CN113870564A - Traffic jam classification method and system for closed road section, electronic device and storage medium - Google Patents

Traffic jam classification method and system for closed road section, electronic device and storage medium Download PDF

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CN113870564A
CN113870564A CN202111247033.2A CN202111247033A CN113870564A CN 113870564 A CN113870564 A CN 113870564A CN 202111247033 A CN202111247033 A CN 202111247033A CN 113870564 A CN113870564 A CN 113870564A
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吴磊
朱文佳
骆乐乐
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Anhui Bai Cheng Hui Tong Technology Co ltd
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Abstract

The invention discloses a traffic jam classification method, a traffic jam classification system, electronic equipment and a storage medium for a closed road section, and belongs to the technical field of intelligent traffic management. The method comprises the steps of selecting a first detection area and a second detection area of a closed road section, determining a first traffic density of the first detection area in a first preset time period according to vehicle passing data, and determining a second traffic density of the second detection area in a second preset time period according to the vehicle passing data; and comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, and judging the traffic jam scene of the road section to be detected to determine whether the traffic jam scene is local road section jam or whole road section jam, so that a traffic management department can conveniently control the traffic state of each road in real time, the urban traffic management is optimized and managed, the running efficiency of the city is improved, and support is provided for the later traffic jam grading elastic alarm.

Description

Traffic jam classification method and system for closed road section, electronic device and storage medium
Technical Field
The invention belongs to the technical field of intelligent traffic management, and particularly relates to a method and a system for classifying traffic jam of a closed road section, electronic equipment and a storage medium.
Background
In recent years, in countries and places, specifications and standards related to urban Traffic operation condition evaluation are successively established, wherein a Traffic Performance Index (TPI), also called a Traffic congestion Index, is a conceptual Index value comprehensively reflecting the smoothness or congestion of an urban road network, and a digital description of road Traffic congestion conditions is realized. Has great significance for the treatment of traffic jam. When the sign of the traffic jam is displayed, the alarm signal can be effectively identified and sent, the control can be performed through a pre-set plan, measures such as on-site traffic order supervision and leading of upstream vehicles to select other paths are enhanced, the traffic jam is relieved and even eliminated, and the traffic jam is prevented from being further aggravated. Therefore, the key point of traffic jam management lies in the timely and effective jam alarm.
At present, the research on a method for analyzing the traffic jam graded elastic alarm of a closed road section based on data collected by an electronic police is less, generally, the method is based on an erected radar or a mode of collecting GPS data, two dimensional indexes of traffic flow and speed are obtained and compared with a threshold value set under the jam condition, and finally the graded alarm is carried out on the jam. Such as the solution of chinese patent publication No. CN 111081019A. The scheme can realize the same type graded alarm of the road section, but the arrangement of front-end equipment needs to be increased, or the data of the floating cars covered by all vehicles on the road section is accessed, so that the construction cost is undoubtedly increased, the data acquisition of the floating cars covered by all vehicles on the road section is difficult to realize, the data acquisition is the limiting factor which causes the data acquisition to be incapable of being widely applied, and meanwhile, the traffic characteristic of dynamic traffic flow is not considered in the prior art, and the problem of frequent alarm jam can not be solved.
In addition, the above scheme does not take into account the influence of the type of traffic jam, such as the jam at the exit of the road and the jam with a traffic accident occurring in the middle of the road, the density of traffic flow in the road, and the queuing form of the jam of the vehicles are different from each other. Therefore, how to determine the congestion type of the traffic road and perform the traffic congestion grading elastic alarm is also an urgent problem to be solved in the field.
Disclosure of Invention
Aiming at the problem of how to determine the congestion type of the closed road section, the invention provides the traffic congestion classification method for the closed road section, which can determine the traffic congestion scene by judging and detecting the traffic flow density in real time, is convenient for traffic management departments to call the module conveniently to judge the congestion, controls the traffic state of each road in real time, carries out scientific decision and management optimization on urban traffic management, and improves the operation efficiency of cities.
This and other objects are at least partly achieved by a method for closed road traffic congestion classification and a method for closed road traffic congestion hierarchical elasticity warning as defined in the appended independent claims.
Specifically, according to a first aspect of the present disclosure, there is provided a method for classifying traffic congestion of a closed road segment, including:
the method comprises the steps that a first detection area and a second detection area of a closed road section are selected, the first traffic density of the first detection area in a first preset time period is determined according to vehicle passing data, and the second traffic density of the second detection area in a second preset time period is determined according to the vehicle passing data; and comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, and judging the traffic jam scene of the road section to be detected to determine whether the traffic jam scene is local road section jam or whole road section jam, so that a traffic management department can conveniently control the traffic state of each road in real time, the city traffic management is scientifically decided and managed and optimized, the city operation efficiency is improved, and support is provided for the following traffic jam grading elastic alarm problem.
The second aspect of the disclosure provides a method for graded elastic early warning of traffic jam of a closed road section, which determines the traffic jam type of a road section to be detected according to the method for classifying the traffic jam of the closed road section; determining the dynamically changed queuing length of the congestion of the road section to be detected according to the traffic congestion type of the road section to be detected; and determining an alarm response grade by taking the dynamically changed queuing length of the congestion of the road section to be detected as a first judgment condition, and outputting the alarm response grade. The multi-level alarm mechanism meeting the actual requirement can be distinguished and set according to the alarm function value, so that a traffic manager can conveniently implement a control plan in a targeted manner and congestion can be eliminated in time. Meanwhile, in order to avoid the occurrence of short-term fluctuation of dynamic data, a reliable elastic mechanism is set according to the first judgment condition and the second judgment condition, so that a model can generate reliable alarm after identifying steady-state traffic jam, help a traffic manager to adopt a corresponding traffic dispersion strategy in time, improve road traffic efficiency and guarantee traffic safety.
Further advantages are achieved by implementing one or more of the features of the dependent claims.
In an exemplary embodiment, the step of acquiring the vehicle passing data in the road section to be tested includes:
receiving the passing data of the bayonet in the first detection area and the vicinity of the second detection area, carrying out cluster analysis on the passing data in the road section to be detected, and eliminating edge fluctuation data;
and respectively determining cluster samples of the vehicle speeds of the positions of the first detection area and the second detection area.
In one exemplary embodiment, the flow density of the first detection area a and the second detection area B is calculated based on cluster samples of the average vehicle speeds of the first detection area and the second detection area, wherein the calculation formula is as follows:
Figure BDA0003321193140000021
in the formula:
Figure BDA0003321193140000022
indicates at a predetermined time period mh(ii) traffic density in the xth detection zone;
c represents the number of lanes of the closed road section;
Ncrepresents mhThe total number of the vehicles passing through the road section detected in the time period;
vjrepresents mhSequentially detecting the vehicle speed of a vehicle place in a certain lane cluster sample in a detection area within a time period;
t1represents mhFirst vehicle detection time and preset time period m in certain lane cluster sample of detection area in time periodhThe interval time of the starting time;
tjrepresents mhAnd in the time interval, the interval time between the detection time of the adjacent vehicles in a certain lane cluster sample of the detection area or the interval time between the end time of the preset time interval and the detection time of the adjacent vehicles.
In an exemplary embodiment, the step of determining the second predetermined period of time includes:
acquiring a first preset time period m of a vehicleiFirst passing data passing through the first detection area A;
taking the time period when the vehicle passes through the second detection area B and reaches the preset value in the first vehicle passing data as a second preset time period mj。
In an exemplary embodiment, the step of determining the traffic jam scene of the road segment to be detected includes:
when it is satisfied with
Figure BDA0003321193140000031
And is
Figure BDA0003321193140000032
The traffic jam scene of the road section to be detected is the whole road section jam; wherein, KmRepresenting the traffic density of the closed road section under the saturated traffic condition of low service level;
Figure BDA0003321193140000033
is indicated at the first predetermined time period miThe traffic density of the inner first detection area;
Figure BDA0003321193140000034
indicates the second predetermined period mjThe traffic density of the second detection zone within.
In an exemplary embodiment, the step of determining the traffic jam scene of the road segment to be detected further includes:
when it is satisfied with
Figure BDA0003321193140000035
Or when satisfying
Figure BDA0003321193140000036
The traffic jam scene of the road section to be detected is local road section jam;
wherein labRepresenting the road section length between AB detection points of the closed road section to be detected; alpha represents the traffic density coefficient of the traffic jam; beta represents an average speed coefficient of traffic jam;
Figure BDA0003321193140000037
and a travel time average value representing a predetermined value of the vehicle passing through the first detection area a and the second detection area B.
In one exemplary embodiment, the first detection region is acquired for a first predetermined period miThe collected first vehicle passing number; acquiring the second detection area in the first preset time period miThe second vehicle passing number is collected;
taking the difference value between the first vehicle passing number and the second vehicle passing number as a second judgment condition;
and determining the alarm response grade according to the first judgment condition and the second judgment condition.
A third aspect of the present invention provides a traffic congestion classification system for a closed road, including:
the target selection module is used for selecting a first detection area and a second detection area of the closed road section, and taking the road section between the first detection area and the second detection area as a road section to be detected;
the data statistics module is used for acquiring vehicle passing data in the road section to be detected according to the preset time granularity;
the calculating module is used for determining the first traffic density of the first detection area in a first preset time period according to the vehicle passing data, and determining the second traffic density of the second detection area in a second preset time period according to the vehicle passing data;
the judging module is used for comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, judging a traffic jam scene of the road section to be detected and outputting the traffic jam scene; wherein the standard traffic density is the traffic density of the closed road section under the maximum passing condition; the traffic jam scene comprises local road congestion and overall road congestion.
A fourth aspect of the present invention provides an electronic device, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected in sequence, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method described above.
A fifth aspect of the invention provides a readable storage medium, the storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method as described above.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the invention, based on the time characteristics of the acquired traffic data and traffic flow, the dynamic fluctuation data of the traffic flow is screened by using a cluster analysis method, so that the precision and reliability of the data depending on the short-term traffic flow are higher, the judgment conditions of various traffic data under two congestion scenes are designed according to the actual conditions, and the judgment and detection are carried out through the real-time traffic flow density traffic congestion, so that the traffic congestion scene can be defined, the traffic management department can conveniently call the module to carry out congestion judgment, and the traffic state of each road is mastered in real time.
(2) According to the invention, by constructing the congestion queuing length function, the actual situation of traffic congestion can be represented timely and accurately, and a multi-level alarm mechanism meeting the actual requirement can be distinguished and set according to the alarm function value, so that a traffic manager can implement a control plan in a targeted manner, and congestion can be eliminated timely.
(3) In order to avoid the occurrence of short-term fluctuation of dynamic data, the invention sets a reliable elastic mechanism through two groups of judgment conditions, so that a model can generate reliable alarm after identifying steady-state traffic jam, help traffic managers to adopt corresponding traffic dispersion strategies in time, improve road traffic efficiency, guarantee traffic safety, reduce social cost and improve social trip efficiency, and has very wide application prospect.
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The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps. In the drawings:
fig. 1 is a flowchart of a method for classifying traffic congestion of a closed road section according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a cluster analysis provided by an embodiment of the present invention;
FIG. 3 is a schematic illustration of a first type of traffic congestion provided by an embodiment of the present invention;
FIG. 4 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application;
FIG. 5 is a schematic illustration of a first type of traffic congestion traffic density provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of a second type of traffic congestion provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of a second type of traffic congestion traffic density and average speed provided by an embodiment of the invention;
fig. 8 is a block diagram of a system for classifying traffic congestion in a closed road according to an embodiment of the present invention.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
Exemplary method
As shown in fig. 1, a method for classifying traffic congestion in a closed road includes the following steps:
s110, selecting a first detection area and a second detection area of the closed road section, and taking the road section between the first detection area and the second detection area as the road section to be detected.
Specifically, the present example is to classify the traffic congestion condition of the closed road segment, where the closed road segment may be an expressway or a city-around expressway. For example, from the entrance to the exit of a certain section at high speed, as the section to be measured. The first detection area and the second detection area are selected on the closed road section according to requirements. In this example, the entry position is generally selected as a starting point in the traffic flow direction, and the collected traffic data as the starting point may be a detection area. For example, as for the detection area of the start point, it is defined as a first detection area a in this example; the exit of the closed link is set as the destination, and the collected passing data set as the destination is set as the second detection area B. It should be understood that, for a closed road section, the starting point and the ending point are selected along the traffic flow direction, and the positions of the starting point and the ending point can be set according to needs, and are not limited by the invention.
And S120, acquiring the passing data in the road section to be detected according to the preset time granularity.
Specifically, the data of passing vehicles at the start and end points of the closed road section are generally acquired by a gate or an electronic police. For example, all the vehicle passing data of the start point and the end point are respectively sorted by taking 1 minute as granularity, and a data table in the detection time sequence is generated. The passing data can comprise detection equipment ID, license plate number, lane number, detection time, place and vehicle speed information and the like, and all data tables are stored in a classified mode according to the lane number. Additionally, it should be understood that the vehicle passing data may be obtained and received from a server.
And S130, determining the first traffic density of the first detection area in a first preset time period according to the traffic passing data, and determining the second traffic density of the second detection area in a second preset time period according to the traffic passing data.
Specifically, as shown in fig. 5 and 7, the traffic density is an important index for measuring the road congestion state, and is a monotonicity index for detecting the road operation state, and as the traffic density gradually increases, the road service level decreases and the road congestion is increased. The speed difference change in the collection time interval reflects the road condition with a certain degree of error. Therefore, the traffic density is selected to judge the traffic jam with high reliability.
For example, the standard traffic density is the traffic density of the closed section under the maximum traffic condition. And under the lowest service level, when the maximum standard unit traffic volume of the cross section of the road can be passed, the traffic flow density of the road section is the traffic flow density under the maximum passing condition. In one exemplary embodiment, the flow density of the first detection area a and the second detection area B is calculated based on cluster samples of the average vehicle speeds of the first detection area and the second detection area, wherein the calculation formula is as follows:
Figure BDA0003321193140000061
in the formula:
Figure BDA0003321193140000062
indicates at a predetermined time period mh(ii) traffic density in the x-th detection zone;
c represents the number of lanes of the closed road section;
Ncrepresents mhThe total number of the vehicles passing through the road section detected in the time period;
vjrepresents mhSequentially detecting the vehicle speed of a vehicle place in a certain lane cluster sample in an x-th detection area within a time period;
t1represents mhFirst vehicle detection time and preset time period m in certain lane cluster sample of the x detection area in time periodhThe interval time of the starting time;
tjrepresents mhAnd (4) the interval time of the detection time of the adjacent vehicles in a certain lane cluster sample of the x-th detection area in the time period.
And S140, comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, judging the traffic jam scene of the road section to be detected, and outputting the traffic jam scene.
Specifically, the second predetermined period mjIs based on the first predetermined time period miSpecifically, the step of determining the second predetermined period of time includes: acquiring first vehicle passing data of a vehicle passing through a first detection area A within a first preset time period; and taking the time period when the vehicle passes through the second detection area B and reaches the preset value in the first vehicle passing data as a second preset time period.
First, the first predetermined time period m is determinediAfter the time interval passes through the first detection area of the starting point, the time interval obtained by more than 50% of vehicles in the end point detection area in the first predetermined time interval is mjM at the second detection areajThe density of the traffic flow in the time interval is
Figure BDA0003321193140000063
The traffic density in the starting point detection area in the time interval is
Figure BDA0003321193140000064
The average travel time of the 50% or more vehicle AB road section is
Figure BDA0003321193140000065
As shown in fig. 3 and 6, the traffic congestion scene in this example includes local link congestion (a first type of congestion scene) and global link congestion (a second type of congestion scene). It should be noted that the main features causing traffic congestion can be divided into two categories: first, due to the increased traffic flow, the road supply cannot meet the traffic demand, creating a congestion that gradually extends from the bottleneck ahead to the periphery along the road line, defined in this example as a global link congestion. Secondly, traffic jam, which is defined as local road congestion in this example, is generated by occupying local lanes of a road section to form a road bottleneck due to an accident or other event occurring at a certain position of the road section.
For example, when it is satisfied
Figure BDA0003321193140000066
And is
Figure BDA0003321193140000067
The traffic jam scene of the road section to be detected is the whole road section jam; wherein, KmRepresenting the traffic density of a closed road segment under saturated traffic conditions of low service level.
Wherein the content of the first and second substances,
Figure BDA0003321193140000068
Figure BDA0003321193140000069
representing the headway of a closed road segment under saturated traffic conditions at low service levels.
When it is satisfied with
Figure BDA00033211931400000610
Or when satisfying
Figure BDA00033211931400000611
And the traffic jam scene of the road section to be detected is local road section jam.
Wherein the content of the first and second substances,
Figure BDA0003321193140000071
is indicated at the first predetermined time period miThe traffic density of the inner first detection area;
Figure BDA0003321193140000072
indicates the second predetermined period mjA traffic density of a second detection zone within; labRepresenting the length of the closed road section to be detected; alpha represents the traffic density coefficient of the traffic jam; beta represents an average speed coefficient of traffic jam;
Figure BDA0003321193140000073
and a travel time average value representing a predetermined value of the vehicle passing through the first detection area a and the second detection area B.
As a variation, the step of acquiring the vehicle passing data in the road section to be detected includes:
receiving the passing data of the gates near the first detection area and the second detection area, performing cluster analysis on the passing data in the road section to be detected, and eliminating edge fluctuation data; and respectively determining clustering samples of the average vehicle speed of the first detection area and the second detection area. As shown in fig. 2, the edge fluctuation data refers to sudden acceleration or deceleration of the vehicle caused by the driver encountering a sudden situation; or the driver does not operate normally, and decelerates freely.
Specifically, for m respectivelyiAnd performing cluster analysis on data in the data tables of the start point and the end point in the time period, eliminating edge fluctuation data influencing the mean value, and respectively calculating the average speed, the traffic flow density and the like of the start point detection area and the end point detection area.
The method for calculating the average vehicle speed of the start-point detection area and the end-point detection area of the closed road section to be detected comprises the following steps:
s112: obtaining miAll vehicle site speeds of a first detection area of a starting point of a closed road section in a time period;
s114, calculating the average value of the vehicle speeds of all vehicle positions
Figure BDA0003321193140000074
With the value of miA center point of a point vehicle speed sample within a time period;
s116: selecting the sample closest to the central point for classification, wherein the number of the samples reaches miAbove a predetermined proportion of the total sample size in the time period, the predetermined percentage in this example is 80%.
S117: and miComparing samples of the vehicle speed categories of the last place in the time period, if the samples are the same, terminating the process, and selecting the sample as a final sample category by taking the category as a reference;
s118: calculating the average value of the vehicle speed categories at the positions above a predetermined ratio
Figure BDA0003321193140000075
Continuing with the new center point of the category as the average value S116, the predetermined proportion is 80% in this example.
Further, m is obtained based on the aboveiAnd (3) clustering samples of the site vehicle speed in a time period, and calculating the traffic flow density of the start and end detection areas of the closed road section:
Figure BDA0003321193140000076
Figure BDA0003321193140000077
in the formula:
Figure BDA0003321193140000078
represents miThe traffic flow density of a first detection area at a starting point in a time period;
Figure BDA0003321193140000079
represents mjTraffic density in the second detection zone over a period of time;
c represents the number of lanes of the closed road section;
Ncrepresents miThe total number of the vehicles passing through the road section detected in the time period;
vjrepresents miSequentially detecting the vehicle speed of a vehicle place in a certain lane cluster sample in a detection area within a time period;
t1represents miThe interval time between the first vehicle detection time in a certain lane cluster sample of the detection area and the starting time of the preset time period in the time period;
tjrepresents miAnd in the time interval, the interval time between the detection time of the adjacent vehicles in a certain lane cluster sample of the detection area or the interval time between the end time of the preset time interval and the detection time of the adjacent vehicles.
As a variation, after determining the traffic congestion type of the road segment to be detected based on the method, the dynamically-changed queuing length of the congestion of the road segment to be detected can be calculated and used as a judgment condition for determining the traffic congestion type grade of the road segment to be detected.
In particular, for the first type of traffic congestion, m needs to be determinediAverage queue length from closed road segment detection area B (end point) to queue end after a time period; for the second type of traffic congestion, m needs to be determinediAverage queue length from the end of the queue at the point S of occurrence of the accident on the closed section after the time period.
In the example, before calculating the dynamically-changed queuing length of the congestion of the road section to be measured, the current time period m of the closed road section needs to be acquirediThe number of vehicles in transit between the detection areas AB is detected in the following specific mode:
s132: taking a certain moment as a starting point, counting all vehicles Q1 passing through the detection starting point within a plurality of hours (time period) of the starting point moment;
s134: counting all vehicles passing the detection starting point from the starting point time to the end point time by taking the time when all vehicles passing the detection starting point within a plurality of hours from the starting point time pass the detection end point as the end point time;
s136: the number of vehicles in transit in the starting state between the detection points AB under the normal traffic condition that the traffic flow of the closed road section is stable from the starting point time to the end point time
Figure BDA0003321193140000081
Figure BDA0003321193140000082
S138: current time interval m of closed road sectioniNumber of vehicles in transit between detection points AB
Figure BDA0003321193140000083
Figure BDA0003321193140000084
In the formula:
Figure BDA0003321193140000085
representing a closed section of road time miThe equivalent number of standard vehicles collected between the detection areas A;
Figure BDA0003321193140000086
representing a closed section of road time miThe number of equivalents of standard vehicle collected between the inner B detection areas,
Figure BDA0003321193140000087
number of vehicles in transit indicating the starting state between detection areas AB under normal traffic conditions
Figure BDA0003321193140000088
According to the steps, a first type of traffic jam type (integral road section jam) time interval m is obtainediLength of queue
Figure BDA0003321193140000089
The calculation method of (c) is as follows:
Figure BDA0003321193140000091
namely:
Figure BDA0003321193140000092
wherein C represents the number of lanes of the closed road section;
Figure BDA0003321193140000093
representing a closed section of road time miThe equivalent number of standard vehicles collected between the detection areas A;
Figure BDA0003321193140000094
representing a closed section of road time miDetecting the equivalent number of standard vehicles collected between areas in the inner B; labAnd the length of the road section between the detection points of the closed road section AB is expressed in m.
Obtaining a second type of traffic congestion(local road segment Congestion) Current time period miLength of queue
Figure BDA0003321193140000095
The calculation method of (c) is as follows:
s142: acquiring the distance l between the accident site and the detection end point of the closed road sectionbsFor example, by driver or police accident location information;
s144: current time period miNumber of vehicles on way from inner accident site to closed road section detection terminal point road section
Figure BDA0003321193140000096
Figure BDA0003321193140000097
S146: current time period miNumber of vehicles on way from inner accident site to closed road section detection terminal point road section
Figure BDA0003321193140000098
Figure BDA0003321193140000099
Namely:
Figure BDA00033211931400000910
s148: current time period miLength of queue
Figure BDA00033211931400000911
Figure BDA00033211931400000912
Namely:
Figure BDA00033211931400000913
in the formula: l0The method comprises the steps of (1) representing the length of a road section occupied by a single vehicle in a congested road section, including the distance between heads, and generally taking the value of 7 m; representing the number of lanes of a closed road section;
Figure BDA00033211931400000914
representing a closed section of road time miThe equivalent number of standard vehicles collected between the detection areas A;
Figure BDA00033211931400000915
representing a closed section of road time miAnd detecting the equivalent number of standard vehicles collected between areas in the inner B.
The invention also provides a method for graded elastic early warning of traffic jam in a closed road section, which comprises the following steps:
s210: determining the dynamically changed queuing length of the congestion of the road section to be detected according to the traffic congestion type of the road section to be detected;
specifically, the dynamically-changed queuing length of the congestion of the road section to be measured can be determined through the steps.
S220: and determining an alarm response grade by taking the dynamically changed queuing length of the congestion of the road section to be detected as a first judgment condition, and outputting the alarm response grade.
In the present example, the queuing length is dynamically changed as a determination condition, and different alarm thresholds are set to perform graded congestion alarm on road traffic conditions, that is, according to the actual situation of a congested road segment, the queuing length threshold is set for a predetermined alarm level, for example, set to 3 response levels (three-level, two-level, one-level), it should be understood that the response level here may be set according to the actual situation, for example, set to 5-level or 7-level, etc. In addition, once traffic events such as sudden changes in flow and severe weather are found, the system can give an alarm and conduct hierarchical management and control on the alarm.
Specifically, for the dynamic change of traffic flow of a closed road section, an elastic space for alarming is set, and the occurrence of short-frequency high-order alarming is avoided. E.g. first type of traffic jam current time period mjThe judgment condition of (1):
Figure BDA0003321193140000101
in the formula:
Figure BDA0003321193140000102
the congestion coefficient lower limit value represents the alarm level s of the closed road section;
Figure BDA0003321193140000103
and the congestion coefficient upper limit value represents the alarm level s of the closed road section.
As a variation, the difference between the first vehicle passing number and the second vehicle passing number may be used as the second judgment condition; and determining the alarm response grade according to the first judgment condition and the second judgment condition.
For example, for a first type of traffic jam, the current time period mjThe judgment condition of (1):
Figure BDA0003321193140000104
and is
Figure BDA0003321193140000105
In the formula:
Figure BDA0003321193140000106
the congestion coefficient lower limit value represents the alarm level s of the closed road section;
Figure BDA0003321193140000107
and the congestion coefficient upper limit value represents the alarm level s of the closed road section.
If the time period m is satisfiediIf the conditions of inequality (1) and inequality (2) are satisfied, the alarm level of the first type of traffic jam is s.
For the second type traffic jam current time period mjThe judgment condition of (1):
Figure BDA0003321193140000108
and is
Figure BDA0003321193140000109
In the formula:
Figure BDA00033211931400001010
the congestion coefficient lower limit value represents the alarm level s of the closed road section;
Figure BDA00033211931400001011
and the congestion coefficient upper limit value represents the alarm level s of the closed road section.
If the time period m is satisfiediIf the conditions of inequality (3) and inequality (4) are satisfied, the alarm level of the first type of traffic jam is s.
Exemplary System
As shown in fig. 8, a closed road traffic congestion classification system includes:
the target selection module 20 is configured to select a first detection area and a second detection area of the closed road section, and use a road section between the first detection area and the second detection area as a road section to be detected;
the data statistics module 30 is used for acquiring vehicle passing data in the road section to be detected according to the preset time granularity;
the calculating module 40 is used for determining a first traffic density of the first detection area in a first preset time period according to the vehicle passing data, and determining a second traffic density of the second detection area in a second preset time period according to the vehicle passing data; the determining of the second predetermined period of time includes: acquiring a first preset time period m of a vehicleiFirst passing data passing through the first detection area A; taking the time period when the vehicle passes through the second detection area B and reaches the preset value in the first vehicle passing data as a second preset time period mj。
Calculating the traffic flow density of the first detection area A and the second detection area B based on the clustering samples of the average vehicle speed of the first detection area and the second detection area, wherein the calculation formula is as follows:
Figure BDA0003321193140000111
in the formula:
Figure BDA0003321193140000112
indicates at a predetermined time period mh(ii) traffic density in the xth detection zone;
c represents the number of lanes of the closed road section;
Ncrepresents mhThe total number of the vehicles passing through the road section detected in the time period;
vjrepresents mhSequentially detecting the vehicle speed of a vehicle place in a certain lane cluster sample in a detection area within a time period;
t1represents mhFirst vehicle detection time and preset time period m in certain lane cluster sample of detection area in time periodhThe interval time of the starting time;
tjrepresents mhAnd in the time interval, the interval time between the detection time of the adjacent vehicles in a certain lane cluster sample of the detection area or the interval time between the end time of the preset time interval and the detection time of the adjacent vehicles.
The judging module 50 is configured to compare the first traffic flow density and the second traffic flow density with the standard traffic flow density, judge a traffic jam scene of the road segment to be detected, and output the traffic jam scene; wherein the standard traffic density is the traffic density of the closed road section under the maximum passing condition; the traffic jam scene comprises local road congestion and overall road congestion.
As a variation, the traffic jam classification system for the closed road section further comprises a data clustering module, wherein the data clustering module is used for receiving the vehicle passing data of the card ports near the first detection area and the second detection area, performing cluster analysis on the vehicle passing data in the road section to be detected, and removing the edge fluctuation data; and respectively determining clustering samples of the average vehicle speed of the first detection area and the second detection area.
The step of judging the traffic jam scene of the road section to be detected comprises the following steps:
when it is satisfied with
Figure BDA0003321193140000113
And is
Figure BDA0003321193140000114
The traffic jam scene of the road section to be detected is the whole road section jam; wherein, KmRepresenting the traffic density of a closed road segment under saturated traffic conditions of low service level.
The step of judging the traffic jam scene of the road section to be detected further comprises the following steps:
when it is satisfied with
Figure BDA0003321193140000121
Or when satisfying
Figure BDA0003321193140000122
The traffic jam scene of the road section to be detected is local road section jam;
wherein labRepresenting the length of the road section between the detection points AB of the closed road section; alpha represents the traffic density coefficient of the traffic jam; beta represents an average speed coefficient of traffic jam;
Figure BDA0003321193140000123
and a travel time average value representing a predetermined value of the vehicle passing through the first detection area a and the second detection area B.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 1. The electronic device may be the mobile device itself, or a stand-alone device separate therefrom, which may communicate with the mobile device to receive the collected input signals therefrom and to transmit the selected goal decision behavior thereto.
FIG. 4 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 4, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 11 to implement the decision-making behavior decision-making methods of the various embodiments of the present application described above and/or other desired functionality.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown). For example, the input device 13 may include various devices such as an on-board diagnostic system (OBD), a Universal Diagnostic Service (UDS), an Inertial Measurement Unit (IMU), a camera, a lidar, a millimeter-wave radar, an ultrasonic radar, an on-board communication (V2X), and the like. The input device 13 may also include, for example, a keyboard, a mouse, and the like. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 4, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in a decision-making behavior decision-making method according to various embodiments of the present application described in the "exemplary methods" section of this specification above.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a decision-making behavior decision method according to various embodiments of the present application, described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims (11)

1. A method for classifying traffic jam of a closed road section, which is characterized by comprising the following steps:
selecting a first detection area and a second detection area of a closed road section, and taking the road section between the first detection area and the second detection area as a road section to be detected;
acquiring vehicle passing data in a road section to be detected according to the preset time granularity;
determining a first traffic density of a first detection area in a first preset time period according to the traffic passing data, and determining a second traffic density of a second detection area in a second preset time period according to the traffic passing data;
comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, and judging a traffic jam scene of the road section to be detected; wherein the standard traffic density is the traffic density of the closed road section under the maximum passing condition.
2. The method for classifying traffic congestion of closed road sections according to claim 1, wherein the step of acquiring vehicle passing data in the road section to be detected comprises:
receiving vehicle passing data of the gates in the first detection area and the second detection area, performing cluster analysis on the vehicle passing data in the road section to be detected, and eliminating edge fluctuation data;
and respectively determining cluster samples of the vehicle speeds of the positions of the first detection area and the second detection area.
3. The method for classifying traffic jam on closed road section according to claim 2, characterized in that the traffic flow density of the first detection area A and the second detection area B is calculated based on the cluster sample of the average vehicle speed of the first detection area and the second detection area, wherein the calculation formula is as follows:
Figure FDA0003321193130000011
in the formula:
Figure FDA0003321193130000012
indicates at a predetermined time period mh(ii) traffic density in the x-th detection zone;
c represents the number of lanes of the closed road section;
Ncrepresents mhThe total number of the vehicles passing through the road section detected in the time period;
vjrepresents mhSequentially detecting the vehicle speed of a vehicle place in a certain lane cluster sample in an x-th detection area within a time period;
t1represents mhDetecting time of the first vehicle in a certain lane cluster sample of the x-th detection area in a time interval and a preset time interval mhThe interval time of the starting time;
tjrepresents mhAnd (4) the interval time of the detection time of the adjacent vehicles in a certain lane cluster sample of the x-th detection area in the time period.
4. The closed road traffic congestion classification method according to claim 3, wherein the step of determining the second predetermined period of time comprises:
acquiring a first preset time period m of a vehicleiFirst passing data passing through the first detection area A;
taking the time interval of the vehicle passing through the second detection area B in the first vehicle passing data and reaching the preset value as a second preset time interval mj。
5. The method for classifying traffic jam on closed road section according to claim 4, wherein the step of judging the traffic jam scene of the road section to be detected comprises the following steps:
when it is satisfied with
Figure FDA0003321193130000021
And is
Figure FDA0003321193130000022
The traffic jam scene of the road section to be detected is the whole road section jam; wherein, KmRepresenting the traffic density of a closed road segment under saturated traffic conditions of low service level,
Figure FDA0003321193130000023
is indicated at the first predetermined time period miThe traffic density of the inner first detection area;
Figure FDA0003321193130000024
indicates the second predetermined period mjThe traffic density of the second detection zone within.
6. The method for classifying traffic jam on closed road section according to claim 4, wherein the step of judging the traffic jam scene of the road section to be detected further comprises the following steps:
when it is satisfied with
Figure FDA0003321193130000025
Or when satisfying
Figure FDA0003321193130000026
The traffic jam scene of the road section to be detected is local road section jam;
wherein the content of the first and second substances,
Figure FDA0003321193130000027
is indicated at the first predetermined time period miThe traffic density of the inner first detection area;
Figure FDA0003321193130000028
indicates the second predetermined period mjA traffic density of a second detection zone within; labRepresenting the length of the closed road section to be detected; alpha represents the traffic density coefficient of the traffic jam; beta represents an average speed coefficient of traffic jam;
Figure FDA0003321193130000029
and a travel time average value representing a predetermined value of the vehicle passing through the first detection area a and the second detection area B.
7. A method for graded elastic early warning of traffic jam of a closed road section is characterized by comprising the following steps:
selecting a first detection area and a second detection area of a closed road section, and taking the road section between the first detection area and the second detection area as a road section to be detected;
acquiring vehicle passing data in a road section to be detected according to the preset time granularity;
determining a first traffic density of a first detection area in a first preset time period according to the traffic passing data, and determining a second traffic density of a second detection area in a second preset time period according to the traffic passing data;
comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, and judging a traffic jam scene of the road section to be detected; wherein the standard traffic density is the traffic density of the closed road section under the maximum passing condition; acquiring a traffic jam type of a road section to be detected;
determining the dynamically changed queuing length of the congestion of the road section to be detected according to the traffic congestion type of the road section to be detected;
and determining an alarm response grade by taking the dynamically changed queuing length of the congestion of the road section to be detected as a first judgment condition, and outputting the alarm response grade.
8. The method for graded elasticity warning of traffic congestion on closed sections of road as claimed in claim 7, wherein:
acquiring a first detection area in a first preset time period miThe collected first vehicle passing number; acquiring the second detection area in the first preset time period mjThe second vehicle passing number is collected;
taking the difference value between the first vehicle passing number and the second vehicle passing number as a second judgment condition;
and determining the alarm response grade according to the first judgment condition and the second judgment condition.
9. A closed road traffic congestion classification system, comprising:
the target selection module is used for selecting a first detection area and a second detection area of the closed road section, and taking the road section between the first detection area and the second detection area as a road section to be detected;
the data statistics module is used for acquiring vehicle passing data in the road section to be detected according to the preset time granularity;
the calculating module is used for determining the first traffic density of the first detection area in a first preset time period according to the vehicle passing data, and determining the second traffic density of the second detection area in a second preset time period according to the vehicle passing data;
the judging module is used for comparing the first traffic flow density and the second traffic flow density with the standard traffic flow density respectively, judging a traffic jam scene of the road section to be detected and outputting the traffic jam scene; wherein the standard traffic density is the traffic density of the closed road section under the maximum passing condition; the traffic jam scene comprises local road congestion and overall road congestion.
10. An electronic device comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being connected in series, the memory being configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-8.
11. A readable storage medium, characterized in that the storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-8.
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