CN112819325B

CN112819325B - Rush hour determination method, apparatus, electronic device, and storage medium

Info

Publication number: CN112819325B
Application number: CN202110129977.3A
Authority: CN
Inventors: 宁志猛
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2021-01-29
Filing date: 2021-01-29
Publication date: 2024-07-05
Anticipated expiration: 2041-01-29
Also published as: CN112819325A

Abstract

The application provides a peak period determining method, a peak period determining device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a first traffic running index of a plurality of first sub-time periods in a commute period and a second traffic running index of a plurality of second sub-time periods in a non-commute period in a target area; selecting a target second sub-time period in a traffic stationary state from a plurality of second sub-time periods according to the second traffic running index of each second sub-time period; calculating a traffic running index threshold value in a traffic stationary state according to a second traffic running index of a target second sub-time period; selecting a first sub-period with the traffic running index larger than the traffic running index threshold value from the plurality of first sub-periods as a target first sub-period; and determining the commute peak time according to the continuity of the first sub-time period of the target. According to the method, the traffic operation index threshold value in the steady state of the traffic is calculated, the commute peak time of the target area is determined, and the accuracy of the peak time is improved.

Description

Rush hour determination method, apparatus, electronic device, and storage medium

Technical Field

The present application relates to the field of information technologies, and in particular, to a peak period determining method, apparatus, electronic device, and storage medium.

Background

With the increase of people's travel demands, transportation has become an important component of people's daily life. When people go out intensively, the road is easy to have larger traffic. The time of day when a large traffic volume occurs is a peak period of the traffic trip.

The early and late peak time of the traffic travel is an important parameter of traffic management, traffic planning and traffic evaluation, and can reflect the traffic travel condition of people. Meanwhile, the peak time in the morning and evening can also be used as an important basis for traffic management decision-making and urban traffic state evaluation.

The current method for determining the peak time of the morning and evening mainly comprises the steps of counting the traffic flow through on-site investigation, and then determining the peak time of the morning and evening according to the quantity of the traffic and the corresponding time.

Disclosure of Invention

In view of the foregoing, it is an object of the present application to provide a peak period determination method, apparatus, electronic device and storage medium to improve the accuracy of commute peak periods.

In a first aspect, an embodiment of the present application provides a peak period determining method, including:

acquiring a first traffic running index of a plurality of first sub-time periods in a commute period and a second traffic running index of a plurality of second sub-time periods in a non-commute period in a target area;

Selecting a target second sub-time period in a traffic stationary state from a plurality of second sub-time periods according to a second traffic operation index of each second sub-time period;

calculating a traffic running index threshold value in a traffic stationary state according to a second traffic running index of the target second sub-time period;

Selecting a first sub-period with the traffic running index larger than the traffic running index threshold value from the plurality of first sub-periods as a target first sub-period;

and determining the commute peak time of the target area according to the continuity of the first sub-time period of the target.

With reference to the first aspect, an embodiment of the present application provides a first possible implementation manner of the first aspect, before the step of obtaining a first traffic running index of a plurality of first sub-time periods in a commute period and a second traffic running index of a plurality of second sub-time periods in a non-commute period in a target area, the method includes:

acquiring the vehicle passing efficiency of each time period in the target area;

and determining the continuous time period of which the vehicle passing efficiency does not accord with the preset numerical range as a commute time period, and determining the continuous time period of which the vehicle passing efficiency accords with the preset numerical range as a non-commute time period.

With reference to the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, wherein the selecting, according to a second traffic running index of each second sub-period, a target second sub-period in which traffic is in a steady state from a plurality of second sub-periods includes:

Calculating the fluctuation amplitude of the traffic operation index of each second sub-time period according to the second traffic operation index;

and selecting a second sub-time period with the fluctuation amplitude of the traffic running index within a preset range from a plurality of second sub-time periods as a target second sub-time period with the traffic in a stable state.

With reference to the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, where calculating, according to the second traffic running index of the target second sub-period, a traffic running index threshold value for which traffic is in a stationary state includes:

selecting a target second traffic running index from the selected second traffic running indexes of the target second sub-time period according to a preset quantile selecting method;

and determining a traffic operation index threshold according to the target second traffic operation index.

With reference to the first aspect, an embodiment of the present application provides a fourth possible implementation manner of the first aspect, where the target second traffic running index is a normal value determined according to distribution conditions corresponding to second traffic running indexes of a plurality of target second sub-time periods; the target second traffic running index has a value greater than a predetermined number of second traffic running indexes of other target second sub-periods.

In a second aspect, an embodiment of the present application provides a peak period determining apparatus, including:

The first acquisition module is used for acquiring a first traffic running index of a plurality of first sub-time periods in the commute time period and a second traffic running index of a plurality of second sub-time periods in the non-commute time period in the target area;

The first selecting module is used for selecting a target second sub-time period with the traffic in a stable state from a plurality of second sub-time periods according to the second traffic operation index of each second sub-time period;

The first calculation module is used for calculating a traffic running index threshold value of the traffic in a steady state according to the second traffic running index of the target second sub-time period;

The second selecting module is used for selecting a first sub-time period with the traffic running index larger than the traffic running index threshold value from a plurality of first sub-time periods as a target first sub-time period;

and the first determining module is used for determining the commute peak time of the target area according to the continuity of the first sub-time period of the target.

In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect, or any of the possible implementations of the first aspect.

In a fourth aspect, embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the first aspect, or any of the possible implementation manners of the first aspect.

In a fifth aspect, embodiments of the present application also provide a computer program product comprising a computer program/instruction which when executed by a processor implements the steps of the first aspect, or any of the possible implementation manners of the first aspect.

The method for determining the peak time period provided by the embodiment of the application comprises the following steps: acquiring a first traffic running index of a plurality of first sub-time periods in a commute period and a second traffic running index of a plurality of second sub-time periods in a non-commute period in a target area; selecting a target second sub-time period in a traffic stationary state from a plurality of second sub-time periods according to the second traffic running index of each second sub-time period; calculating a traffic running index threshold value in a traffic stationary state according to a second traffic running index of a target second sub-time period; selecting a first sub-period with the traffic running index larger than the traffic running index threshold value from the plurality of first sub-periods as a target first sub-period; and determining the commute peak time according to the continuity of the first sub-time period of the target. Compared with the prior art, the traffic operation index threshold value in the steady state of the traffic is accurately calculated by the on-site statistics method, the commute peak time is determined according to the selected first sub-time period which is larger than the traffic operation index threshold value, and the accuracy of the commute peak time is improved.

According to the peak period determining method provided by the embodiment of the application, the target second sub-period in a stable state is selected according to the fluctuation amplitude of the traffic operation index, so that the calculation accuracy is improved, and the accuracy of the determined commute peak period is higher.

According to the peak period determining method provided by the embodiment of the application, the traffic operation index threshold value is calculated according to the target second traffic operation index and the maximum traffic operation index selected by the quantile selecting method, so that the accuracy of the commute peak period is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 shows a flowchart of a peak period determining method according to an embodiment of the present application;

FIG. 2 is a flow chart illustrating another method of peak time period determination provided by an embodiment of the present application;

Fig. 3 is a schematic diagram showing a structure of a peak period determining apparatus according to an embodiment of the present application;

fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that the term "comprising" will be used in embodiments of the application to indicate the presence of the features stated hereafter, but not to exclude the addition of other features.

Peak hours refer to a period of time when a large amount of traffic occurs, and when people concentrate, a large number of vehicles, pedestrians, etc. are concentrated on a traffic route, so that the peak hours usually generate a large amount of traffic, even congestion.

Peak hours typically occur during commute times, near holidays, near the end of holidays, and other special periods. Typically, commute times occur at early and late peaks due to the large traffic volume that people travel to and from the residence and work sites during the day, with the peak periods of commute times occurring primarily in the morning and afternoon.

Holidays or peak hours near the end of holidays are mainly caused by people concentrated leaving or returning to frequent places, but are not normalized peak hours due to holiday arrangement.

Other peak periods occurring in special periods may be peak periods caused by sudden events or concentrated trips of people caused by special events, such as concentrated movement, etc., and the situation may be related to local area policies and belong to sudden peak periods.

The peak period of the commute time may be regarded as a regular peak period, and the peak period occurring on holidays or near the end of holidays and other special periods may be regarded as an irregular peak period.

Considering that the frequent peak time period is more closely related to the travel of people, the method is an important parameter of traffic management, traffic planning and traffic evaluation, can reflect the travel situation of people, and can be used as an important basis for traffic management decision and urban traffic state evaluation, so that the method for determining the peak time period is mainly applicable to the determination process of the frequent peak time period, namely is mainly applicable to the application scene of the commuting time period, and the following implementation mode is provided in combination with the specific application scene 'commuting time period' for enabling the content of the method to be used by people in the field. It will be apparent to those having ordinary skill in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application.

In a flowchart of a peak period determination method shown in fig. 1, the method includes the steps of:

s101: acquiring a first traffic running index of a plurality of first sub-time periods in a commute period and a second traffic running index of a plurality of second sub-time periods in a non-commute period in a target area;

s102: selecting a target second sub-time period with the traffic in a stable state from a plurality of second sub-time periods according to the second traffic operation index of each second sub-time period;

S103: calculating a traffic running index threshold value of the traffic in a steady state according to a second traffic running index of the target second sub-time period;

S104: selecting a first sub-period with the traffic running index larger than the traffic running index threshold value from the plurality of first sub-periods as a target first sub-period;

S105: and determining the commute peak time of the target area according to the continuity of the first sub-time period of the target.

In S101, the commute period refers to a period of time in which a practitioner travels to and from a residence and a work unit or school for work or study or the like. The commute time periods for the different areas may be different, taking into account the time zone in which the different areas are located and the working (or learning) time of the administrative area in which they are located, etc. Specifically, the commute time of the target area may be obtained according to the historical traffic travel data of the people, or the shift-in time and shift-out time of the people in the target area may be counted according to the big data, and then a period of time before and after the shift-in time and the shift-out time may be used as the commute time.

In particular implementations, prior to S101, the commute period and the non-commute period may be acquired as follows:

s201: acquiring the vehicle passing efficiency at each moment in a target area;

s202: and determining the continuous time period of which the vehicle passing efficiency does not accord with the preset numerical range as a commute time period, and determining the continuous time period of which the vehicle passing efficiency accords with the preset numerical range as a non-commute time period.

In S201, the vehicle passing efficiency may refer to the speed of the vehicle passing through the target road section.

In a specific implementation, the vehicle passing efficiency may be determined from the vehicle passing data.

Wherein the vehicle traffic data includes at least one or more of: vehicle passing speed, vehicle passing time, etc.

The vehicle passing speed refers to an average passing speed of the vehicle through the target road section. Here the speed of each vehicle may be obtained from GPS (Global Positioning System ) data of the vehicle, and then the average speed of each vehicle is calculated from the speed of each vehicle.

The slower the vehicle passing speed, the lower the vehicle passing efficiency is characterized.

The vehicle passing time refers to an average passing time length of the vehicle passing through the target road section. Here, the speed of each vehicle may be obtained from the GPS data of the vehicle, and then the average transit time of each vehicle may be calculated from the speed of each vehicle and the length of the target road section. Or directly determining the passing time of each vehicle according to the starting time and the ending time of the vehicle passing through the target road section, and then calculating the average passing time of each vehicle according to the passing time of each vehicle.

The longer the vehicle pass time, the lower the vehicle pass efficiency is characterized.

In S202, the preset data range may be set according to a vehicle passing efficiency range in which the history traffic is in a stationary state. Wherein the traffic stationary state will be described later.

When the vehicle passing efficiency accords with the preset data range, the current moment is in a traffic stable state, namely a non-commute state, so that a continuous time period of which the vehicle passing efficiency accords with the preset data range can be used as a non-commute period; and otherwise, taking a continuous time period with the vehicle passing efficiency which does not accord with the preset data range as a commute time period.

It is to be understood that the commute period obtained here is an inaccurate commute period and is a time range that should contain an accurate commute period.

The commute period may be divided into an early commute period and a late commute period considering that the commute time is mainly distributed in the morning and afternoon.

In a specific implementation, the early commute period and the late commute period may be further divided, respectively, to obtain a plurality of first sub-time periods.

In one embodiment, the commute time period may be divided into a plurality of consecutive first sub-time periods, the time of adjacent two first sub-time periods being consecutive; the commute time period may be divided into a plurality of first sub-time periods according to a preset time interval, and the time intervals of two adjacent first sub-time periods may be the same or different. The duration of each first sub-period may be the same or different.

For example, for an early commute period, the first sub-periods are divided every 20 minutes, each having a duration of 20 minutes.

The non-commute period refers to a period other than the attendance period, and the non-commute period acquired here is also an inaccurate non-commute period. For the early commute period and the late commute period described above, the non-commute period may include an afternoon non-commute period and an evening non-commute period.

Wherein, the non-commute period in the middle of the day refers to a period of time from the end of the early commute period to the beginning of the late commute period in the day; the evening non-commute period refers to a period from the end of the previous day evening commute period to the beginning of the next day early commute period.

In a specific implementation, the non-commute period in the middle of the day and the non-commute period in the evening may be further divided to obtain a plurality of second sub-time periods.

In one embodiment, the non-commute time period may be divided into a plurality of consecutive second sub-time periods, the time of adjacent two second sub-time periods being consecutive; the commute time period may be divided into a plurality of spaced second sub-time periods according to a preset time interval, and the time intervals of two adjacent second sub-time periods may be the same or different. The duration of each second sub-period may be the same or different.

For example, for an inter-noon non-commute period, the second sub-periods are divided every 20 minutes, each having a duration of 20 minutes.

In addition, each second sub-period may be the same as or different from each first sub-period.

The traffic running Index (TRAVEL TIME Index, TTI for short) is an Index for comprehensively evaluating congestion of roads or space areas. TTI refers to the ratio of the actual travel time to the travel time under free stream conditions. The higher the TTI index, the greater the traffic, and the more congested the road.

In implementations, a traffic running index may be obtained for each moment. To reduce the amount of calculation, for example, the traffic running index may be collected every 10 minutes, and 144 pieces of traffic running index data in total are used in the day. The first traffic running index refers to the traffic running index at each time in the first sub-period, and the second traffic running index refers to the traffic running index at each time in the second sub-period. Each moment is the moment of collecting the traffic running index, and the time interval between two adjacent moments is the time interval of collecting the traffic running index. It should be noted that each first sub-period and each second sub-period may include at least one time, and the duration of each first sub-period or each second sub-period is at least the time interval between two adjacent times. For example, the first sub-period is 8:00:00-8:20:00, and the time for collecting the traffic running index is 8:00:00, 8:10:00, and 8:20:00, so that the first sub-period includes 3 time, and the duration of the first sub-period is the sum of the time intervals of 8:00:00-8:10:00:00 and 8:10:00-8:20:00.

To improve the accuracy of the calculation, the traffic running index may be averaged. Specifically, for each moment, the traffic running index of the moment can be collected every day in the historical continuous days, then the traffic running index of each moment is averaged, for example, the traffic running index at 7 am in three continuous days is collected to be 1.21, 1.31 and 1.41, and the average value of the traffic running indexes can be calculated to be 1.31. By calculating the traffic running index average, it is possible to avoid a situation where an error is large due to using only data of a certain day.

The first traffic running index may thus be the average of the traffic running indexes over consecutive preset days for each instant in the first sub-period; the second traffic running index may be a traffic running index mean value for a continuous preset number of days for each time in the second sub-period.

In S102, the traffic stationary state refers to a traffic running state in which the traffic running index is within a preset numerical range. The target second sub-period refers to a second sub-period in which the traffic running index is within a preset numerical range.

Considering that in practical situations, during the evening non-commute period, the number of vehicles and pedestrians going out is relatively small, and no peak is caused basically, so that the influence of traffic flow on the commute peak can be ignored. In the embodiment of the application, the influence of daytime traffic flow on commute peaks is mainly considered, so that the traffic stationary state is the traffic stationary state in daytime. The same early commute period and the same late commute period occur when the traffic flow is large, so that the traffic is usually in an off-peak state or in a congestion state, and therefore the target second sub-period mainly occurs in the non-commute period in the middle of the noon.

In a specific implementation, S102 may include the following steps:

S1021: calculating the fluctuation amplitude of the traffic operation index of each second sub-time period according to the second traffic operation index;

s1022: and selecting a second sub-time period with the fluctuation amplitude of the traffic operation index within the preset fluctuation amplitude range from a plurality of second sub-time periods as a target second sub-time period with the traffic in a stable state.

In S1021, the traffic running index fluctuation amplitude refers to a difference between the second traffic running index and the average of the second traffic running index in each second sub-period.

The second traffic running index average may reflect traffic running conditions in a steady state of traffic. Specifically, a second traffic running index average may be calculated according to the second traffic running index and the number of second sub-time periods for each second sub-time period.

And calculating the fluctuation amplitude of the traffic running index of each second sub-time period according to the difference value of the second traffic running index and the average value of the second traffic running index at each moment.

In S1022, the target second sub-period refers to a second sub-period in which the traffic running index fluctuation amplitude is within the preset range.

Taking the eastern eighth zone in the international time zone as an example, the target second sub-time period of the traffic in the steady state is generally 11:00:00-13:00:00 and 14:00:00-16:00:00, and the fluctuation range of the second traffic running index in the period is in the preset fluctuation range.

In S103, the traffic running index threshold refers to a maximum value of the traffic running index in a steady state of traffic. And when the traffic running index exceeds the traffic running index threshold value, indicating that the traffic is in a non-steady state.

In a specific implementation process, the second traffic running index corresponding to the target second sub-period may have an abnormal value, so in a possible implementation, S103 may be executed according to the following steps:

S1031: selecting a target second traffic running index from the selected second traffic running indexes of the target second sub-time period according to a preset quantile selecting method;

S1032: and determining a traffic operation index threshold according to the target second traffic operation index.

In S1031, the quantile selection method refers to sorting a plurality of data to be selected, and then selecting the data ranked at a preset position as target data.

In the embodiment of the present application, when there are a plurality of target second sub-time periods, that is, a plurality of second traffic running indexes, the plurality of second traffic running indexes may be ordered in order from small to large. Here, it is considered that the maximum value may be an abnormal value, so the maximum value may be excluded, and then a target second traffic running index other than the abnormal maximum value is selected from the remaining second traffic running indexes.

In the specific selection process, considering that the larger target second traffic running index of the normal values is to be selected, the quantile may be set to a quantile of more than 50%, for example, 70%. The selected target second traffic running index in the quantile manner is not necessarily the largest second traffic running index, but is substantially close to the largest second traffic running index, so that in this way the selected larger second traffic running index can be regarded as the target second traffic running index.

In S1032, a traffic running index threshold value in the steady state of traffic may be calculated based on the target second traffic running index.

In the implementation process, the target second traffic operation index may be a normal value determined according to distribution conditions corresponding to the second traffic operation indexes of the plurality of target second sub-time periods; the number of the target second traffic running index is greater than the second traffic running index of the predetermined number of other target second sub-periods.

Specifically, the arrangement may be performed according to the sizes of the second traffic running indexes of the second sub-time periods of the plurality of targets, so as to obtain a distribution ordering from large to small or from small to large. And then selecting normal values except for the abnormal value from the sorted second traffic running indexes. Here, the outlier refers to a second traffic running index that is significantly larger than other data. And the number of the target second traffic operation index is larger than the second traffic operation index of the other target second sub-period of time of the predetermined number, that is, the target second traffic operation index is larger than the second traffic operation index of the other target second sub-period of time of the predetermined number.

However, considering that the traffic running index threshold value in the steady state of the traffic is also related to the maximum value in the first traffic running index, in order to improve the accuracy of the calculation, in another possible embodiment, S103 may be performed according to the following steps:

S1033: determining a maximum traffic running index of the first traffic running indexes of the plurality of first sub-time periods;

S1034: and calculating a traffic operation index threshold value of the traffic in a steady state according to the target second traffic operation index and the maximum traffic operation index.

In S1033, a maximum traffic running index among the first traffic running indexes of the plurality of first sub-periods may be selected by comparison.

In S1034, when the second sub-period corresponding to the target second traffic running index is the time before 12:00:00 noon, the target second traffic running index may be denoted as up_day_tti, and the maximum traffic running index may be denoted as moving_high_tti; the traffic running index threshold is noted as a moving_base_tti.

At this time, the traffic running index threshold, i.e., moving_base_tti= (moving_high_tti-up_day_tti) ×a+up_day_tti. Wherein A is a weight coefficient greater than 0 and less than 1.

When the second sub-period corresponding to the target second traffic running index is the time after 12:00:00 am, the target second traffic running index may be denoted as a down_day_tti, and the maximum traffic running index may be denoted as evening _high_tti.

At this time, the traffic running index threshold, that is evening _high_tti= (evening _high_tti-down_day_tti) ×b+down_day_tti. Wherein B is a weight coefficient greater than 0 and less than 1.

In S104, as described above, the commute peak period should occur within the first sub-period, that is, a case where the traffic running index is greater than the traffic running index threshold value may occur within the first sub-period, and therefore, the first sub-period where the traffic running index is greater than the traffic running index threshold value is selected from the plurality of first sub-periods as the target first sub-period.

In implementations, the target first sub-period may occur during an early commute period as well as during a late commute period.

In S105, considering that the discontinuous time selected according to the traffic running index threshold cannot represent the commute peak period, it is highly likely to be an abnormal value, and therefore, it is necessary to determine the commute peak period of the target area according to the continuity of the target first sub-period.

Specifically, the method comprises the following steps:

S1051: determining at least one continuous time interval composed of a plurality of target first sub-time periods according to the time of each target first sub-time period;

S1052: and determining the commute peak time of the target area according to the time value with the earliest time and the time value with the latest time in the continuous time interval.

In S1051, when there are a plurality of target first sub-periods, at least one continuous time interval may be determined according to the time of each target first sub-period. It is mainly considered here that the discontinuous target first sub-period may belong to the peak of noon or other situations, etc.

In S1052, the start time of the commute peak period may be determined based on the time value of the earliest time in the continuous time period, and the end time of the commute peak period may be determined based on the time value of the latest time in the continuous time period, and further the commute peak period may be determined.

In practice, as previously described, the target first sub-period may occur in the early commute period as well as the late commute period, and thus the commute peak period of the target area may be determined here for the different periods in which the target first sub-period may occur and the continuity of the target first sub-period.

Here, specifically, the start time and the end time of the commute peak of the target area may be determined according to different time periods that may occur in the first sub-time period of the target and the continuity of the first sub-time period of the target, and then the commute peak time of the target area may be determined according to the start time and the end time of the commute peak of the target area.

It is contemplated that commute peak periods may be divided into commute early peak periods and commute late peak periods, with the thresholds for determining the start and end times of the commute early peak periods and the start and end times of the commute late peak periods being different. Thus, the process of determining the commute peak time period can be divided into four processes, namely, the starting time of the commute early peak time period, the ending time of the commute early peak time period, the starting time of the commute late peak time period and the ending time of the commute late peak time period, and the process of determining the traffic running index threshold value can be adjusted according to the four processes.

For the process of determining the start time of the commute early peak period, the target first sub-period occurs in the early commute period, and when determining the commute peak start time of the target region, the traffic running index threshold, i.e. moving_base_tti= (moving_high_tti-up_day_tti) ×a+up_day_tti, in particular moving_base_tti= (moving_high_tti-up_day_tti) ×α+up_day_tti, where α is a weight coefficient greater than 0 and less than 1.

Then, the minimum time of the consecutive time points in the first sub-period of the target is selected as the commute early peak start time of the target area.

For the process of determining the end time of the commute early peak period, the target first sub-period occurs during the early commute period, and when the commute peak end time of the target region is determined, the traffic running index threshold, i.e., moving_base_tti= (moving_high_tti-up_day_tti) ×a+up_day_tti, specifically moving_base_tti= (moving_high_tti-up_day_tti) ×β+up_day_tti, where β is a weight coefficient greater than 0 and less than 1.

Then, the maximum time of the consecutive time points in the first sub-period of the target is selected as the commute early peak end time of the target area.

The above-described process of determining the start time of the commute early peak period and the process of determining the end time of the commute early peak period may be different coefficients. The commute early peak time period may be determined based on a commute early peak start time during which the start time of the commute early peak time period is determined and a commute early peak end time during which the end time of the commute early peak time period is determined.

For the process of determining the start time of the commute peak period, the target first sub-period occurs in the late commute period, and when determining the commute peak start time of the target region, the traffic running index threshold, evening _high_tti= (evening _high_tti-down_day_tti) ×b+down_day_tti, specifically evening _high_tti= (evening _high_tti-down_day_tti) ×γ+down_day_tti, where γ is a weight coefficient greater than 0 and less than 1.

Then, the minimum time of the consecutive time points in the first sub-period of the target is selected as the commute evening peak start time of the target area.

For the process of determining the end time of the commute peak period, the target first sub-period occurs during the late commute period, and when determining the commute peak end start time of the target region, the traffic running index threshold, evening _high_tti= (evening _high_tti-down_day_tti) ×b+down_day_tti, in particular evening _high_tti= (evening _high_tti-down_day_tti) ×δ+down_day_tti, wherein δ is a weight coefficient greater than 0 and less than 1.

Then, the maximum time of the consecutive time points in the first sub-period of the target is selected as the commute evening peak end time of the target area.

The above-described process of determining the start time of the commute late peak period and the process of determining the end time of the commute late peak period may be different coefficients. The commute night peak time period may be determined from the commute night peak start time during which the start time of the commute night peak time period is determined and the commute night peak end time during which the end time of the commute night peak time period is determined.

According to the peak period determining method provided by the embodiment of the application, the commute peak period can be accurately determined, and the peak period determining method provided by the embodiment of the application can be further applied to a signal lamp timing scene, such as another peak period determining method shown in fig. 2, in a flow chart, and specifically comprises the following steps:

s301: acquiring signal lamp timing information of a target intersection in a target area;

s302: determining estimated passing time required by a running vehicle to pass through a target intersection in a commute peak time according to the timing information of the signal lamp;

s303: and adjusting signal lamp timing according to the estimated passing time.

In S301, the signal timing information may be timing information of the signal in different directions of the target intersection.

In S302, the estimated transit time of each vehicle passing through the target intersection in the commute peak period may be determined according to the cycle information of the signal lamps and the number of the traveling vehicles. The estimated time of passage may be an average estimated time of passage of the traveling vehicle.

In S303, according to the estimated traffic time of each direction of the target intersection, when the estimated traffic time of a certain direction is too long, congestion is likely to occur in the direction, and at this time, the display time of the green light in the direction can be properly prolonged, and at the same time, the display time of the green light in other directions can be properly shortened.

In the implementation process, the display time of the red light and the green light can be adjusted, and the timing period of the signal light can be shortened. It should be noted that, according to the estimated traffic time, the implementation of adjusting the signal lamp timing is within the scope of the present application.

The embodiment of the application also provides a method for determining the peak time period, which specifically comprises the following steps:

step 1: acquiring a traffic running index of each target moment in a target area;

In implementations, a traffic running index may be obtained for each moment. In order to reduce the calculation amount, the same time of day can be selected according to a preset selection rule, so that the target time is different times obtained by selecting the same time of day according to the preset selection rule.

In a specific embodiment, a target time may be selected every same time period, for example, a target time may be selected every 10 minutes, a time interval between two adjacent target times is 10 minutes, 144 target times may be obtained in one day, that is, a traffic running index may be collected every 10 minutes, and 144 traffic running index data are all obtained in one day.

TABLE 1

Time of	TTI	Time of	TTI
				2019/11/1 0:00	1.168951	2019/11/2 0:00	1.200312
2019/11/1 0:10	1.158021	2019/11/2 0:10	1.186908
				2019/11/1 0:20	1.14672	2019/11/2 0:20	1.170563
2019/11/1 0:30	1.140603	2019/11/2 0:30	1.159823
				2019/11/1 0:40	1.146251	2019/11/2 0:40	1.16407
2019/11/1 0:50	1.139791	2019/11/2 0:50	1.153767
				2019/11/1 1:00	1.115622	2019/11/2 1:00	1.158381
2019/11/1 1:10	1.128157	2019/11/2 1:10	1.148118
				2019/11/1 1:20	1.117524	2019/11/2 1:20	1.135772
2019/11/1 1:30	1.101958	2019/11/2 1:30	1.125398
				2019/11/1 1:40	1.104051	2019/11/2 1:40	1.124668
2019/11/1 1:50	1.111242	2019/11/2 1:50	1.12254
				2019/11/1 2:00	1.118183	2019/11/2 2:00	1.125279
2019/11/1 2:10	1.116993	2019/11/2 2:10	1.130317
				2019/11/1 2:20	1.094637	2019/11/2 2:20	1.115124
2019/11/1 2:30	1.09997	2019/11/2 2:30	1.119705
				2019/11/1 2:40	1.093993	2019/11/2 2:40	1.119411
2019/11/1 2:50	1.095152	2019/11/2 2:50	1.12334
				2019/11/1 3:00	1.095477	2019/11/2 3:00	1.117015

As in table 1, traffic running index data for 10 minute sampling frequency from zero point to 3 point is shown at 11 months 1 to 2 days 2019.

Step 2: calculating a traffic running index mean value;

in order to improve the accuracy of calculation, traffic running data at each target moment in the historical time period can be averaged, considering that a large error is easily caused when only data of a certain day is selected to determine a commute peak time period. Thus, in particular, for each target instant, the traffic running index for that target instant may be collected daily for a historical succession of days, and then averaged for each instant.

The traffic data may be data of each target time within 30 consecutive days of history, for example, data of each target time within 30 consecutive days of history.

For example, the traffic indexes of 7:00:00 am for three consecutive days are 1.21, 1.31 and 1.41 respectively, and the average value of the traffic indexes for three consecutive days is calculated to be 1.31.

Step 3: acquiring the vehicle passing efficiency of each target moment in the target area;

The vehicle passing efficiency may refer to how fast or slow the vehicle passes through the target road section.

Step 4: determining a continuous time period, in which the vehicle passing efficiency does not accord with the preset numerical range, as a commute time period, and determining a continuous time period, in which the vehicle passing efficiency accords with the preset numerical range, as a non-commute time period;

The commute period refers to a period of time in which a practitioner moves to and from a residence and a work unit or school for work or study or the like. The commute time periods for the different areas may be different, taking into account the time zone in which the different areas are located and the working (or learning) time of the administrative area in which they are located, etc. Specifically, the commute time of the target area may be obtained according to the historical traffic travel data of the people, or the shift-in time and shift-out time of the people in the target area may be counted according to the big data, and then a period of time before and after the shift-in time and the shift-out time may be used as the commute time.

The preset data range may be set according to a vehicle passing efficiency range in which the history traffic is in a steady state.

Step 5: selecting a target time period in a traffic stationary state from the non-commute time period according to the traffic running index of each target time;

the traffic stationary state refers to a traffic running state in which the traffic running index is within a preset numerical range.

Considering that in practical situations, during the evening non-commute period, the number of vehicles and pedestrians going out is relatively small, and no peak is caused basically, so that the influence of traffic flow on the commute peak can be ignored. In the embodiment of the application, the influence of daytime traffic flow on commute peaks is mainly considered, so that the traffic stationary state is the traffic stationary state in daytime. The same early commute period and the same late commute period occur when the traffic flow is large, so that the traffic is usually in a peak-out state or a congestion state, and thus the target period mainly occurs in the non-commute period in the middle of the noon.

In specific implementation, the fluctuation amplitude of the traffic running index at each target moment in the non-commute period can be calculated, and a target time period in which the traffic running index fluctuation amplitude is in a stable state is selected from a plurality of target moments.

The traffic running index fluctuation amplitude refers to the difference between the traffic running index and the average value of the traffic running index during the non-commute period. The preset range refers to the fluctuation range of the traffic running index when the traffic is in a stable state. And when the fluctuation amplitude of the traffic running index is within the preset fluctuation amplitude range, the traffic is in a stable state.

Taking the eastern eighth zone in the international time zone as an example, the target time period of the traffic in a stable state is generally 11:00:00-13:00:00 and 14:00:00-16:00:00, and the fluctuation range of the traffic running index in the time period is in the preset fluctuation range.

Step 6: screening out the target traffic running indexes of other target traffic running indexes with the numerical value larger than the preset number in the target time period according to a preset quantile selecting method aiming at each target time period;

The quantile selecting method is characterized in that a plurality of data to be selected are ordered, and then the data ranked at a preset position are selected as target data.

In general, each target period includes a plurality of target moments, that is, there are a plurality of traffic indexes, where the plurality of traffic indexes may be sorted in order from small to large. Here, it is considered that the maximum value may be an abnormal value, so that the maximum value may be excluded, and then the target traffic running index is selected from the remaining traffic running indexes.

In the specific selection process, the higher target traffic running index of the normal value is selected, so that the quantile may be set to a quantile greater than 50%, for example, 70%. The target traffic running index selected in the quantile manner is larger than the second traffic running index of the predetermined number of other target time periods, so that in this manner, the selected larger traffic running index can be used as the target traffic running index.

TABLE 2

11:00:00	1.33289125
		11:10:00	1.32473046
11:20:00	1.31800604
		11:30:00	1.3132495
11:40:00	1.30735338
		11:50:00	1.30822854
12:00:00	1.30864896
		12:10:00	1.31290242
12:20:00	1.3135345
		12:30:00	1.31026529
12:40:00	1.30873583
		12:50:00	1.306336
13:00:00	1.30733817

Taking the target time period of 11:00:00-13:00:00 as an example, as shown in table 2, traffic running index data of 11:00:00-13:00:00 can be selected, and the traffic running index of 70% quantiles can be 1.313449 according to a predetermined quantile selection method.

Step 7: determining the maximum value of the traffic running index according to the traffic running index at each target moment;

Since the higher the traffic running index, the larger the representative traffic volume, and the more congested the road, the maximum probability of the traffic running index occurs in the commute period. In the implementation process, the maximum value of the traffic running index can be directly determined from the traffic running index corresponding to each target moment contained in the commute time period.

Step 8: calculating a traffic operation index threshold value of each target time period under the steady state of traffic according to the target traffic operation index and the maximum value of the traffic operation index in each target time period;

Considering that the commute peak time of the determined target area can be divided into a commute early peak time and a commute late peak time, the commute early peak time is determined according to the commute early peak start time and the commute early peak end time, and the commute late peak time is determined according to the commute late peak start time and the commute late peak end time. The process of determining the commute peak time period can thus be divided into four processes of determining the start time of the commute early peak time period, determining the end time of the commute early peak time period, determining the start time of the commute late peak time period, and determining the end time of the commute late peak time period.

When the above-mentioned processes are determined, the corresponding traffic running index thresholds are different, and therefore, it is necessary to determine the corresponding traffic running index thresholds for the above-mentioned four processes, respectively.

In a specific implementation, when the target time period corresponding to the target traffic running index is the time before 12:00:00 pm, the target traffic running index may be denoted as up_day_tti, and the maximum traffic running index may be denoted as moving_high_tti; the traffic running index threshold is noted as a moving_base_tti.

When the target time period corresponding to the target traffic running index is 12:00:00 pm, the target traffic running index may be denoted as down_day_tti, and the maximum traffic running index may be denoted as evening _high_tti.

For the process of determining the starting time of the commute early peak time, the target time period corresponding to the target traffic running index is time before 12:00:00 noon, and the traffic running index threshold is specifically moving_base_tti= (moving_high_tti-up_day_tti) ×α+up_day_tti, where α is a weight coefficient greater than 0 and less than 1.

For the process of determining the end time of the commute early peak period, the traffic running index threshold is specifically a moving_base_tti= (moving_high_tti-up_day_tti) ×β+up_day_tti at the end time of the commute peak of the target region, where β is a weight coefficient greater than 0 and less than 1.

For the process of determining the start time of the commute peak period, determining the traffic running index threshold, in particular evening _high_tti= (evening _high_tti-down_day_tti) ×γ+down_day_tti, where γ is a weight coefficient greater than 0 and less than 1, at the start time of the commute peak of the target region.

For the process of determining the end time of the commute evening peak period, determining the traffic running index threshold, in particular evening _high_tti= (evening _high_tti-down_day_tti) ×δ+down_day_tti, where δ is a weight coefficient greater than 0 and less than 1, at the end time of the commute evening peak period of the target region.

Step 9: selecting a target time point set with the traffic running index larger than the traffic running index threshold value from the commute time period;

For the four processes described above for determining commute peak hours, a set of target time points, here denoted as Q1, Q2, Q3, Q4, may be determined separately for each commute period.

Step 10: and determining the commute peak time of the target area according to the continuity of the target time point set.

Considering that the discrete moments selected according to the traffic running index threshold cannot represent the commute peak time, the commute peak time is likely to be an outlier, and therefore the commute peak time of the target area needs to be determined according to the continuity of the target moment point set.

Specifically, for the four processes of determining the commute peak period, the start time of the commute early peak period, the end time of the commute early peak period, the start time of the commute late peak period, and the end time of the commute late peak period may be determined respectively.

For the starting time of the commute early peak time and the starting time of the commute late peak time, the minimum value of continuous target time in the target time point set can be used as the starting time; for the end time of the commute early peak period and the end time of the commute late peak period, the maximum value of the consecutive target time points in the set of target time points may be taken as the end time.

Based on the same inventive concept, the embodiment of the present application further provides a peak period determining device corresponding to the peak period determining method, and since the principle of solving the problem of the device in the embodiment of the present application is similar to that of the peak period determining method in the embodiment of the present application, the implementation of the device may refer to the implementation of the method, and the repetition is omitted.

Referring to fig. 3, a schematic structural diagram of a peak period determining apparatus according to an embodiment of the present application is shown, where the apparatus includes:

A first obtaining module 31, configured to obtain a first traffic running index of a plurality of first sub-periods in a commute period and a second traffic running index of a plurality of second sub-periods in a non-commute period in a target area;

A first selecting module 32, configured to select a target second sub-period in which traffic is in a steady state from a plurality of second sub-periods according to a second traffic running index of each of the second sub-periods;

a first calculating module 33, configured to calculate a traffic running index threshold value of the traffic in a steady state according to the second traffic running index of the target second sub-period;

a second selecting module 34, configured to select, from a plurality of the first sub-periods, a first sub-period with a traffic running index greater than the traffic running index threshold as a target first sub-period;

A first determining module 35 is configured to determine a commute peak period of the target area according to the continuity of the target first sub-period.

In a possible embodiment, the method further includes:

The second acquisition module is used for acquiring the vehicle passing efficiency of each time period in the target area;

The second determining module is used for determining that a continuous time period, in which the vehicle passing efficiency does not accord with a preset numerical range, is a commute time period and determining that the continuous time period, in which the vehicle passing efficiency accords with the preset numerical range, is a non-commute time period.

In one possible implementation, the first selection module 32 includes:

the second calculation module is used for calculating the fluctuation amplitude of the traffic operation index of each second sub-time period according to the second traffic operation index;

And the third selection module is used for selecting a second sub-time period with the fluctuation amplitude of the traffic running index within a preset range from a plurality of second sub-time periods as a target second sub-time period with the traffic in a stable state.

In a possible embodiment, the first determining module 35 includes:

A third determining module, configured to determine at least one continuous time interval composed of a plurality of target first sub-time periods according to the time of each target first sub-time period;

And the fourth determining module is used for determining the commute peak time of the target area according to the time value with the earliest time and the time value with the latest time in the continuous time interval.

In a possible embodiment, the first computing module 33 includes:

The fourth selection module is used for selecting a target second traffic running index from the selected second traffic running indexes of the target second sub-time period according to a preset quantile selection method;

and a fifth determining module, configured to determine a traffic running index threshold according to the target second traffic running index.

In a possible implementation manner, the target second traffic operation index is a normal value determined according to distribution conditions corresponding to second traffic operation indexes of a plurality of target second sub-time periods; the target second traffic running index has a value greater than a predetermined number of second traffic running indexes of other target second sub-periods.

In a possible embodiment, the fifth determining module includes:

a sixth determining module, configured to determine a maximum traffic running index among first traffic running indexes of a plurality of the first sub-periods;

And the third calculation module is used for calculating a traffic operation index threshold value of which the traffic is in a stable state according to the target second traffic operation index and the maximum traffic operation index.

In a possible embodiment, the method further includes:

The third acquisition module is used for acquiring signal lamp timing information of the target intersection in the target area;

A seventh determining module, configured to determine, according to the signal timing information, an estimated passage time required for the traveling vehicle to pass through the target intersection in the commute peak period;

and the adjusting module is used for adjusting the timing of the signal lamp according to the estimated passing time.

The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application, where the electronic device includes: the processor 41, the memory 42 and the bus 43, the memory 42 stores execution instructions, when the electronic device is running, the processor 41 and the memory 42 communicate through the bus 43, and the processor 41 executes the steps of the peak period method provided by the embodiment of the present application stored in the memory 42, including:

In a possible embodiment, before the executing step obtains the first traffic running index of the plurality of first sub-time periods in the commute time period and the second traffic running index of the plurality of second sub-time periods in the non-commute time period in the target area, the processor 41 is further configured to:

In a possible implementation manner, the processor 41 is configured to, when executing the step of selecting, according to the second traffic running index of each of the second sub-periods, a target second sub-period in which traffic is stationary from the plurality of second sub-periods:

In a possible implementation manner, the processor 41 is configured, when executing the step to determine the commute peak period of the target area according to the continuity of the target first sub-period, to:

Determining at least one continuous time interval consisting of a plurality of target first sub-time periods according to the time of each target first sub-time period;

and determining the commute peak time of the target area according to the time value with the earliest time and the time value with the latest time in the continuous time interval.

In a possible implementation manner, the processor 41 is configured to, when executing the step of calculating the traffic running index threshold for the traffic in the stationary state according to the second traffic running index of the second sub-period of time of the target:

In a possible implementation manner, the processor 41 is configured, when executing the step of determining the traffic operation index threshold according to the target second traffic operation index, to:

Determining a maximum traffic running index of a plurality of first traffic running indexes of the first sub-time period;

And calculating a traffic operation index threshold value of the traffic in a steady state according to the target second traffic operation index and the maximum traffic operation index.

In a possible implementation, the processor 41 is further configured to:

Acquiring signal lamp timing information of a target intersection in the target area;

Determining estimated passing time required by a running vehicle to pass through the target intersection in the commute peak time according to the signal lamp timing information;

and adjusting the timing of the signal lamp according to the estimated passing time.

The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to execute the steps of the peak period determining method.

In particular, the storage medium can be a general-purpose storage medium, such as a removable disk, a hard disk, or the like, on which a computer program is executed that is capable of performing the peak period determination method described above.

Embodiments of the present application also provide a computer program product comprising a computer program/instruction which, when executed by a processor, implements the rush hour determination method described above. The specific implementation may refer to a method embodiment, which is not described herein.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the method embodiments, and are not repeated in the present disclosure. In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, and the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, and for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in 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 this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. A peak time determination method, comprising:

Calculating a traffic running index threshold in a traffic stationary state according to the second traffic running index of the target second sub-period, wherein the determining the traffic running index threshold according to the target second traffic running index comprises the following steps:

calculating a traffic operation index threshold value of the traffic in a steady state according to the target second traffic operation index and the maximum traffic operation index;

Selecting a first sub-period with the traffic running index being larger than the traffic running index threshold value from the first sub-periods as a target first sub-period, wherein the traffic running index is an average value determined based on historical data;

2. The rush hour determination method according to claim 1 wherein before the step of obtaining a first traffic running index for a plurality of first sub-periods during a commute period and a second traffic running index for a plurality of second sub-periods during a non-commute period in the target area, the method comprises:

3. The rush hour determination method according to claim 1 wherein the selecting a target second sub-period in which traffic is stationary from a plurality of the second sub-periods according to the second traffic running index for each of the second sub-periods comprises:

4. The rush hour determination method according to claim 1 wherein the determining the commute rush hour for the target area based on the continuity of the target first sub-period comprises:

5. The rush hour determination method according to claim 1 wherein the calculating a traffic running index threshold at which traffic is stationary based on the second traffic running index for the target second sub-period comprises:

And selecting a target second traffic operation index from the selected second traffic operation indexes of the target second sub-time period according to a preset quantile selecting method.

6. The rush hour determination method of claim 5 wherein the target second traffic run index is a normal value determined from a distribution corresponding to a second traffic run index of a plurality of the target second sub-time periods; the target second traffic running index has a value greater than a predetermined number of second traffic running indexes of other target second sub-periods.

7. The rush hour determination method according to claim 1 wherein the method further comprises:

8. Peak time determining apparatus, comprising:

The first computing module includes:

a fifth determining module, configured to determine a traffic operation index threshold according to the target second traffic operation index;

The fifth determination module includes:

The third calculation module is used for calculating a traffic operation index threshold value of the traffic in a steady state according to the target second traffic operation index and the maximum traffic operation index;

9. The rush hour determination apparatus according to claim 8, further comprising:

10. The rush hour determination apparatus of claim 8 wherein the first selection module comprises:

11. The rush hour determination apparatus of claim 8 wherein the first determination module comprises:

12. The rush hour determination apparatus of claim 8 wherein the first calculation module comprises:

and the fourth selection module is used for selecting a target second traffic running index from the selected second traffic running indexes of the target second sub-time period according to a preset quantile selection method.

13. The rush hour determination apparatus according to claim 12 wherein the target second traffic running index is a normal value determined from a distribution situation corresponding to the second traffic running indexes of the plurality of target second sub-periods; the target second traffic running index has a value greater than a predetermined number of second traffic running indexes of other target second sub-periods.

14. The rush hour determination apparatus according to claim 8, further comprising:

15. An electronic 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 over the bus when the electronic device is running, the processor executing the machine-readable instructions to perform the steps of the method of any one of claims 1 to 7.

16. Computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the steps of the method according to any of claims 1 to 7.

17. Computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method according to any of claims 1 to 7.