CN112819325A

CN112819325A - Peak hour determination method, peak hour determination device, electronic equipment and storage medium

Info

Publication number: CN112819325A
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: 2021-05-18
Anticipated expiration: 2041-01-29
Also published as: CN112819325B

Abstract

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

Description

Peak hour determination method, peak hour determination device, electronic equipment and storage medium

Technical Field

The present application relates to the field of information technology, and in particular, to a peak hour determination method, apparatus, electronic device, and storage medium.

Background

With the increase of the travel demand of people, the transportation becomes an important part of the daily life of people. When people go out intensively, the road is easy to have larger traffic volume. The time of day when large traffic volume occurs is the peak time of a trip.

The early and late peak periods of the trip are important parameters of traffic management, traffic planning and traffic evaluation, and can reflect the condition of people on the trip. Meanwhile, the early and late peak periods can also be used as important basis for traffic management decision and urban traffic state evaluation.

At present, the method for determining the peak time periods in the morning and the evening is mainly to count the traffic flow through field investigation, and then determine the peak time periods in the morning and the evening according to the traffic volume and the corresponding time.

Disclosure of Invention

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

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

acquiring first traffic operation indexes of a plurality of first sub-time periods in a commuting period and second traffic operation indexes of a plurality of second sub-time periods in a non-commuting period in a target area;

selecting a target second sub-time period in a steady traffic 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 operation index threshold value in a steady traffic state according to a second traffic operation index of the target second sub-time period;

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

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

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

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

and determining that the continuous time period when the vehicle passing efficiency does not accord with the preset value range is the commuting time period, and determining that the continuous time period when the vehicle passing efficiency accords with the preset value range is the non-commuting time period.

With reference to the first aspect, this embodiment provides a second possible implementation manner of the first aspect, where the selecting a target second sub-period during which traffic is in a steady state from a plurality of second sub-periods according to the second traffic operation index of each second sub-period includes:

for each second sub-time period, calculating the fluctuation range of the traffic operation index of the 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 operation index within a preset range from the plurality of second sub-time periods as a target second sub-time period with the traffic in a steady state.

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

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 selection 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 operation index is a normal value determined according to a distribution situation corresponding to second traffic operation indexes of a plurality of target second sub-time periods; the value of the target second traffic operation index is greater than the second traffic operation index for a predetermined number of other target second sub-time periods.

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

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first traffic operation indexes of a plurality of first sub-time periods in a commuting period and second traffic operation indexes of a plurality of second sub-time periods in a non-commuting period in a target area;

the first selection module is used for selecting a target second sub-time period with steady-state traffic from the 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 operation index threshold value of the traffic in a stable state according to the second traffic operation index of the target second sub-time period;

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

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

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 communicating 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 described above, or any possible implementation of the first aspect.

In a fourth aspect, this application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.

In a fifth aspect, this application further provides a computer program product, which includes a computer program/instruction, and when executed by a processor, the computer program/instruction implements the steps in the first aspect described above or any one of the possible implementation manners of the first aspect.

The peak hour determining method provided by the embodiment of the application comprises the following steps: acquiring first traffic operation indexes of a plurality of first sub-time periods in a commuting period and second traffic operation indexes of a plurality of second sub-time periods in a non-commuting period in a target area; selecting a target second sub-time period in a steady traffic state from a plurality of second sub-time periods according to the second traffic operation index of each second sub-time period; calculating a traffic operation index threshold value in a steady traffic state according to a second traffic operation index of the target second sub-time period; selecting a first sub-time period with the traffic operation index larger than the traffic operation index threshold value from the plurality of first sub-time periods as a target first sub-time period; and determining the commuting peak period according to the continuity of the target first sub-time period. Compared with the prior art, the traffic operation index threshold value under the stable traffic state is accurately calculated through a field statistical method, the commuting peak period is determined according to the selected first sub-period greater than the traffic operation index threshold value, and accuracy of the commuting peak period is improved.

According to the peak hour determining method provided by the embodiment of the application, the target second sub-time period in the stable state is selected according to the fluctuation range of the traffic operation index, the calculation accuracy is improved, and the accuracy of the determined commuting peak hour 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, and the accuracy of the commuting 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 required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart illustrating a peak hour determination method according to an embodiment of the present application;

fig. 2 is a flowchart illustrating another peak hour determination method provided by an embodiment of the present application;

fig. 3 is a schematic structural diagram illustrating a peak hour determination device according to an embodiment of the present application;

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

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

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

The peak time refers to a time period with a large traffic volume, and when people go out intensively, a large number of vehicles, pedestrians and the like are concentrated on a traffic route, so that the large traffic volume and even the congestion usually occur in the peak time.

Rush hour periods typically occur at commute times, near holidays, near the end of holidays, and other special periods, etc. In general, the commute hours have a high early peak and a high late peak due to the high traffic volume of people going to and from the residence and the work place during the day, and the peak hours of the commute hours mainly occur in the morning and afternoon.

The peak time period occurs when the holiday or the holiday is about to end, which is mainly caused by people intensively leaving or returning to the frequent place, but the situation is related to the arrangement of the holiday and is not a normalized peak time period.

The peak time periods occurring in other special periods may be caused by concentrated trips of people due to emergencies or special events, such as concentrated relocation, and the like, which may be related to local area policies and belong to sudden peak time periods.

The peak hours of the commute time may be considered a frequent peak hour, and the peak hours occurring on or near the end of a holiday and other peak hours occurring in special times may be considered non-frequent peak hours.

Considering that frequent peak hours are more closely related to travel of people, the method is an important parameter for traffic management, traffic planning and traffic evaluation, can reflect the traffic travel situation of people, and can also be used as an important basis for traffic management decision and urban traffic state evaluation, so that the peak hour determination method provided by the embodiment of the application is mainly applicable to the frequent peak hour determination process, namely, is mainly applicable to application scenes of commuting hours, and in order to enable technicians in the field to use the content of the application, the following implementation modes are provided by combining a specific application scene of 'commuting hours'. It will be apparent to those skilled 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 the flowchart of a peak hour determination method shown in fig. 1, the following steps are included:

s101: acquiring first traffic operation indexes of a plurality of first sub-time periods in a commuting period and second traffic operation indexes of a plurality of second sub-time periods in a non-commuting period in a target area;

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

s103: calculating a traffic operation index threshold value of the traffic in a steady state according to a second traffic operation index of the target second sub-time period;

s104: selecting a first sub-time period with the traffic operation index larger than the traffic operation index threshold value from the plurality of first sub-time periods as a target first sub-time period;

s105: and determining the commuting peak period of the target area according to the continuity of the target first sub-time period.

In S101, the commute period refers to a time period during which a worker travels to and from a place of residence and a work unit or school for the reason of work, study, or the like. The commute periods may be different in different areas, taking into account differences in time zones in which different areas are located, and in working (or learning) times in administrative areas. Specifically, the commuting period of the target area may be obtained according to the historical traffic travel data of people, or the working time and the working time of people in the target area may be counted according to the big data, and then a period before and after the working time and the working time may be used as the commuting period.

In a specific implementation, before S101, the commute period and the non-commute period may be acquired according to the following steps:

s201: obtaining the vehicle passing efficiency of each moment in a target area;

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

In S201, the vehicle passing efficiency may refer to how fast the vehicle passes through the target road segment.

In particular implementations, vehicle transit efficiency may be determined based on vehicle transit 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 acquired from GPS (Global Positioning System) data of the vehicle, and then the average speed of each vehicle may be calculated from the speed of each vehicle.

When the vehicle passing speed is slower, the vehicle passing efficiency is represented to be lower.

The vehicle transit time refers to an average transit time period for the vehicle to pass through the target link. Here, the speed of each vehicle may be acquired 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 section. Or the passing time of each vehicle is determined directly according to the starting time and the ending time of the vehicles passing through the target road section, and then the average passing time of each vehicle is calculated according to the passing time of each vehicle.

When the vehicle passing time is longer, the vehicle passing efficiency is represented to be lower.

In S202, the preset data range may be set according to a vehicle passage efficiency range in which the historical traffic is in a steady state. Which will be described later.

When the vehicle passing efficiency conforms to the preset data range, the current time is in a traffic steady state, namely a non-commuting state, so that a continuous time period in which the vehicle passing efficiency conforms to the preset data range can be used as a non-commuting time period; and otherwise, taking the continuous time period when the vehicle passing efficiency does not accord with the preset data range as the commuting time period.

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

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

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

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

For example, for the early commute period, a first sub-period is divided every 20 minutes, and the duration of each first sub-period is 20 minutes.

The non-commuting period refers to other periods except the commuting period, and the non-commuting period acquired here is also an inaccurate non-commuting period. For the above-described early commute period and late commute period, the non-commute period may include a noon non-commute period and a night non-commute period.

Wherein the non-commuting period in the noon refers to a time period from the end of the early commuting period to the beginning of the late commuting period in a day; the evening non-commuting period refers to the period of time from the end of the night commuting period of the previous day to the beginning of the morning commuting period of the next day.

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

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

For example, for the non-commuting period in the middle of the day, a second sub-period is divided every 20 minutes, and the duration of each second sub-period is 20 minutes.

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

The traffic operation Index (Travel Time Index, abbreviated as TTI) is used for comprehensively evaluating a congestion Index of a road or a spatial area. The TTI refers to a ratio of an actual travel time to a travel time under a free flow condition. The higher the TTI index, the higher the traffic volume and the more congested the road.

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

To improve the accuracy of the calculation, the traffic operation index may be averaged. Specifically, the traffic operation index of each time may be collected every day in a historical continuous number of days, and then averaged, for example, the traffic operation indexes of 7 am of three consecutive days are collected to be 1.21, 1.31 and 1.41, and the average value of the traffic operation indexes is calculated to be 1.31. By calculating the average value of the traffic running indexes, the situation that the error is large because only data of a certain day is used can be avoided.

Therefore, the first traffic operation index here may be a mean value of the traffic operation indexes for each time in the first sub-period within a preset number of consecutive days; the second traffic operation index may be a mean value of the traffic operation indexes for a preset number of consecutive days for each time within the second sub-period.

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

Considering that in practical situations, in the non-commuting period of night, the number of vehicles and pedestrians going out is relatively small, and the trip peak is not caused basically, the influence of the traffic flow on the commuting peak can be ignored. In the embodiment of the application, the influence of traffic flow in the daytime on commuting peaks is mainly considered, so the traffic steady state is the traffic steady state in the daytime. The traffic vehicle flow is relatively large in the early commuting period and the late commuting period, so that the traffic is usually in a travel peak state or a congestion state, and the target second sub-time period mainly occurs in the noon non-commuting period.

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

s1021: calculating the fluctuation range 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 the plurality of second sub-time periods as a target second sub-time period with the traffic in a steady state.

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

The second traffic operation index average value may reflect a traffic operation condition in a traffic stationary state. Specifically, the second traffic operation index average value may be calculated according to the second traffic operation index of each second sub-period and the number of the second sub-periods.

And aiming at each second sub-time period, calculating the fluctuation range of the traffic operation index of the second sub-time period according to the difference value of the second traffic operation index at each moment and the average value of the second traffic operation index.

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

Taking the eighty-th area in the international time zone as an example, the target second sub-time period when the traffic is 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 operation index in the time period is within the preset fluctuation range.

In S103, the traffic operation index threshold refers to a maximum value of the traffic operation index in a traffic stationary state. When the traffic operation index exceeds the traffic operation index threshold, the traffic is in a non-steady state.

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

s1031: 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 selection 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 pieces 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-periods, that is, there are a plurality of second traffic operation indexes, the plurality of second traffic operation indexes may be sorted in order from small to large. It is considered here that the maximum value may be an abnormal value, and therefore the maximum value may be excluded, and then a target second traffic operation index that is not an abnormal maximum value is selected from the remaining second traffic operation indexes.

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

In S1032, a traffic operation index threshold value in a traffic stationary state may be calculated according to the target second traffic operation index.

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

Specifically, the second traffic operation indexes of the second sub-time periods of the multiple targets may be arranged according to the size of the second traffic operation indexes, so as to obtain distribution sequencing from large to small or from small to large. And then selecting normal values except the abnormal values from the sorted second traffic operation indexes. Here, the abnormal value refers to a second traffic operation index that is significantly larger than other data. And the value of the target second traffic operation index is greater than the second traffic operation indexes of the predetermined number of other target second sub-periods, that is, the target second traffic operation index is greater than the second traffic operation indexes of the predetermined number of other target second sub-periods.

However, considering that the threshold value of the traffic operation index in the steady state is also related to the maximum value of the first traffic operation 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 operation index of the first traffic operation 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, by comparing, the largest of the first traffic operation indexes of the plurality of first sub-periods may be selected.

In S1034, when the second sub-time period corresponding to the target second traffic operation index is the time before 12:00:00 pm, the target second traffic operation index may be recorded as up _ day _ TTI, and the maximum traffic operation index may be recorded as mouning _ high _ TTI; and recording the traffic operation index threshold as morning _ base _ TTI.

At this time, the traffic running index threshold, i.e., the 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-time period corresponding to the target second traffic operation index is the time after 12:00:00 pm, the target second traffic operation index may be recorded as down _ day _ TTI, and the maximum traffic operation index may be recorded as event _ high _ TTI.

At this time, the traffic operation index threshold, namely, the event _ high _ TTI, (event _ 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 rush hour should be the one occurring in the first sub-period, that is, the case where the traffic operation index is greater than the traffic operation index threshold value may occur in the first sub-period, and therefore, the first sub-period where the traffic operation index is greater than the traffic operation index threshold value is selected from the plurality of first sub-periods as the target first sub-period.

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

In S105, considering that the discontinuous time selected according to the traffic operation index threshold value does not represent the commuting peak time period, and is likely to be an abnormal value, the commuting peak time period of the target area needs to be determined according to the continuity of the target first sub-time period.

Specifically, the method may include the steps of:

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

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

In S1052, the start time of the commuting peak period may be determined according to the earliest time value in the continuous time interval, and the end time of the commuting peak period may be determined according to the latest time value in the continuous time interval, so that the commuting peak period may be determined.

In practical cases, as mentioned before, the target first sub-period may occur during the morning commute period as well as during the evening commute period, so here the commute peak period of the target area may be determined for different periods of time during which the target first sub-period may occur as well as for the continuity of the target first sub-period.

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

The thresholds for determining the start and end times of the commuting early peak hours and for determining the start and end times of the commuting late peak hours are different considering that the commuting peak hours can be divided into commuting early peak hours and commuting late peak hours. Therefore, the process of determining the commute rush-hour may be divided into four processes, i.e., a start time of the commute early rush-hour, an end time of the commute early rush-hour, a start time of the commute late rush-hour, and an end time of the commute late rush-hour, and the process of determining the aforementioned calculating the traffic operation index threshold may be adjusted according to the aforementioned four processes.

For the process of determining the start time of the commuting early peak period, the target first sub-time period occurs in the early commuting period, and when the start time of the commuting peak in the target area is determined, the traffic operation index threshold value, i.e. the generating _ base _ TTI (generating _ high _ TTI-up _ day _ TTI) a + up _ day _ TTI, specifically the generating _ base _ TTI (generating _ 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 successive time points in the target first sub-period is selected as the commuting early peak start time of the target area.

For the process of determining the ending time of the commuting early peak period, the target first sub-time period occurs in the early commuting period, and when the ending time of the commuting peak in the target area is determined, the traffic operation index threshold value, i.e. the moving _ base _ TTI (moving _ high _ TTI-up _ day _ TTI) a + up _ day _ TTI, specifically the 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 successive time points in the target first sub-period is selected as the commuting early peak end time of the target area.

The above-described process of determining the start time of the commuting early peak period and the process of determining the end time of the commuting early peak period α and β may be different coefficients. The commuting early peak time period can be determined according to the commuting early peak starting time in the process of determining the starting time of the commuting early peak time period and the commuting early peak ending time in the process of determining the ending time of the commuting early peak time period.

For the process of determining the start time of the commuting late peak period, the target first sub-period occurs in the late commuting period, and when the start time of the commuting peak in the target area is determined, the traffic operation index threshold value is defined, namely, the occurrence _ high _ TTI (occurrence _ high _ TTI-down _ day _ TTI) B + down _ day _ TTI, specifically, the occurrence _ high _ TTI (occurrence _ 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 successive time points in the target first sub-period is selected as the commuting late peak start time of the target area.

For the process of determining the ending time of the commuting late peak period, the target first sub-period occurs in the late commuting period, and the traffic operation index threshold value, namely, the event _ high _ TTI, (event _ high _ TTI-down _ day _ TTI) B + down _ day _ TTI, specifically, the event _ high _ TTI, (event _ high _ TTI-down _ day _ TTI) δ + down _ day _ TTI, is determined at the starting time of the commuting peak of the target area, where δ is a weight coefficient greater than 0 and less than 1.

Then, the maximum time of the successive time points in the target first sub-period is selected as the commuting late peak end time of the target area.

γ and δ in the above-described process of determining the start time of the commuting evening peak hour and the process of determining the end time of the commuting evening peak hour may be different coefficients. The commuting late peak hours can be determined according to the commuting late peak starting time in the process of determining the starting time of the commuting late peak hours and the commuting late peak ending time in the process of determining the ending time of the commuting late peak hours.

According to the peak hour determining method provided by the embodiment of the present application, a commuting peak hour can be accurately determined, and the peak hour determining method provided by the embodiment of the present application can also be applied to a signal lamp timing scene, as shown in a flowchart of another peak hour determining method shown in fig. 2, and specifically may include 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 at a commuting peak period according to signal lamp timing information;

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

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

In S302, an estimated transit time of each vehicle passing through the target intersection during the commuting peak period may be determined according to the period information of the signal lamps and the number of traveling vehicles. The estimated transit time here may be an average estimated transit time 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 appropriately prolonged, and the display time of the green light in other directions can be appropriately shortened.

In the specific implementation process, the display time of the red light and the green light can be adjusted, and the signal lamp timing period can be shortened. It should be noted that the embodiments of adjusting the timing of the traffic lights according to the estimated transit time are all within the scope of the claimed application.

The embodiment of the application further provides a peak hour determining method, which specifically comprises the following steps:

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

In particular implementations, a traffic operating index may be obtained for each time instance. In order to reduce the amount of calculation, the time of the same day may be selected according to a preset selection rule, and therefore the target time is different times obtained by selecting the time of the same day according to the preset selection rule.

In a specific embodiment, one target time may be selected at intervals of the same time period, for example, one target time may be selected at intervals of 10 minutes, a time interval between two adjacent target times is 10 minutes, 144 target times may be obtained in one day, that is, traffic operation indexes may be collected at intervals of 10 minutes, and there are 144 pieces of traffic operation index data in total in one day.

TABLE 1

Time	TTI	Time	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 operation index data for 10 minute sampling frequency from 11 months, 1 day to 2 days, zero to 3 points in 2019 are shown.

Step 2: calculating a traffic operation index mean value;

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

The traffic operation data may be data of each target time within historical continuous days, for example, data of each target time within historical continuous 30 days.

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

And step 3: obtaining the vehicle passing efficiency of each target moment in the target area;

vehicle traffic efficiency may refer to how fast a vehicle is passing through a target road segment.

And 4, step 4: determining a continuous time period when the vehicle passing efficiency does not accord with the preset numerical range as a commuting time period, and determining a continuous time period when the vehicle passing efficiency accords with the preset numerical range as a non-commuting time period;

the commuting period refers to a period of time during which a worker goes to and from a place of residence and a work unit or school for the reason of work or study. The commute periods may be different in different areas, taking into account differences in time zones in which different areas are located, and in working (or learning) times in administrative areas. Specifically, the commuting period of the target area may be obtained according to the historical traffic travel data of people, or the working time and the working time of people in the target area may be counted according to the big data, and then a period before and after the working time and the working time may be used as the commuting period.

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

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

And 5: selecting a target time period in a steady traffic state from the non-commuting time period according to the traffic operation index of each target time;

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

Considering that in practical situations, in the non-commuting period of night, the number of vehicles and pedestrians going out is relatively small, and the trip peak is not caused basically, the influence of the traffic flow on the commuting peak can be ignored. In the embodiment of the application, the influence of traffic flow in the daytime on commuting peaks is mainly considered, so the traffic steady state is the traffic steady state in the daytime. The traffic vehicle flow is large in the early commuting period and the late commuting period, so that the traffic is usually in a travel peak state or a congestion state, and the target time period mainly occurs in the non-commuting period in the middle of the day.

In a specific implementation, the traffic operation index fluctuation amplitude of each target time in the non-commuting period can be calculated, and the time interval of the target time with the traffic operation index fluctuation amplitude within the preset fluctuation amplitude range is selected from the plurality of target times and is the target time period in which the traffic is in a steady state.

The fluctuation range of the traffic operation index refers to the difference value of the traffic operation index and the average value of the traffic operation index in a non-commuting period. The preset amplitude range refers to the fluctuation amplitude range of the traffic operation index when the traffic is in a steady state. When the fluctuation range of the traffic operation index is within the preset fluctuation range, the traffic is in a stable state.

Taking the eighteenth east area in the international time zone as an example, the target time periods when the traffic is in a steady state are generally 11:00:00-13:00:00 and 14:00:00-16:00:00, and the fluctuation range of the traffic operation index in the time period is within the preset fluctuation range.

Step 6: aiming at each target time period, screening out target traffic operation indexes of which the numerical values are greater than a preset number of other target traffic operation indexes in the target time period according to a preset quantile selection method;

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

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

In the specific selection process, considering that the larger target traffic operation index of the normal values is to be selected, a quantile of more than 50%, for example 70%, may be set. The target traffic operation index selected in the quantile manner is greater than the second traffic operation indexes for a predetermined number of other target time periods, and thus in this manner, the selected greater traffic operation index may be used as the target traffic operation 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 operation index data of 11:00:00-13:00:00 is displayed, and according to a predetermined quantile selection method, a traffic operation index of 1.313449 with 70% quantiles can be screened out.

And 7: determining the maximum value of the traffic operation index according to the traffic operation index of each target moment;

since the higher the traffic operation index is, the larger the traffic volume is and the more congested the road is, the maximum probability of the traffic operation index may occur in the commuting period. In a specific implementation process, the maximum value of the traffic operation index can be directly determined from the traffic operation index corresponding to each target time included in the commute period.

And 8: calculating a traffic operation index threshold value of each target time period in a traffic steady state 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 hours of the determined target area may be divided into commute early peak hours determined according to the commute early peak start time and the commute early peak end time and commute late peak hours determined according to the commute late peak start time and the commute late peak end time. The process of determining the commute rush hour may therefore be divided into four processes of determining the start time of the commute early rush hour, determining the end time of the commute early rush hour, determining the start time of the commute late rush hour, and determining the end time of the commute late rush hour.

When the above processes are determined, the corresponding traffic operation index thresholds are different, and therefore, the corresponding traffic operation index thresholds need to be determined for the above four processes respectively.

In specific implementation, when the target time period corresponding to the target traffic operation index is the time before 12:00:00 pm, the target traffic operation index may be recorded as up _ day _ TTI, and the maximum traffic operation index may be recorded as morning _ high _ TTI; and recording the traffic operation index threshold as morning _ base _ TTI.

When the target time period corresponding to the target traffic operation index is the time after 12:00:00 pm, the target traffic operation index may be recorded as down _ day _ TTI, and the maximum traffic operation index may be recorded as event _ high _ TTI.

For the process of determining the start time of the commute early peak period, the target time period corresponding to the target traffic operation index is the time before 12:00:00 at noon, and the traffic operation index threshold is specifically, moving _ base _ TTI ═ α + up _ day _ TTI (moving _ high _ TTI-up _ day _ TTI), where α is a weight coefficient greater than 0 and less than 1.

For the process of determining the ending time of the commuting early peak period, when the ending time of the commuting peak in the target area is determined, the traffic operation index threshold is specifically given as 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.

For the process of determining the start time of the commuting late peak period, when the start time of the commuting peak in the target area is determined, the traffic operation index threshold is (evening _ high _ TTI-down _ day _ TTI) × + down _ day _ TTI, where γ is a weight coefficient greater than 0 and less than 1.

For the process of determining the ending time of the commuting late peak period, when the ending time of the commuting peak in the target area is determined, the traffic operation index threshold value is specifically an 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.

And step 9: selecting a target time point set of which the traffic operation index is greater than a traffic operation index threshold value from the commuting period;

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

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

Considering that discontinuous time moments selected according to the traffic operation index threshold value cannot represent commuting peak time periods and are possibly abnormal values, the commuting peak time periods of the target area need to be determined according to the continuity of the target time point set.

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

Aiming at the starting time of the commuting early peak period and the starting time of the commuting late peak period, the minimum value of continuous target times in the target time point set can be used as the starting time; for the ending time of the commuting early peak period and the ending time of the commuting late peak period, the maximum value of the continuous target times in the target time point set can be used as the ending time.

Based on the same inventive concept, the embodiment of the present application further provides a peak period determination apparatus corresponding to the peak period determination method, and as the principle of solving the problem of the apparatus in the embodiment of the present application is similar to that of the peak period determination method in the embodiment of the present application, the implementation of the apparatus may refer to the implementation of the method, and repeated details are not described again.

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

the first acquiring module 31 is configured to acquire a first traffic operation index of a plurality of first sub-time periods in a commuting period and a second traffic operation index of a plurality of second sub-time periods in a non-commuting period in a target area;

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

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

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

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

In a possible embodiment, the method further comprises:

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

and the second determination module is used for determining that the continuous time period when the vehicle passing efficiency does not accord with the preset numerical range is the commuting time period, and determining that the continuous time period when the vehicle passing efficiency accords with the preset numerical range is the non-commuting time period.

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

the second calculation module is used for calculating the fluctuation range 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 operation index within a preset range from the plurality of second sub-time periods as a target second sub-time period with the traffic in a steady state.

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

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

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

In a possible implementation, the first calculation module 33 includes:

the fourth selection module is used for 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 selection method;

and the fifth determining module is used for determining a traffic operation index threshold according to the target second traffic operation index.

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

In a possible implementation, the fifth determining module includes:

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

and 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.

In a possible embodiment, the method further comprises:

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

the seventh determining module is used for determining the estimated passing time required by a running vehicle to pass through the target intersection in the commuting peak period according to the signal lamp timing information;

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

The description of the processing flow of each module in the device and the interaction flow between the modules may refer to the related description in the above method embodiments, and will not be described in detail here.

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

In one possible embodiment, the processor 41 is further configured to, before the step of obtaining the first traffic operation index of the plurality of first sub-periods in the commute period and the second traffic operation index of the plurality of second sub-periods in the non-commute period in the target area:

In one possible embodiment, the processor 41, when executing the step of selecting a target second sub-period from a plurality of second sub-periods in which the traffic is in a steady state according to the second traffic operation index of each second sub-period, is configured to:

In one possible embodiment, the processor 41, when executing the step of determining the commute peak hour of the target area based on the continuity of the target first sub-period of time, is configured to:

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;

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

In one possible embodiment, the processor 41, when executing the step of calculating the traffic operation index threshold value of the stationary state of the traffic according to the second traffic operation index of the target second sub-period, is configured to:

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

In one possible embodiment, the processor 41, when executing the step of determining the traffic operation index threshold value based on the target second traffic operation index, is configured to:

determining a maximum traffic operation index of a plurality of first traffic operation indexes of the first sub-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 one possible implementation, 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 at the commuting peak time period according to the signal lamp timing information;

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

An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, performs the steps of the peak hour determination method described above.

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

Embodiments of the present application also provide computer program products comprising computer programs/instructions that when executed by a processor implement the peak hour determination methods described above. For specific implementation, reference may be made to the method embodiment, which is not described herein again.

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

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

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

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

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

The embodiment of the application discloses a TS1 and a peak hour determining method, which comprises the following steps:

TS2, the rush hour determination method according to TS1, before the step of obtaining a first traffic operation index for a plurality of first sub-time periods during a commute period and a second traffic operation index for a plurality of second sub-time periods during a non-commute period in the target area, the method comprising:

TS3, the peak hour determination method according to TS1, the selecting a target second sub-period from a plurality of the second sub-periods of time where traffic is in a steady state according to the second traffic operation index of each of the second sub-periods of time, comprising:

TS4, the rush hour determination method according to TS1, the determining the commute rush hour of the target area according to the continuity of the target first sub-period of time, comprising:

TS5, the method for peak hour determination according to TS1, the calculating a traffic operation index threshold value for a stationary state of traffic according to a second traffic operation index of the target second sub-time period, comprising:

TS6, according to the peak hour determining method of TS5, the target second traffic operation index is a normal numerical value determined according to the distribution situation corresponding to the second traffic operation indexes of a plurality of target second sub-time periods; the value of the target second traffic operation index is greater than the second traffic operation index for a predetermined number of other target second sub-time periods.

TS7, the rush hour determination method of TS5, the determining a traffic operation index threshold from the target second traffic operation index, comprising:

TS8, peak hours determination method according to TS1, the method further comprising:

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

TS9, peak hour determination device, comprising:

TS10, peak hour determining apparatus according to TS9, further comprising:

TS11, peak hours determination apparatus according to TS9, the first selection module comprising:

TS12, peak hours determination apparatus according to TS9, the first determination module comprising:

TS13, peak hours determination apparatus according to TS9, the first calculation module comprising:

TS14, the peak period determination device according to TS13, the target second traffic operation index is a normal value determined according to the distribution situation corresponding to the second traffic operation indexes of a plurality of target second sub-time periods; the value of the target second traffic operation index is greater than the second traffic operation index for a predetermined number of other target second sub-time periods.

TS15, peak hour determination device according to TS13, the fifth determination module comprising:

TS16, peak hour determining apparatus according to TS9, further comprising:

Claims

1. A peak hour determination method, comprising:

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

3. The peak hour determination method according to claim 1, wherein said selecting a target second sub-period from a plurality of said second sub-periods with a steady state of traffic based on a second traffic operation index for each of said second sub-periods comprises:

4. The peak hour determination method according to claim 1, wherein said calculating a traffic operation index threshold value for which traffic is in a steady state based on the second traffic operation index for the target second sub-time period comprises:

5. The peak hour determination method according to claim 4, wherein the target second traffic operation index is a normal value determined according to a distribution corresponding to the second traffic operation indexes of the plurality of target second sub-time periods; the value of the target second traffic operation index is greater than the second traffic operation index for a predetermined number of other target second sub-time periods.

6. The peak hour determination method of claim 4, wherein determining a traffic operation index threshold based on the target second traffic operation index comprises:

7. Peak hour determination apparatus, comprising:

8. 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 via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 6.

9. Computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.

10. Computer program product, characterized in that it comprises computer programs/instructions which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.