CN109035775A - A kind of method and device of emergency event identification - Google Patents
A kind of method and device of emergency event identification Download PDFInfo
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- CN109035775A CN109035775A CN201810963330.9A CN201810963330A CN109035775A CN 109035775 A CN109035775 A CN 109035775A CN 201810963330 A CN201810963330 A CN 201810963330A CN 109035775 A CN109035775 A CN 109035775A
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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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract
The invention discloses a kind of method and devices of emergency event identification, this method includes the traffic data for obtaining at least one data source in section to be identified, according to the traffic data of at least one data source, determine the traffic congestion distinguishing indexes of at least one data source, according to the historical data of the traffic congestion distinguishing indexes of at least one data source in section to be identified and traffic congestion distinguishing indexes, determine the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is assessed, determine the assessed value in section to be identified, if the assessed value in section to be identified is greater than first threshold, then determine that section to be identified is the section that emergency event occurs.Traffic behavior is differentiated by multi-data source, the inherent limitation for differentiating traffic behavior based on single source data is overcome, ensure that the accuracy for differentiating result and practical applicability.
Description
Technical field
The present embodiments relate to the method and devices that technical field of transportation more particularly to a kind of emergency event identify.
Background technique
In intelligent transportation field, burst traffic congestion information can be obtained in time, it will have pole to traffic administration and control
It is big to help, be conducive to improve traffic control level, reduce urban road congestion degree.With information-based and artificial intelligence technology
Development, traffic management department can obtain the traffic data of more and more different data sources, for section burst traffic congestion event
Judgement provides foundation.The traffic congestion event of section burst at present mainly passes through manual video inspection and citizen report two kinds of approach
Obtain, manual inspection low efficiency, quantity are few, mainly reported with citizen based on, thus citizen the complaint amount of emergency event is occupied it is high
Under not, on the one hand can not real time reaction traffic circulation state, another aspect traffic incident easily cause burst traffic gather around
It is stifled, if will to will cause section vehicle pass-through not in time slow for processing, sprawling is lined up until overflowing, leads to greater area of traffic
Paralysis.
Summary of the invention
The embodiment of the present invention provides a kind of method and device of emergency event identification, overcomes and differentiates friendship based on single data source
The inherent limitation of logical state, improves the accuracy for differentiating result and practical applicability.
Method for distinguishing is known in a kind of emergency event provided in an embodiment of the present invention, comprising:
Obtain the traffic data of at least one data source in section to be identified；
According to the traffic data of at least one data source, the traffic congestion identification of at least one data source is determined
Index；
According to the traffic congestion distinguishing indexes and traffic congestion of at least one data source in the section to be identified
The historical data of distinguishing indexes determines the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes；
The catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is assessed, determines institute
State the assessed value in section to be identified；
If the assessed value in the section to be identified is greater than first threshold, it is determined that the section to be identified is that burst thing occurs
The section of part.
Traffic behavior is differentiated by multi-data source, overcomes the intrinsic office for differentiating traffic behavior based on single source data
It is sex-limited, it ensure that the accuracy for differentiating result and practical applicability.
Optionally, the traffic congestion distinguishing indexes of at least one data source according to the section to be identified with
And the historical data of traffic congestion distinguishing indexes, determine the mutation in the corresponding section to be identified of each traffic congestion distinguishing indexes
Situation, comprising:
For any one traffic congestion distinguishing indexes of at least one data source, the traffic congestion identification is obtained
Historical data before the current time of index in preset time period；
According to the mean value and variance of the historical data of the traffic congestion distinguishing indexes, determine that the traffic congestion identification refers to
First range threshold of target historical data；
If the traffic distinguishing indexes are greater than the first range threshold of its historical data, it is determined that the traffic distinguishing indexes
It mutates；
Determine in continuous n time interval whether identify with the presence of the time interval traffic greater than the first amount threshold
Index mutates, if so, determining that the catastrophe in the corresponding section to be identified of the traffic congestion distinguishing indexes is
It is doubtful to have emergency event, otherwise, it determines the catastrophe in the corresponding section to be identified of the traffic distinguishing indexes is without prominent
Hair event, wherein n is positive integer.
Optionally, the catastrophe to the corresponding section to be identified of each traffic congestion distinguishing indexes carries out
Assessment, determines the assessed value in the section to be identified, comprising:
It gives a mark to the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, and according to
The weight of each traffic congestion distinguishing indexes determines the gross score in the section to be identified；The gross score in the section to be identified
For all traffic congestion distinguishing indexes in addition to the index of crossing score it is cumulative；
The gross score in the section to be identified is determined as to the assessed value in the section to be identified.
Optionally, at least one data source includes one of following data source or any combination:
Microwave data source, electricity alert data source, loop data source, Online Map data source.
Optionally, after the determination section to be identified is that the section of emergency event occurs, further includes:
If the crossing index in the section to be identified is congestion, it is determined that the section to be identified is traffic burst congestion shape
State.
Correspondingly, the embodiment of the invention also provides a kind of devices of emergency event identification, comprising:
Acquiring unit, the traffic data of at least one data source for obtaining section to be identified；
Processing unit determines at least one data source for the traffic data according at least one data source
Traffic congestion distinguishing indexes；According to the traffic congestion distinguishing indexes of at least one data source in the section to be identified with
And the historical data of traffic congestion distinguishing indexes, determine the mutation in the corresponding section to be identified of each traffic congestion distinguishing indexes
Situation；The catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is assessed, determine described in
The assessed value in section to be identified；If the assessed value in the section to be identified is greater than first threshold, it is determined that the section to be identified
For the section that emergency event occurs.
Optionally, the processing unit is specifically used for:
For any one traffic congestion distinguishing indexes of at least one data source, the traffic congestion identification is obtained
Historical data before the current time of index in preset time period；
According to the mean value and variance of the historical data of the traffic congestion distinguishing indexes, determine that the traffic congestion identification refers to
First range threshold of target historical data；
If the traffic distinguishing indexes are greater than the first range threshold of its historical data, it is determined that the traffic distinguishing indexes
It mutates；
Determine in continuous n time interval whether identify with the presence of the time interval traffic greater than the first amount threshold
Index mutates, if so, determining that the catastrophe in the corresponding section to be identified of the traffic congestion distinguishing indexes is
It is doubtful to have emergency event, otherwise, it determines the catastrophe in the corresponding section to be identified of the traffic distinguishing indexes is without prominent
Hair event, wherein n is positive integer.
Optionally, the processing unit is specifically used for:
It gives a mark to the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, and according to
The weight of each traffic congestion distinguishing indexes determines the gross score in the section to be identified；The gross score in the section to be identified
For all traffic congestion distinguishing indexes in addition to the index of crossing score it is cumulative；
The gross score in the section to be identified is determined as to the assessed value in the section to be identified.
Optionally, at least one data source includes one of following data source or any combination:
Microwave data source, electricity alert data source, loop data source, Online Map data source.
Optionally, the processing unit is also used to:
After the determination section to be identified is that the section of emergency event occurs, if the road in the section to be identified
Mouth index is congestion, it is determined that the section to be identified is traffic burst congestion status.
Correspondingly, the embodiment of the invention also provides a kind of calculating equipment, comprising:
Memory, for storing program instruction；
Processor executes above-mentioned burst according to the program of acquisition for calling the program instruction stored in the memory
The method of event recognition.
Correspondingly, the embodiment of the invention also provides a kind of computer-readable non-volatile memory medium, including computer
Readable instruction, when computer is read and executes the computer-readable instruction, so that computer executes above-mentioned emergency event and knows
Method for distinguishing.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly introduced, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill in field, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is the flow diagram that method for distinguishing is known in a kind of emergency event provided in an embodiment of the present invention；
Fig. 2 is a kind of structural schematic diagram of the device of emergency event identification provided in an embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
It is described in detail to one step, it is clear that described embodiments are only a part of the embodiments of the present invention, rather than whole implementation
Example.Based on the embodiments of the present invention, obtained by those of ordinary skill in the art without making creative efforts
All other embodiment, shall fall within the protection scope of the present invention.
Fig. 1 illustratively shows the process that method for distinguishing is known in a kind of emergency event provided in an embodiment of the present invention, the stream
The device that journey can be identified by emergency event executes.
As shown in Figure 1, the process specifically includes:
Step 101, the traffic data of at least one data source in section to be identified is obtained.
In embodiments of the present invention, which may include one of following data source or any combination: micro-
The alert data source of wave data source, electricity, loop data source, Online Map data source etc..
It should be noted that the type of above-mentioned data source is only example effect, the quantity of data source is not specifically limited,
The data source of other types can also be increased in the specific implementation.For example, the Online Map data source can for Amap or
The Online Maps data source such as Baidu map.
After obtaining at least one data source in section to be identified, data on flows is based on to every kind of data source and judges that equipment is
No exception, the recording equipment exception information if abnormal.
Step 102, according to the traffic data of at least one data source, the traffic of at least one data source is determined
Congestion distinguishing indexes.
Different data source correspondences possess different traffic congestion distinguishing indexes, such as: the traffic congestion in loop data source
Distinguishing indexes can lose for the equal occupation rate of vehicle, crossing green light；The traffic congestion distinguishing indexes in microwave data source can be section
Average speed, road-section average flow, road-section average occupation rate；The traffic congestion distinguishing indexes of the alert data source of electricity can be flat for section
Equal speed, upstream vehicle pass through ratio, queue length, flow；The traffic congestion distinguishing indexes of Online Map data source can be
Road average-speed, road congestion state.
Above-mentioned traffic congestion distinguishing indexes are only example effects, can increase as the case may be in practical application and add deduct
It is few.
Different traffic congestion distinguishing indexes needs are respectively calculated, and will be detailed below calculating process, wherein
Line source of map data is by taking Amap data source as an example.
1) the traffic congestion distinguishing indexes for calculating loop data source may include the equal occupation rate of vehicle, the loss of crossing green light.
A) the equal occupation rate of vehicle is calculated using formula (1).
The formula (1) are as follows:
Wherein,For average occupancy of the section l in time period t；N is number of track-lines；oi,tTo pass through road in time period t
The occupation rate of the phase j of section l；qi,tTo pass through the flow of the phase j of section l in time period t.
B) crossing green light loses
The sum of crossing green light loss=each entrance ingress green light loss.
2) calculate microwave data source traffic congestion distinguishing indexes may include road average-speed, road-section average flow,
Road-section average occupation rate.
A) road average-speed is calculated using formula (2).
The formula (2) are as follows:
Wherein,The average speed (meter per second) for being section l in time period t；N is number of track-lines；vi,tFor in time period t
Pass through the speed (meter per second) of the lane i of section l；qi,tTo pass through the vehicle number () of the lane i of section l in time period t.
B) road-section average flow is calculated using formula (3).
The formula (3) are as follows:
Wherein,For the vehicle number that average every lane in time period t by section l passes through, qi,tTo lead in time period t
Cross the vehicle number of the lane i of section l；N is number of track-lines.
C) road-section average occupation rate is calculated using formula (4).
The formula (4) are as follows:
Wherein,For average occupancy of the section l in time period t；N is number of track-lines；oi,tTo pass through road in time period t
The occupation rate of the lane i of section l, qi,tTo pass through the flow of the lane i of section l in time period t.
3) the traffic congestion distinguishing indexes for calculating the alert data source of electricity may include that road average-speed, upstream vehicle pass through ratio
Example, queue length, flow.
A) road average-speed is calculated using formula (5).
The formula (5) are as follows:
Wherein,The average speed (meter per second) for being section l in time period t；L be section l length (rice), n be when
Between section l upstream and downstream crossing is matched in section t vehicle number；tk,tAfter cancelling noise, pass through the vehicle of section l in time period t
The journey time of k.
B) upstream vehicle passes through ratio
Enter the flow Q in section in statistical time range firstin, then count the vehicle number Q being matched in the periodma, finally
It calculates upstream vehicle and passes through ratio: Rup_cr=Qin/Qma。
C) queue length
Each time interval calculates in time period t, the queue length of every entrance driveway.The data storage will act as history stream
Measure data.
D) flow
Each time interval, in statistical time section t, every entrance driveway passes through vehicle number, i.e. number after removal repeated data
According to item number.The data storage will act as historical traffic data.
4) calculating the traffic congestion distinguishing indexes of Amap data source may include road average-speed, section congestion shape
State.
A) road average-speed is calculated using formula (6).
The formula (6) are as follows:
Wherein,The average speed (meter per second) for being section l in time period t；N is that section l is effective in time period t
Data volume；vk,tTo pass through the speed (meter per second) of the vehicle k of section l in time period t.
B) road congestion state
High moral block status is divided into several grades such as unobstructed, congestion and heavy congestion, determines road according to high moral block status
Section congestion calculates congestion index.
Step 103, according to the traffic congestion distinguishing indexes of at least one data source in the section to be identified and
The historical data of traffic congestion distinguishing indexes determines the mutation feelings in the corresponding section to be identified of each traffic congestion distinguishing indexes
Condition.
After obtaining each traffic congestion distinguishing indexes, section to be identified is just differentiated according to its corresponding historical data
Catastrophe.The catastrophe, which can be divided into, doubtful has catastrophic event and without catastrophic event.
It is specifically as follows: for any one traffic congestion distinguishing indexes of at least one data source, obtains traffic congestion
Historical data before the current time of distinguishing indexes in preset time period, according to the equal of the historical data of traffic congestion distinguishing indexes
Value and variance, determine the first range threshold of the historical data of traffic congestion distinguishing indexes, if traffic distinguishing indexes are gone through greater than it
First range threshold of history data, it is determined that traffic distinguishing indexes mutate, and whether determine has greatly in continuous n time interval
In the first amount threshold time interval there are the mutation of traffic distinguishing indexes, if so, determining traffic congestion distinguishing indexes
The catastrophe in corresponding section to be identified has emergency event to be doubtful, otherwise, it determines traffic distinguishing indexes are corresponding to be identified
The catastrophe in section is no emergency event, wherein n is positive integer.First range threshold can be carried out according to historical data
It determines, is mainly determined according to the mean value of historical data and variance.
For example, firstly, calculating threshold value based on historical data.15 minutes historical datas, meter before acquisition current time
Calculate the mean value of historical dataAnd varianceAnd calculate 3 σ ranges of historical dataIfThen think beyond historical range.
Then each indexes suddenly changed differentiation of multi-data source is carried out, such as:
1) index in microwave data source
(1) if the current time obtained index (speed, flow, occupation rate) in microwave data source is beyond its history model
It encloses, then it is assumed that mutation；
(2) continuous n time interval has the index of m% to mutate, then it is assumed that and it is doubtful to have emergency event, otherwise it is assumed that
Without emergency event.N and m is positive integer.
2) index of the alert data source of electricity
A) flow is mutated
Step 1, for data on flows, with 5 minutes for time window, 1 minute is time interval, each time interval, statistics
In current time window, every entrance driveway passes through vehicle number.
Step 2, since historical data is 1 minute data, so needing to convert 5-minute data for historical data, with 5 points
The multiple 1 minute data on flows of history are merged into the flow of multiple 5 minutes windows by clock time window.
Step 3, each time interval calculate the mean value and variance of 5 minutes flows of its corresponding multiple time window of history.
If present flow rate exceeds its historical range, then it is assumed that flow mutates.
If continuous n time interval has the mutation of m% flow, then it is assumed that it is doubtful to have emergency event, otherwise it is assumed that nothing
Emergency event.
B) velocity jump
Step 1, each time interval (default 1 minute can configure) calculate the vehicle of crossing in 5 minutes windows (configurable) and put down
Equal speed.That is the average speed of each vehicle in 5 minutes windows.
Step 2 obtains historical average speeds according to historical data securing mechanism and determines historical data acquisition time range,
Obtaining has the historical speed data of intersection with the time range, and calculates historical average speeds and variance.If present period is flat
Equal speed exceeds its historical range, then it is assumed that speed mutates.
Step 3 has the mutation of m% speed if continuous n time interval, then it is assumed that and it is doubtful to have emergency event, otherwise
Think no emergency event.
C) upstream vehicle is mutated by ratio
Step 1, if current point in time, upstream vehicle is more than 3 σ ranges of historical data by ratio, then it is assumed that should be into
Mouth road upstream vehicle passes through ratio and mutates.
Step 2, continuous n time interval have m% entrance driveway upstream vehicle to pass through ratio and mutate, then it is assumed that should be into
Mouth road corresponding road section is doubtful emergency event, otherwise it is assumed that without emergency event.
D) queue length is mutated
Step 1, if current point in time, queue length is more than 3 σ ranges of historical data, then it is assumed that the entrance driveway is lined up
Length mutates.
Step 2, continuous n time interval have m% queue length mutation, then it is assumed that the entrance driveway corresponding road section is doubted
Seemingly there is emergency event.
3) index of high moral data source
(1) if current time obtained speed is beyond its historical range, then it is assumed that mutation；
(2) continuous n time interval has the index of m% to mutate, then it is assumed that and it is doubtful to have emergency event, otherwise it is assumed that
Without emergency event.
4) index in loop data source
(1) if current time obtained data target (occupation rate, green damage) is beyond its historical range, then it is assumed that mutation；
(2) continuous n time interval has the index of m% to mutate, then it is assumed that and it is doubtful to have emergency event, otherwise it is assumed that
Without emergency event.
Step 104, the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is commented
Estimate, determines the assessed value in the section to be identified.
When determining the assessed value in section to be identified, can be realized by way of marking, specific:
It gives a mark to the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, and is gathered around according to each traffic
The weight of stifled distinguishing indexes determines that the gross score in section to be identified, the gross score in the section to be identified are in addition to the index of crossing
The score of all traffic congestion distinguishing indexes it is cumulative after obtain.The gross score in the section to be identified can be determined as wait know
The assessed value in other section.
Specific scoring process is as follows:
Whether section to be identified occurs emergency event, needs first to carry out the catastrophe of each traffic congestion distinguishing indexes
Marking, wherein the catastrophe in the corresponding section to be identified of traffic congestion distinguishing indexes is doubtful when having emergency event, and giving a mark is
1；When the catastrophe in the corresponding section to be identified of traffic congestion distinguishing indexes is no emergency event, giving a mark is -1；Traffic congestion
When data are not present in distinguishing indexes, giving a mark is 0.Marking table shown in can specifically 1 being shown in Table.
Table 1
α, β in above-mentioned table 1 are weight coefficient.Wherein, due to the different corresponding weighteds of data source, identical data
The weight of each index is also different in source, and the corresponding weight of identical index of different data sources is also different.In addition to Amap number
Outside according to the road conditions index in source, if certain index thinks that doubtful emergency event occurs, it is scored at 1；If the index does not have
Data source, then the index is scored at 0；If the index, in history normal range (NR), which is scored at -1 point.
After marking is completed, so that it may add up after the corresponding weight of the score of each index is multiplied, in this way
Just obtain the total score in the section.
It should be noted that the above-mentioned catastrophe to the corresponding section to be identified of each traffic congestion distinguishing indexes is commented
The method estimated is only example effect, and the method that others can be used for assessing is all applied to the embodiment of the present invention.
Step 405, if the assessed value in the section to be identified is greater than first threshold, it is determined that the section to be identified is hair
The section of raw emergency event.
For example, when the assessed value in section to be identified is the total score in section to be identified, if the total score in section to be identified
Greater than first threshold, show that the section is that section occurs for emergency event.The first threshold can be empirically arranged.
Then according to road conditions indication information, it is determined whether be congestion.If road conditions index is congestion, the road can be determined
Section is traffic burst congested link.
Above-described embodiment shows to obtain the traffic data of at least one data source in section to be identified, according at least one
The traffic data of data source determines the traffic congestion distinguishing indexes of at least one data source, according to section to be identified extremely
A kind of traffic congestion distinguishing indexes of few data source and the historical data of traffic congestion distinguishing indexes, determine that each traffic congestion is known
The catastrophe in the corresponding section to be identified of other index, to the mutation feelings in the corresponding section to be identified of each traffic congestion distinguishing indexes
Condition is assessed, and determines the assessed value in section to be identified, if the assessed value in section to be identified is greater than first threshold, it is determined that wait know
Other section is the section that emergency event occurs.Traffic state judging technology based on multi-source big data can using coil, electricity it is alert,
The collected traffic data of microwave, Online Map differentiates traffic behavior, overcomes and differentiates traffic shape based on single source data
The inherent limitation of state ensure that the accuracy for differentiating result and practical applicability, and to same index according to different data
Source is weighted processing, strengthens differentiation dynamics, so that differentiating that result more meets driver's impression.
Based on the same technical idea, Fig. 2 illustratively shows a kind of emergency event provided in an embodiment of the present invention and knows
The structure of other device, the device can execute the process of emergency event identification.
As shown in Fig. 2, the device includes:
Acquiring unit 201, the traffic data of at least one data source for obtaining section to be identified；
Processing unit 202 determines at least one data for the traffic data according at least one data source
The traffic congestion distinguishing indexes in source；According to the traffic congestion distinguishing indexes of at least one data source in the section to be identified
And the historical data of traffic congestion distinguishing indexes, determine the prominent of the corresponding section to be identified of each traffic congestion distinguishing indexes
Become situation；The catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is assessed, determines institute
State the assessed value in section to be identified；If the assessed value in the section to be identified is greater than first threshold, it is determined that the road to be identified
Section is that the section of emergency event occurs.
Optionally, the processing unit 202 is specifically used for:
For any one traffic congestion distinguishing indexes of at least one data source, the traffic congestion identification is obtained
Historical data before the current time of index in preset time period；
According to the mean value and variance of the historical data of the traffic congestion distinguishing indexes, determine that the traffic congestion identification refers to
First range threshold of target historical data；
If the traffic distinguishing indexes are greater than the first range threshold of its historical data, it is determined that the traffic distinguishing indexes
It mutates；
Determine in continuous n time interval whether identify with the presence of the time interval traffic greater than the first amount threshold
Index mutates, if so, determining that the catastrophe in the corresponding section to be identified of the traffic congestion distinguishing indexes is
It is doubtful to have emergency event, otherwise, it determines the catastrophe in the corresponding section to be identified of the traffic distinguishing indexes is without prominent
Hair event, wherein n is positive integer.
Optionally, the processing unit 202 is specifically used for:
It gives a mark to the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, and according to
The weight of each traffic congestion distinguishing indexes determines the gross score in the section to be identified；The gross score in the section to be identified
For all traffic congestion distinguishing indexes in addition to the index of crossing score it is cumulative；
The gross score in the section to be identified is determined as to the assessed value in the section to be identified.
Optionally, at least one data source includes one of following data source or any combination:
Microwave data source, electricity alert data source, loop data source, Online Map data source.
Optionally, the processing unit 202 is also used to:
After the determination section to be identified is that the section of emergency event occurs, if the road in the section to be identified
Mouth index is congestion, it is determined that the section to be identified is traffic burst congestion status.
Based on the same technical idea, the embodiment of the invention also provides a kind of calculating equipment, comprising:
Memory, for storing program instruction；
Processor executes above-mentioned burst according to the program of acquisition for calling the program instruction stored in the memory
The method of event recognition.
Based on the same technical idea, the embodiment of the invention also provides a kind of computer-readable non-volatile memories to be situated between
Matter, including computer-readable instruction, when computer is read and executes the computer-readable instruction, so that computer executes
It states emergency event and knows method for distinguishing.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (12)
1. method for distinguishing is known in a kind of emergency event characterized by comprising
Obtain the traffic data of at least one data source in section to be identified；
According to the traffic data of at least one data source, determine that the traffic congestion identification of at least one data source refers to
Mark；
It is identified according to the traffic congestion distinguishing indexes of at least one data source in the section to be identified and traffic congestion
The historical data of index determines the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes；
The catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is assessed, determine it is described to
Identify the assessed value in section；
If the assessed value in the section to be identified is greater than first threshold, it is determined that the section to be identified is that emergency event occurs
Section.
2. the method as described in claim 1, which is characterized in that at least one number according to the section to be identified
According to the traffic congestion distinguishing indexes in source and the historical data of traffic congestion distinguishing indexes, each traffic congestion distinguishing indexes pair are determined
The catastrophe in the section to be identified answered, comprising:
For any one traffic congestion distinguishing indexes of at least one data source, the traffic congestion distinguishing indexes are obtained
Current time before historical data in preset time period；
According to the mean value and variance of the historical data of the traffic congestion distinguishing indexes, the traffic congestion distinguishing indexes are determined
First range threshold of historical data；
If the traffic distinguishing indexes are greater than the first range threshold of its historical data, it is determined that the traffic distinguishing indexes occur
Mutation；
It whether determines in continuous n time interval with the presence of the time interval traffic distinguishing indexes greater than the first amount threshold
It mutates, if so, determining that the catastrophe in the corresponding section to be identified of the traffic congestion distinguishing indexes is doubtful
There is emergency event, otherwise, it determines the catastrophe in the corresponding section to be identified of the traffic distinguishing indexes is without burst thing
Part, wherein n is positive integer.
3. the method as described in claim 1, which is characterized in that described corresponding to each traffic congestion distinguishing indexes described
The catastrophe in section to be identified is assessed, and determines the assessed value in the section to be identified, comprising:
It gives a mark to the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, and according to described
The weight of each traffic congestion distinguishing indexes determines the gross score in the section to be identified；The gross score in the section to be identified be except
The score of all traffic congestion distinguishing indexes except the index of crossing adds up；
The gross score in the section to be identified is determined as to the assessed value in the section to be identified.
4. the method as described in claim 1, which is characterized in that it is described at least one data source include one of following data source or
Any combination:
Microwave data source, electricity alert data source, loop data source, Online Map data source.
5. such as the described in any item methods of Claims 1-4, which is characterized in that in the determination section to be identified be hair
After the section of raw emergency event, further includes:
If the crossing index in the section to be identified is congestion, it is determined that the section to be identified is traffic burst congestion status.
6. a kind of device of emergency event identification characterized by comprising
Acquiring unit, the traffic data of at least one data source for obtaining section to be identified；
Processing unit determines the friendship of at least one data source for the traffic data according at least one data source
Logical congestion distinguishing indexes；According to the traffic congestion distinguishing indexes of at least one data source in the section to be identified and friendship
The historical data of logical congestion distinguishing indexes, determines the mutation feelings in the corresponding section to be identified of each traffic congestion distinguishing indexes
Condition；The catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes is assessed, determine it is described to
Identify the assessed value in section；If the assessed value in the section to be identified is greater than first threshold, it is determined that the section to be identified is
The section of emergency event occurs.
7. device as claimed in claim 6, which is characterized in that the processing unit is specifically used for:
For any one traffic congestion distinguishing indexes of at least one data source, the traffic congestion distinguishing indexes are obtained
Current time before historical data in preset time period；
According to the mean value and variance of the historical data of the traffic congestion distinguishing indexes, the traffic congestion distinguishing indexes are determined
First range threshold of historical data；
If the traffic distinguishing indexes are greater than the first range threshold of its historical data, it is determined that the traffic distinguishing indexes occur
Mutation；
It whether determines in continuous n time interval with the presence of the time interval traffic distinguishing indexes greater than the first amount threshold
It mutates, if so, determining that the catastrophe in the corresponding section to be identified of the traffic congestion distinguishing indexes is doubtful
There is emergency event, otherwise, it determines the catastrophe in the corresponding section to be identified of the traffic distinguishing indexes is without burst thing
Part, wherein n is positive integer.
8. device as claimed in claim 6, which is characterized in that the processing unit is specifically used for:
It gives a mark to the catastrophe in the corresponding section to be identified of each traffic congestion distinguishing indexes, and according to described
The weight of each traffic congestion distinguishing indexes determines the gross score in the section to be identified；The gross score in the section to be identified be except
The score of all traffic congestion distinguishing indexes except the index of crossing adds up；
The gross score in the section to be identified is determined as to the assessed value in the section to be identified.
9. device as claimed in claim 6, which is characterized in that it is described at least one data source include one of following data source or
Any combination:
Microwave data source, electricity alert data source, loop data source, Online Map data source.
10. such as the described in any item devices of claim 6 to 9, which is characterized in that the processing unit is also used to:
After the determination section to be identified is that the section of emergency event occurs, if the crossing in the section to be identified refers to
It is designated as congestion, it is determined that the section to be identified is traffic burst congestion status.
11. a kind of calculating equipment characterized by comprising
Memory, for storing program instruction；
Processor requires 1 to 5 according to the program execution benefit of acquisition for calling the program instruction stored in the memory
Described in any item methods.
12. a kind of computer-readable non-volatile memory medium, which is characterized in that including computer-readable instruction, work as computer
When reading and executing the computer-readable instruction, so that computer executes such as method described in any one of claim 1 to 5.
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