the predetermined period of time.
7. An excavating device for a hot spot area, comprising:
the gridding module is used for gridding a target area to obtain each grid area in the target area;
the quantity module is used for obtaining the quantity of the vehicles passing through each grid area by using the running track of each vehicle;
the level module is used for obtaining the level of each grid area by using the number of vehicles passing through each grid area;
and the hot spot module is used for determining the hot spot area in the target area by utilizing the grade of each grid area.
8. The apparatus of claim 7, wherein the level module comprises:
the clustering submodule is used for clustering by using the number of vehicles passing through each grid area to obtain a plurality of clustering results, and the clustering results respectively correspond to a plurality of levels;
and the determining submodule is used for determining the grade of each grid region according to the clustering result of each grid region.
9. The apparatus of claim 7, wherein the hotspot module is further configured to determine that the grid area is a hotspot area if the rank of the grid area is higher than a preset threshold.
10. The apparatus of claim 7, further comprising:
and the marking module is used for marking the grade of each grid area in the map of the target area.
11. The apparatus of claim 10, wherein the labeling module is further configured to label a color corresponding to a level of each of the grid areas in the map of the target area to obtain a traffic thermodynamic diagram of the target area.
12. The apparatus of any one of claims 7 to 11, further comprising:
the acquisition module is used for acquiring the running track of each vehicle in a preset time period;
the quantity module is further used for determining the quantity of the vehicles passing through each grid area in a preset time period by using the running tracks of the vehicles in the preset time period;
the level module is further used for obtaining the level of each grid area in the preset time period by using the number of vehicles passing through each grid area in the preset time period;
the hot spot module is further configured to determine a hot spot region in the target region in the predetermined period of time using a rank of each of the grid regions in the predetermined period of time.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
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