CN111063189B

CN111063189B - Traffic flow processing method and device and electronic equipment

Info

Publication number: CN111063189B
Application number: CN201911072472.7A
Authority: CN
Inventors: 肖楠; 胡楠; 余亮; 马力
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2019-11-05
Filing date: 2019-11-05
Publication date: 2022-05-03
Anticipated expiration: 2039-11-05
Also published as: CN111063189A

Abstract

The invention discloses a method and a device for processing traffic flow and electronic equipment, wherein the processing method comprises the following steps: acquiring a path set in a preset area and a traffic flow monitored by road flow sensing equipment arranged in the preset area in at least one time period as a target equipment traffic flow; acquiring a corresponding relation between each path in the path set and each road flow sensing device, wherein the corresponding relation represents a relation between a road section included in each path and the road flow sensing device; and determining the total traffic flow of each path in each time period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period.

Description

Traffic flow processing method and device and electronic equipment

Technical Field

The present invention relates to the field of traffic control technologies, and in particular, to a method and an apparatus for processing traffic flow, an electronic device, and a computer readable medium.

Background

With the popularization of GPS (Global Positioning System) devices, more and more vehicles in cities record tracks by GPS when traveling. The trajectory is big data for estimating urban trip information. The method is important for urban traffic management to master the complete urban traffic trip information.

The existing trajectory may be obtained by a GPS device provided on the vehicle or by a navigation application. Extracting trajectories from navigation applications has become an important data source for mastering urban travel characteristics. However, not all vehicle trips use the designated navigation application, and therefore, the trajectory extracted by the designated navigation application is only one sample of the total trip data, not the full amount of data.

Various road traffic sensing devices (such as coils, geomagnetism, radars, bayonet cameras and the like) are deployed in each current city, and the road traffic sensing devices can monitor the total traffic flow passing through the deployed position. However, these road flow sensing devices only monitor the cross-sectional flow and do not know the traffic flow of each specific path in each time period. Moreover, because the deployment cost and the maintenance cost of the road traffic sensing equipment are high, the full coverage of a road network cannot be achieved.

Therefore, the traffic flow of each path needs to be calculated by using the traffic flow monitored by the road flow sensing device.

Disclosure of Invention

The invention aims to provide a new technical scheme for obtaining the full traffic flow of each path by using the traffic flow monitored by a road flow sensing device.

According to a first aspect of the present invention, there is provided a traffic flow processing method, including:

acquiring a path set in a preset area and a traffic flow monitored by road flow sensing equipment arranged in the preset area in at least one time period as a target equipment traffic flow;

acquiring a corresponding relation between each path in the path set and each road flow sensing device, wherein the corresponding relation represents a relation between a road section included in each path and a road section provided with the road flow sensing device;

and determining the total traffic flow of each path in each time period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period.

Optionally, the at least one time period is a time period in the target statistical period.

Optionally, the processing method further includes:

acquiring a sampling track of the preset area in each time period of the target statistical period;

determining the number of sampling tracks corresponding to each path in the corresponding time period of the target statistical period according to the sampling tracks in each time period of the target statistical period, and taking the number as the target sampling traffic flow;

in each time period of the target statistical period, the traffic flow passing through each road flow sensing device and providing a sampling track;

wherein the determining the total traffic flow of each path in each time period comprises:

and determining the total traffic flow of each path in each time period of the target statistical period according to the traffic flow which passes through each road traffic sensing device in each time period of the target statistical period and provides a sampling track and the target sampling traffic flow of each path in each time period of the target statistical period.

Optionally, the step of determining the total traffic flow of each path in each time period of the target statistics period includes:

according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period of the target counting period, constructing a first target expression by taking the full traffic flow of each path in each time period of the target counting period as a variable;

constructing a second target expression by taking the full traffic flow of each path in each time period of the target counting period as a variable according to the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period of the target counting period, the traffic flow of each road traffic sensing equipment which is monitored by each road traffic sensing equipment in each time period of the target counting period and provided with a sampling track, and the target sampling traffic flow of each path in each time period of the target counting period;

obtaining a target function according to the first target expression and the second target expression;

and solving the objective function, and determining the value of the total traffic flow of each path in each time period of the target statistical cycle under the condition that the value of the objective function is minimum.

Optionally, the step of constructing the first target expression includes:

setting a device traffic flow vector according to the traffic flow of the target device monitored by each road traffic sensing device in each time period of the target counting period;

setting a path full-volume vehicle flow vector by taking the full-volume vehicle flow of each path in each time period of the target statistical period as a variable;

constructing a sparse matrix according to the corresponding relation;

and constructing the first target expression according to the equipment traffic flow vector, the path full-quantity traffic flow vector and the sparse matrix.

Optionally, the processing method includes:

acquiring traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistical periods as historical device traffic flow;

constructing a first diagonal matrix according to the historical device traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistical periods, and obtaining the first target expression according to the first diagonal matrix; wherein the first diagonal matrix is used for representing the fluctuation degree of the historical equipment traffic flow in the plurality of historical statistic periods.

Optionally, the step of constructing a first diagonal matrix according to the historical traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistics periods includes:

for each road flow sensing device, respectively determining the variance of the monitored vehicle flow of the historical device in each time period;

and constructing the first diagonal matrix according to the corresponding variance of each road traffic sensing device in each time period.

Optionally, the step of constructing the second target expression includes:

setting a path sampling traffic flow vector according to the target sampling traffic flow of each path in each time period of the target statistical period;

determining the permeability of the preset area according to the traffic flow which is monitored by each road traffic sensing device in each time period of a target counting period and provides a sampling track and the traffic flow of each road traffic sensing device in each time period of the target counting period; wherein the permeability represents a traffic flow ratio of a sampling track provided in the preset area;

and constructing the second target expression according to the path sampling vehicle flow vector, the path full-quantity vehicle flow vector and the permeability.

Optionally, the processing method further includes:

acquiring the sampling traffic flow of each path in each time period of a plurality of historical statistical periods to serve as the historical path sampling traffic flow;

the constructing the second target expression comprises:

constructing a second diagonal matrix according to the historical path sampling traffic flow and the permeability of each path in each time period of a plurality of historical statistic periods, wherein the second diagonal matrix is used for expressing the fluctuation degree of the historical path sampling traffic flow in the plurality of historical statistic periods;

and obtaining the second target expression according to the path sampling traffic flow vector, the path full-quantity traffic flow vector and the second diagonal matrix.

Optionally, the step of constructing the second diagonal matrix includes:

for each path, respectively determining the variance of the historical path sampling traffic flow in each time period of a plurality of historical statistical periods;

and constructing the second diagonal matrix according to the corresponding variance and permeability of each path in each time period.

Optionally, the step of determining the permeability of the preset area includes:

determining the sum of the traffic flow which provides the sampling track and is monitored by all the road flow sensing devices in all time periods of a target counting period as track traffic flow sum;

determining the sum of the traffic flow of the target equipment monitored by all the road traffic sensing equipment in all the time periods of the target counting period as the sum of the traffic flow of the equipment;

and obtaining the permeability according to the track traffic flow sum and the equipment traffic flow sum.

Optionally, the first target expression and the second target expression are both semi-positive definite quadratic expressions.

Optionally, the step of obtaining the path set in the preset area includes:

extracting at least one pair of travel combinations in the preset area, wherein the travel combinations comprise departure points and arrival points;

and for each pair of travel combinations, acquiring a set number of paths with the shortest path length to obtain the path set.

Optionally, the processing method further includes:

and carrying out traffic control on the preset area according to the total traffic flow of each path in each time period of the target statistical period.

Optionally, the step of performing traffic control on the preset area according to the total traffic flow of each route in each time period of the target statistical cycle includes:

according to the total traffic flow of each path in each time period of the target statistical cycle, obtaining the predicted traffic flow of the target path in the target time period of the future statistical cycle;

and carrying out traffic control on the crossing through which the target path passes according to the predicted traffic flow of the target path in the target time period of the future statistical cycle.

Optionally, the traffic flow is a traffic flow of a designated vehicle.

According to a second aspect of the present invention, there is provided a traffic flow processing apparatus, comprising:

the system comprises a path flow acquisition module, a target device traffic flow acquisition module and a traffic flow monitoring module, wherein the path flow acquisition module is used for acquiring a path set in a preset area and the traffic flow monitored by road flow sensing equipment arranged in the preset area in at least one time period as target device traffic flow;

a corresponding relation obtaining module, configured to obtain a corresponding relation between each path in the path set and each road traffic sensing device, where the corresponding relation indicates a relation between a road segment included in each path and a road segment provided with a road traffic sensing device;

and the total traffic flow determining module is used for determining the total traffic flow of each path in each time period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period.

According to a third aspect of the invention, there is provided an electronic device comprising the processing apparatus according to the second aspect of the invention; or a processor and a memory for storing executable instructions for controlling the processor to perform the processing method according to the first aspect of the invention.

According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the processing method according to the first aspect of the present invention.

In the embodiment of the invention, the total traffic flow of each path in each time period of the target counting period is determined according to the traffic flow monitored by the road traffic sensing equipment in each time period of the target counting period and the corresponding relation between each path and the road traffic sensing equipment. In this way, the total traffic flow of each path in each time period can be more accurate and the real-time performance is higher.

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a block diagram of one example of a hardware configuration of a transportation system that can be used to implement an embodiment of the present invention.

FIG. 2 is a flow chart illustrating a method of handling traffic flow in accordance with an embodiment of the present invention;

FIG. 3 is a schematic diagram of an example of a preset area according to an embodiment of the present invention;

fig. 4 is a flowchart illustrating an example of a traffic flow processing method according to an embodiment of the present invention;

fig. 5 is a flowchart illustrating another example of a traffic flow processing method according to an embodiment of the present invention;

FIG. 6 is a functional block diagram of one example of a traffic flow processing device according to an embodiment of the present invention;

FIG. 7 is a functional block diagram of another example of a traffic flow processing device according to an embodiment of the present invention;

fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< hardware configuration >

FIG. 1 is a schematic view of a transportation system that may be used to implement any embodiment of the present invention.

As shown in fig. 1, the transportation system 100 includes a server 1000, a client 2000, a vehicle 3000, a road traffic awareness device 4000, and a network 5000.

The server 1000 provides a service point for processes, databases, and communications facilities. The server 1000 may be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, etc.

In one example, the server 1000 may be as shown in fig. 2, including a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600. Although the server may also include speakers, microphones, etc., these components are reasonably irrelevant to the present invention and are omitted here.

The processor 1100 may be, for example, a central processing unit CPU, a microprocessor MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, an infrared interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1150 is, for example, a liquid crystal display panel, an LED display panel touch display panel, or the like. Input devices 1160 may include, for example, a touch screen, a keyboard, and the like.

In the present embodiment, the client 2000 is an electronic device having a communication function, a map function, and a positioning function. The client 2000 may be a mobile terminal, such as a mobile phone, a laptop, a tablet, a palmtop, etc. In one example, the client 2000 is a location-enabled device, and in another example, the client 2000 may be a device providing a travel trajectory of a user, for example, a mobile phone installed with an Application (APP) supporting a mapping service.

As shown in fig. 1, the client 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, a speaker 2700, a microphone 2800, and so on. The processor 2100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 2200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 2300 includes, for example, a USB interface, a headphone interface, and the like. Communication device 2400 is capable of wired or wireless communication, for example. The display device 2500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 2600 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 2700 and the microphone 2800.

The vehicle 3000 is any vehicle having a positioning function, and may be, for example, an automobile, a bicycle, a power-assisted vehicle, an electric vehicle, a motorcycle, or the like. In one example, the vehicle 3000 may include any one or more of a GPS location module, a beidou location module, and a galileo location module. The vehicle may upload its own travel track to the server 1000.

In this embodiment, the road traffic sensing device 4000 may be a coil, a geomagnetic sensor, a radar, a bayonet camera, or the like, and may be used to monitor the traffic passing through the deployed position.

The network 5000 may be a wireless communication network, a wired communication network, a local area network, or a wide area network. In the article management system shown in fig. 1, a vehicle 3000 and a server 1000, a client 2000 and a server 1000, and a road traffic sensing device 4000 and a server 1000 may communicate with each other through a network 5000. Further, the vehicle 3000 may be the same as or different from the server 1000, the client 2000 may be the same as or different from the server 1000, and the network 5000 on which the road traffic sensing apparatus 4000 communicates with the server 1000 may be provided.

It should be understood that although fig. 1 shows only one server 1000, client 2000, vehicle 3000, it is not meant to limit the corresponding number, and multiple servers 1000, clients 2000, vehicles 3000 may be included in the transportation system 100.

The traffic system 100 shown in fig. 1 is merely illustrative and is in no way intended to limit the present invention, its application, or uses.

Although fig. 1 shows only one server 1000, one client 2000 and one vehicle 3000, it should be understood that, in a specific application, the transportation system 100 may include a plurality of servers 1000, a plurality of clients 2000, a plurality of vehicles 3000 and a plurality of road flow sensing devices 4000 according to actual requirements.

In an embodiment of the present invention, the memory 1200 of the server 1000 is configured to store instructions for controlling the processor 1100 to operate to execute the traffic processing method according to the embodiment of the present invention.

Although a number of devices are shown in fig. 1 for server 1000, the present invention may relate to only some of the devices, for example, server 1000 may relate to only memory 1200 and processor 1100.

In an embodiment of the present invention, the memory 2200 of the client 2000 is configured to store instructions for controlling the processor 2100 to execute the method for processing the traffic flow provided by the client 2000 according to the embodiment of the present invention.

Although a number of devices are shown in fig. 1 for client 2000, the present invention may relate to only some of the devices, for example, client 2000 may relate to only memory 2200 and processor 2100.

In the above description, the skilled person will be able to design instructions in accordance with the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

< method examples >

In the present embodiment, a method for processing a traffic flow is provided. The processing method may be implemented by an electronic device. The electronic device may be a server 1000 as shown in fig. 1.

As shown in fig. 2, the method for processing the traffic flow according to the present embodiment may include the following steps S1000 to S3000:

step S1000, acquiring a path set in a preset area and a traffic flow monitored by road flow sensing equipment arranged in the preset area in at least one time period as a target equipment traffic flow.

The preset area in this embodiment may be a traffic area selected in a city according to an application scenario or a specific requirement. For example, the predetermined area may be as shown in fig. 3.

The manner of acquiring the path set in the preset area may include any one or any combination of the following:

exhausting all paths in the preset area and constructing a path set;

acquiring a plurality of paths in a preset area according to an application scene or specific requirements to form a path set;

and extracting at least one pair of travel combinations in the preset area, wherein each pair of travel combinations comprises a departure point and an arrival point, and acquiring a set number of paths with the shortest path length for each pair of travel combinations to obtain a path set.

In the present embodiment, the path may be represented as a sequence of intersections or a sequence of road segments. The road segment in this embodiment may refer to a traffic line between two adjacent intersections on the traffic network.

In one embodiment, the path may comprise at least one segment, and the partial segments comprised by different paths may coincide, i.e. different paths may comprise partially identical segments.

The preset area can be an area in which at least one road traffic sensing device is selected to be arranged in a city according to an application scene or specific requirements. The road traffic sensing device in this embodiment may be a coil, a geomagnetic sensor, a radar, a bayonet camera, or the like. Each road traffic sensing device may monitor the traffic passing through its own deployed location. The traffic flow monitored by the road flow sensing equipment is full data.

In one embodiment, the at least one time period may be a time period within the target statistical period. The target statistical period may be set in advance according to an application scenario or specific requirements, for example, the statistical period may be one day or one hour. Further, each statistical cycle may be divided into a plurality of time segments. In the case that the statistical period is one day, the 24 hours a day may be divided into 24 time periods; in the case where the statistical period is one hour, it may be that one hour is divided into 6 time periods.

In one embodiment, the traffic flow according to the present invention may be the traffic flow of a given vehicle. The designated vehicle may be a vehicle for transporting an item, and may be a logistics vehicle, for example.

Step S2000, a corresponding relationship between each path in the path set and each road traffic sensing device is obtained.

Wherein the corresponding relation represents the relation between the road section included in each path and the road section provided with the road flow sensing device.

For example, for a preset area as shown in fig. 3, the acquired path set includes paths L1, L2, L3, L4, and L5, the road traffic sensing devices set in the preset area may include 1 to 6, it may be determined that the road traffic sensing device 6 is set on a link included in the path L1, the road traffic sensing devices 1 and 5 are set on a link included in the path L2, the road traffic sensing device 1 is set on a link included in the path L3, the road traffic sensing devices 2, 3, and 4 are set on a link included in the path 4, and the road traffic sensing devices 1, 3, and 4 are set on a link included in the path 5.

And step S3000, determining the total traffic flow of each path in each time period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period.

In one embodiment, at least one time period is a time period in the target statistical period, and then, according to the correspondence and the target device traffic flow monitored by each road traffic sensing device in each time period, it is determined that the total traffic flow of each path in each time period may be: and determining the total traffic flow of each path in each time period in the target counting period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period in the target counting period.

< first embodiment >

In the first embodiment, determining the total traffic flow of each path in each time period of the target statistics period according to the correspondence and the target device traffic flow monitored by each road traffic sensing device in each time period of the target statistics period may include steps S3100 to S3300 as follows:

and step S3100, constructing a first target expression by taking the total traffic flow of each path in each time period in the target counting period as a variable according to the corresponding relation and the traffic flow of the target device monitored by each road traffic sensing device in each time period in the target counting period.

In one embodiment, the step of constructing the first target expression may include steps S3110-S3140 as shown below:

step S3110, a device traffic flow vector is set according to the traffic flow of the target device monitored by each road traffic sensing device in each time period of the target counting period.

In one example, the device traffic may be a column vector. The elements of each line in the device traffic flow respectively correspond to the target device traffic flow monitored by one road traffic sensing device in one time period of the target statistics cycle.

In one example, a combination of one road flow sensing device and one time period may be taken as one device period combination, i.e. one device period combination having a corresponding time period and road flow sensing device.

For example, in the case of acquiring the traffic flow of the target device monitored by N road traffic sensing devices in M time periods of the target statistics period in step S1000, the nth (N ∈ [1, N ]) may be set]) A road flow sensing device and M (M E [1, M)]) The combination of time segments as the j (j ∈ [1, M × N)]) Combining the device periods, and combining the nth (N epsilon [1, N)]) The M (M belongs to [1, M ] of the road flow sensing equipment in the target statistical period]) The traffic flow of the target equipment monitored in a time period is used as the traffic flow f of the target equipment corresponding to the jth equipment time period combination_jThe target equipment traffic flow f_jNamely, as an element in the equipment traffic flow vector, obtaining the equipment traffic flow vector of M x N rows

And S3120, setting a path total traffic flow vector by taking the total traffic flow of each path in each time period of the target statistical period as a variable.

In one example, the path full-volume traffic vector may also be a column vector. The elements of each line in the path full traffic flow vector respectively correspond to the full traffic flow of one path in one time period of the target statistical cycle.

In one example, a combination of one path and one time period may be taken as one path period combination, where one path period combination has a corresponding time period and path.

For example, in the case that the L paths in the path set are acquired in step S1000, and the traffic flows of the target devices monitored by the N road traffic sensing devices in M time periods of the target statistical period are acquired, the L (L ∈ [1, L ]) may be set]) The M (M E [1, M) th path]) The combination of time segments as the i (i ∈ [1, M × L)]) The path periods are combined, the L (L is E [1, L)]) The M (M is equal to [1, M) of each path in the target statistical period]) The total traffic flow of each time segment is used as the total traffic flow g corresponding to the ith path segment combination_iThe total traffic flow g_iNamely, the path full-volume vehicle flow vector of M x L rows is obtained as one element in the path full-volume vehicle flow vector

Step S3130, constructing a sparse matrix according to the corresponding relationship.

In one example, the rows, which may be a sparse matrix, correspond to path period combinations and the columns correspond to device period combinations. Specifically, the sequence of the equipment time period combinations corresponding to each column in the sparse matrix is the same as the sequence of the equipment time period combinations corresponding to each row in the equipment traffic flow vector; the sequence of the path time interval combinations corresponding to each row in the sparse matrix is the same as the sequence of the path time interval combinations in the path full-volume vehicle flow vector.

Specifically, when the sparse matrix is constructed, the road traffic sensing device corresponding to the jth device time interval combination is passed through the path corresponding to the ith path time interval combination, and the time period corresponding to the jth device time interval combination is the same as the time period corresponding to the ith path time interval combination, the element values corresponding to the device time interval combination j and the path time interval combination i in the sparse matrix are set to 1, and the remaining element values are set to 0, so that the sparse matrix a with M × L rows and M × N columns is obtained.

For example, for the correspondence between the paths and the road traffic sensing devices as shown in fig. 3, in the case where the number M of time periods within the target statistical period is 1, the order of the paths corresponding to each row is L1, L2, L3, L4 and L5, and the order of the road traffic sensing devices corresponding to each column is 1, 2, 3, 4, 5, 6, then the constructed sparse matrix a may be:

and S3140, constructing a first target expression according to the equipment traffic flow vector, the path full-quantity traffic flow vector and the sparse matrix.

In one example, the first target expression may be a semi-orthodefinite quadratic expression. On this basis, according to the device traffic flow vector, the path full-volume traffic flow vector and the sparse matrix, the constructed first target expression may be:

in one embodiment, the processing method may further include steps S3150 to S3160 as follows:

step S3150, the traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistics periods is obtained and used as the traffic flow of the historical device.

In this embodiment, the durations of the history statistical period and the target statistical period are the same, wherein the time period of the history statistical period corresponds to the time period of the target statistical period.

For example, the historical statistical period and the target statistical period are both one day, and then the time period from 10 to 11 points of the historical statistical period corresponds to the time period from 10 to 11 points of the target statistical period.

Step S3160, a first diagonal matrix is constructed according to the historical device traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistics periods.

Wherein the first diagonal matrix is used for representing the fluctuation degree of the historical equipment traffic flow in the plurality of historical statistic periods.

In one embodiment, the step of constructing the first diagonal matrix includes steps S3161 to S3162 as follows:

step S3161, for each road flow sensing device, the variance of the monitored historical device traffic flow in each time period is respectively determined.

For example, in the case of acquiring the traffic flow of the target device monitored by N road traffic sensing devices in M time periods of T historical statistics periods, the nth (N ∈ [1, N ∈ N)]) A road flow sensing device is arranged at the T (T epsilon [1, T)]) The traffic flow of the historical equipment monitored in the mth time period of the historical statistical period is f_n，t，mThen, the variance of the historical vehicle flow monitored by the nth road flow sensing device in the mth time period may be:

step S3162, a first diagonal matrix is constructed according to the corresponding variance of each road flow sensing device in each time period.

For example, the nth (N ∈ [1, N)]) A road flow sensing device and M (M E [1, M)]) The combination of time segments as the j (j ∈ [1, M × N)]) Combining the time periods of the devices, and monitoring the variance s of the vehicle flow of the historical device by the nth road flow sensing device in the mth time period_n，m ²As the variance s corresponding to the jth device period combination_j ²。

Then, the inverse of the variance corresponding to each device period combination may be used as an element on a diagonal of the first diagonal matrix, and the values of elements outside the diagonal may be set to 0, so as to obtain the first diagonal matrix U with M × N rows and M × N columns.

Further, the first diagonal matrix U may also be represented as:

U＝diag{min{s_j ^-2，δ}}

wherein δ is an arbitrary small positive number set in advance according to an application scenario, specific requirements, or experimental data.

And the ordering of the device time interval combination corresponding to each element on the diagonal line of the constructed first diagonal matrix is the same as the ordering of the device time interval combination corresponding to each row in the device traffic flow vector.

In this embodiment, step S3140 may further include: according to the traffic flow vector of the equipment

Full-volume traffic flow vector of path

And constructing a first target expression by using the sparse matrix A and the first diagonal matrix U.

Specifically, the constructed first target expression may be:

in step S3200, an objective function is obtained according to the first objective expression.

In this embodiment, the first target expression may be used as the target function.

Specifically, the objective function Q can be expressed as:

and S3300, solving the objective function, and determining the value of the total traffic flow of each path in each time period of the target statistical period under the condition that the value of the objective function is minimum, so that the value of the total traffic flow of each path in each time period of the target statistical period is greater than or equal to zero.

In one example, a solver (e.g., CVXOPT) may be used to solve the objective function.

< second embodiment >

In the second embodiment, the processing method may further include steps S4100 to S4300 shown below:

step S4100, acquiring a sampling trajectory of the preset region in each time period of the target statistical period.

The sampling trajectory in the present embodiment may be acquired by a 6PS device provided on the vehicle, or may be acquired by a specified navigation application. The sampling track can comprise a spatial position sequence of an intersection or a road section which is passed by the vehicle in the traveling process and departure time. The path matched with each sampling track can be determined according to the spatial position sequence, and the time period corresponding to each sampling track can be determined according to the starting time.

Step S4200, determining the number of sampling trajectories of each path in each time period of the target statistical period according to the sampling trajectories in each time period, respectively, as a target sampling traffic flow.

Step S4300, obtaining the traffic flow passing through each road flow sensing device and providing a sampling track in each time period of the target counting period.

In one example, the traffic flow passing through each road flow sensing device and providing the sampling trajectory in each time period of the target statistics period may be obtained according to a corresponding relationship between each path and each road flow sensing device in each time period of the target statistics period and a target sampling traffic flow of each path in each time period of the target statistics period.

For example, at L1(L1 ∈ [1, L ]]) The sum of the paths L2(L2 ∈ [1, L ]]The N-th path (N ∈ [1, N ]) is set on the l1 ≠ l2]) The l1 th path is at the M (M is equal to [1, M ] th of the target statistical period]) The target sampling traffic flow in each time period is

The l2 th path is at the targetThe target sampling traffic flow in the mth time segment of the statistical cycle is

Then, the traffic flow passing through the nth road flow sensing device and providing the sampling trajectory in the mth time period of the target statistical cycle is:

on the basis, the total traffic flow of each path in each time period of the target counting period is determined according to the traffic flow which passes through each road flow sensing device in each time period of the target counting period and provides the sampling track and the target sampling traffic flow of each path in each time period of the target counting period.

Specifically, determining the total traffic flow of each path in each time period in the target statistical period may include the following steps S4400 to S4700:

step S4400, according to the correspondence and the target device traffic flow monitored by each road traffic sensing device in each time period of the target statistics period, constructing a first target expression by using the full amount of traffic flow of each path in each time period of the target statistics period as a variable.

In this embodiment, the manner of constructing the first target expression may refer to the foregoing first embodiment, and is not described herein again.

Step S4500, constructing a second target expression by taking the full traffic flow of each path in each time period of the target counting period as a variable according to the traffic flow of the target device monitored by each road traffic sensing device in each time period of the target counting period, the traffic flow of each road traffic sensing device which is monitored in each time period of the target counting period and provides a sampling track, and the target sampling traffic flow of each path in each time period of the target counting period.

In one embodiment, the step of constructing the second target expression may include steps S4510 to S4530 shown below:

and step S4510, setting a path sampling traffic flow vector according to the target sampling traffic flow of each path in each time period of the target statistical period.

In one example, the path sample traffic vector may be a column vector. The elements of each row in the path sampling traffic flow vector respectively correspond to the target sampling traffic flow of one path in one time period of the target statistical period.

For example, the L (L ∈ [1, L ]) can be set]) The M (M E [1, M) th path]) The combination of time segments as the i (i ∈ [1, M × L)]) The path periods are combined, the L (L is E [1, L)]) The M (M is equal to [1, M) of each path in the target statistical period]) The target sampling traffic flow of each time period is used as the target sampling traffic flow corresponding to the ith path time period combination

The target sampling traffic flow

Namely, the path sampling traffic flow vector is obtained as an element in the path sampling traffic flow vector

In one example, the ordering of the device time period combinations corresponding to each row in the path sampling traffic flow vector is the same as the ordering of the device time period combinations corresponding to each row in the path full-volume traffic flow vector.

Step S4520, determining the permeability of the preset area according to the traffic flow rate of the sampling trajectory monitored by each road traffic sensing device in each time period of the target statistics period and the traffic flow rate of the target device monitored by each road traffic sensing device in each time period of the target statistics period.

Wherein the permeability represents a traffic flow ratio providing a sampling trajectory within a preset area.

In one embodiment, the step of determining the permeability of the preset area comprises:

step S4521, determining the sum of the traffic flows which are monitored by all the road traffic sensing devices in all time periods of the target counting period and provide the sampling track as the track traffic flow sum.

At the N-th (N is equal to [1, N ]]) A road flow sensing device and M (M E [1, M)]) The combination of time segments as the j (j ∈ [1, M × N)]) Under the condition of time interval combination of each device, the traffic flow which provides the sampling track and corresponds to the time interval combination of the jth device is

The sum of the trajectory traffic can be expressed as

And S4522, determining the sum of the traffic flow of the target equipment monitored by all the road traffic sensing equipment in all the time periods of the target statistical period as the sum of the traffic flow of the equipment.

N (N is equal to [1, N ]]) A road flow sensing device and M (M E [1, M)]) The combination of time segments as the j (j ∈ [1, M × N)]) The time interval combination of each device, and the traffic flow f of the target device corresponding to the time interval combination of the jth device_jThe sum of the device traffic may be expressed as Σ_jf_j。

And step S4523, obtaining the permeability according to the track traffic flow sum and the equipment traffic flow sum.

In one embodiment, the sum of the trajectory traffic flow may be determined

And the traffic flow sum sigma of the equipment_jf_jAs the ratio between, as the permeability

In another embodiment, the sum of the trajectory traffic is used

And the traffic flow sum sigma of the equipment_jf_jThe equation for determining the permeability α can be expressed as:

and step S4530, constructing a second target expression according to the path sampling traffic flow vector, the path full-quantity traffic flow vector and the permeability.

In one example, the second target expression may be a semi-orthodefinite quadratic expression. On the basis, the traffic flow vector is sampled according to the path

Full-volume traffic flow vector of path

And permeability α, the first target expression constructed may be:

in one embodiment, the processing method may further include step S4540 shown as follows:

and step S4540, acquiring the number of sampling tracks of each path in each time period of a plurality of historical statistical cycles as the historical path sampling traffic.

Step S4530 may further include steps S4531 to S4532 as follows:

step S4531, sampling traffic flow according to the historical path of each path in each time period of a plurality of historical statistic periods, and constructing a second diagonal matrix.

And the second diagonal matrix is used for representing the fluctuation degree of the historical path sampling vehicle flow in a plurality of historical statistical periods.

In one embodiment, the step of constructing the second diagonal matrix includes steps S4531-1 to S4531-2 as follows:

in step S4531-1, for each path, the variance of the historical path sample traffic flow in each time period is determined, respectively.

For example, when the traffic flow of the target device monitored by the L paths in M time periods of the T historical statistics periods is obtained, the L (L ∈ [1, L ∈ L)]) At T (n ∈ [1, T)]) The historical sampling traffic flow in the mth time period of the historical statistical period is g_l，t，mThen, the L (L e [1, L)]) The variance of the historical sampled traffic for each path over the mth time period may be:

and step S4531-2, constructing a second diagonal matrix according to the corresponding variance and permeability of each path in each time period.

In one example, a combination of one path and one time period may be taken as one path period combination, i.e., one path period combination has a corresponding time period and path.

For example, the L (L ∈ [1, L ]) can be set]) The M (M E [1, M) th path]) The combination of time segments as the i (i ∈ [1, M × L)]) Combining time periods of the equipment, and sampling the variance s of the historical traffic flow of the I paths in the mth time period_l，m ²As the variance s corresponding to the ith path period combination_i ²。

Then, the product of the inverse of the variance and the permeability corresponding to each path segment combination may be used as the diagonal elements of the second diagonal matrix, and the values of the elements outside the diagonal lines may be set to 0, so as to obtain a second diagonal matrix V with M × L rows and M × L columns.

Further, the second diagonal matrix V may also be represented as:

V＝diag{min{α²s_i ^-2，δ}}

The ordering of the path time interval combination corresponding to each element on the diagonal line of the constructed second diagonal matrix is the same as the ordering of the equipment time interval combination corresponding to each row in the path sampling traffic flow vector, and the ordering of the equipment time interval combination corresponding to each row in the path full-quantity traffic flow vector.

Step S4532, sampling traffic flow vector according to path

Full-volume traffic flow vector of path

And a second diagonal matrix V to obtain a second target expression.

Specifically, the constructed second target expression may be:

step S4600, obtain an objective function according to the first target expression and the second target expression.

Specifically, the first target expression and the second target expression may be summed to obtain the target function.

The objective function Q can be expressed as:

step S4700, solving the objective function, and determining the value of the total traffic flow of each path in each time period of the target statistical period under the condition that the value of the objective function is minimum, so that the value of the total traffic flow of each path in each time period of the target statistical period is greater than or equal to the value of the target sampling traffic flow of the corresponding path in the corresponding time period of the target statistical period.

In one embodiment, after obtaining the total traffic volume of each path in each time period of the target statistical period, the processing method may further include:

and carrying out traffic control on the preset interval according to the total traffic flow of each path in each time period of the target statistical period.

Specifically, the traffic signal lamps in the preset area can be controlled according to the mode of carrying out traffic control on the preset area according to the obtained total track; travel demands in a preset area can be determined; traffic simulation can be performed aiming at a preset area; and the traffic guidance can be performed aiming at the preset area.

In one example, the manner of traffic control for the preset area may include:

according to the total traffic flow of the target path in the target time period of the target statistical cycle, obtaining the predicted traffic flow of the target path in the target time period of the future statistical cycle;

and carrying out traffic control on the crossing passed by the target path according to the predicted traffic flow of the target path in the target time period of the future statistical cycle.

In this embodiment, a prediction model may be adopted to obtain the predicted traffic flow of the target path in the target time period of the future statistical cycle according to the total traffic flow of the target path in the target time period of the target statistical cycle.

Specifically, the specific way of performing traffic control on the intersection through which the target path passes may include: and correspondingly controlling at least one of the signal period duration of signal lamps of the intersections passed by the target path, the split of at least one phase and the phase difference of at least one phase of the intersections.

The phase in this embodiment is known in the art. For example, it may include that within a signal cycle, a sequence of signal states of one or several traffic flows with the same signal light color is called a phase. The phases are divided according to the time sequence of the signal display obtained by the traffic flow, and there are several phases according to different time sequence arrangements. Each control state corresponds to a different set of lamp color combinations, called a phase. In short, one phase is also referred to as one control state. For another example, the signal display states corresponding to a group of traffic flows which do not conflict with each other and simultaneously obtain the right of way may be referred to as phases. It can be seen that the phases are divided according to the alternation of the right of way in the crossing in one signal period.

The signal period duration comprises the time required for the signal to run for one cycle, including the change of the signal lamp, and is equal to the sum of the green, yellow and red lamp times; and also equal to the sum of the green and yellow lamp times (which are typically fixed) required for all phases.

The split ratio is the proportional time available for the vehicle to pass through during one period of the signal light. I.e. the ratio of the green time of a certain phase to the period duration. The green time may be an actual green time or an effective green time.

The actual green light time may be the time taken for the green light to turn on until the green light is turned off. Effective green time: including the actual vehicle transit time that is effectively utilized, which is equal to the sum of the green light time and the yellow light time minus the loss time. The lost time comprises two parts, namely the time when the green light signal is turned on and the vehicle is started; when the green light is turned off and the yellow light is turned on, only the vehicle passing the stop line can pass continuously, so that a part of the lost time is the delay time of the acceleration ending of the actual green light time minus the starting time. The end lag time is the fraction of the yellow lamp time that is effectively utilized. The loss time for each phase is the difference between the start delay time and the end delay time.

Phase difference: the two signal intersections refer to the difference between the start times of green (or red) lights in the same phase of two adjacent intersections.

The above definitions are only for exemplifying the description of the specific embodiments of the present invention and are not to be construed as limiting the scope of the invention.

For example, the manner of performing traffic control on the intersection passed by the target path in the target time period of the future statistical cycle may include: the phase difference of the phase corresponding to the target path at the crossing where the target path passes is set, so that the vehicle can enjoy the green wave effect of continuously passing through the crossings without stopping when running along the target path.

< example 1>

Fig. 4 is a method for processing the traffic flow according to an example, which describes the step of determining the total traffic flow passing through each route only based on the traffic flow monitored by the road flow sensing device. The processing method may include the following steps S4001 to S4011:

step S4001, a path set in a preset area and traffic flow monitored by road traffic sensing equipment arranged in the preset area in at least one time period of a target statistic cycle are obtained and used as traffic flow of target equipment.

Step S4002, acquiring a corresponding relation between each path in the path set and each road traffic sensing device.

Step S4003, according to the traffic flow of the target device monitored by each road traffic sensing device in each time period of the target counting period, setting a device traffic flow vector.

For example, in the case of acquiring the traffic flow of the target device monitored by N road traffic sensing devices in M time periods of the target statistics period in step S4001, the nth (N ∈ [1, N ]) may be set]) A road flow sensing device and M (M E [1, M)]) The combination of time segments as the j (j ∈ [1, M × N)]) Combining the device periods, and combining the nth (N epsilon [1, N)]) The M (M belongs to [1, M ] of the road flow sensing equipment in the target statistical period]) The traffic flow of the target equipment monitored in a time period is used as the traffic flow f of the target equipment corresponding to the jth equipment time period combination_jThe target equipment traffic flow f_jNamely, as an element in the equipment traffic flow vector, obtaining the equipment traffic flow vector of M x N rows

Step S4004, setting a path full-volume traffic flow vector by taking the full-volume traffic flow of each path in each time period of the target statistical period as a variable.

When L paths are included in the path set obtained in step S4001, the L-th path (L ∈ [1, L ∈ L)]) The M (M E [1, M) th path]) The combination of time segments as the i (i ∈ [1, M × L)]) The path periods are combined, the L (L is E [1, L)]) The total traffic flow of the mth time segment of the target statistical cycle of each path is used as the total traffic flow g corresponding to the ith path time segment combination_iThe total traffic flow g_iNamely, the path full-volume vehicle flow vector of M x L rows is obtained as one element in the path full-volume vehicle flow vector

And S4005, constructing a sparse matrix according to the corresponding relation.

For example, a route corresponding to the ith route period combination passes through the road traffic sensing device corresponding to the jth device period combination, and the time period corresponding to the jth device period combination is the same as the time period corresponding to the ith route period combination, the element values corresponding to the device period combination j and the route period combination i in the sparse matrix are set to 1, and the remaining element values are set to 0, so that a sparse matrix a with M × L rows and M × N columns is obtained.

Step S4006, the traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistics periods is obtained and used as the traffic flow of the historical device.

Step S4007, for each road traffic sensing device, determining the variance of the historical device traffic monitored in each time period.

Under the condition of acquiring the traffic flow of the target equipment monitored by the N road flow sensing equipment in M time periods of T historical statistics periods, the traffic flow of the historical equipment monitored by the nth road flow sensing equipment in the mth time period of the T historical statistics period is f_n，t，mThen, the nth road traffic sensing device is at the mth timeThe variance of the historical device traffic monitored over the period of time may be:

the variance s of the vehicle flow of the historical equipment monitored by the nth road flow sensing equipment in the mth time period_n，m ²As the variance s corresponding to the jth device period combination_j ²。

Step S4008, a first diagonal matrix is constructed according to the corresponding variance of each road traffic sensing device in each time period.

The first diagonal matrix U may also be represented as:

U＝diag{min{s_j ^-2，δ}}

step S4009, a first target expression is constructed according to the equipment traffic flow vector, the path full-quantity traffic flow vector, the sparse matrix and the first diagonal matrix.

The first target expression constructed may be:

step S4010, the first target expression is taken as a target function.

Step S4011, solving the objective function, determining a value of the total traffic flow of each path in each time period of the target statistics period when the value of the objective function is minimum, and making the value of the total traffic flow of each path in each time period of the target statistics period greater than or equal to zero.

< example 2>

Fig. 5 is a method for processing a traffic flow according to another example based on example 1, which describes a step of determining a total traffic flow passing through each route according to a traffic flow monitored by a road flow sensing device and a sampling trajectory. The processing method may include the following steps S5001 to S5011:

step S5001, a sampling trajectory of the preset area in each time period of the target statistical period is obtained.

Step S5002, determining the number of sampling trajectories corresponding to each path in each time period of the target statistical period according to the sampling trajectories in each time period, as the target sampling traffic flow.

Step S5003, obtaining the traffic flow passing through each road flow sensing device and providing a sampling track in each time period of the target counting period.

At L1(L1 ∈ [1, L ]]) The sum of the paths L2(L2 ∈ [1, L ]]The N-th path (N ∈ [1, N ]) is set on the l1 ≠ l2]) The l1 th path is at the M (M is equal to [1, M ] th of the target statistical period]) The target sampling traffic flow in each time period is

The target sampling traffic flow of the l2 th path in the m time segment of the target statistical period is

step S5004, setting a path sampling traffic flow vector according to the target sampling traffic flow of each path in each time period of the target statistical period.

Can convert L (L E [1, L ]]) The M (M E [1, M) th path]) The combination of time segments as the i (i ∈ [1, M × L)]) The path periods are combined, the L (L is E [1, L)]) The M (M is equal to [1, M) of each path in the target statistical period]) The target sampling traffic flow of each time period is used as the target sampling traffic flow corresponding to the ith path time period combination

The target sampling traffic flow

Step S5005, determining the sum of the traffic flows which are monitored by all the road traffic sensing devices in all time periods of the target counting period and provide the sampling track as the track traffic flow sum.

The sum of the trajectory traffic can be expressed as

Step S5006, determining the sum of the traffic flow of the target device monitored by all the road traffic sensing devices in all the time periods of the target counting period as the sum of the traffic flow of the device.

And S5007, obtaining the permeability according to the track traffic flow sum and the equipment traffic flow sum.

The formula for the permeability α can be expressed as:

step S5008, acquiring the number of sampling tracks of each path in each time period of a plurality of historical statistical cycles as the historical path sampling traffic flow.

In step S5009, the variance of the historical path sample traffic volume in each time period is determined for each path, respectively.

Under the condition of acquiring the traffic flow of the target equipment monitored by the L paths in M time periods of the T historical statistical periods, determining that the L belongs to [1, L ∈ [ ]]) At T (n ∈ [1, T)]) The historical sampling traffic flow in the mth time period of the historical statistical period is g_l，t，mThen, the L (L e [1, L)]) The variance of the historical sampled traffic for each path over the mth time period may be:

can convert L (L E [1, L ]]) The M (M E [1, M) th path]) The combination of time segments as the i (i ∈ [1, M × L)]) Combining time periods of the equipment, and sampling the variance s of the historical traffic flow of the I paths in the mth time period_l，m ²As the variance s corresponding to the ith path period combination_i ²。

Step S5010, constructing a second diagonal matrix according to the variance and permeability of each path in each time period.

The second diagonal matrix V may also be represented as:

V＝diag{min{α²s_i ^-2，δ}}

step S5011, a second target expression is obtained according to the path sampling traffic flow vector, the path full-quantity traffic flow vector and the second diagonal matrix.

The second target expression constructed may be:

step S5012, obtaining an objective function according to the first target expression and the second target expression.

The objective function Q can be expressed as:

step S5013, solving the objective function, determining a value of a total traffic flow of each path in each time period of the target statistics period when the value of the objective function is minimum, and making the value of the total traffic flow of each path in each time period of the target statistics period greater than or equal to a value of a target sampling traffic flow of the corresponding path in a corresponding time period of the target statistics period.

< apparatus embodiment >

In this embodiment, a vehicle flow rate processing device 6000 is provided, as shown in fig. 6, and includes a path flow rate obtaining module 6100, a correspondence relation obtaining module 6200, and a total flow rate determining module 6300. The path flow acquiring module 6100 is configured to acquire a path set in a preset area and a traffic flow monitored by a road flow sensing device arranged in the preset area in at least one time period, as a target device traffic flow; the corresponding relation obtaining module 6200 is configured to obtain a corresponding relation between each path in the path set and each road traffic sensing device in each time period of the target statistics cycle, where the corresponding relation represents a relation between a road segment included in each path and a road segment provided with the road traffic sensing device; the total traffic flow determining module 6300 is configured to determine the total traffic flow of each path in each time period of the target statistics period according to the correspondence and the traffic flow of the target device monitored by each road traffic sensing device in each time period of the target statistics period.

In one embodiment, the traffic flow is the traffic flow of a given vehicle.

In one embodiment, the at least one time period is a time period within the target statistical period.

In one embodiment, as shown in fig. 7, the processing device 6000 may further include:

a sampling trajectory acquisition module 6400, configured to acquire a sampling trajectory of a preset area in each time period of a target statistics period;

the sampling traffic acquiring module 6500 is configured to determine, according to the sampling trajectory in each time period of the target statistics period, the number of corresponding sampling trajectories in a time period corresponding to the target statistics period of each path, and use the number as the target sampling traffic;

a trajectory-providing-flow obtaining module 6600, configured to provide a vehicle flow of the sampling trajectory through each road flow sensing device in each time period of the target statistics period;

the full traffic determination module 6300 may further be configured to: and determining the total traffic flow of each path in each time period of the target statistical period according to the traffic flow which passes through each road traffic sensing device in each time period of the target statistical period and provides the sampling track and the target sampling traffic flow of each path in each time period of the target statistical period.

In one embodiment, the full traffic determination module 6300 is specifically configured to:

constructing a second target expression by taking the total traffic flow of each path in each time period of the target counting period as a variable according to the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period of the target counting period, the traffic flow of each road traffic sensing equipment which is monitored by each road traffic sensing equipment in each time period of the target counting period and the target sampling traffic flow of each path in each time period of the target counting period;

In one embodiment, constructing the first target expression includes:

constructing a sparse matrix according to the corresponding relation;

and constructing a first target expression according to the equipment traffic flow vector, the path full-quantity traffic flow vector and the sparse matrix.

In one embodiment, the processing device 6000 may further include:

the module is used for acquiring traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistical periods and taking the traffic flow as historical device traffic flow;

the module is used for constructing a first diagonal matrix according to the historical device traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistical periods, so as to obtain a first target expression according to the first diagonal matrix; the first diagonal matrix is used for representing the fluctuation degree of the historical equipment vehicle flow in a plurality of historical statistic periods.

In one embodiment, constructing the first diagonal matrix according to the historical traffic flow monitored by each road traffic sensing device in each of the plurality of historical statistical periods comprises:

and constructing a first diagonal matrix according to the variance of each road flow sensing device in each time period.

In one embodiment, constructing the second target expression includes:

determining the permeability of a preset area according to the traffic flow which is monitored by each road flow sensing device in each time period of a target counting period and provides a sampling track and the traffic flow of each road flow sensing device in each time period of the target counting period; the permeability represents the traffic flow ratio of a sampling track provided in a preset area;

and constructing a second target expression according to the path sampling traffic flow vector, the path full-quantity traffic flow vector and the permeability.

In one embodiment, the processing device 6000 further comprises:

the module is used for acquiring the sampling traffic flow of each path in each time period of a plurality of historical statistical cycles and taking the sampling traffic flow as the sampling traffic flow of the historical path;

constructing the second target expression includes:

according to the historical path sampling traffic flow and permeability of each path in each time period of a plurality of historical statistical periods, constructing a second diagonal matrix, wherein the second diagonal matrix is used for expressing the fluctuation degree of the historical path sampling traffic flow in the plurality of historical statistical periods;

and obtaining a second target expression according to the path sampling traffic flow vector, the path full-quantity traffic flow vector and the second diagonal matrix.

In one embodiment, constructing the second diagonal matrix comprises:

and constructing a second diagonal matrix according to the corresponding variance of each path in each time period and the permeability.

In one embodiment, determining the permeability of the preset area comprises:

determining the sum of the traffic flow of the target equipment monitored by all the road traffic sensing equipment in all time periods of a target counting period as the sum of the traffic flow of the equipment;

and obtaining the permeability according to the sum of the track traffic flow and the sum of the equipment traffic flow.

In one embodiment, the first target expression and the second target expression are both semi-positive definite quadratic expressions.

In one embodiment, obtaining the set of paths within the preset area comprises:

extracting at least one pair of travel combinations in a preset area, wherein the travel combinations comprise departure points and arrival points;

and for each pair of travel combinations, acquiring a set number of paths with the shortest path length to obtain a path set.

In one embodiment, the processing device 6000 further comprises:

and the module is used for carrying out traffic control on the preset area according to the total traffic flow of each path in each time period of the target statistical period.

In one embodiment, the traffic control of the preset area according to the total traffic flow of each path in each time period of the target statistical period comprises:

It will be appreciated by those skilled in the art that the traffic flow processing device 6000 can be implemented in various ways. For example, the processor may be configured by instructions to implement the processing device 6000 for traffic flow. For example, the instructions may be stored in ROM and read from ROM into a programmable device when the device is activated to implement the processing device 6000 for traffic flow. For example, the traffic handling device 6000 may be cured into a dedicated device (e.g., an ASIC). The traffic handling device 6000 may be divided into separate units or may be combined together. The processing device 6000 of the traffic flow may be realized by one of the various implementations described above, or may be realized by a combination of two or more of the various implementations described above.

In this embodiment, the traffic flow processing device 6000 may have various implementation forms, for example, the traffic flow processing device 6000 may be any functional module running in a software product or an application program providing the traffic flow processing service, or a peripheral insert, a plug-in, a patch, etc. of the software product or the application program, and may also be the software product or the application program itself.

< electronic apparatus >

In this embodiment, an electronic device 8000 is also provided. The electronic device 8000 may be the server 1000 shown in fig. 1.

In one aspect, as shown in fig. 8, the electronic device 8000 may include the aforementioned traffic flow processing device 6000, which is used to implement the traffic flow processing method according to any embodiment of the present invention.

In another aspect, as shown in FIG. 8, the electronic device 8000 may also include a processor 8100 and a memory 8200, the memory 8200 for storing executable instructions; the processor 8100 is configured to operate the electronic device 8000 to perform a traffic flow processing method according to any of the embodiments of the present invention according to a control of an instruction.

< computer-readable storage Medium >

In the present embodiment, there is also provided a computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing a method of processing a traffic flow according to any embodiment of the present invention.

The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for processing traffic flow comprises the following steps:

determining the total traffic flow of each path in each time period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period;

determining the total traffic flow of each path in each time period according to the corresponding relationship and the traffic flow of the target device monitored by each road traffic sensing device in each time period, including:

according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period, constructing a first target expression by taking the full traffic flow of each path in each time period as a variable;

obtaining a target function according to the first target expression;

solving the objective function, determining the value of the total traffic flow of each path in each time period under the condition that the value of the objective function is minimum, so that the value of the total traffic flow of each path in each time period is greater than or equal to zero,

wherein the step of constructing the first target expression comprises:

setting a device traffic flow vector according to the traffic flow of the target device monitored by each road traffic sensing device in each time period;

setting a path full-volume vehicle flow vector by taking the full-volume vehicle flow of each path in each time period as a variable;

constructing a sparse matrix according to the corresponding relation;

constructing a first diagonal matrix according to the historical device traffic flow monitored by each road traffic sensing device in each time period of a plurality of historical statistical periods;

and constructing the first target expression according to the equipment traffic flow vector, the path full-quantity traffic flow vector, the sparse matrix and the first diagonal matrix.

2. The method of claim 1, wherein the at least one time period is a time period within a target statistical period.

3. The processing method of claim 2, wherein the processing method further comprises:

4. The process of claim 3, wherein the step of determining a full amount of traffic for each path for each time period of the target statistical period comprises:

5. The processing method of claim 4, wherein the step of constructing a first target expression comprises:

constructing a sparse matrix according to the corresponding relation;

6. The processing method according to claim 5, wherein the processing method comprises:

7. The processing method according to claim 6, wherein the step of constructing a first diagonal matrix according to the historical traffic monitored by each road traffic sensing device in each of a plurality of historical statistical cycles comprises:

8. The process of claim 5, wherein said step of constructing a second target expression comprises:

9. The processing method of claim 8, wherein the processing method further comprises:

the constructing the second target expression comprises:

10. The processing method of claim 9, wherein the step of constructing a second diagonal matrix comprises:

for each path, respectively determining the variance of the historical path sampling traffic flow in each time period;

11. The process of claim 8, wherein the step of determining the permeability of the preset area comprises:

12. The processing method according to claim 5, wherein the first target expression and the second target expression are both semi-orthodefinite quadratic expressions.

13. The processing method according to claim 2, wherein the step of obtaining the set of paths within the preset area comprises:

14. The processing method according to any one of claims 2 to 13, wherein the processing method further comprises:

15. The processing method according to claim 14, wherein the step of controlling the traffic of the preset area according to the total traffic flow of each path in each time period of the target statistical cycle comprises:

16. The method of claim 1, wherein the traffic volume is a traffic volume of a given vehicle.

17. A traffic flow processing device, comprising:

a corresponding relation obtaining module, configured to obtain a corresponding relation between each path in the path set and each road traffic sensing device, where the corresponding relation indicates a relation between a road segment included in each path and a road segment provided with the road traffic sensing device;

the total traffic flow determining module is used for determining the total traffic flow of each path in each time period according to the corresponding relation and the traffic flow of the target equipment monitored by each road traffic sensing equipment in each time period;

obtaining a target function according to the first target expression;

the step of constructing the first target expression comprises:

constructing a sparse matrix according to the corresponding relation;

18. An electronic device comprising the processing apparatus of claim 17; or, comprising a processor and a memory for storing executable instructions for controlling the processor to perform a processing method according to any one of claims 1 to 16.

19. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the processing method of any one of claims 1 to 16.