CN116229714A

CN116229714A - Traffic characteristic obtaining method, device, equipment and storage medium

Info

Publication number: CN116229714A
Application number: CN202310117594.3A
Authority: CN
Inventors: 肖逸雄; 袁昊; 路新江; 周景博; 窦德景
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2023-02-09
Filing date: 2023-02-09
Publication date: 2023-06-06

Abstract

The disclosure provides a traffic characteristic obtaining method, a device, equipment and a storage medium, relates to the field of data processing, and particularly relates to the field of intelligent traffic. The specific implementation scheme is as follows: acquiring first description information of a congestion road section in a target area in different time periods; determining first change information representing a change over time of a congestion road section in the target area based on the obtained first description information; obtaining second description information of each type of road section in the target area at the reference moment in different time periods; performing information fitting based on the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion unblocking rate to obtain second change information which is successfully fitted with the first change information; and obtaining the traffic characteristics of the target area based on the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information. By applying the scheme provided by the embodiment of the invention, the efficiency in obtaining the traffic characteristics can be improved.

Description

Traffic characteristic obtaining method, device, equipment and storage medium

Technical Field

The disclosure relates to the technical field of data processing, and in particular relates to the technical field of intelligent transportation.

Background

With the rapid development of various intelligent technologies, intelligent traffic technologies are gradually introduced into urban traffic control, so that urban roads are kept smooth, and congestion is reduced. When urban traffic is controlled based on the intelligent traffic technology, traffic characteristics such as road section congestion characteristics, vehicle driving characteristics and the like in an urban area can be analyzed in real time, a traffic regulation strategy for avoiding or relieving traffic congestion in the area is generated based on the analysis result, and then traffic control is performed according to the generated traffic regulation strategy.

Therefore, the traffic characteristics are key information on which traffic regulation strategies are generated in the intelligent traffic technology, and play a key role in the intelligent traffic field.

In the prior art, a machine learning model for obtaining traffic characteristics is generally trained based on a large number of sample traffic data, and then the traffic data is processed based on the trained machine learning model in the application process to obtain the traffic characteristics.

Disclosure of Invention

The present disclosure provides a traffic characteristic obtaining method, device, equipment and storage medium.

According to an aspect of the present disclosure, there is provided a traffic characteristic obtaining method including:

acquiring first description information of a congestion road section in a target area in different time periods;

Determining first change information representing a change over time of a congestion road section in the target area based on the obtained first description information;

obtaining second description information of each type of road section in the target area at the reference moment in the different time periods;

performing information fitting based on the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion dredging rate to obtain second change information which is successfully fitted with the first change information;

and obtaining the traffic characteristics of the target area based on the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information.

According to another aspect of the present disclosure, there is provided a traffic characteristic obtaining apparatus including:

the first descriptive information acquisition module is used for acquiring first descriptive information of the congestion road sections in the target area in different time periods;

a first change information obtaining module, configured to determine, based on the obtained first description information, first change information that characterizes a change over time of a congestion road section in the target area;

the second descriptive information obtaining module is used for obtaining second descriptive information of each type of road section in the target area at the reference moment in the different time periods;

The information fitting module is used for carrying out information fitting based on the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion dredging rate to obtain second change information which is successfully fitted with the first change information;

and the traffic characteristic obtaining module is used for obtaining the traffic characteristic of the target area based on the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the traffic characteristic obtaining method described previously.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned traffic characteristic obtaining method.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, is the aforementioned traffic feature obtaining method.

From the above, when the traffic characteristics are obtained by applying the scheme provided by the embodiment of the present disclosure, first change information representing the time change of the congestion road section in the target area is determined based on the first description information of the road section in the target area, then information fitting is performed based on the second description information of each type of road section in the target area at the reference time in different time periods, the preset value corresponding to the congestion propagation rate and the preset value corresponding to the congestion dredging rate, so as to obtain second change information successfully fitted with the first change information, and further, the traffic characteristics of the target area can be successfully obtained based on the value of the congestion propagation rate and the value of the congestion dredging rate corresponding to the second change information.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a flow chart of a first traffic feature obtaining method according to an embodiment of the disclosure;

Fig. 2 is a schematic diagram of a congestion section duty ratio change trend according to an embodiment of the present disclosure;

fig. 3 is a flow chart of a second traffic characteristic obtaining method according to an embodiment of the disclosure;

fig. 4 is a schematic view of a first fitting effect provided by an embodiment of the present disclosure;

FIG. 5 is a schematic view of a second fitting effect provided by an embodiment of the present disclosure;

FIG. 6 is a schematic view of a third fitting effect provided by an embodiment of the present disclosure;

fig. 7 is a schematic view of a fourth fitting effect provided by an embodiment of the present disclosure;

fig. 8 is a schematic diagram of a traffic characteristic variation trend provided in an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a traffic feature obtaining device according to an embodiment of the present disclosure;

fig. 10 is a block diagram of an electronic device for implementing a traffic feature acquisition method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

First, description will be made of an execution body of the scheme provided by the embodiment of the present disclosure.

The implementation main body of the scheme provided by the embodiment of the disclosure is as follows: any one electronic device with functions of data processing, data storage and the like.

The following describes in detail the traffic characteristic obtaining scheme provided by the embodiments of the present disclosure.

Referring to fig. 1, a flow chart of a first traffic feature generating method according to an embodiment of the disclosure is provided, where the method includes the following steps S101 to S105.

Step S101: first description information of the congestion road sections in the target area in different time periods is obtained.

The different time periods may be a plurality of arbitrary time periods before the current time, may be continuous time periods, or may be discontinuous time periods.

The embodiments of the present disclosure are not limited to specific durations of the respective time periods, and for example, the durations of the above time periods may be 5 minutes, 8 minutes, 10 minutes, or the like. And, the time periods of each time period may be the same or different.

In one case, the different time periods may be selected from two time periods of 6:00-10:00 and 17:00-20:00. The advantages of this option are described later.

The target area may be any area included in an administrative division such as a city, county, or the like.

The manner in which the road segments contained in the target area are determined is first described below.

Specifically, the road segment point string information representing the road segment position can be obtained, and then the intersection of the road segment point string information and the county-wide area is taken, so that the target road segment point string information located in the target area is determined, and the target road segment represented by the target road segment point string information can be determined.

The road segment point string information may be composed of points expressed by longitude and latitude coordinates.

The specific manner of obtaining the first description information will be described again.

From the viewpoint of the obtaining flow of the first description information, the first description information corresponding to different time periods can be obtained in the following manner.

In one embodiment, for each time period, an actual vehicle passing speed of each road section in the target area in the time period may be obtained, and then a road section with the corresponding actual vehicle passing speed smaller than a preset speed is determined as a congestion road section corresponding to the time period, so as to obtain first description information corresponding to the determined congestion road section, that is, the first description information corresponding to the time period.

For example, if the actual vehicle passing speed of the road section p1 in the target area within the time period t1 is V1 and V1 is smaller than the preset speed V, the target road section p1 is a congested road section within the sub-time period t 1.

The corresponding actual vehicle passing speed for a road section may be the average, median, etc. of all the speeds of vehicles passing through the road section in the time period.

In another embodiment, for each time period, an actual vehicle passing speed and an unobstructed passing speed of each road section in a target area in the time period can be obtained, and first description information corresponding to the time period is determined based on the obtained actual vehicle passing speed and the unobstructed passing speed. The detailed description will be given in the following examples, which will not be described in detail here.

From the viewpoint of the content contained in the first description information, the first description information corresponding to different time periods can be obtained in the following manner.

In one embodiment, the number of congested road segments in the target area in different time periods may be obtained as the first description information.

Obviously, in this case, the larger the first description information is, the larger the number of the congested road segments in the target area is, which means that the congestion condition of the target area is more serious; and vice versa, no more so is done here.

In another embodiment, the duty ratio information of the congested road segment in the target area in different time periods may be obtained as the first description information.

The above-described duty information is a duty ratio of the number of congested road segments to the number of road segments of each type, and may also be referred to as a congestion index.

Obviously, in this case, the larger the first description information is, the higher the ratio of the congested road segments in the target area is, which means that the congestion condition of the target area is more serious; and vice versa, no more so is done here.

Therefore, the duty ratio information is the duty ratio of the congestion road sections in the target areas in different time periods, reflects the relative quantity relation between the congestion road sections and all road sections, and can more comprehensively represent the overall congestion condition in the target areas in different time periods.

Step S102: first change information characterizing a change over time of a congested road segment in a target area is determined based on the obtained first description information.

The first change information may be an array, key value pairs, curves, etc.

For example, the identification of each time period and the number of congestion segments corresponding to the time period are used as array elements, and an array formed by the array elements is determined as first change information; for another example, the identifier of each time slot and the number of congestion segments corresponding to the time slot are respectively used as key value pairs, and the obtained key value pairs are determined as the first change information.

It will be appreciated that, depending on the first description information, the meaning of the first variation information representation is also different:

if the first description information is the number of the congestion road sections in the target area in different time periods, the first change information represents the change of the number of the congestion road sections in the target area along with time; and if the first description information is the duty ratio of the congestion road section in the target area in different time periods, the first change information represents the change of the duty ratio of the congestion road section in the target area along with time.

Step S103: and obtaining second descriptive information of each type of road section in the target area at the reference moment in different time periods.

The reference time may be any time within each different time period, and may be, for example, the start time of the first time period with the earliest time sequence.

Other specific road segment types included in each type of road segment are not limited by the disclosed embodiments, except for congested road segments.

In one case, each type of road section includes: a congested segment, an uncongested segment that is prone to being congested by propagation, and a unblocked segment that transitions from congested to uncongested.

The road section information processing method can comprise rich road section types, can represent various different states of the road section, and is beneficial to obtaining more comprehensive second description information based on various road sections.

In this step, the manner of determining the second description information of each type of link from the target area may be obtained on the basis of the aforementioned determination of the congestion link from the target area, with the differences only in the link type and the determination reference, which will be described briefly below.

In one embodiment, the number of each type of road segments in the target area at the reference time in the different time periods may be obtained as the second description information.

In another embodiment, the duty ratio of each type of road section in the target area at the reference time in the different time periods may be obtained as the second description information.

In this case, the obtained duty ratio is the duty ratio of each type of road section in the target area at the reference time in different time periods, reflects the relative number relationship between each type of road section and all road sections, and can more comprehensively represent the relative number relationship of each type of road section at the reference time in different time periods.

Step S104: and performing information fitting based on the obtained second description information, the preset value corresponding to the congestion propagation rate and the preset value corresponding to the congestion unblocking rate to obtain second change information which is successfully fitted with the first change information.

The above mentioned congestion propagation rate characterization: the average number of uncongested segments that can be effectively transmitted over a period of time. Where infection means that uncongested segments are converted into congested segments.

The congestion dredging rate is characterized in that: the number of congestion segments that can be dredged in a time period is the ratio of the total number of segments. Where unblocking means that a congested segment is converted into an uncongested segment.

Specifically, first, second change information representing the change of the congestion road section in the target area along with time is determined based on the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion dredging rate, and then information fitting is performed in the following manner, so that second change information successfully fitted with the first change information is finally obtained:

in one embodiment, the second variation information may be continuously adjusted until the second variation information is successfully fitted to the first variation information, so as to obtain second variation information successfully fitted to the first variation information.

In another embodiment, the second change information may be updated by repeatedly adjusting the value of the congestion propagation rate and the value of the congestion circulation rate until the updated second change information is successfully fitted with the first change information. The specific implementation manner is shown in step S304-step S305 in the embodiment shown in fig. 3, and will not be described herein.

Step S105: and obtaining the traffic characteristics of the target area based on the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information.

By combining the steps, the second change information is correlated with the value of the congestion propagation rate and the value of the congestion removal rate, so that the value of the congestion propagation rate and the value of the congestion removal rate corresponding to the second change information can be determined.

The value of the congestion propagation rate corresponding to the second change information is respectively as follows: the congestion propagation rate and the congestion unblocking rate when the second change information and the first change information are successfully fitted are enabled to be the same, and therefore the target congestion propagation rate and the target congestion unblocking rate can be considered to be basically matched with the time-dependent change of the congestion road section represented by the first change information.

Specifically, a ratio between the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information may be calculated, and then the ratio is determined as the traffic characteristic of the target area.

The ratio can also be called as a basic infection number R0, and represents the number of the effectively-infected road sections in the whole target time period of each congestion road section in the target area, so that the congestion propagation capacity of the congestion road sections in the target area can be better reflected.

Therefore, the traffic characteristics can be obtained without using a machine learning model, so that the time for collecting a large amount of sample data and training the machine learning model by using the sample data is saved, and the efficiency for obtaining the traffic characteristics is improved.

Moreover, training of the machine learning model generally depends on a large amount of road section sample data, in the field, the collection difficulty of the road section sample data is high, and the conditions of low model training efficiency and poor training effect caused by lack of the sample data are easy to occur. When the traffic characteristics are obtained by applying the scheme provided by the embodiment of the disclosure, the occurrence of the situation can be avoided.

In addition, when the traffic characteristics are obtained, the method comprehensively analyzes all road sections contained in the target area, and integrally considers the traffic network formed by all road sections in the target area. Therefore, compared with the queuing theory and the motion wave theory of only a single link when the traffic characteristics are obtained, the scheme can comprehensively obtain the traffic characteristics through the angles of all target road sections in the target area, particularly can effectively discover the space-time characteristics of the urban traffic network structure, and is suitable for traffic characteristic extraction of the road network scale.

Another way of obtaining the first description information corresponding to the different time periods mentioned above is explained below.

Specifically, for each time period, the first description information of the congestion road section in the target area in the time period may be obtained according to the following steps a and B:

step A: and obtaining the actual vehicle passing speed and the unimpeded passing speed of each road section in the target area in the time period.

The above-mentioned non-blocking traffic speed can be understood as: the speed of vehicles passing through the road section under the condition of smooth road section. The speed limit may be the maximum speed limit of each road section, or may be a speed obtained by counting the vehicle speed of the vehicle in each road section under the condition that the road section is smooth in advance.

And (B) step (B): and determining a congestion road section from the road sections based on the obtained actual vehicle passing speed and the non-blocking passing speed, and obtaining first description information corresponding to the determined congestion road section.

Wherein a congested road segment may be determined from the road segments based on the obtained actual vehicle passing speed and the unobstructed passing speed in the following manner.

In the first way, the ratio between the actual vehicle passing speed and the unimpeded passing speed of each road section in the time period can be calculated, and the road section with the corresponding ratio smaller than the preset ratio is determined as the congestion road section.

The above-mentioned preset ratio may also be referred to as a congestion threshold value, and may be set empirically by a worker, for example, may be 0.65, 0.7, 0.75, or the like.

It will be appreciated that the smaller the above ratio of road segments, the greater the relative difference between the actual vehicle traffic speed and the unimpeded traffic speed for each road segment, i.e. the more severe the congestion condition of the road segment during the time period. Therefore, when the above ratio is smaller than the preset ratio, the link may be considered as a congested link for the period of time.

In the method, the calculated ratio is the ratio between the actual vehicle passing speed and the unimpeded passing speed, the ratio represents the relative difference between the actual average passing speed and the unimpeded passing speed of the vehicle, and the congestion condition of the road section in a certain time period can be more accurate.

In the second way, the difference between the unobstructed traffic speed and the actual vehicle traffic speed of each road section in the time period can be calculated, and the road section with the corresponding difference smaller than the preset difference is determined as the congestion road section.

The preset difference may be set empirically by a worker, for example, 10, 15, 20, etc.

It will be appreciated that the smaller the above-mentioned difference value corresponding to a road segment, the larger the absolute difference between the actual vehicle traffic speed and the unimpeded traffic speed of each road segment, that is, the more serious the congestion condition of the road segment in the time period. Therefore, when the above-mentioned difference is smaller than the preset difference, the link may be considered as a congested link in the period.

From the above, the congestion road sections corresponding to each time period are determined according to the actual vehicle passing speed and the unimpeded passing speed of each road section in the time period, so that the congestion road sections corresponding to each time period can be accurately and reasonably determined based on the difference between the actual average speed and the determination of the unimpeded passing speed, and further the first description information corresponding to the determined congestion road sections can be obtained.

The reason for selecting the different time periods from the two time periods of 6:00-10:00 and 17:00-20:00 mentioned above will be described again.

Referring to fig. 2, a congestion road section duty ratio change trend chart is provided for an embodiment of the present disclosure.

Fig. 2 is a graph obtained by counting the proportion of the congestion road segments in a certain area under each date in 1 month in a real life scene, wherein different curves in fig. 2 represent different dates in 1 month, and the abscissa represents time in hours; the ordinate indicates the actual occupancy of the congested road segment, i.e. the congestion index.

The following information can be summarized from fig. 2:

the period of highest congestion index in a day is mainly concentrated in two time periods of 6:00-10:00 and 17:00-20:00, which can be called as an early peak period and a late peak period, and the congestion index of the late peak period is generally higher than that of the early peak period, and the early and late peak periods in the week and the weekend are generally staggered.

Based on the analysis, the target time period is determined to be two time periods of 6:00-10:00 and 17:00-20:00, so that the traffic characteristics of the time period with the highest congestion index, namely the traffic characteristics of the time period with the most serious congestion condition, are obtained.

In one embodiment of the present disclosure, after obtaining the traffic characteristics, traffic control information for the target area may also be generated based on the traffic characteristics and the congestion optimization objective for the target area.

The congestion optimization objective described above may be to minimize the total duration of congestion, to minimize the total number of congested road segments, etc.

The embodiment of the disclosure does not limit a specific way of generating traffic control information of a target area, for example, when traffic characteristics are greater than an early warning threshold, traffic control information representing early warning of a congestion area, early warning of a congestion period and the like can be generated. Thus, the traffic control of the target area based on the traffic control information is facilitated, and the congestion condition of the target road section in the target area is avoided or relieved.

On the basis of the embodiment shown in fig. 1, when the second change information successfully fitted with the first change information is obtained, the second change information can be updated by repeatedly adjusting the value of the congestion propagation rate and the value of the congestion circulation rate until the updated second change information is successfully fitted with the first change information. In view of the foregoing, embodiments of the present disclosure provide a second traffic characteristic obtaining method.

Referring to fig. 3, a flow chart of a second traffic characteristic obtaining method according to an embodiment of the disclosure is shown, where the method includes the following steps S301 to S306.

Step S301: first description information of the congestion road sections in the target area in different time periods is obtained.

Step S302: first change information characterizing a change over time of a congested road segment in a target area is determined based on the obtained first description information.

Step S303: and obtaining second descriptive information of each type of road section in the target area at the reference moment in different time periods.

The steps S301 to S303 are the same as the steps S101 to S103 in the embodiment shown in fig. 1, and are not described herein.

Step S304: and determining second change information representing the change of the congestion road section in the target area along with time based on the obtained second description information, the preset value corresponding to the congestion propagation rate and the preset value corresponding to the congestion unblocking rate.

In one embodiment, since the second description information is description information of each type of road segment in the target area at the reference time, the time-dependent change condition of the congestion road segment in each period before the reference time and after the reference time can be determined by taking the reference time as a starting point and combining the congestion propagation rate and the congestion dredging rate, and further, the second change information representing the time-dependent change of the congestion road segment in the target area can be obtained according to the determined change condition.

In another embodiment, the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion dredging rate may be used as model parameters of an infectious disease model, third description information of the congestion road section in the target area in the different time periods is predicted, and then second change information representing the time change of the congestion road section in the target area is obtained based on the predicted third description information. Detailed description will be given hereinafter with reference to the detailed examples, which will not be described in detail herein.

Step S305: updating the second change information by repeatedly adjusting the value of the congestion propagation rate and the value of the congestion circulation rate until the updated second change information is successfully fitted with the first change information table.

Specifically, this step may be performed as the following steps C to D, respectively.

Step C: and (3) adjusting the value of the congestion propagation rate and the value of the congestion circulation rate, and re-determining the change information representing the change of the congestion road section along with the time in the target area according to the obtained second description information, the value of the congestion propagation rate after adjustment and the value of the congestion circulation rate after adjustment.

Step D: and updating the second change information into the redetermined change information, judging whether the fitting is successful, and if not, returning to the step of adjusting the value of the congestion propagation rate and the value of the congestion dredging rate until the fitting is successful.

The manner of judging the success of the fitting is as follows:

in one embodiment, the reference time period may be determined from different time periods, and if the first sub-information corresponding to the reference time period in the second variation information is the same as the second sub-information corresponding to the reference time in the first variation information, it is determined that the second variation information and the first variation information are successfully fitted.

In another embodiment, a similarity between the second variation information and the first variation information may be obtained, and if the similarity is not smaller than a preset threshold, it is determined that the second variation information and the first variation information are successfully fitted.

Among them, the above-described similarity can be obtained in the following manner.

In the first manner, an error between the sub information corresponding to the same period in the second variation information and the first variation information may be calculated, and the above-described similarity may be obtained based on the obtained error.

The error may be a variance, a mean square error, etc.

In the second mode, curve fitting can be performed on the second change information and the first change information respectively to obtain a first change curve and a second change curve, then the distance between corresponding target points in the first change curve and the second change curve is determined, and the similarity is obtained based on the obtained distance.

The similarity between the second change information and the first change information can accurately represent the fitting degree between the two information, so that the similarity is used as a judging standard for judging whether the fitting is successful, and the prediction change area and the actual change area can be successfully fitted.

Step S306: and obtaining the traffic characteristics of the target area based on the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information.

The above steps are the same as step S105 in the embodiment shown in fig. 1, and will not be repeated here.

From the above, after the initial second change information is determined, the second change information can be updated by repeatedly adjusting the value of the congestion propagation rate and the value of the congestion unblocking rate until the updated second change information is successfully fitted with the first change information table, so that whether the second change information is successfully fitted with the first change information or not can be continuously estimated based on the updated second change information, and the second change information when the fitting is successful can be obtained gradually.

The manner in which the aforementioned second change information characterizing the change over time of the congested road section in the target area is obtained based on the infectious disease model is specifically described below.

The embodiment of the present disclosure is not limited to the specific infectious disease model used when the second variation information is obtained, and for example, the infectious disease model may be an SI model, an SIR model, an SIRs model, an SEIR model, or the like, and the difference between the models is that model parameters related to the models are different.

The inventors found in practice that the SIR model is more effective in obtaining the above-described second variation information, and therefore, the manner of obtaining the second information will be described below taking the SIR model as an example.

First, some concepts to be referred to in the following examples will be explained.

Class S road segment: congestion segments.

Class I road segment: uncongested road segments that are prone to being congested by propagation.

R-type road segment: transitioning from congested to uncongested open road segments.

The rule of the S-type road section propagation congestion is as follows:

1. class S segments are effectively contacted with class I segments, i.e., infected, and also transition to class I segments, a process that may be referred to as congestion infection.

2. Class I segments may be unblocked, transitioning to class R segments, a process that may be referred to as congestion unblocking.

3. The class R road segments will not get stuck again.

Based on the above understanding, an SIR model expressed by the following differential equation can be established:

s (t), I (t) and R (t) respectively represent the number of S-type road sections, I-type road sections and R-type road sections in the time period t, and the number needs to be obtained in advance; s (t), I (t) and R (t) respectively represent the duty ratio of S-type road sections, I-type road sections and R-type road sections in the time period t; beta and gamma denote the aforementioned congestion propagation rate and congestion unblocking rate, respectively.

It can be seen that given the preset β and γ, the final I (t) can be solved by solving the differential equation above and substituting s_0, i_0, r_0 into the resulting general solution: the duty ratio of the class I road section in the time period t is the second variation information.

Wherein s_0, i_0 and r_0 are respectively: and when the reference time t=0, the duty ratio of the S-type road section, the I-type road section and the R-type road section corresponds to the second description information.

From the above, based on the ideas of congestion infection and congestion unblocking, the second change information representing the time-dependent change of the congestion road section in the target area can be obtained rapidly and accurately through the infectious disease model.

As can be seen from the above examples, the updated second change information can be obtained by adjusting β and γ, and therefore, the second change information when the fitting with the first change information is successful can be obtained by repeatedly adjusting β and γ.

The fitting effect of the second variation information with respect to the first variation information when the fitting based on the SIR model is successful will be described below.

Referring first to fig. 4 and 5, a first fitting effect schematic diagram and a second fitting effect schematic diagram provided by an embodiment of the disclosure are shown.

Fig. 4 is a schematic diagram of a fitting effect of an early peak time of the target area A1, and an abscissa in fig. 5 represents a time sequence number of each time in the early peak time, and an ordinate represents a duty ratio of a crowded road section; the curve labeled I represents the curve corresponding to the second variation information obtained based on the SIR model, and the curve labeled c (t) represents the curve corresponding to the first variation information.

Similarly, fig. 5 is a schematic diagram of a fitting effect for the target area A1 during the late peak period, and the coordinates and curves in fig. 5 have meanings similar to those in fig. 4, and only differ from the corresponding period, and are not described again.

As is apparent from a combination of fig. 4 and fig. 5, the second variation information obtained based on the SIR model can be well fitted to the first variation information for both the early peak period and the late peak period of the target region p 1.

Referring to fig. 6 and fig. 7, a third fitting effect schematic diagram and a fourth fitting effect schematic diagram provided by the embodiments of the present disclosure are shown.

Fig. 6 and fig. 7 are schematic diagrams of fitting effects for the early peak period and the late peak period of the target area A2, respectively, and the meanings of coordinates and curves in fig. 5 and fig. 6 are similar to those in fig. 4, and the differences are only that the target area and the period are different, and are not repeated here.

As is also evident from a combination of fig. 6 and fig. 7, the second variation information obtained based on the SIR model can be well fitted to the first variation information for both the early peak period and the late peak period of the target area A2.

The fitting effect for other target areas is not illustrated by one more drawing.

In summary, the second variation information obtained based on the SIR model has a better fitting effect on the first variation information.

The resulting traffic characteristics are analyzed as follows.

Referring to fig. 8, a schematic diagram of a traffic characteristic change trend is provided in an embodiment of the disclosure.

Wherein, different curves in each coordinate system in fig. 8 represent traffic characteristic change trend curves corresponding to early peak (moving) and late peak (afternoon), and p represents a preset ratio in the foregoing, namely a congestion threshold; the abscissa represents each date number within 1 month, and the ordinate represents traffic characteristics; curves in each coordinate system respectively represent traffic characteristics corresponding to the early peak time period and the late peak time period; the curve in the gray area corresponds to the weekend period.

As can be seen from fig. 8, no matter what kind of congestion threshold, the traffic characteristics obtained are the smallest at the weekend, that is, the road section is the lowest in congestion. Traffic characteristics at peak hours in the morning and evening present a v-shaped trend during weekdays other than weekends. Wherein, the traffic characteristics of Monday and Friday are larger than those of Monday, and the traffic characteristics of the early peak period are faster than those of the late peak period, which basically accords with the condition of fast blocking and fast scattering of the early peak period in life.

Corresponding to the traffic characteristic obtaining method, the embodiment of the disclosure also provides a traffic characteristic obtaining device.

Referring to fig. 9, a schematic structural diagram of a traffic characteristic obtaining device according to an embodiment of the present disclosure is provided, where the device includes the following modules 901 to 905.

A first description information obtaining module 901, configured to obtain first description information of a congested road section in a target area in different time periods;

a first change information obtaining module 902, configured to determine, based on the obtained first description information, first change information that characterizes a change over time of a congestion road segment in the target area;

a second description information obtaining module 903, configured to obtain second description information of each type of road section in the target area at the reference moment in the different time periods;

the information fitting module 904 is configured to perform information fitting based on the obtained second description information, a preset value corresponding to the congestion propagation rate, and a preset value corresponding to the congestion unblocking rate, so as to obtain second change information that is successfully fitted with the first change information;

the traffic characteristic obtaining module 905 is configured to obtain the traffic characteristic of the target area based on the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information.

In one embodiment of the present disclosure,

the first description information obtaining module 901 is specifically configured to obtain, as first description information, duty ratio information of a congested road section in a target area in different time periods;

And/or

The second description information obtaining module 903 is specifically configured to obtain, as second description information, the duty ratio information of each type of road section in the target area at the reference moment in the different time periods.

In one embodiment of the disclosure, the traffic characteristic obtaining module 905 is specifically configured to calculate a ratio between a value of the congestion propagation rate and a value of the congestion unblocking rate corresponding to the second change information; and determining the ratio as the traffic characteristics of the target area.

The ratio can represent the number of the effectively transmitted road sections of each congestion road section in the target area in the whole target time period, and can better reflect the congestion propagation capacity of the congestion road sections in the target area.

In one embodiment of the disclosure, the information fitting module 904 includes:

the second change information determining submodule is used for determining second change information representing the change of the congestion road section in the target area along with time based on the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion dredging rate;

and the second change information updating sub-module is used for updating the second change information by repeatedly adjusting the value of the congestion propagation rate and the value of the congestion unblocking rate until the updated second change information is successfully fitted with the first change information table.

In one embodiment of the disclosure, the second change information determining submodule is specifically configured to predict third description information of a congested road segment in the target area in the different time periods by using the obtained second description information, a preset value corresponding to a congestion propagation rate, and a preset value corresponding to a congestion dredging rate as model parameters of an infectious disease model; and obtaining second change information representing the time change of the congestion road section in the target area based on the third predicted description information.

In one embodiment of the disclosure, the second change information updating sub-module is specifically configured to adjust the value of the congestion propagation rate and the value of the congestion unblocking rate; according to the obtained second description information, the value after the congestion propagation rate adjustment and the value after the congestion unblocking rate adjustment, redetermining the change information representing the change of the congestion road section along with time in the target area, and updating the second change information into redetermined change information; obtaining the similarity between the second change information and the first change information; and returning to the step of adjusting the value of the congestion propagation rate and the value of the congestion unblocking rate until the similarity is smaller than the preset threshold value under the condition that the similarity is not smaller than the preset threshold value.

In one embodiment of the present disclosure, the first description information obtaining module 901 includes:

the speed obtaining submodule is used for obtaining the actual vehicle passing speed and the unimpeded passing speed of each road section in the target area in each time period;

and the congestion road section determining submodule is used for determining the congestion road section from each road section based on the obtained actual vehicle passing speed and the obtained unimpeded passing speed and obtaining first description information corresponding to the determined congestion road section.

In one embodiment of the disclosure, the congestion road section determining submodule is specifically configured to calculate a ratio between an actual vehicle passing speed and an unobstructed passing speed of each road section in the time period; and determining the road section with the ratio smaller than the preset ratio as a congestion road section.

In one embodiment of the present disclosure, the various types of road segments include: a congested segment, an uncongested segment that is prone to being congested by propagation, and a unblocked segment that transitions from congested to uncongested.

In one embodiment of the present disclosure, the apparatus further comprises:

and the traffic regulation information generation module is used for generating traffic regulation information of the target area based on the traffic characteristics and the congestion optimization target aiming at the target area. According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Thus, the traffic control of the target area based on the traffic control information is facilitated, and the congestion condition of the target road section in the target area is avoided or relieved.

In one embodiment of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

In one embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the aforementioned traffic characteristic obtaining method is provided.

In one embodiment of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the aforementioned traffic feature acquisition method.

Fig. 10 shows a schematic block diagram of an example electronic device 1000 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 10, the apparatus 1000 includes a computing unit 1001 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.

Various components in device 1000 are connected to I/O interface 1005, including: an input unit 1006 such as a keyboard, a mouse, and the like; an output unit 1007 such as various types of displays, speakers, and the like; a storage unit 1008 such as a magnetic disk, an optical disk, or the like; and communication unit 1009 such as a network card, modem, wireless communication transceiver, etc. Communication unit 1009 allows device 1000 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.

The computing unit 1001 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1001 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the respective methods and processes described above, such as a traffic characteristic obtaining method. For example, in some embodiments, the traffic characteristic obtaining method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1000 via ROM 1002 and/or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by computing unit 1001, one or more steps of the traffic characteristic obtaining method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to perform the traffic feature acquisition method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

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

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

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

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A traffic characteristic obtaining method, comprising:

2. The method of claim 1, wherein,

the obtaining the first description information of the congestion road section in the target area in different time periods includes:

the method comprises the steps of obtaining the duty ratio information of a congestion road section in a target area in different time periods, and taking the duty ratio information as first description information;

and/or

The obtaining the second description information of each type of road section in the target area at the reference time in the different time periods includes:

and obtaining the duty ratio information of each type of road section in the target area at the reference moment in the different time periods as second description information.

3. The method of claim 1, wherein the obtaining the traffic characteristics of the target area based on the value of the congestion propagation rate and the value of the congestion relief rate corresponding to the second change information includes:

calculating the ratio between the value of the congestion propagation rate and the value of the congestion unblocking rate corresponding to the second change information;

and determining the ratio as the traffic characteristics of the target area.

4. The method according to any one of claims 1-3, wherein the performing information fitting based on the obtained second description information, a preset value corresponding to a congestion propagation rate, and a preset value corresponding to a congestion unblocking rate, to obtain second change information that is successfully fitted with the first change information, includes:

Determining second change information representing the change of the congestion road section in the target area along with time based on the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion unblocking rate;

and updating the second change information by repeatedly adjusting the value of the congestion propagation rate and the value of the congestion unblocking rate until the updated second change information is successfully fitted with the first change information table.

5. The method of claim 4, wherein the determining second change information characterizing a change over time of the congested road segment in the target area based on the obtained second description information, a preset value corresponding to a congestion propagation rate, and a preset value corresponding to a congestion unblocking rate includes:

predicting third description information of the congested road sections in the target area in different time periods by taking the obtained second description information, a preset value corresponding to the congestion propagation rate and a preset value corresponding to the congestion dredging rate as model parameters of an infectious disease model;

and obtaining second change information representing the time change of the congestion road section in the target area based on the third predicted description information.

6. The method of claim 4, wherein updating the second change information by iteratively adjusting the value of the congestion propagation rate and the value of the congestion relief rate until the updated second change information is successfully fitted with the first change information comprises:

Adjusting the value of the congestion propagation rate and the value of the congestion dredging rate;

according to the obtained second description information, the value after the congestion propagation rate adjustment and the value after the congestion unblocking rate adjustment, redetermining the change information representing the change of the congestion road section along with time in the target area, and updating the second change information into redetermined change information;

obtaining the similarity between the second change information and the first change information;

and returning to the step of adjusting the value of the congestion propagation rate and the value of the congestion unblocking rate until the similarity is smaller than the preset threshold value under the condition that the similarity is not smaller than the preset threshold value.

7. The method of claim 1, wherein the obtaining the first description information of the congested road segment in the target area within the different time periods includes:

for each time period, first description information of a congested road segment in a target area within the time period is obtained in the following manner:

obtaining the actual vehicle passing speed and the unimpeded passing speed of each road section in the target area in the time period;

and determining a congestion road section from the road sections based on the obtained actual vehicle passing speed and the non-blocking passing speed, and obtaining first description information corresponding to the determined congestion road section.

8. The method of claim 7, wherein the determining a congested road segment from among road segments based on the obtained actual vehicle traffic speeds and the unobstructed traffic speeds comprises:

calculating the ratio of the actual vehicle passing speed to the unimpeded passing speed of each road section in the time period;

and determining the road section with the corresponding ratio smaller than the preset ratio as the congestion road section.

9. The method according to any one of claims 1-3, 5-8, wherein,

the road sections of various types include: a congested segment, an uncongested segment that is prone to being congested by propagation, and a unblocked segment that transitions from congested to uncongested.

10. The method of any of claims 1-3, 5-8, further comprising:

and generating traffic regulation information of the target area based on the traffic characteristics and the congestion optimization target for the target area.

11. A traffic characteristic obtaining device comprising:

12. The apparatus of claim 11, wherein,

the first descriptive information obtaining module is specifically configured to obtain, as first descriptive information, duty ratio information of a congested road section in a target area in different time periods;

and/or

The second descriptive information obtaining module is specifically configured to obtain, as second descriptive information, duty ratio information of each type of road section in the target area at the reference moments in the different time periods.

13. The apparatus of claim 11, wherein,

the traffic characteristic obtaining module is specifically configured to calculate a ratio between a value of the congestion propagation rate and a value of the congestion unblocking rate corresponding to the second change information; and determining the ratio as the traffic characteristics of the target area.

14. The apparatus of any of claims 11-13, wherein the information fitting module comprises:

15. The apparatus of claim 14, wherein,

the second change information determining submodule is specifically configured to predict third description information of a congested road section in the target area in different time periods by using the obtained second description information, a preset value corresponding to a congestion propagation rate and a preset value corresponding to a congestion dredging rate as model parameters of an infectious disease model; and obtaining second change information representing the time change of the congestion road section in the target area based on the third predicted description information.

16. The apparatus of claim 14, wherein,

the second change information updating sub-module is specifically configured to adjust the value of the congestion propagation rate and the value of the congestion unblocking rate; according to the obtained second description information, the value after the congestion propagation rate adjustment and the value after the congestion unblocking rate adjustment, redetermining the change information representing the change of the congestion road section along with time in the target area, and updating the second change information into redetermined change information; obtaining the similarity between the second change information and the first change information; and returning to the step of adjusting the value of the congestion propagation rate and the value of the congestion unblocking rate until the similarity is smaller than the preset threshold value under the condition that the similarity is not smaller than the preset threshold value.

17. The apparatus of claim 11, wherein the first description information obtaining module comprises:

18. The apparatus of claim 17, wherein,

the congestion road section determining submodule is specifically used for calculating the ratio between the actual vehicle passing speed and the unimpeded passing speed of each road section in the time period; and determining the road section with the ratio smaller than the preset ratio as a congestion road section.

19. The device according to any one of claims 11-13, 15-18, wherein,

20. The apparatus of any one of claims 11-13, 15-18, further comprising:

and the traffic regulation information generation module is used for generating traffic regulation information of the target area based on the traffic characteristics and the congestion optimization target aiming at the target area.

21. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-10.