CN106781460A - A kind of road section traffic volume state determines method and device - Google Patents
A kind of road section traffic volume state determines method and device Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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Abstract
The embodiment of the present invention discloses a kind of road section traffic volume state and determines method and device.Methods described includes：According to default traffic behavior grade, the traffic historical data sample to target road section carries out traffic behavior grading；Obtain after being graded through traffic behavior, each default characteristic parameter in the corresponding traffic historical data sample of each traffic behavior grade；According to each default characteristic parameter and its corresponding traffic behavior grade, the corresponding relation of the default characteristic parameter and traffic behavior grade is obtained；The current each default characteristic parameter of target road section is obtained, and according to the default characteristic parameter and the corresponding relation of traffic behavior grade, obtains the current traffic behavior grade of target road section.Described device is based on methods described, and methods described can accurately determine the traffic behavior of target road section, and then solve target road section congestion wrong report problem.
Description
Technical field
The present invention relates to intelligent transportation field, and in particular to a kind of road section traffic volume state determines method and device.
Background technology
With developing rapidly for intelligent transportation system technology, increasing city establishes advanced traffic-information service
System.Real time traffic data is obtained and by integrated treatment by data collecting system, transportation information service systems can be used for pre-
Test cross is through-flow, and issues real-time road condition information by network, broadcast, mobile phone, variable message board or on-vehicle navigation apparatus etc., to tie
It is that traveler plans optimal path to close transport information.Additionally, transport information can provide traffic control and management for traffic control department
Foundation, for roading department road equipment of making rational planning for provides reference frame, reaches alleviation traffic congestion, the mesh of energy-saving and emission-reduction
's.
There is very big difference in influence and ordinary road of the special road sections such as signal lamp, gateway, charge station to traffic behavior.
For example there is the crossing of traffic lights, when vehicle runs into red light, that is, unblock a road, vehicle still needs parking waiting, if
Processing system is left intact, and the road conditions of waiting signal lamp can falsely determine that to be congestion when will be unimpeded, causes the friendship of mistake
Logical state, therefore, the traffic behavior in signal lamp coverage needs specially treated.
The patent of Publication No. CN102280031A (2011.12.14) proposes that a kind of crossing based on floating car data is handed over
Logical state identification method, the method is based on crossing traffic signal controlling cycle duration, and the threshold value according to setting is predicted and sentences
Disconnected traffic behavior.Because Signalized control cycle duration not immobilizes, for example traffic-police can be according to actual traffic shape
State changes Signalized control cycle duration, or induction type signal lamp can be automatically according to magnitude of traffic flow Regulate signal lamp controlling cycle
Duration.Therefore the crossing traffic state recognition method based on Signalized control cycle duration is implemented and is difficult fixed light control
Cycle duration processed, is difficult to realize in actual applications.
The patent application of Publication No. CN101593431 (2009.12.02) proposes a kind of vehicular traffic of auto-control crossing
The method of state, its reasonable traveling according to the distance between current crossing continuous vehicle platoon more long, each vehicle less than setting
Distance and judge the track congestion less than the reasonable travel speed of setting in halted state or travel speed.So
And, predetermined threshold value is less than merely with travel speed, judge congestion in road situation without considering other factors such as time etc.,
It is irrational.
The patent application of Publication No. CN101470956 (2009.07.01) proposes a kind of using ground induction coil detection crossing
The simple and easy method of congestion status and traffic light control system in this way.The method is in crossing and/or crossing
Whether embedded multiple ground induction coils on the road of adjacent edges, detection vehicle number exceedes the quantitative criteria of congestion, and when lasting
When time meets or exceeds the time standard for assert congestion, then crossing congestion is judged.However, buried on road feeling line multiplely
Not only input cost is high for circle, and can unavoidably ground-to-ground face damages.The method does not have practical prospect.
Therefore, the traffic behavior of target road section how is accurately determined, and then solves target road section congestion wrong report problem, tool
It is of great significance.
The content of the invention
For defect of the prior art, the embodiment of the present invention provides a kind of road section traffic volume state and determines method and device.
On the one hand, the embodiment of the present invention proposes that a kind of road section traffic volume state determines method, including：
According to default traffic behavior grade, the traffic historical data sample to target road section carries out traffic behavior grading；
Obtain after being graded through traffic behavior, each default spy in the corresponding traffic historical data sample of each traffic behavior grade
Levy parameter；
According to each default characteristic parameter and its corresponding traffic behavior grade, the default characteristic parameter is obtained
With the corresponding relation of traffic behavior grade；
The current each default characteristic parameter of target road section is obtained, and according to the default characteristic parameter and traffic behavior etc.
The corresponding relation of level, obtains the current traffic behavior grade of target road section；
Wherein, the default characteristic parameter is used to represent the transport condition of vehicle, and the default characteristic parameter includes：
The stop frequency of vehicle in unit interval, stop and run at a low speed duration, stop and run at a low speed distance.
The road section traffic volume state that the embodiment of the present invention is proposed determines method, due to the historical traffic state according to target road section
Data, generate the corresponding relation of default characteristic parameter and traffic behavior, therefore, the corresponding relation have high accuracy with
It is representative.Afterwards, then the default characteristic parameter current by collecting target road section, and according to the corresponding relation tried to achieve mesh is obtained
The current traffic behavior in mark section, therefore, the traffic behavior for getting has high accuracy.
On the other hand, the embodiment of the present invention also proposes a kind of road section traffic volume state determination device, it is characterised in that including：
Condition evaluation module, for according to default traffic behavior grade, to the traffic historical data sample of target road section
Carry out traffic behavior grading；
Parameter acquisition module, for obtaining and being graded through traffic behavior after, the corresponding traffic history number of each traffic behavior grade
According to each default characteristic parameter in sample；
Relationship determination module, for according to each default characteristic parameter and its corresponding traffic behavior grade, obtaining
The corresponding relation of the default characteristic parameter and traffic behavior grade；
State determining module, for obtaining the current each default characteristic parameter of target road section, and according to the default spy
The corresponding relation of parameter and traffic behavior grade is levied, the current traffic behavior grade of target road section is obtained；
Wherein, the default characteristic parameter is used to represent the transport condition of vehicle, and the default characteristic parameter includes：
The stop frequency of vehicle in unit interval, stop and run at a low speed duration, stop and run at a low speed distance.
The road section traffic volume state determination device that the embodiment of the present invention is proposed, due to the historical traffic state according to target road section
Data, generate the corresponding relation of default characteristic parameter and traffic behavior, therefore, the corresponding relation have high accuracy with
It is representative.Afterwards, then the default characteristic parameter current by collecting target road section, and according to the corresponding relation tried to achieve mesh is obtained
The current traffic behavior in mark section, therefore, the traffic behavior for getting has high accuracy.
Brief description of the drawings
Fig. 1 is the schematic flow sheet that road section traffic volume state of the present invention determines embodiment of the method；
Fig. 2 is the structural representation of road section traffic volume state determination device embodiment of the present invention.
Specific embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is explicitly described, it is clear that described embodiment be the present invention
A part of embodiment, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not having
The every other embodiment obtained under the premise of creative work is made, the scope of protection of the invention is belonged to.
Fig. 1 is the schematic flow sheet that road section traffic volume state of the present invention determines embodiment of the method, and referring to Fig. 1, the present embodiment is public
Open a kind of road section traffic volume state and determine method, including：
S1, according to default traffic behavior grade, traffic behavior is carried out to the traffic historical data sample of target road section and is commented
Level；
S2, obtain and graded through traffic behavior after, it is each default in the corresponding traffic historical data sample of each traffic behavior grade
Characteristic parameter；
S3, according to each default characteristic parameter and its corresponding traffic behavior grade, obtain the default feature
The corresponding relation of parameter and traffic behavior grade；
S4, the current each default characteristic parameter of acquisition target road section, and according to the default characteristic parameter and traffic shape
The corresponding relation of state grade, obtains the current traffic behavior grade of target road section；
Wherein, the default characteristic parameter is used to represent the transport condition of vehicle, and the default characteristic parameter includes：
The stop frequency of vehicle in unit interval, stop and run at a low speed duration, stop and run at a low speed distance.
The road section traffic volume state that the embodiment of the present invention is proposed determines method, due to the historical traffic state according to target road section
Data, generate the corresponding relation of default characteristic parameter and traffic behavior, therefore, the corresponding relation have high accuracy with
It is representative.Afterwards, then the default characteristic parameter current by collecting target road section, and according to the corresponding relation tried to achieve mesh is obtained
The current traffic behavior in mark section, therefore, the traffic behavior for getting has high accuracy.
It should be noted that the executive agent of this method embodiment is computer.
Specifically, in step sl, the default traffic behavior grade can be set to 3：Unimpeded, slow, congestion.If
Further subdivision, can also be divided into 4 grades by traffic behavior：It is unimpeded, slow, compared with congestion, heavy congestion.May be appreciated
It is that the grade of traffic behavior can be divided according to actual needs, and the embodiment of the present invention is not construed as limiting to this.
The traffic historical data sample of the target road section can be the traffic data in target road section preset time range.
For example, it may be before target road section current time in 2 months, the traffic historical data at each moment, or
Before target road section current time in 2 months, the traffic historical data of special time period, such as morning peak time period 7：00-9：00,
And the evening peak time period 18:00-20:00 traffic historical data.Certainly, the traffic historical data sample of the target road section
Can be adjusted according to actual needs, the present embodiment is not construed as limiting to this.
The traffic historical data sample to target road section carries out traffic behavior grading be may include：By transferring target road
The image data that the imaging monitoring device of section is recorded, and default traffic behavior grade is combined, the traffic to target road section is gone through
History data sample carries out traffic behavior grading.
In step s 2, after the traffic behavior grading to traffic historical data sample is completed, each traffic behavior grade is then
At least part of traffic historical data sample is had to correspond to therewith.
For example, certain day morning 7 before target road section current time in 2 months：00-7：10, the traffic shape of target road section
State is slow, then the traffic data sample of the time period is one of corresponding part traffic historical data sample of slow grade.
Further, it is determined that after the corresponding traffic historical data sample of each traffic behavior grade, then obtaining the traffic
Each default characteristic parameter in historical data sample.
The default characteristic parameter includes：The stop frequency of the vehicle in unit interval, stop and run at a low speed duration,
Stop and run at a low speed distance.
Wherein, the stop frequency refers to that target road section causes the number of times that the vehicle stops within the unit interval because of congestion, when
Between of short duration (such as less than 5 seconds) or long-time exception parking (such as traffic accident) be not counted in stop frequency.
The parking and run at a low speed duration and refer to that vehicle causes the vehicle stop within the unit interval and low speed row because of congestion
The time span sailed.Under normal circumstances, congestion is more serious, stops and to run at a low speed the time more long.
The parking and run at a low speed distance and refer to because caused by congestion, the vehicle is stopped and low speed within the unit interval
The distance of traveling.Under normal circumstances, congestion is more serious, congestion scope is bigger, vehicle parking and to run at a low speed distance more long.
It is understood that in the corresponding traffic historical data sample of each traffic behavior grade, in any time period
Any vehicle for, its default characteristic parameter is not necessarily equal to the default feature of any other vehicles in the time period
Parameter, i.e.,：Each traffic behavior grade to that should have different default characteristic parameters, stop frequency in each vehicle unit time,
Duration is stopped and run at a low speed, stop and runs at a low speed distance and be different.
In step s3, it is described according to the default characteristic parameter and its corresponding traffic behavior grade, obtain described
Default characteristic parameter includes with the corresponding relation of traffic behavior grade：
S31, according to default interval threshold, the default characteristic parameter is divided into the numerical intervals of predetermined number；
Specifically, if representing parking with ρ and running at a low speed the numerical intervals of duration t, dash when will stop and running at a low speed
It is divided into M numerical intervals, θ1、θ2、…、θM-1Represent parking and run at a low speed the default interval threshold of duration, then：
Wherein, θ1＜ θ2＜ ... ＜ θM-1。
It is H numerical intervals by stop frequency n points if representing the numerical intervals of stop frequency with σ,The default interval threshold of stop frequency n is represented, then：
Wherein,
If representing parking with γ and running at a low speed the numerical intervals apart from d, will stop and run at a low speed distance and be divided into N number of area
Between, μ1、μ2、…、μN-1The stop frequency default interval thresholds of d are represented, then：
Wherein, μ1＜ μ2＜ ... ＜ μN-1。
If withDefault traffic behavior grade is represented, then
Wherein, K is the number of default traffic behavior grade.
S32, by multinomial preference pattern (Multinomial Logistic) algorithm with regress analysis method, obtain each described default
Characteristic parameter numerical intervals combination correspondence traffic behavior grade probability；
Specifically, defineA length of ρ, stop frequency are σ, stop and low when representing the parking of vehicle and running at a low speed
The traffic behavior grade of fast operating range γ,
By traffic behavior gradeUsed as reference category, then the multinomial selection of other traffic behavior grades is returned
Model is：
In formula, β0、β1、β2、β3It is model coefficient.For reference category, all coefficients in model are zero, i.e.,
ProbabilityComputing formula is：
With the corresponding traffic behavior grade of most probable value represent parking and when running at a low speed a length of ρ, stop frequency be σ,
Distance is stopped and run at a low speed for the numerical intervals of γ combine corresponding traffic behavior gradeSpecific formula for calculation is：
Every group of parking and the numerical intervals combination condition for running at a low speed duration, stop frequency, stopping and run at a low speed distance
Under, the traffic behavior for having a corresponding traffic behavior, all numerical intervals combinations is taken together as the default spy
The corresponding relation of parameter and traffic behavior grade is levied, it can be showed by form, it is specific as shown in table 1：
The default characteristic parameter of table 1 and traffic behavior grade mapping table
In step s 4, the characteristic parameter of target road section current preset can be obtained, and obtains its each numerical intervals, you can
Contrasted with table 1 by by the combination of numerical intervals, get the current traffic behavior grade of target road section.
Specifically, the data sample of the vehicle travel situations of target road section current time predetermined number can be randomly selected, is obtained
Each default characteristic parameter of those data samples is taken, the average value of all default characteristic parameters is then taken, that is, tries to achieve unit
The average stop frequency of vehicle in time, it is average stop and run at a low speed duration, it is average stop and run at a low speed distance, and according to
The combination of the numerical intervals belonging to those average values, contrasts by with table 1, gets current traffic behavior of target road section etc.
Level.
Alternatively, the data sample of target road section current time all vehicle travel situations can be also obtained, and obtains those
Each default characteristic parameter of data sample, then according to the combination of the corresponding numerical intervals of the default characteristic parameter of each vehicle,
The traffic behavior grade of respective amount is obtained, occurs the maximum traffic shape of frequency in the traffic behavior grade for taking those respective amounts
State grade, as the traffic behavior grade at target road section current time.
Fig. 2 is the structural representation of road section traffic volume state determination device embodiment of the present invention, and referring to Fig. 2, the present embodiment is public
A kind of road section traffic volume state determination device is opened, including：
Condition evaluation module 1, for according to default traffic behavior grade, to the traffic historical data sample of target road section
Carry out traffic behavior grading；
Parameter acquisition module 2, for obtaining and being graded through traffic behavior after, the corresponding traffic history number of each traffic behavior grade
According to each default characteristic parameter in sample；
Relationship determination module 3, for according to each default characteristic parameter and its corresponding traffic behavior grade, obtaining
The corresponding relation of the default characteristic parameter and traffic behavior grade；
State determining module 4, for obtaining the current each default characteristic parameter of target road section, and according to the default spy
The corresponding relation of parameter and traffic behavior grade is levied, the current traffic behavior grade of target road section is obtained；
Wherein, the default characteristic parameter is used to represent the transport condition of vehicle, and the default characteristic parameter includes：
The stop frequency of vehicle in unit interval, stop and run at a low speed duration, stop and run at a low speed distance.
The road section traffic volume state determination device that the embodiment of the present invention is proposed, due to the historical traffic state according to target road section
Data, generate the corresponding relation of default characteristic parameter and traffic behavior, therefore, the corresponding relation have high accuracy with
It is representative.Afterwards, then the default characteristic parameter current by collecting target road section, and according to the corresponding relation tried to achieve mesh is obtained
The current traffic behavior in mark section, therefore, the traffic behavior for getting has high accuracy.
Specifically, the default traffic behavior grade can be set to 3：Unimpeded, slow, congestion.If further subdivision,
Also traffic behavior can be divided into 4 grades：It is unimpeded, slow, compared with congestion, heavy congestion.It is understood that traffic behavior
Grade can be divided according to actual needs, and the embodiment of the present invention is not construed as limiting to this.
The traffic historical data sample of the target road section can be the traffic data in target road section preset time range.
For example, it may be before target road section current time in 2 months, the traffic historical data at each moment, or
Before target road section current time in 2 months, the traffic historical data of special time period, such as morning peak time period 7：00-9：00,
And the evening peak time period 18:00-20:00 traffic historical data.Certainly, the traffic historical data sample of the target road section
Can be adjusted according to actual needs, the present embodiment is not construed as limiting to this.
The Condition evaluation module 1 can be specifically for：The shadow recorded by the imaging monitoring device for transferring target road section
As data, and default traffic behavior grade is combined, the traffic historical data sample to target road section carries out traffic behavior grading.
After the traffic behavior grading that Condition evaluation module 1 completes to traffic historical data sample, each traffic behavior grade
At least part of traffic historical data sample is then had to correspond to therewith.
For example, certain day morning 7 before target road section current time in 2 months：00-7：10, the traffic shape of target road section
State is slow, then the traffic data sample of the time period is one of corresponding part traffic historical data sample of slow grade.
Further, it is determined that after the corresponding traffic historical data sample of each traffic behavior grade, the parameter acquiring mould
Block 2 then obtains each default characteristic parameter in the traffic historical data sample.
The default characteristic parameter includes：The stop frequency of the vehicle in unit interval, stop and run at a low speed duration,
Stop and run at a low speed distance.
Wherein, the stop frequency refers to that target road section causes the number of times that the vehicle stops within the unit interval because of congestion, when
Between of short duration (such as less than 5 seconds) or long-time exception parking (such as traffic accident) be not counted in stop frequency.
The parking and run at a low speed duration and refer to that vehicle causes the vehicle stop within the unit interval and low speed row because of congestion
The time span sailed.Under normal circumstances, congestion is more serious, stops and to run at a low speed the time more long.
The parking and run at a low speed distance and refer to because caused by congestion, the vehicle is stopped and low speed within the unit interval
The distance of traveling.Under normal circumstances, congestion is more serious, congestion scope is bigger, vehicle parking and to run at a low speed distance more long.
It is understood that in the corresponding traffic historical data sample of each traffic behavior grade, in any time period
Any vehicle for, its default characteristic parameter is not necessarily equal to the default feature of any other vehicles in the time period
Parameter, i.e.,：Each traffic behavior grade to that should have different default characteristic parameters, stop frequency in each vehicle unit time,
Duration is stopped and run at a low speed, stop and runs at a low speed distance and be different.
The relationship determination module 3 specifically for：
According to default interval threshold, the default characteristic parameter is divided into the numerical intervals of predetermined number；
Specifically, if representing parking with ρ and running at a low speed the numerical intervals of duration t, dash when will stop and running at a low speed
It is divided into M numerical intervals, θ1、θ2、…、θM-1Represent parking and run at a low speed the default interval threshold of duration, then：
Wherein, θ1＜ θ2＜ ... ＜ θM-1。
It is H numerical intervals by stop frequency n points if representing the numerical intervals of stop frequency with σ,The default interval threshold of stop frequency n is represented, then：
Wherein,
If representing parking with γ and running at a low speed the numerical intervals apart from d, will stop and run at a low speed distance and be divided into N number of area
Between, μ1、μ2、…、μN-1The stop frequency default interval thresholds of d are represented, then：
Wherein, μ1＜ μ2＜ ... ＜ μN-1。
If withDefault traffic behavior grade is represented, then
Wherein, K is the number of default traffic behavior grade.
The relationship determination module 3 also particularly useful for：Returned by multinomial preference pattern (Multinomial Logistic)
Return parser, obtain the probability of the combination correspondence traffic behavior grade of each default characteristic parameter numerical intervals；
Specifically, defineA length of ρ, stop frequency are σ, stop and low when representing the parking of vehicle and running at a low speed
The traffic behavior grade of fast operating range γ,
By traffic behavior gradeUsed as reference category, then the multinomial selection of other traffic behavior grades is returned
Model is：
In formula, β0、β1、β2、β3It is model coefficient.For reference category, all coefficients in model are zero, i.e.,
ProbabilityComputing formula is：
With the corresponding traffic behavior grade of most probable value represent parking and when running at a low speed a length of ρ, stop frequency be σ,
Distance is stopped and run at a low speed for the numerical intervals of γ combine corresponding traffic behavior gradeSpecific formula for calculation is：
Every group of parking and the numerical intervals combination condition for running at a low speed duration, stop frequency, stopping and run at a low speed distance
Under, the traffic behavior for having a corresponding traffic behavior, all numerical intervals combinations is taken together as the default spy
The corresponding relation of parameter and traffic behavior grade is levied, it can be showed by form, it is specific as shown in table 1：
The default characteristic parameter of table 1 and traffic behavior grade mapping table
The state determining module 4 can obtain the characteristic parameter of target road section current preset, and obtain its each numerical value area
Between, then contrasted with table 1 by by the combination of numerical intervals, get the current traffic behavior grade of target road section.
Specifically, the state determining module 4 can randomly select the vehicle traveling of target road section current time predetermined number
The data sample of situation, obtains each default characteristic parameter of those data samples, then takes all default characteristic parameters
Average value, that is, try to achieve the average stop frequency of vehicle in the unit interval, averagely stop and run at a low speed duration, averagely stop and low
Fast operating range, and the numerical intervals according to belonging to those average values combination, contrasted by with table 1, get target road section
Current traffic behavior grade.
Alternatively, the state determining module 4 can also obtain the number of target road section current time all vehicle travel situations
According to sample, and each default characteristic parameter of those data samples is obtained, then according to the default characteristic parameter correspondence of each vehicle
Numerical intervals combination, obtain respective amount traffic behavior grade, go out in the traffic behavior grade for taking those respective amounts
The maximum traffic behavior grade of existing frequency, as the traffic behavior grade at target road section current time.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
Modified with to the technical scheme described in foregoing embodiments, or equivalent is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (6)
1. a kind of road section traffic volume state determines method, it is characterised in that including：
According to default traffic behavior grade, the traffic historical data sample to target road section carries out traffic behavior grading；
Obtain after being graded through traffic behavior, each default feature ginseng in the corresponding traffic historical data sample of each traffic behavior grade
Number；
According to each default characteristic parameter and its corresponding traffic behavior grade, obtain the default characteristic parameter and hand over
The corresponding relation of logical state grade；
The current each default characteristic parameter of target road section is obtained, and according to the default characteristic parameter and traffic behavior grade
Corresponding relation, obtains the current traffic behavior grade of target road section；
Wherein, the default characteristic parameter is used to represent the transport condition of vehicle, and the default characteristic parameter includes：Unit
The stop frequency of vehicle in time, stop and run at a low speed duration, stop and run at a low speed distance.
2. method according to claim 1, it is characterised in that described according to the default characteristic parameter and its corresponding
Traffic behavior grade, obtain the default characteristic parameter includes with the corresponding relation of traffic behavior grade：
According to default interval threshold, each default characteristic parameter is divided into the numerical intervals of predetermined number；
Obtain the probability of the combination correspondence traffic behavior grade of each default characteristic parameter numerical intervals；
By the traffic behavior grade of the maximum probability, the combination as each default characteristic parameter numerical intervals is corresponding
Traffic behavior grade；
According to the corresponding traffic behavior grade of combination of each default characteristic parameter numerical intervals, the default spy is obtained
Levy the corresponding relation of parameter and traffic behavior grade.
3. method according to claim 2, it is characterised in that each default characteristic parameter numerical intervals of acquisition
The probability of combination correspondence traffic behavior grade include：
By multinomial preference pattern algorithm with regress analysis method, the combination correspondence for obtaining each default characteristic parameter numerical intervals is handed over
The probability of logical state grade.
4. a kind of road section traffic volume state determination device, it is characterised in that including：
Condition evaluation module, for according to default traffic behavior grade, the traffic historical data sample to target road section to be carried out
Traffic behavior is graded；
Parameter acquisition module, for obtaining and being graded through traffic behavior after, the corresponding traffic historical data sample of each traffic behavior grade
Each default characteristic parameter in this；
Relationship determination module, for according to each default characteristic parameter and its corresponding traffic behavior grade, obtaining described
The corresponding relation of default characteristic parameter and traffic behavior grade；
State determining module, for obtaining the current each default characteristic parameter of target road section, and joins according to the default feature
Number and the corresponding relation of traffic behavior grade, obtain the current traffic behavior grade of target road section；
Wherein, the default characteristic parameter is used to represent the transport condition of vehicle, and the default characteristic parameter includes：Unit
The stop frequency of vehicle in time, stop and run at a low speed duration, stop and run at a low speed distance.
5. device according to claim 4, it is characterised in that the relationship determination module specifically for：
According to default interval threshold, each default characteristic parameter is divided into the numerical intervals of predetermined number；
Obtain the probability of the combination correspondence traffic behavior grade of each default characteristic parameter numerical intervals；
By the traffic behavior grade of the maximum probability, the combination as each default characteristic parameter numerical intervals is corresponding
Traffic behavior grade；
According to the corresponding traffic behavior grade of combination of each default characteristic parameter numerical intervals, the default spy is obtained
Levy the corresponding relation of parameter and traffic behavior grade.
6. device according to claim 5, it is characterised in that the relationship determination module also particularly useful for：
By multinomial preference pattern algorithm with regress analysis method, the combination correspondence for obtaining each default characteristic parameter numerical intervals is handed over
The probability of logical state grade.
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| CN107331172A (en) * | 2017-08-25 | 2017-11-07 | 青岛海信网络科技股份有限公司 | A kind of traffic phase adjusting method and device based on road traffic state |
| CN109849935A (en) * | 2019-02-20 | 2019-06-07 | 百度在线网络技术（北京）有限公司 | A kind of method of controlling security, device and storage medium |
| CN110782659A (en) * | 2019-09-09 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Road condition determining method, road condition determining device, server and storage medium |
| CN112598199A (en) * | 2021-01-29 | 2021-04-02 | 杭州易龙安全科技有限公司 | Monitoring and early warning method based on decision tree algorithm |
| CN112700650A (en) * | 2021-01-29 | 2021-04-23 | 杭州易龙安全科技有限公司 | Safe intelligent monitoring and early warning method |
| CN113628454A (en) * | 2021-08-18 | 2021-11-09 | 中国联合网络通信集团有限公司 | Traffic signal lamp control method, device and system |
| CN116824863A (en) * | 2023-08-28 | 2023-09-29 | 速度时空大数据研究(深圳)有限公司 | Intelligent road network monitoring system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101271622A (en) * | 2007-03-22 | 2008-09-24 | 上海经达实业发展有限公司 | Instant condition discrimination and inducing system of urban highway traffic |
| CN101710449A (en) * | 2009-12-04 | 2010-05-19 | 吉林大学 | Traffic flow running rate recognizing method based on bus GPS data |
| CN102460534A (en) * | 2009-04-22 | 2012-05-16 | 因瑞克斯有限公司 | Predicting expected road traffic conditions based on historical and current data |
| CN104851293A (en) * | 2015-05-18 | 2015-08-19 | 安徽科力信息产业有限责任公司 | Road section traffic congestion index evaluation method based on spot speed |
| CN105389987A (en) * | 2015-12-03 | 2016-03-09 | 青岛海信网络科技股份有限公司 | Road traffic condition prediction method and device |
-
2016
- 2016-11-30 CN CN201611090785.1A patent/CN106781460B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101271622A (en) * | 2007-03-22 | 2008-09-24 | 上海经达实业发展有限公司 | Instant condition discrimination and inducing system of urban highway traffic |
| CN102460534A (en) * | 2009-04-22 | 2012-05-16 | 因瑞克斯有限公司 | Predicting expected road traffic conditions based on historical and current data |
| CN101710449A (en) * | 2009-12-04 | 2010-05-19 | 吉林大学 | Traffic flow running rate recognizing method based on bus GPS data |
| CN104851293A (en) * | 2015-05-18 | 2015-08-19 | 安徽科力信息产业有限责任公司 | Road section traffic congestion index evaluation method based on spot speed |
| CN105389987A (en) * | 2015-12-03 | 2016-03-09 | 青岛海信网络科技股份有限公司 | Road traffic condition prediction method and device |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107331172A (en) * | 2017-08-25 | 2017-11-07 | 青岛海信网络科技股份有限公司 | A kind of traffic phase adjusting method and device based on road traffic state |
| CN107331172B (en) * | 2017-08-25 | 2020-11-20 | 青岛海信网络科技股份有限公司 | Traffic phase adjusting method and device based on road traffic state |
| CN109849935A (en) * | 2019-02-20 | 2019-06-07 | 百度在线网络技术（北京）有限公司 | A kind of method of controlling security, device and storage medium |
| CN109849935B (en) * | 2019-02-20 | 2021-02-02 | 百度在线网络技术（北京）有限公司 | Security control method, device and storage medium |
| CN110782659A (en) * | 2019-09-09 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Road condition determining method, road condition determining device, server and storage medium |
| CN110782659B (en) * | 2019-09-09 | 2021-06-01 | 腾讯科技（深圳）有限公司 | Road condition determining method, road condition determining device, server and storage medium |
| CN112598199A (en) * | 2021-01-29 | 2021-04-02 | 杭州易龙安全科技有限公司 | Monitoring and early warning method based on decision tree algorithm |
| CN112700650A (en) * | 2021-01-29 | 2021-04-23 | 杭州易龙安全科技有限公司 | Safe intelligent monitoring and early warning method |
| CN113628454A (en) * | 2021-08-18 | 2021-11-09 | 中国联合网络通信集团有限公司 | Traffic signal lamp control method, device and system |
| CN116824863A (en) * | 2023-08-28 | 2023-09-29 | 速度时空大数据研究(深圳)有限公司 | Intelligent road network monitoring system |
| CN116824863B (en) * | 2023-08-28 | 2023-12-26 | 深圳市天速智能有限公司 | Intelligent road network monitoring system |
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