CN113053103B

CN113053103B - Traffic simulation model generation method, traffic flow prediction method and related device

Info

Publication number: CN113053103B
Application number: CN202110191302.1A
Authority: CN
Inventors: 孙伟力; 杨的
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2021-02-19
Filing date: 2021-02-19
Publication date: 2022-07-05
Anticipated expiration: 2041-02-19
Also published as: CN113053103A

Abstract

The application provides a traffic simulation model generation method, a traffic flow prediction method and a related device, wherein the traffic simulation model generation method comprises the following steps: acquiring track data of the floating car running in a preset range, wherein the preset range comprises preset time and a preset area; acquiring the monitoring traffic flow for monitoring all vehicles passing through a preset road section within a preset range; estimating the traffic flow of all vehicles on all road sections in a preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range; and distributing the estimated traffic flow in the global OD matrix to a preset traffic network, and adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model. The method and the device can reduce development cost and time cost consumed in the traffic simulation model generation process.

Description

Traffic simulation model generation method, traffic flow prediction method and related device

Technical Field

The application relates to the technical field of traffic simulation, in particular to a traffic simulation model generation method, a traffic flow prediction method and a related device.

Background

With the increasing urbanization pace of China, the problem of urban traffic congestion is increasingly aggravated. The urban traffic jam relieving is a complex and challenging work, any traffic jam relieving scheme needs to be analyzed from the perspective of a system and cannot be focused on only one intersection or road section, so that a traffic simulation tool is needed to model a jam area or even the whole urban range, different jam relieving schemes are simulated, and a scheme with the optimal jam relieving effect on the whole system is selected.

The amount of collected and analyzed travel data required by the conventional traffic simulation modeling method is very large, so that the development cost and the time cost of the traffic simulation system modeling are increased.

Disclosure of Invention

In view of the above, an object of the embodiments of the present application is to provide a traffic simulation model generation method, a traffic flow prediction method, and a related device, which can reduce development cost and time cost consumed in a traffic simulation model generation process.

According to one aspect of the present application, a computer device is provided that may include one or more storage media and one or more processors in communication with the storage media. One or more storage media store machine-readable instructions executable by a processor. When the computer device is operated, the processor is communicated with the storage medium through the bus, and the processor executes the machine readable instructions to execute one or more of the following operations:

in a first aspect, an embodiment of the present application provides a traffic simulation model generation method, including: acquiring track data of a floating vehicle running in a preset range, wherein the preset range comprises preset time and a preset area; acquiring the monitoring traffic flow for monitoring all vehicles passing through a preset road section in the preset range; estimating the traffic flow of all vehicles on all road sections in the preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range; and distributing the estimated traffic flow in the global OD matrix to a preset traffic network, and adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

In a second aspect, an embodiment of the present application provides a traffic flow prediction method, including: acquiring a scene to be analyzed; and analyzing the scene to be analyzed by utilizing a preset traffic simulation model to obtain the predicted flow under the scene to be analyzed, wherein the traffic simulation model is generated by the traffic simulation model generation method.

In a third aspect, an embodiment of the present application provides a traffic simulation model generation apparatus, including: a data acquisition module to: acquiring track data of a floating vehicle running in a preset range, wherein the preset range comprises preset time and a preset area, and acquiring monitoring traffic flow monitored by all vehicles passing through a preset road section in the preset range; the estimation module is used for estimating the traffic flow of all vehicles on all road sections in the preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range; and the generation module is used for distributing the estimated traffic flow in the global OD matrix to a preset traffic network, and adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

In a fourth aspect, an embodiment of the present application provides a traffic flow prediction apparatus, including: the scene acquisition module is used for acquiring a scene to be analyzed; and the analysis module is used for analyzing the scene to be analyzed by utilizing a preset traffic simulation model to obtain the predicted flow under the scene to be analyzed, wherein the traffic simulation model is generated by the traffic simulation model generation method.

In a fifth aspect, the present application provides a computer device, including a processor, a storage medium and a bus, where the storage medium stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the storage medium communicate with each other through the bus, and the processor executes the machine-readable instructions to perform the steps of the method.

In a sixth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method.

In a seventh aspect, the present application provides a computer program product, which includes a computer program/instruction, and when the computer program/instruction is executed by a processor, the steps of the method are implemented.

The traffic simulation model generation method provided by the embodiment of the application comprises the steps of firstly, acquiring track data of a floating car running in a preset range, wherein the preset range comprises a preset time and a preset area, then acquiring the monitoring traffic flow for monitoring all vehicles passing through the preset road section within the preset range, and then according to the track data and the monitoring traffic flow, estimating the traffic flow of all vehicles on all road sections in the preset range to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range, and finally, the estimated traffic flow in the global OD matrix is distributed to a preset traffic network, and adjusting the estimated traffic flow in the overall OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

In the above embodiment, estimated traffic flows of all vehicles in all road sections in the preset range can be estimated according to the track data of the floating vehicle running in the preset range and the monitored traffic flows of all vehicles passing through all road sections in the preset range, and then the estimated traffic flows are adjusted according to the monitored traffic flows of the preset road sections, so that a traffic simulation model closer to the actual traffic simulation model is finally obtained.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 shows a flowchart of a traffic simulation model generation method provided in an embodiment of the present application.

Fig. 2 shows a flowchart of a specific method for obtaining a global OD matrix in the traffic simulation model generation method provided in the embodiment of the present application.

Fig. 3 is a flowchart illustrating a specific method for obtaining a sample matrix in the traffic simulation model generation method according to the embodiment of the present application.

Fig. 4 is a flowchart illustrating a specific method for estimating traffic flow of all vehicles on all road segments within a preset range in the traffic simulation model generation method provided in the embodiment of the present application.

Fig. 5 is a flowchart illustrating a specific method for obtaining a traffic simulation model in the traffic simulation model generation method provided in the embodiment of the present application.

Fig. 6 is a flowchart illustrating a specific method for obtaining a preset traffic network in the traffic simulation model generation method according to the embodiment of the present application.

Fig. 7 shows an exemplary diagram of a road segment aggregation in a navigation road network provided in an embodiment of the present application.

Fig. 8 shows a flowchart of a traffic flow prediction method provided in an embodiment of the present application.

Fig. 9 is a flowchart illustrating a specific method for performing traffic flow prediction with respect to a traffic flow to be analyzed in the traffic flow prediction method according to the embodiment of the present application.

Fig. 10 is a flowchart illustrating a specific method for predicting traffic flow according to a road condition of a road segment to be analyzed in the traffic flow prediction method according to the embodiment of the present application.

Fig. 11 shows an example diagram of traffic prediction for a road condition of a road segment to be analyzed according to an embodiment of the present application.

Fig. 12 is a schematic structural diagram illustrating a traffic simulation model generating apparatus according to an embodiment of the present application.

Fig. 13 is a schematic structural diagram illustrating a traffic flow prediction apparatus according to an embodiment of the present application.

Fig. 14 shows a schematic structural diagram of a computer device provided in an embodiment of the present application.

Icon: 10-a computer device; 11-a network port; 12-a processor; 13-a communication bus; 14-a storage medium; 15-I/O interface; 100-traffic simulation model generation means; 110-an obtaining module; 120-prediction module; 130-a generating module; 200-traffic flow prediction means; 210-a scene acquisition module; 220-analysis Module.

Detailed Description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

The Positioning technology used in the present application may be based on a Global Positioning System (GPS), a Global Navigation Satellite System (GLONASS), a COMPASS Navigation System (COMPASS), a galileo Positioning System, a Quasi-Zenith Satellite System (QZSS), a Wireless Fidelity (WiFi) Positioning technology, or the like, or any combination thereof. One or more of the above-described positioning systems may be used interchangeably in this application.

In order to analyze the reason of urban traffic congestion from the perspective of a system, the urban traffic congestion cannot be focused on one or a plurality of intersections or road sections, and modeling needs to be performed on peak hours of a congested area or a preset area and even on the whole urban area, so as to simulate different congestion relieving schemes and select a scheme with the optimal congestion relieving effect on the whole system.

In order to obtain a more accurate traffic simulation model, in the development process of the existing traffic simulation model, a large amount of travel data in a large-scale preset area needs to be collected and analyzed, so that the development cost and the time cost of the traffic simulation model are increased greatly.

Based on the above defects, the embodiment of the application provides a traffic simulation model generation method, and the core improvement point is as follows: the estimated traffic flow of all vehicles on all road sections in the preset range is estimated through the track data of the floating vehicles in all vehicles on all road sections in the preset range and the monitored vehicle flow of all vehicles on all road sections in the preset range, so that the development cost and the time cost consumed in the generation process of the traffic simulation model can be reduced, and the traffic simulation model is better suitable for small and medium-sized cities. The technical solution of the present application is explained below by means of possible implementations.

As shown in fig. 1, fig. 1 is a flowchart illustrating a traffic simulation model generation method provided in an embodiment of the present application, where the method includes:

s101, acquiring track data of the floating car running in a preset range, wherein the preset range comprises preset time and a preset area.

S102, acquiring the monitored traffic flow for monitoring all vehicles passing through the preset road section in the preset range.

S103, estimating the traffic flow of all vehicles on all road sections in a preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range.

And S104, distributing the estimated traffic flow in the overall OD matrix to a preset traffic network, and adjusting the estimated traffic flow in the overall OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

In the above step S101, the floating car may be a bus, a taxi, a network appointment car, or the like, which is mounted with a vehicle-mounted GPS positioning device and travels on an urban main road. The track data of the floating car is used for representing the data of the trip information of the floating car, for example, the track data of the floating car can be obtained according to the vehicle position, direction and speed information which is regularly recorded in the running process of a taxi equipped with a GPS, and can also be obtained according to the information of a trip starting point, a trip route, a trip end point and the like in the order information of the taxi appointment.

Because the traffic conditions in different areas may be very different, and even the traffic conditions in different time periods in the same area are also very different, the traffic simulation model generated in the embodiment of the present application is specific to a preset range, the preset range includes a space dimension and a time dimension, and the preset range includes the preset area in terms of the space dimension, and the preset area may be an area of the whole city range, for example, a city, or an area of a certain area range of a certain city, for example, a high-new area of the city. In terms of time dimension, the granularity of the time dimension may be quarterly, or a working day or a rest day of a certain quarterly, or may be a peak time period of work day during work or work, and the granularity of the specific time dimension may be set as required.

In step S102, the preset road segments are some of all the road segments in the preset range, and the preset road segments may be main roads in the preset range, for example, the prefecture avenues in the metropolitan high new area, or some randomly selected road segments, for example, the prefecture three road segments and the prefecture five road segments in the metropolitan high new area, or road segments leading to the preset campus, for example, road segments leading to the prefecture software park.

The monitoring mode for monitoring all vehicles passing through the preset road section can be as follows: shoot through the mode that sets up the monitoring camera at the default highway section, carry out vehicle identification to the image of shooing again, carry out vehicle statistics again at last and obtain, also can be: the traffic flow is obtained by adopting a special traffic flow observation instrument, and the traffic flow observation instrument can be but is not limited to an ultrasonic traffic flow instrument, a microwave traffic flow instrument, a ground induction coil traffic flow instrument and the like.

In step S103, the OD matrix is sorted by rows (start point region) and columns (destination point region) in the preset traffic partition, and the travel amount (also referred to as OD amount) of the residents or vehicles between any two partitions is used as an element of the matrix, and the data in the matrix is the traffic flow from the area a to the area B. For example, the OD matrix is illustrated in table 1, taking traffic as an example:

TABLE 1

	A	B	C
				A
	100	200	300
				B	300	200	100
C	200	300	400

In table 1, the traffic flow from traffic zone a to traffic zone B is 200, and the traffic flow from traffic zone C to traffic zone B is 300.

In the embodiment of the present application, the global OD matrix is used to represent the estimated traffic flow of all vehicles on all road sections within the preset range, and the element in the ith row and the jth column in the global OD matrix represents: and (4) with i as a starting point and j as an end point, estimating the number of all vehicles passing through in unit time between the starting point and the end point, namely the estimated traffic flow.

Since the global OD matrix is estimated according to the track data and the monitored vehicle traffic, the global OD matrix needs to be adjusted in order to make the global OD matrix closer to the actual condition, and the specific adjustment method is described in step S104.

In step S104, the preset traffic network may be used to represent lane information of all road segments in a preset area of a preset range, where the lane information includes, but is not limited to, a geographical location characteristic of a start point or an end point of a lane, a direction characteristic of the lane, a category characteristic of the lane, and the like.

In the embodiment of the application, the estimated traffic flow in the global OD matrix can be distributed by using a preset traffic distribution algorithm to obtain the traffic flow distributed by each road section in the preset traffic network, and then the monitored traffic flow of the preset road section is compared with the traffic flow distributed by the preset road section to gradually adjust the global OD matrix, so that the traffic flow distributed by the preset road section and the monitored traffic flow of the preset road section reach the preset proximity degree, and at this time, a traffic simulation model is obtained.

It should be noted that the traffic simulation model is a simulation of the traffic conditions in the preset range by using the historical trajectory data and the historical monitored traffic data in the preset range, for example, the traffic simulation model is a simulation of the traffic conditions in the rush hour of the metropolis high-new area in the working day by using the trajectory data and the monitored traffic in the rush hour of the metropolis high-new area in 6 months in 2020, and then the traffic simulation model can be used for predicting the traffic conditions in the rush hour of the metropolis high-new area in the future working day.

According to the four steps provided by the embodiment of the application, the estimated vehicle flow of all vehicles on all road sections in the preset range is estimated through the track data of the floating vehicles in all vehicles on all road sections in the preset range and the monitored vehicle flow of all vehicles monitored on all road sections in the preset range, so that the development cost and the time cost consumed in the generation process of the traffic simulation model are reduced, and the traffic simulation model is not only suitable for large cities, but also better suitable for small and medium cities.

In a specific embodiment, the track data may include a start point, an end point, and a travel route of the floating vehicle, in order to obtain a global OD matrix according to the track data and the monitored traffic flow, on the basis of fig. 1, an embodiment of the present application provides a specific implementation manner, please refer to fig. 2, fig. 2 shows a flowchart of a specific method for obtaining the global OD matrix in the traffic simulation model generation method provided by the embodiment of the present application, and step S103 includes the following sub-steps:

and a substep S1031 of aggregating the starting point and the end point in the track data to obtain a sample OD matrix, wherein each element in the sample OD matrix represents a first traffic volume of the floating car between the starting point represented by the row and the end point represented by the column.

And a substep S1032, counting the floating cars comprising the preset road section in the driving route to obtain a second car flow of the floating cars.

And a substep S1033 of estimating the traffic flow of all vehicles in all road sections in the preset range according to the monitored traffic flow, the second traffic flow and the sample OD matrix to obtain a global OD matrix.

As a specific embodiment, the starting point, the ending point and the driving route of the floating car in the track data may be obtained from order related information of the floating car recorded in the background, for example, some order related information includes a starting point prefecture three-street subway station, an ending point high and new subway station, a driving route which is a prefecture avenue middle section, a prefecture ave north section, a prefecture ave and an weather road. As another specific embodiment, the start point, the end point, and the travel route of the floating car in the trajectory data may be acquired from the travel information recorded by the GPS of the floating car.

In sub-step S1031, the sample OD matrix is similar to the global OD matrix described above, however, the sample OD matrix is for a floating car and is obtained by aggregating the sample OD matrix according to the start point and the end point in the track data, that is, each element in the sample OD matrix represents the first traffic volume of the floating car between the start point represented by the row and the end point represented by the column.

In the sub-step S1032, the predetermined number of road sections may be multiple, and for any predetermined road section, the floating cars in the driving route including the predetermined road section are counted to obtain the second vehicle flow rate, that is, the second vehicle flow rate refers to the number of vehicles passing through all floating cars in the predetermined road section within a predetermined range, for example, the predetermined road section is a road section a, the number of orders of the floating cars within the predetermined range is 1000, and the number of orders of the driving route including the road section a is 700, and then the second vehicle flow rate may be considered as 700.

In the above sub-step S1033, the monitored vehicle flow and the second vehicle flow may be both bidirectional vehicle flows or unidirectional vehicle flows in a preset direction, but the two flows are kept consistent, that is, when the monitored vehicle flow includes bidirectional vehicle flows of all vehicles in a preset road section, the corresponding second vehicle flow also includes bidirectional vehicle flows of floating vehicles in the preset road section, when the monitored vehicle flow includes unidirectional vehicle flows in a preset direction of all vehicles in the preset road section, the corresponding second vehicle flow also includes unidirectional vehicle flows in a preset direction of floating vehicles in the preset road section and the monitored vehicle flow, for example, two intersections in the road section a are respectively intersection 1 and intersection 2, and when the monitored vehicle flow is vehicle flows of all vehicles in the road section a from intersection 1 to intersection 2, the second vehicle flow is vehicle flow of floating vehicles in the road section a from intersection 1 to intersection 2.

In the three substeps provided by the embodiment of the application, the second traffic flow corresponding to the monitored traffic flow is obtained according to the track data, and then the traffic flows of all vehicles on all road sections in the preset range are estimated according to the monitored traffic flow, the second traffic flow and the sample OD matrix, so that the data processing amount is simplified, and the accuracy of the estimated global OD matrix is improved.

In this embodiment of the present application, in order to further simplify the data amount to be processed and improve the processing efficiency when obtaining the sample OD matrix, on the basis of fig. 2, an embodiment of the present application provides a specific implementation manner, please refer to fig. 3, fig. 3 shows a flowchart of a specific method for obtaining the sample OD matrix in the traffic simulation model generation method provided in the embodiment of the present application, and sub-step S1031 includes the following sub-steps:

and a substep S10311, dividing the preset area according to the preset traffic cell to obtain a plurality of sub-areas.

And a substep S10312, counting floating vehicle flow rates between the source sub-region to which the starting point belongs and the destination sub-region to which the end point belongs to obtain a sample OD matrix, wherein each element in the sample OD matrix represents the floating vehicle flow rate between the source sub-region represented by the row and the destination sub-region represented by the column.

In the above sub-step S10311, the preset traffic cell is a scheme provided for reducing the complexity of the traffic control and management system, and improving the system reliability and the system development requirement, and the purpose of dividing the preset traffic cell is to comprehensively understand the time and space distribution characteristics of the traffic flow between the traffic sources, theoretically, the smaller the division of the preset traffic cell is, the better the division of the preset traffic cell is, but the smaller the division of the preset traffic cell is, the larger the processed data amount is, generally, the smaller the division of the preset traffic cell in the urban traffic plan, the smaller the division of the traffic region in the regional traffic plan, the smaller the division of the traffic cell in the place where the traffic conflict occurs, and the larger the division of the traffic cell in the opposite direction. The embodiment of the present application does not limit the specific division manner of the preset traffic cell.

In the above sub-step S10312, the source sub-region is a sub-region to which the starting point belongs, the destination sub-region is a sub-region to which the end point belongs, and the floating car flow rate represented by each element in the sample OD matrix is granular by using the sub-region. For example, the sample OD matrix at road segment granularity is illustrated in table 2:

TABLE 2

	Section a	Section b	Section c	Road section d	Section e	Road section f
							Section a	10	20	30	40	50	60
Section b	10	10	10	10	10	10
							Section c	20	30	40	50	60	70
Section d	20	20	20	20	20	20
							Section e	30	40	50	60	70	80
Section f	10	20	30	40	50	60

If the road section a and the road section b belong to the sub-region 1, the road section c, the road section d and the road section e belong to the sub-region 2, and the road section f belongs to the sub-region 3, the sample OD matrix at this time is as an example in table 3:

TABLE 3

	Subregion 1	Sub-region 2	Subregion 3
				Subregion 1	50	150	70
Sub-region 2	160	390	170
				Subregion 3	30	120	60

As can be seen by comparing table 2 and table 3, the size of the sample OD matrix was reduced from 6 x 6 to 3 x 3, and therefore, the amount of data processed was greatly reduced.

On the basis of fig. 2, an embodiment of the present application further provides a specific implementation manner of estimating vehicle flows of all vehicles in all road segments within a preset range, please refer to fig. 4, fig. 4 shows a flowchart of a specific method of estimating vehicle flows of all vehicles in all road segments within a preset range in a traffic simulation model generation method provided in the embodiment of the present application, and the substep S1033 further includes:

and a substep S10331 of calculating a sample expansion factor based on the monitored traffic flow and the second traffic flow.

And a substep S10332, performing sample expansion on the sample OD matrix according to the sample expansion factor to obtain a global OD matrix.

In the sub-step S10331, the sample expansion factor is used to characterize a relationship between the monitored vehicle traffic and the second vehicle traffic, and as a specific implementation, the sample expansion factor is (second vehicle traffic)/(monitored vehicle traffic). When the preset road sections are multiple, the monitoring traffic flow also corresponds to multiple values, the second traffic flow also corresponds to multiple values, the multiple monitoring traffic flows and the second traffic flow can be summed respectively, and then the formula is utilized: the sample expansion factor is obtained by (sum of the second vehicle flow)/(sum of the monitored vehicle flow), and for example, the preset section includes: the corresponding monitoring vehicle flow of the road section a, the road section b and the road section c is respectively as follows: 100. 200 and 300, the corresponding second vehicle flow respectively is as follows: 10. if the sample expansion factor is (10+20+30)/(100+200+300) ═ 0.1, 20 and 30, it may be possible to calculate one sample expansion factor for each preset link and then average all sample expansion factors, for example, the preset links include: the corresponding monitoring vehicle flow of the road section a, the road section b and the road section c is respectively as follows: 100. 100 and 300, the corresponding second vehicle flow rates are respectively as follows: 10. 20 and 30, then:

the expansion factor ═ ((10/100) + (20/100) + (30/300))/3 ═ 0.13.

In the foregoing sub-step S10332, as a specific implementation manner, for any one element in the sample OD matrix, the element is divided by the sample spreading factor to obtain a value of an element corresponding to a position of the element in the global OD matrix. For example, if the sample expansion factor is 0.1, the global OD matrix after sample expansion is shown in table 4 below for the sample OD matrix in table 3 above:

TABLE 4

	Subregion 1	Sub-region 2	Subregion 3
				Subregion 1	500	1500	700
Sub-region 2	1600	3900	1700
				Subregion 3	300	1200	600

As another specific embodiment, when the sample OD matrix is expanded, some elements in the sample OD matrix may be scaled up or down based on the expansion factor according to actual conditions, for example, in table 4, it is known that the ratio of the second vehicle flow rate from sub-area 2 to sub-area 3 to the monitored vehicle flow rate is greater than the expansion factor, and in this case, scaling up may be performed based on the expansion factor, for example, scaling up the expansion factor by 0.01 based on 0.1, and then 1700/(0.1+0.01) ═ 1545.

Because the global OD matrix is obtained by sample expansion of the sample OD matrix according to the sample expansion factor, when the sample expanded global OD matrix is used for traffic simulation modeling, a certain deviation will certainly exist between the sample expanded global OD matrix and an actual traffic condition, and in order to make the accuracy of the traffic simulation model obtained by modeling higher, on the basis of fig. 1, the embodiment of the present application further provides a specific implementation manner for adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain the traffic simulation model, please refer to fig. 5, fig. 5 shows a flow chart of a specific method for obtaining the traffic simulation model in the traffic simulation model generation method provided by the embodiment of the present application, and step S104 includes the following substeps:

and a substep S1041 of distributing the estimated traffic flow in the global OD matrix to a preset traffic network by using a preset traffic distribution algorithm to obtain a simulated traffic flow of a preset road section.

And a substep S1042 of defining a loss function according to the simulated traffic flow and the monitored traffic flow.

And a substep S1043 of adjusting the estimated traffic flow in the global OD matrix according to the loss function until the loss function meets a preset condition to obtain a traffic simulation model, wherein the traffic simulation model comprises the corresponding adjusted global OD matrix when the loss function meets the preset condition.

In the above sub-step S1041, the preset traffic distribution algorithm may be, but is not limited to, a Frank-Wolfe based traffic distribution algorithm, a gr (o) bner base based traffic distribution algorithm, and the like. The estimated traffic flow in the global OD matrix can be distributed to a preset traffic network by using a preset traffic distribution algorithm, and the monitored traffic flow of a preset road section is obtained in advance through monitoring, namely the monitored traffic flow of the preset road section is a real value, and the simulated traffic flow distributed to the preset road section through the preset traffic distribution algorithm is a predicted value, so that the estimated traffic flow in the global OD matrix can be adjusted through the monitored traffic flow, and the traffic simulation model is closer to the actual traffic condition.

In the above sub-step S1042, the loss function is used to characterize the proximity between the simulated traffic flow and the monitored traffic flow. As one embodiment, the loss function may be defined by a difference between the simulated traffic flow and the monitored traffic flow, and as another embodiment, the loss function may be defined by a mean square error between the simulated traffic flow and the monitored traffic flow.

In the sub-step S1043, the preset condition may be that the loss function reaches the preset minimum value only, or may be a combination of the number of times of adjustment and the loss function reaching the preset minimum value. In the embodiment of the present application, the estimated traffic flow in the global OD matrix is adjusted by using a preset optimization algorithm according to a loss function, where the preset optimization algorithm may be, but is not limited to, a synchronous disturbance Stochastic Approximation algorithm (SPSA) algorithm, a gradient descent method, a genetic algorithm, and the like. When the loss function meets the preset condition, the corresponding adjusted global OD matrix is also called as a reference OD matrix of the traffic simulation model. The traffic simulation model comprises a reference OD matrix, a preset traffic network and a preset traffic distribution algorithm, so that the traffic flow in a preset range can be predicted by using the traffic simulation model, and the traffic condition in the preset range can be timely adjusted and scheduled.

It should be noted that, the above steps in fig. 1 to fig. 5 may be correspondingly replaced or combined to achieve the corresponding technical effect, for example, the sub-steps S1031 to S1033 of the step S103 in fig. 2 may also replace the step S103 in fig. 3 to fig. 5 to achieve the corresponding technical effect, and the sub-steps S1041 to S1043 of the step S104 in fig. 5 may also replace the step S104 in fig. 2 to fig. 4 to achieve the corresponding technical effect.

In this embodiment, in order to facilitate implementation of traffic simulation and simplify a generation process of a traffic simulation model, an implementation manner of obtaining a preset traffic network is further provided in an embodiment of the present application, please refer to fig. 6, where fig. 6 shows a flowchart of a specific method for obtaining a preset traffic network in a traffic simulation model generation method provided in the embodiment of the present application, and the method includes the following steps:

and step S200, acquiring a navigation road network.

Step S201, aggregating road segments located between the same pair of intersections in the navigation road network into one road segment, so as to obtain a preset traffic road network.

In the step S200, the navigation road network is used to represent a road topology structure in the navigation engine, a road segment in the navigation road network is a section of road, also called link, and is a basic unit of a road model in the navigation system, and because the granularity of the link in the navigation road network is too fine, if the navigation road network is directly used to generate a traffic simulation model, the involved calculation amount is too large, which is not beneficial to traffic simulation, and in order to reduce the calculation amount, the link in the navigation road network needs to be simplified, so as to generate a preset traffic road network meeting the simulation requirement.

In step S201, in order to increase the granularity of links, the links located between the same pair of intersections are aggregated into one link, and the number of the links in the preset traffic network obtained thereby can be greatly reduced, for example, as shown in fig. 7, fig. 7 is an exemplary diagram of the aggregation of the links in the navigation network provided by the embodiment of the present application, and in fig. 7, a total of 3 links located between intersection 1 and intersection 2 are respectively: the 3 links can be aggregated into a road section in the north section of the skyway, the middle section of the skyway and the south section of the skyway, that is, in the preset traffic network, the 3 links belong to the same road section.

It should be noted that fig. 7 is only an exemplary diagram of a road segment aggregation, and a specific implementation manner of aggregating road segments located between the same pair of intersections is different according to different practical application scenarios.

In this embodiment, after obtaining the traffic simulation model, in order to predict the traffic flow in the scene to be analyzed by using the traffic simulation model, an embodiment of the present application provides a traffic flow prediction method, please refer to fig. 8, where fig. 8 shows a flowchart of the traffic flow prediction method provided in the embodiment of the present application, and the method includes the following steps:

step S300, acquiring a scene to be analyzed;

step S301, analyzing a scene to be analyzed by using a preset traffic simulation model to obtain a predicted flow under the scene to be analyzed, wherein the traffic simulation model is generated by the traffic simulation model generation method.

In the step S300, the scenes to be analyzed at least include the following two scenes: (1) for example, a large exposition is expected to be held in an exposition center, the traffic flow of some road sections around the exposition center will change, and according to the scale of the exposition, the traffic value after the traffic flow of the road section is increased can be estimated and used as the traffic flow to be analyzed. (2) The scene to be analyzed comprises road conditions of the road section to be analyzed, and the road conditions of the road section to be analyzed at the position at least comprise the following three conditions: (a) the road section to be analyzed is interrupted and cannot pass through; (b) limiting the flow of the road section to be analyzed, and limiting the traffic; (c) the road section to be analyzed is a newly added road section.

In the above step S301, if the preset traffic simulation model is a simulation of traffic conditions in the preset range, the preset traffic simulation model is also used to predict the traffic flow in the preset range, for example, if the preset traffic simulation model is a simulation of traffic conditions in the rush hour of the metropolitan high-new area, the preset traffic simulation model is also used to predict traffic conditions in the rush hour of the future metropolitan high-new area.

In a specific implementation, the preset traffic simulation model may include a reference OD matrix, a preset traffic network, and a preset traffic distribution algorithm, where the reference OD matrix may be an adjusted global OD matrix in the traffic simulation model, for example, the adjusted global OD matrix may be an adjusted global OD matrix corresponding to a loss function meeting a preset condition, and the loss function is defined according to the simulated traffic flow and the monitored traffic flow. The preset traffic network and the preset traffic distribution algorithm are both corresponding to the traffic simulation model.

Referring to fig. 9, fig. 9 is a flowchart illustrating a specific method for performing traffic flow prediction for a traffic flow to be analyzed in the traffic flow prediction method provided in the embodiment of the present application, where step S310 includes the following sub-steps:

and a substep S301-10, updating the reference OD matrix according to the traffic flow to be analyzed to obtain the OD matrix to be analyzed.

And the substeps S301-11, distributing the traffic flow in the OD matrix to be analyzed to a preset traffic network by using a preset traffic distribution algorithm to obtain the predicted traffic flow in the scene to be analyzed.

Referring to fig. 10, fig. 10 shows a flowchart of a specific method for traffic flow prediction for road conditions of a road section to be analyzed in the traffic flow prediction method provided in the embodiment of the present application, and step S301 further includes the following sub-steps:

and a substep S301-20, updating the preset traffic network according to the road condition of the road section to be analyzed.

And substeps S301-21, distributing the traffic flow in the reference OD matrix to the updated preset traffic network by using a preset traffic distribution algorithm to obtain the predicted traffic flow in the scene to be analyzed.

Referring to fig. 11, fig. 11 is a diagram illustrating an example of traffic prediction for road conditions of a road section to be analyzed according to an embodiment of the present application, where fig. 11(a) is a reference traffic flow in a reference scene obtained by allocating the traffic flow in the reference OD matrix to a preset traffic network, and fig. 11(b) is a predicted traffic flow in the analysis scene obtained when a road section in a black oval frame stops passing.

It should be noted that, regardless of which kind of scene to be analyzed, the traffic flow in the reference OD matrix may be distributed to a preset traffic network to obtain the reference traffic flow in the reference scene, and the influence on the traffic flow in the reference scene in the scene to be analyzed may be evaluated by comparing the reference traffic flow with the predicted traffic flow in the scene to be analyzed, where the influence may be an increase in the traffic flow or a sudden increase in the transit time, and a traffic regulation measure that may be taken for the influence, or may even evaluate the effectiveness of the regulation measure.

Based on the same inventive concept, the embodiment of the present application further provides a traffic simulation model generation apparatus 100 corresponding to the traffic simulation model generation method, and since the principle of solving the problem of the apparatus in the embodiment of the present application is similar to that of the traffic simulation model generation method in the embodiment of the present application, the implementation of the apparatus may refer to the implementation of the method, and repeated details are not repeated.

Referring to fig. 12, fig. 12 is a schematic structural diagram of a traffic simulation model generation apparatus 100 according to an embodiment of the present application, where the traffic simulation model generation apparatus 100 includes a data acquisition module 110, an estimation module 120, and a generation module 130.

The data acquisition module 110 is configured to acquire track data of the floating vehicle traveling within a preset range, where the preset range includes a preset time and a preset area, and a monitored traffic flow rate monitored by all vehicles passing through a preset road section within the preset range is acquired.

As a specific embodiment, the data obtaining module 110 is further configured to: acquiring a navigation road network; and aggregating the road sections between the same pair of intersections in the navigation road network into one road section to obtain the preset traffic road network.

And the estimation module 120 is configured to estimate the traffic flow of all vehicles on all road sections within the preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, where the global OD matrix includes the estimated traffic flow of all vehicles on all road sections within the preset range.

As a specific implementation manner, the track data includes a starting point, an end point and a driving route of the floating car, and the estimation module 120 is specifically configured to: aggregating the starting point and the end point in the track data to obtain a sample OD matrix, wherein each element in the sample OD matrix represents a first vehicle flow rate of the floating vehicle between the starting point represented by the row and the end point represented by the column; counting the floating cars comprising the preset road section in the driving route to obtain a second car flow of the floating cars; and estimating the traffic flow of all vehicles in all road sections in the preset range according to the monitored traffic flow, the second traffic flow and the sample OD matrix to obtain the global OD matrix.

As a specific embodiment, the estimation module 120 is specifically configured to, when performing the step of aggregating the starting point and the ending point in the track data to obtain the sample OD matrix: dividing the preset area according to a preset traffic cell to obtain a plurality of sub-areas; and counting the floating vehicle flow between the source sub-region to which the starting point belongs and the destination sub-region to which the end point belongs to obtain the sample OD matrix, wherein each element in the sample OD matrix represents the floating vehicle flow between the source sub-region represented by the row and the destination sub-region represented by the column.

As a specific implementation manner, the estimation module 120 is further specifically configured to: calculating a sample expansion factor according to the monitored vehicle flow and the second vehicle flow; and carrying out sample expansion on the sample OD matrix according to the sample expansion factor to obtain the global OD matrix.

The generating module 130 is configured to distribute the estimated traffic flow in the global OD matrix to a preset traffic network, and adjust the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

As a specific implementation manner, the generating module 130 is specifically configured to: distributing the estimated traffic flow in the global OD matrix to a preset traffic network by using a preset traffic distribution algorithm to obtain the simulated traffic flow of the preset road section; defining a loss function according to the simulated traffic flow and the monitored traffic flow; and adjusting the estimated traffic flow in the global OD matrix according to the loss function until the loss function meets a preset condition to obtain a traffic simulation model, wherein the traffic simulation model comprises the corresponding adjusted global OD matrix when the loss function meets the preset condition.

Based on the same inventive concept, a traffic flow prediction apparatus 200 corresponding to a traffic flow prediction method is further provided in the embodiments of the present application, and as the principle of solving the problem of the apparatus in the embodiments of the present application is similar to that of the traffic flow prediction method in the embodiments of the present application, reference may be made to the implementation of the apparatus, and repeated details are not repeated.

Referring to fig. 13, fig. 13 is a schematic structural diagram illustrating a traffic flow prediction apparatus 200 according to an embodiment of the present application. The traffic flow prediction apparatus 200 includes a scene acquisition module 210 and an analysis module 220.

A scene acquisition module 210 configured to: and acquiring a scene to be analyzed.

An analysis module 220 to: and analyzing the scene to be analyzed by utilizing a preset traffic simulation model to obtain the predicted flow under the scene to be analyzed, wherein the traffic simulation model is generated by the traffic simulation model generation method.

As a specific implementation manner, the scene to be analyzed includes a traffic flow to be analyzed, the traffic simulation model includes a reference OD matrix, a preset traffic network, and a preset traffic distribution algorithm, and the analysis module 220 is specifically configured to: updating the reference OD matrix according to the traffic flow to be analyzed to obtain an OD matrix to be analyzed; and distributing the traffic flow in the OD matrix to be analyzed to the preset traffic network by using the preset traffic distribution algorithm to obtain the predicted traffic flow in the scene to be analyzed.

As a specific implementation manner, the scene to be analyzed further includes a road condition of the road segment to be analyzed, the traffic simulation model includes a reference OD matrix, a preset traffic network and a preset traffic distribution algorithm, and the analysis module 220 is specifically configured to: updating the preset traffic network according to the road condition of the road section to be analyzed; and distributing the traffic flow in the reference OD matrix to the updated preset traffic network by using the preset traffic distribution algorithm to obtain the predicted traffic flow in the scene to be analyzed.

The modules in the traffic simulation model generation apparatus or the modules in the traffic flow prediction apparatus may be connected or communicated with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.

Referring to fig. 14, fig. 14 shows a schematic structural diagram of a computer device 10 provided in the embodiment of the present application, and the computer device 10 may be a general-purpose computer or a special-purpose computer, both of which may be used to implement the traffic simulation model generation method or the traffic flow prediction method in the embodiment of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.

For example, the computer device 10 may include a network port 11 connected to a network, one or more processors 12 for executing program instructions, a communication bus 13, and a storage medium 14 of a different form, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The computer device 10 also includes an Input/Output (I/O) interface 15 between the computer and other Input/Output devices (e.g., keyboard, display screen).

For ease of illustration, only one processor is depicted in computer device 10. However, it should be noted that the computer device 10 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the computer device 10 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, the first processor performs step a and the second processor performs step B, or the first processor and the second processor perform steps a and B together.

Embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform steps of a traffic simulation model generation method or steps of a traffic flow prediction method of embodiments of the present application.

Embodiments of the present application provide a computer program product comprising a computer program/instructions that, when executed by a processor, perform the steps of the traffic simulation model generation method or the steps of the traffic flow prediction method of embodiments of the present application.

It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the system and the apparatus described above may refer to the corresponding process in the method embodiment, and is not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media capable of storing program codes.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A traffic simulation model generation method, characterized in that the method comprises:

acquiring track data of a floating vehicle running in a preset range, wherein the preset range comprises preset time and a preset area;

acquiring the monitoring traffic flow for monitoring all vehicles passing through a preset road section in the preset range;

estimating the traffic flow of all vehicles on all road sections in the preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range;

and distributing the estimated traffic flow in the global OD matrix to a preset traffic network, and adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

2. The method according to claim 1, wherein the track data comprises a starting point, an end point and a driving route of the floating car, and the step of estimating the traffic flow of all the vehicles in all the road sections within the preset range according to the track data and the monitored traffic flow to obtain the global OD matrix comprises:

aggregating the starting point and the end point in the track data to obtain a sample OD matrix, wherein each element in the sample OD matrix represents a first vehicle flow rate of the floating vehicle between the starting point represented by the row and the end point represented by the column;

counting the floating cars comprising the preset road section in the driving route to obtain a second car flow of the floating cars;

and estimating the traffic flow of all vehicles in all road sections in the preset range according to the monitored traffic flow, the second traffic flow and the sample OD matrix to obtain the global OD matrix.

3. The method according to claim 2, wherein the step of estimating the traffic flow of all vehicles in all road segments within the preset range according to the monitored traffic flow, the second traffic flow and the sample OD matrix to obtain the global OD matrix comprises:

calculating a sample expansion factor according to the monitored vehicle flow and the second vehicle flow;

and carrying out sample expansion on the sample OD matrix according to the sample expansion factor to obtain the global OD matrix.

4. The method according to claim 1, wherein the step of distributing the estimated traffic flow in the global OD matrix to a preset traffic network and adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model comprises:

distributing the estimated traffic flow in the global OD matrix to a preset traffic network by using a preset traffic distribution algorithm to obtain the simulated traffic flow of the preset road section;

defining a loss function according to the simulated traffic flow and the monitored traffic flow;

and adjusting the estimated traffic flow in the global OD matrix according to the loss function until the loss function meets a preset condition to obtain a traffic simulation model, wherein the traffic simulation model comprises the corresponding adjusted global OD matrix when the loss function meets the preset condition.

5. The method of claim 2, wherein aggregating the start and end points in the trace data to obtain a sample OD matrix comprises:

dividing the preset area according to a preset traffic cell to obtain a plurality of sub-areas;

and counting the floating vehicle flow between the source sub-region to which the starting point belongs and the destination sub-region to which the end point belongs to obtain the sample OD matrix, wherein each element in the sample OD matrix represents the floating vehicle flow between the source sub-region represented by the row and the destination sub-region represented by the column.

6. The method of claim 1, further comprising:

acquiring a navigation road network;

and aggregating the road sections between the same pair of intersections in the navigation road network into one road section to obtain the preset traffic road network.

7. A traffic flow prediction method, characterized in that the method comprises:

acquiring a scene to be analyzed;

analyzing the scene to be analyzed by using a preset traffic simulation model to obtain the predicted flow under the scene to be analyzed, wherein the traffic simulation model is generated by any one of the methods of claims 1-6.

8. The method according to claim 7, wherein the scene to be analyzed includes traffic flow to be analyzed, the traffic simulation model includes a reference OD matrix, a preset traffic network and a preset traffic distribution algorithm, and the step of analyzing the scene to be analyzed by using the preset traffic simulation model to obtain the predicted traffic flow under the scene to be analyzed includes:

updating the reference OD matrix according to the traffic flow to be analyzed to obtain an OD matrix to be analyzed;

and distributing the traffic flow in the OD matrix to be analyzed to the preset traffic network by using the preset traffic distribution algorithm to obtain the predicted traffic flow in the scene to be analyzed.

9. The method according to claim 8, wherein the scene to be analyzed further includes a road condition of a road section to be analyzed, and the step of analyzing the scene to be analyzed by using a preset traffic simulation model to obtain the predicted flow rate under the scene to be analyzed includes:

updating the preset traffic road network according to the road condition of the road section to be analyzed;

and distributing the traffic flow in the reference OD matrix to the updated preset traffic network by using the preset traffic distribution algorithm to obtain the predicted traffic flow in the scene to be analyzed.

10. An apparatus for generating a traffic simulation model, the apparatus comprising:

a data acquisition module to: acquiring track data of a floating vehicle running in a preset range, wherein the preset range comprises preset time and a preset area, and acquiring monitoring traffic flow monitored by all vehicles passing through a preset road section in the preset range;

the estimation module is used for estimating the traffic flow of all vehicles on all road sections in the preset range according to the track data and the monitored traffic flow to obtain a global OD matrix, wherein the global OD matrix comprises the estimated traffic flow of all vehicles on all road sections in the preset range;

and the generation module is used for distributing the estimated traffic flow in the global OD matrix to a preset traffic network, and adjusting the estimated traffic flow in the global OD matrix according to the monitored traffic flow to obtain a traffic simulation model.

11. A traffic flow prediction apparatus, characterized in that the apparatus comprises:

the scene acquisition module is used for acquiring a scene to be analyzed;

the analysis module is used for analyzing the scene to be analyzed by utilizing a preset traffic simulation model to obtain the predicted flow under the scene to be analyzed, wherein the traffic simulation model is generated by any one method of claims 1-6.

12. A computer device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the computer apparatus is operating, the processor executing the machine-readable instructions to perform the steps of the method of any one of claims 1 to 6 or the steps of the method of any one of claims 7 to 9.

13. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 4 or the steps of the method according to any one of claims 7 to 9.