CN112419728B - Method, device, equipment and storage medium for determining road condition information - Google Patents
Method, device, equipment and storage medium for determining road condition information Download PDFInfo
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- CN112419728B CN112419728B CN202011316824.1A CN202011316824A CN112419728B CN 112419728 B CN112419728 B CN 112419728B CN 202011316824 A CN202011316824 A CN 202011316824A CN 112419728 B CN112419728 B CN 112419728B
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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
Abstract
The application discloses a method, a device, equipment and a storage medium for determining road condition information, which can be applied to the field of intelligent traffic, and are particularly used for intelligent traffic planning and intelligent traffic management. The specific implementation scheme is as follows: acquiring first road network information of a plurality of first road sections; acquiring second road network information of a plurality of second road sections and road condition information of each of the plurality of second road sections; and for each of a plurality of first road segments: determining a matched road section aiming at each first road section in a plurality of second road sections according to the adjacent road section of each first road section in the plurality of first road sections, the second road network information and a preset mapping model; and determining the road condition information of each first road section according to the road condition information of the matched road section.
Description
Technical Field
The present application relates to the field of intelligent transportation, specifically to the field of intelligent transportation planning and the field of intelligent transportation management, and more specifically to a method, an apparatus, a device, and a storage medium for determining road condition information.
Background
The road condition information can be used as reference information for daily travel of people and can also be used as important business information in industries such as traffic planning or traffic management. In order to improve the service processing efficiency, service personnel in the industries of traffic planning, traffic management and the like can acquire road condition information from a platform providing a map navigation function. The traffic planning or traffic management industry generally has a self-drawing road network, and road condition information acquired from a platform providing a map navigation function is a standard road network set for the platform. However, the self-drawn road network and the standard road network are different, and the problem that the acquired road condition information is inaccurate relative to the self-drawn road network exists.
Disclosure of Invention
A method, apparatus, device and storage medium for determining traffic information for improving accuracy of the traffic information are provided.
According to a first aspect, there is provided a method for determining traffic information, the method comprising: acquiring first road network information of a plurality of first road sections; acquiring second road network information of a plurality of second road sections and road condition information of each of the plurality of second road sections; for each of a plurality of first road segments: determining a matched road section aiming at each first road section in a plurality of second road sections according to the adjacent road section of each first road section in the plurality of first road sections, the second road network information and a preset mapping model; and determining the road condition information of each first road section according to the road condition information of the matched road section.
According to a second aspect, there is provided an apparatus for determining traffic information, the apparatus comprising: the first information acquisition module is used for acquiring first road network information of a plurality of first road sections; the second information acquisition module is used for acquiring second road network information of a plurality of second road sections and road condition information of each of the plurality of second road sections; a matching segment determination module to, for each of a plurality of first segments: determining a matched road section aiming at each first road section in a plurality of second road sections according to the adjacent road section of each first road section in the plurality of first road sections, the second road network information and a preset mapping model; and the road condition information determining module is used for determining the road condition information of each first road section according to the road condition information of the matched road section.
According to a third aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for determining traffic information provided by the present application.
According to a fourth aspect, a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the method of determining traffic information provided herein is provided.
According to a fifth aspect, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of determining road condition information provided herein.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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 schematic view of an application scenario of a method, an apparatus, a device and a storage medium for determining traffic information according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating a method for determining traffic information according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of determining a matching road segment for each first road segment according to an embodiment of the application;
FIG. 4 is a schematic illustration of a determination of matching road segments for each first road segment in accordance with another embodiment of the present application;
fig. 5A to 5E are schematic diagrams illustrating a principle of determining a mapping road segment or a mapping road segment group according to a preset rule according to an embodiment of the present application;
fig. 6 is a schematic diagram illustrating a principle of determining traffic information of each first road segment according to an embodiment of the present application;
fig. 7A to 7C are road condition information mapping comparison graphs of a first road network and a second road network obtained by the method for determining road condition information according to the embodiment of the present application;
fig. 8 is a block diagram illustrating a structure of an apparatus for determining traffic information according to an embodiment of the present disclosure; and
fig. 9 is a block diagram of an electronic device for implementing the method for determining traffic information 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.
The application provides a method for determining road condition information. The method comprises the steps of firstly obtaining first road network information of a plurality of first road sections, and obtaining second road network information of a plurality of second road sections and road condition information of each of the plurality of second road sections. Then, for each of the plurality of first road segments, a matching road segment for each of the plurality of second road segments is determined according to the neighboring road segments of each of the plurality of first road segments, the second road network information, and the preset mapping model. And finally, determining the road condition information of each first road section according to the road condition information of the matched road section aiming at each first road section.
An application scenario of the method and apparatus provided by the present application will be described below with reference to fig. 1.
Fig. 1 is an application scenario diagram of a method, an apparatus, a device and a storage medium for determining traffic information according to an embodiment of the present application.
As shown in fig. 1, the application scenario 100 of this embodiment may include, for example, a first terminal device 110, a second terminal device 120, and a server 130. The first terminal device 110 may interact with the server 130 through a network to acquire data from the server 130 or transmit data to the server 130 through the network. User 140 may transmit data to first terminal device 110 through second terminal device 120.
According to the embodiment of the present application, the first terminal device 110 and the second terminal device 120 may be, for example, various electronic devices having a processing function, including but not limited to a smart phone, a tablet computer, a laptop portable computer, a desktop computer, a server, and the like. The first terminal device 110 and the second terminal device 120 may, for example, be installed with client applications, such as a data processing type application, a mapping type application, a search type application, social platform software, and the like (for example only). The user 140 may, for example, send data to the first terminal device 110 via social platform software in the second terminal device 120.
According to an embodiment of the present application, the server 130 may be a server that provides various services, such as a background management server (for example only) that provides support for the running of an electronic map client application. The server 130 may be, for example, an application server, a server of a distributed system, or a server incorporating a blockchain. Alternatively, the server may also be a virtual server, a cloud server, or the like.
According to the embodiment of the present application, the data sent by the second terminal device 120 to the first terminal device 110 may be, for example, a road network information text 150 of an electronic map drawn by the user 140 via the second terminal device 120. The first terminal device 110 may obtain, in response to receiving the road network information text 150, the road network information text 160 of the navigation electronic map and the road condition information of each road segment in the road network information of the navigation electronic map within a preset time period from the server 130. The first terminal device 110 may map the road network information in the road network information text 150 and the road network information text 160 via the client application, and feed back the road condition information 170 of each road segment in the road network information described in the road network information text 150 to the second terminal device 120 according to the mapping result.
In this way, for the user 140, by sending the road network information text 150 of the drawn electronic map to the first terminal device 110, not only the road condition information 170 of each road segment in the self-drawn road network can be accurately acquired, but also complex calculations such as coordinate conversion are not required, so that the user experience can be effectively improved.
According to an embodiment of the present application, the functions of the first terminal device 110 and the server 130 may also be integrated in the same electronic device, for example. For example, the server may receive the road network information text 150 and feed back the road condition information to the second terminal device 120 according to the locally stored road network information text 160.
It should be noted that the method for determining the traffic information provided in the embodiment of the present application may be generally executed by the first terminal device 110, or may be executed by the server 130. Accordingly, the apparatus for determining traffic information provided in the embodiment of the present application may be generally disposed in the first terminal device 110, or may be disposed in the server 130.
It should be understood that the types of the first terminal device, the second terminal device and the server in fig. 1 are merely illustrative. There may be any type of first terminal device, second terminal device, and server, as the implementation requires.
In the following, the method for determining traffic information provided in the embodiment of the present application is described in detail with reference to the application scenario described in fig. 1 and with reference to fig. 2 to 7C.
Fig. 2 is a flowchart illustrating a method for determining traffic information according to an embodiment of the present disclosure.
As shown in fig. 2, the method 200 for determining traffic information according to the embodiment may include operation S210, operation S230, operation S250, and operation S270.
In operation S210, first road network information of a plurality of first road segments is acquired.
According to the embodiment of the present application, the first network information may be sent by the user through the terminal device, or may be transmitted by the user through a storage medium (e.g., a hard disk, a usb disk, an optical disk), or the like. The plurality of first road segments are all road segments shown by a certain regional road network map drawn by the user through the terminal device, or a plurality of road segments included in a target region which is drawn by the user in the certain regional road network map shown by the terminal device. The road network map of a certain area may be, for example, a road network map of an area range such as a town, a county, an urban area, or a province. A plurality of roads are displayed in the road network map, and each road in the plurality of roads is divided into at least one first road section due to the intersection relation between the road and other roads. Each first road section can be a straight line section or a curve section.
According to an embodiment of the present application, the first road network information of the plurality of first road segments may be recorded with attribute information of each of the first road segments. The attribute information may include, for example, a start point position, an intermediate point position, an end point position, and the like of the link.
In operation S230, second road network information of a plurality of second road segments and road condition information of each of the plurality of second road segments are acquired.
According to the embodiment of the application, the second road network information of the plurality of second road segments can be obtained from a server providing service support for the map navigation client application. The plurality of second road segments are all road segments displayed in the electronic map displayed by the map navigation client application. Alternatively, the plurality of second road segments may be all road segments of a matching area in the electronic map, wherein the matching area is determined according to the acquisition condition transmitted by the first terminal device.
According to the embodiment of the present application, the second road network information of the plurality of second road segments may record attribute information of each second road segment, and the second road network information is similar to the first road network information, and is not described herein again. The road condition information of each of the plurality of second road segments may include, for example, an average speed of the plurality of vehicles passing through each road segment within a preset time period, the number of vehicles passing through each road segment per unit time, and the like. In an embodiment, the average speed included in each road condition information may include an average speed for each of a plurality of preset time periods.
In operation S250, for each of the plurality of first road segments, a matching road segment for each of the plurality of second road segments is determined according to the neighboring road segments of each of the plurality of first road segments, the second road network information, and the preset mapping model.
According to an embodiment of the present application, the preset mapping model may be, for example, a map matching model or a map matching algorithm. The operation S250 may input information of each first road segment in the first road network information, information of a road segment adjacent to each first road segment, and second road network information into a preset mapping model, so as to obtain a second road segment matched with each first road segment.
Illustratively, the map matching Model may be, for example, a Hidden Markov Model (HMM). The model may be specifically configured to compare the first road segment with a plurality of second road segments, and find a route that is closest to the trajectory of the first road segment from the second road segments as the second road segment that matches the first road segment. It is to be understood that the map matching model is only an example to facilitate understanding of the present application, and the present application is not limited thereto, for example, the map matching model may also be a kalman filter model, a fuzzy logic model, or the like.
Illustratively, the map Matching algorithm may be, for example, an ST-Matching algorithm, which is a global algorithm capable of integrating geometric information, road topology information, road attribute information (e.g., speed limit of each road), and the like to determine a second road segment Matching each first road segment among a plurality of second road segments. It is to be understood that the map matching algorithm described above is merely an example to facilitate understanding of the present application, and the present application is not limited thereto.
In operation S270, traffic information for each first road segment is determined with respect to the traffic information for the matching road segment for each first road segment.
According to the embodiment of the application, the traffic information of the second road section matched with each first road section can be used as the traffic information of each first road section. For example, in the case where the road condition information includes a speed, the embodiment may take a speed of the second section matched with the first section as a speed of the first section. In the case where the traffic information includes the congestion degree, the congestion degree of the second section that matches the first section may be used as the congestion degree of the first section.
In summary, according to the embodiment of the application, the second road segment matched with each first road segment is determined according to the preset mapping model, and when the matched road segment is determined, not only the information of each first road segment but also the information of the adjacent road segments of the first road segment are considered, so that the matched road segment can be determined more accurately. Therefore, the accuracy of the first road condition information determined according to the second road condition information can be improved, and better service can be provided for users. This is because the accuracy of map matching increases as the number of input track points increases. Especially for the first road section with short length, the accuracy of the determined matching road section can be obviously improved. Furthermore, the method for determining the road condition information can enable the user to obtain the road condition information of the road network road section only by providing the road network information without converting the road condition information of the navigation map, thereby being beneficial to improving the service processing efficiency of the user.
The principle of obtaining the matching road section of each first road section according to the preset mapping model will be described below with reference to fig. 3 to 4.
Fig. 3 is a schematic diagram of determining a matching road segment for each first road segment according to an embodiment of the present application.
According to the embodiment of the application, when determining the matching road segment, a mapping road segment having a mapping relation with each first road segment in the plurality of second road segments may be determined according to the first road network information and the preset mapping model. And then determining a mapping road section group which has a mapping relation with each first road section in a plurality of second road sections according to the adjacent road sections, the second road network information and a preset map mapping model. Finally, a matching road segment for each first road segment is determined according to the mapping road segment and the mapping road segment group. According to the embodiment, the matching road sections determined according to a single road section and the matching road section groups determined according to a plurality of adjacent road sections are fused, so that the accuracy of the determined matching road sections can be improved, and the accuracy of the road condition information provided for the user can be improved conveniently.
Illustratively, in the aforementioned acquired road network information, attribute information of each road segment is included. The attribute information includes head node position information, tail node position information, and position information of a plurality of intermediate points between the head node and the tail node of the road section. As in the embodiment 300 shown in fig. 3, when determining the mapped road segment, the attribute information 311 of each first road segment and the attribute information of all the second road segments in the second network information 312 may be input into the map matching model 320, and the mapped road segment 331 is obtained via the map matching model 320. Similarly, when determining the mapped link group, the attribute information 311 of each first link, the attribute information of all the second links in the second network information 312, and the attribute information 313 of the adjacent link may be input into the map matching model 320, and the mapped link group 332 is obtained via the map matching model 320. The mapped road segment group 332 may include, for example, a second road segment matching each first road segment and a second road segment matching an adjacent road segment, which are determined via the map matching model 320. It is to be understood that the present application does not limit the number of second road segments in the mapped road segments and the number of second road segments in the mapped road segment group. For example, in the case where the length of the first link is short and the length of the second link is long, the second link matching each first link and the second link matching the adjacent link may be the same link. In the case where the length of the first link is long and the length of the second link is short, the map link may be constituted by a plurality of adjacent second links.
For example, after the mapping road segment 331 and the mapping road segment group 332 are obtained, the second road segments included in both the mapping road segment 331 and the mapping road segment group 332 may be determined to be the second road segments matched with each of the first road segments as the matching road segments 340 by comparing the mapping road segment 331 and the mapping road segment group 332. For example, if the mapped road segment 331 includes a second road segment a and a second road segment b, and the mapped road segment group 332 includes a second road segment b, a second road segment c, and a second road segment d, it is determined that the matching road segment is the second road segment b.
For example, in a case where the mapping road segment group 332 includes the mapping road segment 331, that is, the mapping road segment 331 is a subset of the mapping road segment group 332, the mapping road segment 331 may be determined as a matching road segment for each first road segment.
For example, in a case where the mapped link group does not include the mapped link, a link other than the matching link for the adjacent link in the mapped link group is determined as the matching link for each first link. For example, in a case where the mapped road segment group 332 having a mapping relationship with the first road segment a does not include the mapped road segment 331 having a mapping relationship with the first road segment a, and the mapped road segment group 332 does not intersect with the mapped road segment 331, the mapped road segment group 332 may be first saved. After the matching road sections of the adjacent road sections B of the first road section a are obtained, the matching road sections of the adjacent road sections B are removed from the stored mapping road section group 332, and the remaining second road sections are used as the matching road sections of the first road section a.
Fig. 4 is a schematic diagram of determining a matching road segment for each first road segment according to another embodiment of the present application.
According to the embodiment of the application, the accuracy of the matched road section obtained by the previous embodiment depends on the accuracy of the map matching model to a certain extent. In consideration of the diversity and complexity of the actual link types, the map matching model may have a plurality of mapped links or mapped link groups. For this reason, when the map matching model determines that a plurality of mapped link groups are obtained, the plurality of mapped link groups may be used as candidate link groups. And finally, determining a road section from the alternative road sections as a mapping road section, and determining a road section group from the alternative road section group as a mapping road section group.
For example, as in the embodiment 400 shown in fig. 4, when determining the map segments, the attribute information of all the second segments in the attribute information 411 and the second network information 412 of each first segment may be input into the map matching model 420. At least one alternative road segment 431 having a mapping relation with each first road segment in the plurality of second road segments is obtained through the map matching model 420. Then, according to the preset similarity rule, one of the at least one candidate road segment 431 is determined as the mapping road segment 451. Similarly, when determining the mapped link group, at least one alternative link group 432 for each first link may be determined via the map matching model 420 with the attribute information 411 of each first link, the attribute information of all second links in the second network information 412, and the attribute information 413 of the adjacent link as inputs to the map matching model 420. Then, according to the preset similarity rule, one of the at least one alternative road segment group 432 is determined as the mapping road segment group 452. Finally, the matching road segment 440 is determined from the mapping road segment 451 and the mapping road segment group 452.
According to the embodiment of the application, the preset similarity rule may determine, for example, a similarity relationship between each candidate road segment and the first road segment according to a similarity between the attribute information of the candidate road segment and the attribute information of the first road segment, and determine the candidate road segment with the highest similarity to the attribute information of the first road segment as the mapping road segment. In a similar manner, the candidate link group having the highest similarity to the attribute information of the first link and the link adjacent to the first link may be determined as the mapping link group.
Illustratively, the degree of similarity between two pieces of attribute information may be determined, for example, from the degree of similarity between two vectors representing the two pieces of attribute information. Wherein, the similarity between two vectors can be represented by any one of the following: cosine similarity, Euclidean distance, Manhattan distance, Jacard similarity coefficient, etc.
According to the embodiment of the present application, the preset similarity rule may also determine the similarity between each candidate segment and the first segment by using an algorithm such as a euclidean Distance, a Dynamic Time Warping (DTW), a Hausdorff Distance (Hausdorff Distance), and the like, and finally determine the candidate segment with the highest similarity to the first segment as the mapping segment. In a similar manner, the candidate link group having the highest similarity to the first link and the links adjacent to the first link may be determined as the mapped link group.
According to an embodiment of the present application, the presetting of the similar rule may also include, for example: and comparing the attributes of the two road sections, and if the difference of the attributes of the two road sections does not meet the preset condition, determining that the similarity relation between the two road sections is not similar. The preset similarity rule will be described in detail below with reference to fig. 5A to 5E.
Fig. 5A to 5E are schematic diagrams illustrating a principle of determining a mapped road segment or a mapped road segment group according to a preset rule according to an embodiment of the present application.
According to an embodiment of the present application, the preset similarity rule may be that a similarity relationship between two road segments is determined according to a difference between lengths of the two road segments and a smaller length of the lengths of the two road segments.
For example, in the embodiment 510 shown in fig. 5A, for the road segment 511 and the road segment 512, the respective lengths of the two road segments, that is, the mileage traveled by the vehicle through the road segment 511 and the mileage traveled through the road segment 512, are determined. The smaller of the two lengths is then determined to be the length of the road segment 511. The absolute value of the difference between the two lengths is then determined and a first ratio of the absolute value of the difference to the length of the road segment 511 is determined. And if the first ratio is greater than or equal to the first preset ratio, determining that the two road sections are not similar. And finally determining the alternative road section similar to the first road section in the plurality of alternative road sections as the mapping road section. It can be understood that the value of the first preset ratio can be set according to actual requirements, and the application does not limit the value. For example, the first predetermined ratio may be any value less than 0.2.
Illustratively, the attribute information of the link includes position information of the head node, position information of the tail node, and position information of the plurality of intermediate nodes. The length of a road segment may be represented by the cumulative sum of the lengths of straight lines between sets of adjacent nodes. For example, for road segment 511, there is a head node O11Intermediate node O12～O15And tail node O16. The length of the road section 511 is a head node O11And intermediate node O12Straight line distance between, intermediate node O12And intermediate node O13Linear distance therebetween, …, and intermediate node O15And tail node O16The sum of the linear distances therebetween.
According to an embodiment of the present application, the preset similarity rule may be a rule for determining a similarity relationship between two road segments according to a distance difference between head nodes of the two road segments and an average value of lengths of the two road segments.
Illustratively, in the embodiment 520 shown in fig. 5B, for the road segment 521 and the road segment 522, the head node P of the road segment 521 is determined first11And head node P of road section 52212The distance between the two head nodes can be determined by two coordinate values of the two head nodes in the same coordinate system, and the distance is a straight-line distance between the two head nodes. An average of the lengths of the two segments is then determined, and a second ratio of the difference in distance between the head nodes of the two segments to the average is determined. And if the second ratio is smaller than a second preset ratio, determining that the two road sections are similar, otherwise, determining that the two road sections are not similar. It can be understood that the value of the second preset ratio can be set according to actual requirements, and the application does not limit the value. For example, the second predetermined ratio may be any value less than 0.1.
According to an embodiment of the present application, the preset similarity rule may determine a similarity relationship between two road segments according to an angle value between a direction in which the two road segments respectively point from the head node to the tail node and a preset direction. The attribute information of the road section comprises position information of a head node and position information of a tail node.
For example, in the embodiment 530 as shown in fig. 5C, for the road segment 531 and the road segment 532, the head node Q of the road segment 531 may be determined first11Pointing to tail node Q12In the direction of dotted line Q in FIG. 5C11Q12Determines the head node Q of the road segment 53221Pointing to tail node Q22In the direction of dotted line Q in FIG. 5C21Q22Is directed in the direction of pointing. The dotted line Q is then determined11Q12Is directed at an angle theta to the north direction N1Determining the dotted line Q21Q22Is directed at an angle theta to the north direction N2. When the required rotation angle of the pointing direction rotating to the due north direction along the clockwise direction is less than or equal to 180 degrees, the included angle between the pointing direction and the due north direction is taken as the value of the rotation angle, and when the required rotation angle of the pointing direction rotating to the due north direction along the clockwise direction is more than 180 degrees, the included angle between the pointing direction and the due north direction is taken as the difference between the rotation angle and 360 degrees. At the angle theta1And angle theta2And then determining whether the absolute value of the difference value of the two included angles is smaller than a preset angle value, if so, determining that the two road sections are similar, otherwise, determining that the two road sections are not similar. By this similarity rule, alternative road sections that are similar in length to the first road section but opposite in direction of travel of the first road section can be excluded. It can be understood that the preset angle value can be set according to actual requirements, and the preset angle value is not limited in the application. For example, the preset angle value may be any value not greater than 30 °. By setting the preset angle value, the road section of the driving main road can be distinguished from the road section of the branch road of the driving main road.
According to an embodiment of the application, the preset similarity rule may determine a similarity relationship between two road segments according to maximum curvature values of the two road segments, wherein the attribute information of the road segments includes curvature values at various positions of the road segments. It will be appreciated that the curvature values at the various locations may include curvature values at each of the head, tail, and intermediate nodes, and that the curvature values may also be represented by a radius of curvature, for example.
For example, in the embodiment 540 shown in fig. 5D, for the road segment 541 and the road segment 542, a maximum curvature value of a plurality of curvature values in the attribute information of the road segment 541 may be determined first, and a maximum curvature value of a plurality of curvature values in the attribute information of the road segment 542 may be determined. A difference between the two maximum curvature values is then determined, and in case the difference is smaller than a preset difference, the road section 541 and the road section 542 are determined to be similar, otherwise the road section 541 and the road section 542 are determined to be dissimilar. The preset difference value may be set according to an actual requirement, which is not limited in the present application, and for example, the preset difference value may be any value not greater than 0.2/m.
By presetting the similarity rule, the embodiment can avoid having the same or similar head nodes R1And tail node R2However, a case where two links belonging to different branches are determined as mapped links to each other.
According to an embodiment of the application, the preset similarity rule may determine a similarity relationship between two road segments according to road types of the two road segments, for example, where the attribute information of the road segment includes the road type of the road segment.
Illustratively, the road type may include, for example, a viaduct, a main road, a sub road, a ramp, and the like. This embodiment may be implemented by determining dissimilarity between two road segments in the case where the road types of the two road segments are not identical. In the case where the road types of the two road segments coincide, it is determined that the two road segments are similar. For example, in the embodiment 550 shown in fig. 5E, the segment 551 belongs to one segment in the main road and the segment 552 belongs to one segment in the side road, although the head nodes S of the two segments11And head node S21Near and tail nodes S12And tail node S22The attributes are similar, and the maximum curvature values of the two road sections are similar, because the two road sections are similarThe road types of the individual road segments are different, and thus it should be determined that there is a dissimilarity between the two road segments. It is to be understood that the above-described road types are merely examples to facilitate understanding of the present application, and the present application is not limited thereto.
It is to be understood that in determining whether two road segments are similar, two or more similarity rules may be combined to determine a similarity relationship between the two road segments. For example, it may be determined whether the road types of the two road segments are the same, and in the case that the road types are the same, the similarity relationship between the two road segments may be determined according to the difference between the lengths of the two road segments and the smaller length of the lengths of the two road segments. Alternatively, the difference between the lengths of the two road segments and the smaller length of the lengths of the two road segments may be determined, and it may be determined whether the ratio of the difference to the smaller length is smaller than a first preset ratio. And under the condition that the distance between the head nodes of the two road sections is smaller than the first preset ratio, determining the similarity relation between the two road sections according to the distance difference between the head nodes of the two road sections and the average value of the lengths of the two road sections. And under the condition that the ratio of the distance difference to the average value is smaller than a second preset ratio, determining the similarity relation between the two road sections according to the respective maximum curvature values of the two road sections. By combining two or more similarity rules to determine the similarity relationship between two road segments, the accuracy of the finally determined mapping road segment can be improved.
According to the embodiment of the application, the various preset similarity rules can be combined, a weight is assigned to each preset similarity rule, and if the similarity of the two road sections is determined according to the similarity rules, the similarity relation is the similarity with the value of 1. If the two road sections are not similar, the similarity relationship is the similarity with the value of 0. And calculating the weighted sum of the similarity obtained by the multiple preset similarity rules according to the distributed weights, and taking the weighted sum as the final similarity between the two road sections. And selecting the road section with the highest similarity with the first road section from the plurality of candidate road sections as the mapping road section when the number of the candidate road sections is multiple. Similarly, one from a plurality of alternative road segment groups may be selected as the mapping road segment group.
Fig. 6 is a schematic diagram illustrating a principle of determining traffic information of each first road segment according to an embodiment of the present application.
According to the embodiment of the application, when the traffic information of each first road section is determined according to the traffic information of the matched road section, the speed of the vehicle passing through each first road section can be determined firstly, and then the traffic information of the first road section is determined according to the speed.
For example, in the case that the matching road segment of each first road segment is one second road segment, it may be determined that the speed of the matching road segment in the acquired road condition information of the second road segment is the speed of the first road segment. In the case where the matching section is at least two second sections, the average speed of the vehicle passing through the at least two second sections may be taken as the speed of the vehicle passing through the first section. And under the condition that the road condition information is the speed of the road section, taking the determined speed of the first road section as the road condition information. For example, in the embodiment 600 depicted in fig. 6, the second road segment matched with the first road segment 611 includes a second road segment 621, a second road segment 622, and a second road segment 623, and the speed of the vehicle passing through the second road segment 621, the second road segment 622, and the second road segment 623 is V respectively1、V2、V3The lengths of the second road segment 621, the second road segment 622, and the second road segment 623 are L, respectively1、L2、L3The rate at which the vehicle passes through the first segment 611, V ═ may be determined (L)1+L2+L3)/(L1/V1+L2/V2+L3/V3). The second road segment matched with the first road segment 612 is the second road segment 623, it can be determined that the speed of the vehicle passing through the first road segment 612 is V3。
In summary, according to the present application, when the matching road segment includes a plurality of second road segments, the speed of the plurality of second road segments is used as the speed of the first road segment, so that the accuracy of the determined speed of the first road segment can be improved, and the user experience can be improved.
According to an embodiment of the present application, when the traffic information further includes a congestion degree indicating a congestion degree of a road segment, the embodiment may use the congestion degree of a second road segment matching the first road segment as the congestion degree of the first road segment.
For example, the acquired first road network information may include a congestion degree-speed relationship, and after obtaining a speed of the vehicle passing through the first road segment, it may be determined that a congestion degree corresponding to the speed of the vehicle passing through the first road segment in the congestion degree-speed relationship is a congestion degree of the first road segment, and the congestion degree is used as the road condition information. Since the determination criteria and the setting purpose of the road congestion degree are different for different mechanisms, the congestion degree of the second link may be different from the congestion degree of the first link required by the user. In this embodiment, after the speed of the first segment is obtained, the congestion degree of the first segment is determined according to the speed and the congestion degree-speed relationship provided by the user. By the embodiment, the accuracy of the road condition information fed back to the user can be improved, so that the user experience is improved.
According to the method for determining the road condition information, the method is verified, the matched road section of the first road section in the first road network information can be accurately obtained, and the road condition information of the first road section is accurately obtained. The congestion degree and road network distribution of the two road networks obtained by the method for determining road condition information provided by the present application will be shown in comparison with fig. 7A to 7C.
Fig. 7A to 7C are road condition information mapping comparison graphs of a first road network and a second road network obtained by the method for determining road condition information according to the embodiment of the present application.
As shown in fig. 7A to 7B, left diagrams of fig. 7A and 7B are a forward road condition information display diagram and a reverse road condition information display diagram of the first road network determined by the method for determining road condition information according to the present application, respectively, and right diagrams of fig. 7A and 7B are a forward road condition information display diagram and a reverse road condition information display diagram of the second road network displayed by the map navigation client application, respectively. As can be seen from fig. 7A, by the method for determining traffic information according to the present application, most (for example, more than 97%) of the traffic information of the first road segment can be obtained, and the distribution of the congestion degree has a high matching degree with the distribution of the congestion degree shown by the map navigation client application. The darker the display color of the link, the higher the degree of congestion of the road.
As shown in fig. 7C, the left side of the graph is the road condition detail graph of the first road network determined by the method for determining road condition information of the present application, and the right side of the graph is the road condition detail graph of the second road network displayed by the map navigation client application. As can be seen from the traffic detail diagram, according to the method for determining traffic information of the present application, the traffic information of different road segments belonging to the same road in the first road network can be determined, and even if there is a local difference (for example, in the area defined by the dashed circle in fig. 7C, the congestion degree of the first road segment is different from the congestion degree of the second road segment), the difference may be caused by different standards set for the congestion degree by different mechanisms. Overall, the mapping result accuracy of the road condition information is high, and the requirements of users can be met.
The method for determining the road condition information can solve the problem that a user feels pain when using external road condition data. The method can be applied, for example, in the following scenarios.
For example, the method for determining road condition information of the present application may be applied to checking and analyzing a traffic model. For example, the speed data of the first road segment obtained by mapping is loaded into a model network of the user, so that the travel time of the road segment can be checked well, for example, the congestion degree of the first road segment at different time intervals can be shown in the form of a network isochrone diagram, and thus, the traffic model and the road network travel time obtained according to the big data can be compared intuitively.
For example, the method for determining traffic information of the present application may be applied to analysis of traffic congestion and analysis of traffic operation index. For example, in the current state of the industry, each city realizes vehicle speed analysis of partial roads and traffic jam analysis for a parcel according to video data and the like. To implement this analysis, each city needs to develop a separate analysis system, which is costly. By using the method, the road condition information provided by the map navigation client application can be loaded to the user-defined road network, and a brand-new and convenient analysis tool is provided for various traffic current state analyses and annual report achievements. The core input data of the traffic operation index analysis is the speed data of the road, and the server corresponding to the map navigation client application can provide low-cost and high-efficiency data guarantee for the urban traffic operation index analysis. With the integrated development of multiple regions, the demand for analyzing large traffic data across administrative areas is continuously increased, and the system obstacles can be overcome by relying on road condition information provided by map navigation client application, so that the large traffic data analysis across the administrative areas is realized.
For example, the method for determining road condition information can fuse the display and the application of a traffic big data platform. For example, many cities try to develop a traffic big data and model integrated information platform, and such a platform is often required to realize "one map" management of various traffic survey data, automatically collected big data and traffic simulation data. The so-called 'one map' is not a map in form, but is to aggregate various data by technical means to realize unified image and data association, and further realize the fusion analysis and application of multivariate data. The realization of data fusion application can be promoted by mapping and importing the road condition information of the map navigation client application into the user-defined road network of the big data application platform.
Fig. 8 is a block diagram illustrating a structure of an apparatus for determining traffic information according to an embodiment of the present disclosure.
As shown in fig. 8, the apparatus 800 for determining traffic information according to this embodiment may include a first information obtaining module 810, a second information obtaining module 830, a matching section determining module 850, and a traffic information determining module 870.
The first information obtaining module 810 is configured to obtain first road network information of a plurality of first road segments. In an embodiment, the first information obtaining module 810 may be configured to perform the operation S210 described above, for example, and is not described herein again.
The second information obtaining module 830 is configured to obtain second road network information of a plurality of second road segments and road condition information of each of the plurality of second road segments. In an embodiment, the second information obtaining module 830 may be configured to perform the operation S230 described above, for example, and is not described herein again.
The matched segment determination module 850 is configured to, for each of a plurality of first segments: and determining a matched road section aiming at each first road section in the plurality of second road sections according to the adjacent road section of each first road section in the plurality of first road sections, the second road network information and the preset mapping model. In an embodiment, the matching section determining module 850 may be configured to perform the operation S250 described above, for example, and will not be described herein again.
The traffic information determining module 870 is configured to determine the traffic information of each first road segment according to the traffic information of the matched road segment. In an embodiment, the traffic information determining module 870 may be configured to perform the operation S270 described above, for example, and is not described herein again.
Fig. 9 is a block diagram of an electronic device for implementing the method for determining traffic information according to the embodiment of the present application.
There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product. The computer program product comprises a computer program which, when executed by a processor, may implement the method of any of the embodiments described above.
Fig. 9 is a block diagram of an electronic device implementing a method for determining traffic information 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 exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 9, the electronic device 900 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 of a GUI on an external input/output apparatus (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 some of the necessary operations (e.g., as an array of servers, a group of blade servers, or a multi-processor system). Fig. 9 illustrates an example of a processor 901.
The memory 902, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for underestimating traffic information in the embodiment of the present application (for example, the first information obtaining module 810, the second information obtaining module 830, the matching section determining module 850, and the traffic information determining module 870 shown in fig. 8). The processor 901 executes various functional applications and data processing of the server by running non-transitory software programs, instructions and modules stored in the memory 902, so as to implement the method for determining traffic information in the above method embodiment.
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 use of the electronic device that determines the traffic information, 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 a memory remotely located with respect to the processor 901, which may be connected via a network to an electronic device that determines road condition information. 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 implementing the method for determining traffic information 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. 9 illustrates the connection by a bus as an example.
The input device 903 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device that determines traffic information, such as 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, and the like. 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 ASICs (application specific integrated circuits), 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.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
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 (10)
1. A method for determining traffic information, comprising:
acquiring first road network information of a plurality of first road sections;
acquiring second road network information of a plurality of second road sections and road condition information of each of the plurality of second road sections; and
for each of the plurality of first road segments:
determining a matching road segment for each of the plurality of first road segments in the plurality of second road segments according to the adjacent road segment of each of the plurality of first road segments, the second road network information and a preset mapping model;
determining the road condition information of each first road section according to the road condition information of the matched road section;
wherein determining a matching road segment for the each first road segment in the plurality of second road segments comprises:
determining a mapping road section which has a mapping relation with each first road section in the plurality of second road sections according to the second road network information and the preset mapping model;
determining a mapping road section group having a mapping relation with each first road section in the plurality of second road sections according to the adjacent road sections, the second road network information and the preset mapping model; and
and determining a matching road section aiming at each first road section according to the mapping road section and the mapping road section group.
2. The method according to claim 1, wherein the road network information includes attribute information of the road segment; determining a mapping road segment of the plurality of second road segments having a mapping relationship with each of the first road segments comprises:
taking the attribute information of each first road section and the second network information as the input of the preset mapping model to obtain at least one alternative road section which has a mapping relation with each first road section in the plurality of second road sections; and
and determining one of the at least one alternative road section as the mapping road section according to a preset similarity rule.
3. The method according to claim 1, wherein the road network information includes attribute information of the road segment; determining a mapping road segment group having a mapping relationship with each of the first road segments among the plurality of second road segments includes:
taking the attribute information of each first road section, the attribute information of the adjacent road sections and the second road network information as the input of the preset mapping model to obtain at least one alternative road section group aiming at each first road section; and
and determining one of the at least one alternative road section group as the mapping road section group according to a preset similarity rule.
4. The method according to any of claims 1-3, wherein the determining a matching segment for the each first segment from the mapped segment and the mapped segment group comprises:
determining the mapping road segment as a matching road segment for each first road segment in case that the mapping road segment is included in the mapping road segment group;
in a case where the mapped link group does not include the mapped link, determining a link in the mapped link group other than the matched link for the adjacent link as a matched link for the each first link.
5. The method of claim 1, wherein the traffic information for each second road segment comprises a speed of the vehicle passing through the each second road segment; determining the traffic information of each first segment includes, in case that the matching segment includes at least two second segments:
determining an average velocity of the vehicle through the at least two second road segments as a velocity of the vehicle through each of the first road segments; and
and determining the road condition information of each first road section according to the speed of the vehicle passing through each first road section.
6. The method of claim 5, wherein the first road network information comprises a congestion degree-rate relationship for each first road segment; determining the traffic information of each first road section according to the speed of the vehicle passing through each first road section further comprises:
and determining the congestion degree of each first road section according to the speed of the vehicle passing through each first road section and the congestion degree-speed relation of each first road section, wherein the congestion degree of each first road section is used as the road condition information of each first road section.
7. The method according to claim 2 or 3, wherein the preset similarity rule comprises at least one of:
determining a similarity relationship between two road sections according to a difference between the lengths of the two road sections and a smaller length of the lengths of the two road sections;
determining a similarity relation between two road sections according to a distance difference between head nodes of the two road sections and an average value of lengths of the two road sections, wherein attribute information of the road sections comprises position information of the head nodes;
determining a similarity relation between two road sections according to an included angle value between a direction of each of the two road sections from the head node to the tail node and a preset direction, wherein the attribute information of the road sections comprises position information of the head node and position information of the tail node;
determining a similarity relation between two road sections according to respective maximum curvature values of the two road sections, wherein the attribute information of the road sections comprises the curvature values of the road sections at various positions;
and determining the similarity relation between the two road sections according to the road types of the two road sections, wherein the attribute information of the road sections comprises the road types of the road sections.
8. An apparatus for determining traffic information, comprising:
the first information acquisition module is used for acquiring first road network information of a plurality of first road sections;
the second information acquisition module is used for acquiring second road network information of a plurality of second road sections and road condition information of each of the plurality of second road sections;
a matching segment determination module to, for each of the plurality of first segments: determining a matching road segment for each of the plurality of first road segments in the plurality of second road segments according to the adjacent road segment of each of the plurality of first road segments, the second road network information and a preset mapping model; and
the road condition information determining module is used for determining the road condition information of each first road section according to the road condition information of the matched road section;
the matching section determination module is specifically configured to:
determining a mapping road section which has a mapping relation with each first road section in the plurality of second road sections according to the second road network information and the preset mapping model;
determining a mapping road section group having a mapping relation with each first road section in the plurality of second road sections according to the adjacent road sections, the second road network information and the preset mapping model; and
and determining a matching road section aiming at each first road section according to the mapping road section and the mapping road section group.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform: the method of any one of claims 1-7.
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