Path Flow Determination Method, Device, Electronic Device, and Storage Medium

Abstract

The present disclosure provides a method, apparatus, electronic device and storage medium for determining path traffic, which relates to the field of artificial intelligence, and particularly to fields such as autonomous driving and intelligent transportation. The implementation solution is to obtain first data associated with a vehicle driving trajectory and second data associated with road monitoring within a preset time period for a selected road network area, as well as a path set associated with a specified intersection within the road network area; based on the first data, perform path integrity expansion on the path set to obtain a first expanded path set; based on the second data, weight a traffic matrix representing the initial origin-destination traffic of the road network area to obtain a weighted traffic matrix; based on the weighted traffic matrix, determine an initial path traffic corresponding to the first expanded path set; and based on a first turning traffic determined via the second data and a second turning traffic determined via the initial path traffic, update the initial path traffic to obtain an updated path traffic.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, and in particular to fields such as autonomous driving and intelligent transportation. Specifically, it relates to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for determining path flow. Background Art

[0002] With the growth of social economy and the progress of science and technology, the urbanization process in countries around the world has been accelerating continuously, and the scale of the urban population has also been increasing accordingly. At the same time, the problem of traffic congestion caused by the imbalance between traffic supply and demand has gradually become prominent, restricting the further development of cities in various aspects.

[0003] As one of the basic data indicators of the urban transportation system, path flow describes the travel distribution status between each pair of origin-destination in the entire traffic network, directly reflecting the spatio-temporal distribution of vehicle flow in the traffic network, and is of great significance for the management and control, development planning, and long-term prediction of urban road traffic. Therefore, obtaining accurate path flow has become a hot research topic in the field of transportation. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for determining path flow.

[0005] According to one aspect of the present disclosure, there is provided a method for determining path flow, including obtaining first data associated with vehicle driving trajectories and second data associated with road monitoring within a preset time period for a selected road network area, as well as a path set associated with a specified intersection within the road network area, where the starting point and ending point of the paths in the path set correspond to the specified intersection; based on the first data, expanding the path integrity of the path set to obtain a first expanded path set; based on the second data, weighting a flow matrix representing the initial origin-destination flow of the road network area to obtain a weighted flow matrix, where the flow matrix of the initial origin-destination flow is determined based on the supplemented first data obtained by supplementing the trajectory integrity of the vehicle driving trajectories; based on the weighted flow matrix, determining an initial path flow corresponding to the first expanded path set; and based on the first turning flow determined via the second data and the second turning flow determined via the initial path flow, updating the initial path flow to obtain an updated path flow.

[0006] According to another aspect of the present disclosure, there is provided a path traffic determination device, including an acquisition module configured to acquire first data associated with vehicle driving trajectories and second data associated with road monitoring within a selected road network area during a preset time period, as well as a path set associated with a specified intersection within the road network area, wherein the starting point and the ending point of the paths in the path set correspond to the specified intersection; a path expansion module configured to perform path integrity expansion on the path set based on the first data to obtain a first expanded path set; a weighting module configured to weight a traffic matrix representing the initial origin-destination traffic of the road network area based on the second data to obtain a weighted traffic matrix, wherein the traffic matrix of the initial origin-destination traffic is determined based on the supplemented first data obtained by performing trajectory integrity supplementation on the vehicle driving trajectories; a path traffic determination module configured to determine an initial path traffic corresponding to the first expanded path set based on the weighted traffic matrix; and a first path traffic update module configured to update the initial path traffic based on a first turning traffic determined via the second data and a second turning traffic determined via the initial path traffic to obtain an updated path traffic.

[0007] According to another aspect of the present disclosure, there is provided an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method provided above in the present disclosure.

[0008] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method provided above in the present disclosure.

[0009] According to another aspect of the present disclosure, there is provided a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the method provided above.

[0010] According to one or more embodiments of the present disclosure, the determination of path traffic can be achieved in a simpler and more efficient manner.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Description of the Drawings

[0012] The accompanying drawings exemplarily illustrate embodiments and form a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram of an exemplary system in which various methods described herein can be implemented according to an embodiment of the present disclosure is shown;

[0014] Figure 2 A flowchart of a path traffic determination method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A schematic diagram of a road network area according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A schematic diagram of a turning traffic convergence curve according to an embodiment of the present disclosure is shown;

[0017] Figure 5 A structural block diagram of a path traffic determination device according to an embodiment of the present disclosure is shown;

[0018] Figure 6 A structural block diagram of a path traffic determination device according to another embodiment of the present disclosure is shown;

[0019] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement an embodiment of the present disclosure is shown. Detailed Embodiments

[0020] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements and are not intended to limit the positional relationship, timing relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, and in certain cases, based on the context description, they may also refer to different instances.

[0022] In the description of the various examples in this disclosure, the terms used are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically defined, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any and all possible combinations of the listed items.

[0023] In the related art, the traditional method for determining path flow generally uses a two-layer path flow estimation model to implement. The upper layer of the path flow estimation model estimates the demand allocation matrix based on the user equilibrium assumption, and the lower layer of the path flow estimation model estimates the path flow based on the allocation matrix obtained by solving the upper layer. The two layers in the path flow estimation model are executed in an iterative manner until convergence is reached. Only when the user equilibrium assumption fits the real-world traffic conditions very well, it may be practical to use such a path flow estimation model.

[0024] However, with the development of urban roads and the continuous increase in the number of vehicles, it is difficult to make the user equilibrium assumption under the condition of variable road conditions. This traditional path flow determination method that relies on the user equilibrium assumption is difficult to apply. At the same time, making the user equilibrium assumption also requires a lot of time, making the traditional path flow estimation method not only technically complex but also less efficient.

[0025] In view of the above technical problems, according to one aspect of the present disclosure, a method for determining path flow is provided.

[0026] Before describing in detail the method for determining path flow according to the embodiments of the present disclosure, first, in combination with Figure 1 a schematic diagram of an exemplary system in which the various methods and apparatuses described herein can be implemented is described.

[0027] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein can be implemented according to an embodiment of the present disclosure is shown. Referring to Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 that couple the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0028] In an embodiment of the present disclosure, the server 120 can run one or more services or software applications that enable the execution of the method for determining path flow.

[0029] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0030] In Figure 1 the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that may be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be understood that various different system configurations are possible, which may be different from system 100. Thus, Figure 1 is an example of a system for implementing the various methods described herein and is not intended to be limiting.

[0031] Users may use client devices 101, 102, 103, 104, 105, and / or 106 to obtain the results of path traffic determination. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via this interface. Although Figure 1 only six client devices are depicted, those skilled in the art will be able to understand that the present disclosure may support any number of client devices.

[0032] Client devices 101, 102, 103, 104, 105, and / or 106 can include various types of computing devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices, etc. These computing devices can run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT WindowsMobile OS, iOS, Windows Phone, Android. Portable handheld devices can include cellular phones, smartphones, tablets, personal digital assistants (PDAs), etc. Wearable devices can include head-mounted displays (such as smart glasses) and other devices. Gaming systems can include various handheld gaming devices, Internet-enabled gaming devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and can use various communication protocols.

[0033] Network 110 can be any type of network known to those skilled in the art, which can support data communication using any one of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, token ring, wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (such as Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0034] Server 120 can include one or more general-purpose computers, dedicated server computers (such as PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframes, server clusters, or any other suitable arrangement and / or combination. Server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for the server). In various embodiments, server 120 can run one or more services or software applications that provide the functions described below.

[0035] The computing unit in server 120 can run one or more operating systems including any of the above operating systems and any commercially available server operating systems. Server 120 can also run any one of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0036] In some embodiments, server 120 can include one or more applications to analyze and combine data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 can also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0037] In some embodiments, server 120 can be a server of a distributed system, or a server incorporating a blockchain. Server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system, which solves the defects of high management difficulty and weak business scalability existing in traditional physical hosts and virtual private server (VPS) services.

[0038] System 100 can also include one or more databases 130. In certain embodiments, these databases can be used to store data and other information. For example, one or more of databases 130 can be used to store information such as audio files and video files. Databases 130 can reside in various locations. For example, the databases used by server 120 can be local to server 120, or can be remote from server 120 and can communicate with server 120 via a network-based or dedicated connection. Databases 130 can be of different types. In certain embodiments, the databases used by server 120 can be, for example, relational databases. One or more of these databases can store, update, and retrieve data to and from the databases in response to commands.

[0039] In certain embodiments, one or more of databases 130 can also be used by applications to store application data. The databases used by applications can be different types of databases, such as key-value repositories, object repositories, or conventional repositories supported by a file system.

[0040] Figure 1The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatuses described in this disclosure.

[0041] The path traffic determination method according to an embodiment of this disclosure is described in detail below.

[0042] Figure 2 A flowchart of a path traffic determination method 200 according to an embodiment of this disclosure is shown; as Figure 2 shown, the method 200 includes steps S202, S204, S206, S208, and S210.

[0043] In step S202, first data associated with vehicle driving trajectories, second data associated with road monitoring, and a path set associated with a specified intersection within the road network area are obtained within a preset time period, where the start and end points of the paths in the path set correspond to the specified intersection.

[0044] In an example, the road network area can be any selected area as needed. For example, Figure 3 A schematic diagram of a road network area according to an embodiment of this disclosure is shown, as Figure 3 shown, the abscissa in the figure represents longitude, and the ordinate represents latitude; the black dots represent intersections, and the lines connecting two black dots represent road segments. From Figure 3 it can be seen that this area contains a total of 91 intersections and 289 road segments.

[0045] In an example, the first data associated with vehicle driving trajectories can be license plate data, and the first data associated with vehicle driving trajectories can be obtained through one or more road monitoring means.

[0046] In an example, the road monitoring can be radar-vision devices and checkpoint devices, and the second data associated with road monitoring can be radar-vision device data and checkpoint data obtained through the radar-vision devices and checkpoint devices.

[0047] In an example, the specified intersection can be any intersection specified within the road network area. For example, it can be Figure 3 the 31 external intersections and 60 internal intersections in the shown road network area. The path set associated with the specified intersection within the road network area can be a path set formed with the 31 external intersections and 60 internal intersections in the road network area as the start and end points. For example, the specified intersection N k represents the kth intersection in the road network area, and the road segment Link k，k+1 represents the road segment from the specified intersection N k to the specified intersection N k+1 The path Path k，l ={[N k, N k+1 , ..., N l-1 , N l ,} represents the path from the specified intersection N k to the specified intersection N l .

[0048] In step S204, based on the first data, the path set is expanded for path integrity to obtain a first expanded path set;

[0049] In the example, the start and end points of the paths in the path set correspond to the specified intersections, while when the vehicle is actually driving, the start and end points of the path may be arbitrary intersections, that is, the paths in the path set may be missing relative to the actual driving path of the vehicle. The first data can be license plate data. Based on the license plate data associated with the vehicle driving trajectory, the actual driving path of the vehicle is obtained, and then the actual driving path of the vehicle is added to the path set, and the path set is expanded for integrity to obtain a first expanded path set.

[0050] In step S206, based on the second data, the flow matrix representing the initial origin-destination flow of the road network area is weighted to obtain a weighted flow matrix, where the flow matrix of the initial origin-destination flow is determined based on the supplemented first data obtained by supplementing the trajectory integrity of the vehicle driving trajectory.

[0051] Exemplarily, the first data can be license plate data. The first data can include both continuous vehicle driving trajectories and discontinuous vehicle trajectories caused by equipment loss and missed inspections. The missing vehicle driving trajectories in the first data are complemented to obtain the supplemented license plate data. In the supplemented license plate data, the flows with the same origin-destination are extracted to obtain the flow matrix of the initial origin-destination flow of the road network area. The flow matrix is shown in the following table:

[0052]

[0053] where N k represents the k-th intersection, represents the origin-destination flow.

[0054] Exemplarily, since the first data associated with the vehicle driving trajectory usually has missing parts, in order to reduce the impact of the missing first data on the determined path flow, the flow matrix of the initial origin-destination flow is weighted by the second data associated with road monitoring to obtain a weighted flow matrix.

[0055] In step S208, based on the weighted flow matrix, the initial path flow corresponding to the first expanded path set is determined;

[0056] Exemplarily, the determination of path flow can be achieved through a path flow estimation model, which can consist of two layers of ordinary least squares (OLS) estimation models. Among them, the first layer of the ordinary least squares estimation model can be:

[0057]

[0058]

[0059] where v τ，a is the flow of section a within time τ; is the flow of section a obtained through road monitoring within time τ; TV τ，a is the turning flow of section a within time τ, including the left-turn flow LT τ，a , the straight-through flow ST τ，a and the right-turn flow RT τ，a ; is the turning flow of section a obtained through road monitoring within time τ; is the flow matrix of the origin-destination flow from origin r to destination s within time τ; is the distribution matrix of the proportion of the origin-destination flow from r to s allocated to path j within time τ; is the flow of path j from r to s within time τ; is the path-section flow incidence rate. If path j passes through section a, otherwise it is 0; is the path-turning flow incidence rate. If path j has the corresponding turn at section a, otherwise it is 0.

[0060] The second layer of the ordinary least squares estimation model is used to solve the flow matrix of the origin-destination flow. The objective function is the weighted sum of the squares of the differences between the turning flows of each section and the origin-destination flow and the true values. To avoid excessive turning flows in some cases, a constraint that the turning flow is less than the turning capacity is added.

[0061] The second layer of the ordinary least squares estimation model can be:

[0062]

[0063]

[0064] where is the weighted flow matrix, TV a，c is the capacity of the corresponding turn of section a, and the meanings of other variables are the same as those in the first layer of the ordinary least squares estimation model.

[0065] Exemplarily, TVa，c It can be determined by turning to the corresponding green signal ratio, number of lanes, and saturation flow rate:

[0066] TV a，c = r sat × λ T × l T

[0067] Wherein, r sat is the saturation flow; λ T is the green signal ratio of the phase corresponding to the turn T; l T is the number of lanes related to the turn T.

[0068] Exemplarily, by using the weighted traffic flow matrix as the input of the path traffic estimation model, the initial path traffic corresponding to the first augmented path set can be determined, and the initial path traffic can include the traffic flow matrix and the turning matrix

[0069] In step S210, based on the first turning traffic determined via the second data and the second turning traffic determined via the initial path traffic, the initial path traffic is updated to obtain the updated path traffic.

[0070] Exemplarily, the second data can be the data of the radar-vision device obtained by the radar-vision device. The first turning traffic determined via the second data can include the left-turn traffic, straight-through traffic, and right-turn traffic corresponding to multiple road segments actually passed by the vehicle trajectory. The second turning traffic determined by the initial path traffic can include the left-turn traffic, straight-through traffic, and right-turn traffic corresponding to each road segment included in the path set.

[0071] Exemplarily, based on the first turning traffic determined via the second data and the second turning traffic determined via the initial path traffic, the initial path traffic is updated to obtain the updated path traffic. When the updated path traffic meets the preset criteria, the update is stopped.

[0072] Exemplarily, the preset criteria can be:

[0073]

[0074] Wherein, MAPE is the mean absolute percentage error, x i is the i-th turning traffic in the second turning traffic; is the i-th turning traffic in the first turning traffic, and M is the total number of turning traffic.

[0075] Exemplarily, Figure 4 shows a schematic diagram of the turning traffic convergence curve according to an embodiment of the present disclosure, as Figure 4As shown, the abscissa is the number of iterations, that is, the k value, and the ordinate is the mean absolute percentage error (MAPE). If the difference between the MAPE of the (k + 1)-th time and the MAPE of the k-th time is less than the threshold, that is, the difference between the MAPE of the (k + 1)-th time and the MAPE of the k-th time satisfies the following formula:

[0076]

[0077] where ε is the threshold, and the specific value can be preset by observing the convergence curve. At this time, the update of the path flow ends, and the obtained flow matrix and the allocation matrix are the updated path flows.

[0078] According to the path flow determination method of the present disclosure embodiment, by means of the first data associated with the vehicle driving trajectory, the second data associated with the road monitoring, and the path set associated with the designated intersections within the road network area, it is possible to avoid the traditional method that depends on pre-calibrating network parameters, so that the path flow determination process only needs to obtain the first data associated with the vehicle driving trajectory and the second data associated with the road monitoring to achieve the determination of the path flow and ensure the accuracy of the determination result. Thereby reducing the complexity of the path flow determination method, thus reducing the implementation difficulty of the path flow determination method and improving the efficiency of determining the path flow.

[0079] It should be noted that in the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of information related to the vehicle driving trajectory and road monitoring comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0080] The following further describes various aspects of the path flow determination method according to the present disclosure embodiment.

[0081] According to some embodiments, based on the first turning flow determined via the second data and the second turning flow determined via the initial path flow, the initial path flow is updated to obtain the updated path flow, including determining, based on the first turning flow, the first missing path not included in the first extended path set; adding the first missing path and the second missing path determined based on the difference between the second turning flow and the first turning flow to the first extended path set to obtain the second extended path set; and determining the updated path flow corresponding to the second extended path set.

[0082] Exemplarily, the second data may be the data of the radar-vision device obtained by the radar-vision device. The radar-vision device usually only collects the straight-through flow and the left-turn flow. However, when determining the path flow, the integrity of each turning flow will affect the accuracy of the path flow. Therefore, the right-turn flow also needs to be obtained. The turning flow of the right turn is:

[0083]

[0084] Wherein, and respectively represent the link flow, left-turn flow, straight-through flow, and right-turn flow corresponding to the link a with the time period τ, starting point r, and ending point s.

[0085] Exemplarily, the second data may be the data of the radar-vision device obtained by the radar-vision device, and the first turning flow may be the actual turning flow in the vehicle driving trajectory determined from the radar-vision device data. According to the first turning flow, the path sets corresponding to each turn can be determined, and the path sets corresponding to each turn are compared with the first expanded path set to determine the first missing paths not included in the first expanded path set.

[0086] Exemplarily, the second turning flow can be determined through the initial path flow, that is, the turning flow corresponding to the first expanded path set. The second turning flow is compared with the first turning flow, and the paths with the difference between the second turning flow and the first turning flow greater than a certain threshold are determined as the second missing paths.

[0087] Exemplarily, the first missing paths and the second missing paths are added to the first expanded path set. That is, the parameters in the path flow estimation model in the foregoing embodiment are updated to obtain an updated path flow estimation model.

[0088] Exemplarily, the initial path flow is used as the input value of the updated path flow estimation model to obtain the updated path flow corresponding to the second expanded path set.

[0089] According to the embodiments of the present disclosure, based on the first turning flow, the first missing paths not included in the first expanded path set are determined; the first missing paths and the second missing paths determined based on the difference between the second turning flow and the first turning flow are added to the first expanded path set to obtain a second expanded path set, so that the path set better conforms to the selected road network area, thereby making the determined path flow corresponding to the second expanded path set more accurate.

[0090] According to some embodiments, the second data includes traffic monitoring device data and checkpoint data; wherein, based on the second data, the traffic matrix representing the initial origin-destination flow of the road network area is weighted to obtain a weighted traffic matrix, including determining a first number of origin vehicles at the origin and a first number of destination vehicles at the destination according to the traffic monitoring device data; determining a second number of origin vehicles at the origin and a second number of destination vehicles at the destination according to the checkpoint data; determining an origin vehicle recognition rate based on the first number of origin vehicles and the second number of origin vehicles, and determining a destination vehicle recognition rate based on the first number of destination vehicles and the second number of destination vehicles; and weighting the traffic matrix based on the smaller one of the origin vehicle recognition rate and the destination vehicle recognition rate to obtain a weighted traffic matrix.

[0091] Exemplarily, in practical applications, both the traffic monitoring device data and the checkpoint data will have missing values compared to the real data. To make the determined traffic matrix closer to the real value, the traffic matrix of the initial origin-destination flow can be weighted by the traffic monitoring device data and the checkpoint data.

[0092] Exemplarily, within the time interval τ, the first number of origin vehicles detected by the traffic monitoring device at the origin r is The first number of destination vehicles detected at the destination s is The second number of origin vehicles detected by the checkpoint device at the origin r is The second number of destination vehicles detected at the destination s is

[0093] Based on the first number of origin vehicles and the second number of origin vehicles The determined origin vehicle recognition rate is:

[0094]

[0095] Based on the first number of destination vehicles and the second number of destination vehicles The determined destination vehicle recognition rate is:

[0096]

[0097] Based on the origin vehicle recognition rate and the destination vehicle recognition rate The smaller one of them, for the traffic matrix representing the initial origin-destination flow of the road network area is weighted:

[0098]

[0099] Wherein, is the weighted traffic matrix.

[0100] According to an embodiment of the present disclosure, by weighting the traffic matrix of the initial origin-destination flow with radar-vision device data and bayonet data, the weighted traffic matrix can be made to better conform to the selected road network area, thereby obtaining more accurate path flows.

[0101] According to some embodiments, based on the first data, path integrity of a path set is augmented to obtain a first augmented path set, including based on the first data, supplementing the trajectory integrity of vehicle driving trajectories to obtain supplemented first data, where the supplemented first data represents the actual vehicle driving path; and adding the actual vehicle driving path to the path set to obtain the first augmented path set.

[0102] Exemplarily, the first data may include both continuous vehicle driving trajectories and discontinuous vehicle trajectories caused by equipment loss and missed inspections. Supplementing the discontinuous trajectories in the first data to obtain continuous vehicle driving trajectories, that is, obtaining the supplemented first data.

[0103] Exemplarily, the augmented first data represents the actual vehicle driving path. Adding the actual vehicle driving path to the path set to obtain the first augmented path set

[0104] According to an embodiment of the present disclosure, based on the first data, supplementing the trajectory integrity of vehicle driving trajectories to obtain the actual vehicle driving path, and adding the actual vehicle driving path to the path set to obtain the first augmented path set, making the first augmented path set better conform to the actual path set, thereby improving the accuracy and reliability of the determined path flow.

[0105] According to some embodiments, based on the first data, supplementing the trajectory integrity of vehicle driving trajectories to obtain supplemented first data, including based on the first data, using a first shortest path algorithm to determine missing trajectories not included in the vehicle driving trajectories; and complementing the missing trajectories into the vehicle driving trajectories to obtain the supplemented first data.

[0106] Exemplarily, the first shortest path algorithm may be the Dijkstra shortest path algorithm.

[0107] Exemplarily, the first data may be license plate data. The first data may include both continuous vehicle driving trajectories and discontinuous vehicle trajectories caused by equipment loss and missed inspections. Calculating the shortest path of the vehicle driving trajectories using the first shortest path algorithm, and then determining the missing trajectories not included in the vehicle driving trajectories. Supplementing the missing trajectories into the vehicle driving trajectories can obtain the first data that only includes continuous vehicle driving trajectories, that is, obtaining the supplemented first data.

[0108] According to an embodiment of the present disclosure, the first shortest path algorithm can be used to efficiently determine the missing trajectories not included in the vehicle driving trajectory, improving the integrity of the path.

[0109] According to some embodiments, the path set associated with a specified intersection in the road network area is obtained by using the second shortest path algorithm with the specified intersection as the starting point and the ending point of the path.

[0110] Exemplarily, the second shortest path algorithm can be the k shortest path algorithm, where k is the number of shortest paths obtained based on the same starting point and ending point.

[0111] According to an embodiment of the present disclosure, obtaining the path set associated with the specified intersection in the road network area by using the second shortest path algorithm with the specified intersection as the starting point and the ending point of the path can make the number of paths in the obtained path set more in line with the requirements when determining the path flow, reducing the solution variables while ensuring the accuracy of the path flow and improving the solution efficiency.

[0112] Figure 5 The structural block diagram of a path flow determination device 500 according to an embodiment of the present disclosure is shown. As Figure 5 shown, the device 500 includes: an acquisition module 510 configured to acquire first data associated with the vehicle driving trajectory and second data associated with road monitoring within a preset time period for a selected road network area, as well as a path set associated with a specified intersection in the road network area, where the starting point and the ending point of the path in the path set correspond to the specified intersection; a path expansion module 520 configured to perform path integrity expansion on the path set based on the first data to obtain a first expanded path set; a weighting module 530 configured to weight a flow matrix representing the initial origin-destination flow of the road network area to obtain a weighted flow matrix, where the flow matrix of the initial origin-destination flow is determined based on the supplemented first data obtained by performing trajectory integrity supplementation on the vehicle driving trajectory; a path flow determination module 540 configured to determine an initial path flow corresponding to the first expanded path set based on the weighted flow matrix; and a first path flow update module 550 configured to update the initial path flow based on a first turning flow determined via the second data and a second turning flow determined via the initial path flow to obtain an updated path flow.

[0113] According to an embodiment of the present disclosure, it is possible to avoid the method in the traditional method that relies on pre-calibrating network parameters, so that the path traffic determination process only needs to obtain the first data associated with the vehicle driving trajectory and the second data associated with road monitoring, and then the determination of path traffic can be realized and the accuracy of the determination result can be ensured. Thereby, the complexity of the path traffic determination method is reduced, the implementation difficulty of the path traffic determination method is reduced, and the efficiency of determining path traffic is improved.

[0114] Figure 6 FIG. shows a structural block diagram of a vehicle speed measurement device 600 according to another embodiment of the present disclosure. As Figure 6 shown, the device 600 may include an acquisition module 610, a path expansion module 620, a weighting module 630, a path traffic determination module 640, and a first path traffic update module 650. The acquisition module 610, the path expansion module 620, the weighting module 630, the path traffic determination module 640, and the first path traffic update module 650 correspond to the acquisition module 510, the path expansion module 520, the weighting module 530, the path traffic determination module 540, and the first path traffic update module 550 as Figure 5 shown, and thus the details thereof will not be described herein again.

[0115] In an example, the first path traffic update module 650 includes: a missing path determination module 6501 configured to determine a first missing path not included in the first expanded path set based on the first turning traffic; a first path addition module 6502 configured to add the first missing path and a second missing path determined based on the difference between the second turning traffic and the first turning traffic to the first expanded path set to obtain a second expanded path set; and a second path traffic update module 6503 configured to determine an updated path traffic corresponding to the second expanded path set.

[0116] Thereby, the path traffic corresponding to the second expanded path set determined can be made more accurate.

[0117] In the example, the second data includes radar-vision device data and checkpoint data; among which, the weighting module 630 includes a first vehicle number determination module 6301 configured to determine a first starting vehicle number at the starting point and a first ending vehicle number at the ending point according to the radar-vision device data; a second vehicle number determination module 6302 configured to determine a second starting vehicle number at the starting point and a second ending vehicle number at the ending point according to the checkpoint data; a vehicle recognition rate determination module 6303 configured to determine a starting point vehicle recognition rate based on the first starting vehicle number and the second starting vehicle number, and determine an ending point vehicle recognition rate based on the first ending vehicle number and the second ending vehicle number; and a traffic matrix weighting module 6304 configured to weight the traffic matrix based on the smaller one of the starting point vehicle recognition rate and the ending point vehicle recognition rate to obtain a weighted traffic matrix.

[0118] Thus, by weighting the traffic matrix of the initial origin-destination flow with the radar-vision device data and the checkpoint data, the weighted traffic matrix can be made to better match the selected road network area, and thus more accurate path flows can be obtained.

[0119] In the example, the path expansion module 620 may include:

[0120] A path supplement module 6201 configured to supplement the integrity of the vehicle driving trajectory based on the first data to obtain the supplemented first data, where the supplemented first data represents the actual vehicle driving path; and a second path addition module 6202 configured to add the actual vehicle driving path to the path set to obtain a first expanded path set.

[0121] Thus, the first expanded path set can be made to better match the actual path set, thereby improving the accuracy and reliability of the determined path flow.

[0122] In the example, the path supplement module 6201 may include:

[0123] A missing trajectory determination module 6201a configured to determine, based on the first data, a missing trajectory not included in the vehicle driving trajectory using a first shortest path algorithm; and

[0124] A missing trajectory determination module 6201b configured to complete the missing trajectory into the vehicle driving trajectory to obtain the supplemented first data.

[0125] Thus, using the first shortest path algorithm can efficiently determine the missing trajectory not included in the vehicle driving trajectory, improving the integrity of the path.

[0126] In the example, the set of paths associated with a specified intersection within the road network area is obtained according to the second shortest path algorithm with the specified intersection as the starting point and the ending point of the path.

[0127] Thereby, the number of paths in the obtained set of paths can better meet the requirements when determining path flow, reducing the solution variables while ensuring the accuracy of path flow and improving the solution efficiency.

[0128] According to another aspect of the present disclosure, there is also provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in the above embodiments.

[0129] According to another aspect of the present disclosure, there is also provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method in the above embodiments.

[0130] According to another aspect of the present disclosure, there is also provided a computer program product, including a computer program, wherein the computer program implements the method in the above embodiments when executed by a processor.

[0131] Reference Figure 7 , a block diagram of an electronic device 700 that can be used as a server or a client of the present disclosure will now be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0132] As Figure 7 shown, the electronic device 700 includes a computing unit 701, which can execute various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0133] Multiple components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 can be any type of device capable of inputting information into the electronic device 700. The input unit 706 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function controls of the electronic device, and can include but are not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output unit 707 can be any type of device capable of presenting information, and can include but are not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 708 can include but are not limited to a magnetic disk, an optical disc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include but are not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0134] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 executes the various methods and processes described above, such as the path traffic determination method. For example, in some embodiments, the path traffic determination method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the path traffic determination method described above can be executed. Alternatively, in other embodiments, the computing unit 701 can be configured to execute the path traffic determination method in any other suitable manner (e.g., by means of firmware).

[0135] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0136] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0137] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball), by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), the Internet, and blockchain networks.

[0140] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating blockchain.

[0141] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0142] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples may be omitted or replaced by their equivalent elements. In addition, the steps may be executed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein may be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A method for determining path flow, comprising: Obtaining first data associated with vehicle driving trajectories and second data associated with road monitoring within a selected road network area during a preset time period, as well as a path set associated with a specified intersection within the road network area, wherein the starting and ending points of the paths in the path set correspond to the specified intersection; Based on the first data, performing path integrity expansion on the path set to obtain a first expanded path set; Based on the second data, weighting a flow matrix representing the initial origin-destination flow of the road network area to obtain a weighted flow matrix, wherein the flow matrix of the initial origin-destination flow is determined based on the supplemented first data obtained by performing trajectory integrity supplementation on the vehicle driving trajectories; Based on the weighted flow matrix, determining an initial path flow corresponding to the first expanded path set; and Based on a first turning flow determined via the second data and a second turning flow determined via the initial path flow, updating the initial path flow to obtain an updated path flow, wherein the weighting the flow matrix representing the initial origin-destination flow of the road network area based on the second data to obtain a weighted flow matrix includes: Based on the second data, determining a vehicle recognition rate within the road network area; and Based on the vehicle recognition rate within the road network area, weighting the flow matrix to obtain the weighted flow matrix, and wherein the updating the initial path flow to obtain an updated path flow based on the first turning flow determined via the second data and the second turning flow determined via the initial path flow includes: Based on the first turning flow, determining a first missing path not included in the first expanded path set; Adding the first missing path and a second missing path determined based on the difference between the second turning flow and the first turning flow to the first expanded path set to obtain a second expanded path set; and Determining the updated path flow corresponding to the second expanded path set.

2. The method according to claim 1, wherein The second data includes radar-vision device data and bayonet data; wherein the determining the vehicle recognition rate within the road network area based on the second data includes: According to the radar-vision device data, determining a first number of vehicles at the starting point and a first number of vehicles at the ending point; According to the bayonet data, determining a second number of vehicles at the starting point and a second number of vehicles at the ending point; Based on the first number of vehicles at the starting point and the second number of vehicles at the starting point, determining a starting-point vehicle recognition rate, and based on the first number of vehicles at the ending point and the second number of vehicles at the ending point, determining an ending-point vehicle recognition rate; and Based on the smaller of the starting-point vehicle recognition rate and the ending-point vehicle recognition rate, determining the vehicle recognition rate within the road network area.

3. The method according to claim 1, wherein, The performing path integrity expansion on the path set based on the first data to obtain a first expanded path set includes: Based on the first data, perform trajectory integrity supplementation on the vehicle driving trajectory to obtain the supplemented first data, where the supplemented first data represents the actual vehicle driving path; and Add the actual vehicle driving path to the path set to obtain the first augmented path set.

4. The method according to claim 3, wherein The performing trajectory integrity supplementation on the vehicle driving trajectory based on the first data to obtain the supplemented first data includes: Based on the first data, use the first shortest path algorithm to determine the missing trajectory not included in the vehicle driving trajectory; and Complete the missing trajectory into the vehicle driving trajectory to obtain the supplemented first data.

5. The method according to any one of claims 1 to 4, wherein The path set associated with the specified intersection in the road network area is obtained according to the second shortest path algorithm with the specified intersection as the start point and the end point of the path.

6. A path flow determination device, comprising: An acquisition module configured to acquire first data associated with a vehicle driving trajectory, second data associated with road monitoring, and a path set associated with a specified intersection in a selected road network area within a preset time period, where the start point and the end point of the path in the path set correspond to the specified intersection; A path augmentation module configured to perform path integrity augmentation on the path set based on the first data to obtain a first augmented path set; A weighting module configured to weight a flow matrix representing the initial origin-destination flow of the road network area based on the second data to obtain a weighted flow matrix, where the flow matrix of the initial origin-destination flow is determined based on the supplemented first data obtained by performing trajectory integrity supplementation on the vehicle driving trajectory; A path flow determination module configured to determine an initial path flow corresponding to the first augmented path set based on the weighted flow matrix; and A first path flow update module configured to obtain an updated path flow by updating the initial path flow based on a first turning flow determined via the second data and a second turning flow determined via the initial path flow, where the weighting module is configured to: Based on the second data, determine the vehicle recognition rate in the road network area; and Based on the vehicle recognition rate in the road network area, weight the flow matrix to obtain the weighted flow matrix, and wherein the first path flow update module includes: A missing path determination module configured to determine a first missing path not included in the first augmented path set based on the first turning flow; A first path addition module configured to add the first missing path and a second missing path determined based on the difference between the second turning flow and the first turning flow to the first augmented path set to obtain a second augmented path set; and A second path flow update module configured to determine the updated path flow corresponding to the second augmented path set.

7. The device according to claim 6, wherein The second data includes vehicle detection and video data and checkpoint data; Among them, the weighting module includes: A first vehicle number determination module configured to determine a first starting vehicle number at the starting point and a first ending vehicle number at the ending point according to the vehicle detection and video data; A second vehicle number determination module configured to determine a second starting vehicle number at the starting point and a second ending vehicle number at the ending point according to the checkpoint data; A vehicle recognition rate determination module configured to determine a starting point vehicle recognition rate based on the first starting vehicle number and the second starting vehicle number, and determine an ending point vehicle recognition rate based on the first ending vehicle number and the second ending vehicle number; and A traffic matrix weighting module configured to determine the vehicle recognition rate within the road network area based on the smaller one of the starting point vehicle recognition rate and the ending point vehicle recognition rate, and weight the traffic matrix based on the vehicle recognition rate within the road network area to obtain the weighted traffic matrix.

8. The apparatus according to claim 6, wherein The path expansion module includes: A path supplement module configured to supplement the integrity of the vehicle driving trajectory based on the first data to obtain the supplemented first data, where the supplemented first data represents the actual vehicle driving path; and A second path addition module configured to add the actual vehicle driving path to the path set to obtain the first expanded path set.

9. The apparatus according to claim 8, wherein The path supplement module includes: A missing trajectory determination module configured to determine, based on the first data, a missing trajectory not included in the vehicle driving trajectory by using a first shortest path algorithm; and A missing trajectory determination module configured to complete the missing trajectory into the vehicle driving trajectory to obtain the supplemented first data.

10. The apparatus according to any one of claims 6 to 9, wherein, The path set associated with the specified intersection within the road network area is obtained by using a second shortest path algorithm with the specified intersection as the starting point and the ending point of the path.

11. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-5.

13. A computer program product comprising a computer program, wherein, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-5.

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