CN109035775B - Method and device for identifying emergency - Google Patents
Method and device for identifying emergency Download PDFInfo
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- CN109035775B CN109035775B CN201810963330.9A CN201810963330A CN109035775B CN 109035775 B CN109035775 B CN 109035775B CN 201810963330 A CN201810963330 A CN 201810963330A CN 109035775 B CN109035775 B CN 109035775B
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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
Abstract
The invention discloses a method and a device for identifying an emergency, wherein the method comprises the steps of acquiring traffic data of at least one data source of a road section to be identified, determining a traffic jam identification index of the at least one data source according to the traffic data of the at least one data source, determining the mutation condition of the road section to be identified corresponding to each traffic jam identification index according to the traffic jam identification index of the at least one data source of the road section to be identified and historical data of the traffic jam identification index, evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, determining the evaluation value of the road section to be identified, and determining the road section to be identified as the road section with the emergency if the evaluation value of the road section to be identified is greater than a first threshold value. The traffic state is judged through multiple data sources, the inherent limitation of judging the traffic state based on single-source data is overcome, and the accuracy and the actual applicability of a judgment result are ensured.
Description
Technical Field
The embodiment of the invention relates to the technical field of traffic, in particular to a method and a device for identifying an emergency.
Background
In the intelligent traffic field, the sudden traffic jam information can be timely acquired, great help is provided for traffic management and control, the traffic management and control level is favorably improved, and the urban road jam degree is reduced. With the development of informatization and artificial intelligence technology, traffic management departments can acquire more and more traffic data of different data sources, and a basis is provided for judging the sudden traffic jam event of a road section. At present, the emergent traffic jam event of a road section is mainly obtained through two ways of manual video inspection and citizen reporting, the manual inspection efficiency is low, the number is small, and citizen reporting is mainly used as the main mode, so that the complaint amount of citizens to the emergent event is high, on one hand, the emergent traffic jam can not be reflected in real time, on the other hand, the emergent traffic jam can be easily caused by the emergent event, if the handling is not timely, the vehicles on the road section can pass slowly, and the queuing and the spreading can be carried out until the emergent traffic jam occurs, so that the traffic.
Disclosure of Invention
The embodiment of the invention provides a method and a device for identifying an emergency, which overcome the inherent limitation of judging the traffic state based on a single data source and improve the accuracy and the actual applicability of a judgment result.
The method for identifying the emergency event provided by the embodiment of the invention comprises the following steps:
acquiring traffic data of at least one data source of a road section to be identified;
determining a traffic jam identification index of the at least one data source according to the traffic data of the at least one data source;
determining mutation conditions of the road sections to be identified corresponding to the traffic jam identification indexes according to the traffic jam identification indexes of the at least one data source of the road sections to be identified and historical data of the traffic jam identification indexes;
evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the evaluation value of the road section to be identified;
and if the evaluation value of the road section to be identified is greater than a first threshold value, determining that the road section to be identified is the road section with the emergency.
The traffic state is judged through multiple data sources, the inherent limitation of judging the traffic state based on single-source data is overcome, and the accuracy and the actual applicability of a judgment result are ensured.
Optionally, the determining, according to the traffic congestion identification index of the at least one data source of the road segment to be identified and the historical data of the traffic congestion identification index, a sudden change condition of the road segment to be identified corresponding to each traffic congestion identification index includes:
aiming at any traffic jam identification index of the at least one data source, acquiring historical data in a preset time period before the current time of the traffic jam identification index;
determining a first range threshold of the historical data of the traffic jam identification index according to the mean value and the variance of the historical data of the traffic jam identification index;
if the traffic identification index is larger than a first range threshold of historical data of the traffic identification index, determining that the traffic identification index is mutated;
determining whether the traffic identification index has sudden change in time intervals larger than a first quantity threshold value in n continuous time intervals, if so, determining that the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is suspected to have an emergency, otherwise, determining that the sudden change situation of the road section to be identified corresponding to the traffic identification index is not the emergency, wherein n is a positive integer.
Optionally, the evaluating the sudden change condition of the road segment to be identified corresponding to each traffic congestion identification index to determine the evaluation value of the road segment to be identified includes:
scoring the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the total score of the road section to be identified according to the weight of each traffic jam identification index; the total score of the road section to be identified is the accumulation of scores of all traffic jam identification indexes except the intersection index;
and determining the total score of the road section to be identified as the evaluation value of the road section to be identified.
Optionally, the at least one data source includes one or any combination of the following data sources:
the system comprises a microwave data source, an electric alarm data source, a coil data source and an online map data source.
Optionally, after determining that the road segment to be identified is a road segment in which an emergency occurs, the method further includes:
and if the intersection index of the road section to be identified is congestion, determining that the road section to be identified is in a traffic burst congestion state.
Correspondingly, an embodiment of the present invention further provides an emergency event identification apparatus, including:
the acquisition unit is used for acquiring traffic data of at least one data source of the road section to be identified;
the processing unit is used for determining a traffic jam identification index of the at least one data source according to the traffic data of the at least one data source; determining mutation conditions of the road sections to be identified corresponding to the traffic jam identification indexes according to the traffic jam identification indexes of the at least one data source of the road sections to be identified and historical data of the traffic jam identification indexes; evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the evaluation value of the road section to be identified; and if the evaluation value of the road section to be identified is greater than a first threshold value, determining that the road section to be identified is the road section with the emergency.
Optionally, the processing unit is specifically configured to:
aiming at any traffic jam identification index of the at least one data source, acquiring historical data in a preset time period before the current time of the traffic jam identification index;
determining a first range threshold of the historical data of the traffic jam identification index according to the mean value and the variance of the historical data of the traffic jam identification index;
if the traffic identification index is larger than a first range threshold of historical data of the traffic identification index, determining that the traffic identification index is mutated;
determining whether the traffic identification index has sudden change in time intervals larger than a first quantity threshold value in n continuous time intervals, if so, determining that the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is suspected to have an emergency, otherwise, determining that the sudden change situation of the road section to be identified corresponding to the traffic identification index is not the emergency, wherein n is a positive integer.
Optionally, the processing unit is specifically configured to:
scoring the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the total score of the road section to be identified according to the weight of each traffic jam identification index; the total score of the road section to be identified is the accumulation of scores of all traffic jam identification indexes except the intersection index;
and determining the total score of the road section to be identified as the evaluation value of the road section to be identified.
Optionally, the at least one data source includes one or any combination of the following data sources:
the system comprises a microwave data source, an electric alarm data source, a coil data source and an online map data source.
Optionally, the processing unit is further configured to:
and after the road section to be identified is determined to be the road section with the emergency, if the intersection index of the road section to be identified is congested, determining that the road section to be identified is in a traffic emergency congestion state.
Correspondingly, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the emergency identification method according to the obtained program.
Accordingly, embodiments of the present invention also provide a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a computer, the computer-readable instructions cause the computer to perform the above method for identifying an emergency event.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for identifying an emergency event according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an emergency recognition apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 schematically illustrates a flow of a method for emergency event identification, which may be performed by an apparatus for emergency event identification according to an embodiment of the present invention.
As shown in fig. 1, the process specifically includes:
In an embodiment of the present invention, the at least one data source may include one or any combination of the following data sources: a microwave data source, an electric alarm data source, a coil data source, an online map data source, etc.
It should be noted that the types of the data sources are only exemplary, the number of the data sources is not particularly limited, and other types of data sources may be added in the specific implementation. For example, the online map data source may be an online map data source such as a grand map or a Baidu map.
After at least one data source of the road section to be identified is obtained, judging whether equipment is abnormal or not for each data source based on flow data, and recording equipment abnormal information if the equipment is abnormal.
And 102, determining a traffic jam identification index of the at least one data source according to the traffic data of the at least one data source.
Different data sources correspondingly have different traffic jam identification indexes, such as: the traffic jam identification index of the coil data source can be the average vehicle occupancy and the green light loss at the intersection; the traffic jam identification index of the microwave data source can be road section average speed, road section average flow and road section average occupancy; the traffic jam identification index of the electric alarm data source can be the average speed of a road section, the passing proportion of upstream vehicles, the queuing length and the flow; the traffic jam identification index of the online map data source can be the average speed of the road section and the jam state of the road section.
The traffic jam identification index is only used as an example, and can be increased or decreased according to specific situations in practical application.
Different traffic jam identification indexes need to be calculated respectively, and the calculation process will be described in detail below, wherein the online map data source is a high-grade map data source as an example.
1) The traffic jam identification index of the coil data source can be calculated to include the average vehicle occupancy and the green light loss of the intersection.
a) The vehicle average occupancy is calculated using formula (1).
The formula (1) is:
wherein the content of the first and second substances,
the average occupancy rate of the road section l in the time period t is shown; n is the number of lanes; oi,tThe occupancy of the phase j of the passing road section l in the time period t; q. q.si,tIs the flow through phase j of the segment l over the time period t.
b) Green light loss at crossing
The green light loss at the intersection is the sum of the green light losses at each entrance section.
2) The traffic jam identification index for calculating the microwave data source can comprise the average speed of the road section, the average flow of the road section and the average occupancy of the road section.
a) The link average speed is calculated using formula (2).
The formula (2) is:
wherein the content of the first and second substances,
average speed (m/s) for the section of road i over a time period t; n is the number of lanes; v. ofi,tThe speed (m/sec) of a lane i passing through a section l for a time period t; q. q.si,tThe number of vehicles (vehicles) passing through the lane i of the link i in the time period t.
b) The link average flow is calculated using equation (3).
The formula (3) is:
wherein the content of the first and second substances,
the average number of vehicles per lane passing through the section l in the time period t, qi,tThe number of vehicles passing through the lane i of the road section l in the time period t; n is the number of lanes.
c) The link average occupancy is calculated using formula (4).
The formula (4) is:
wherein the content of the first and second substances,
the average occupancy rate of the road section l in the time period t is shown; n is the number of lanes; oi,tOccupancy of a lane i through a section of road i during a time period t, qi,tIs the flow through lane i of road section i during time period t.
3) The traffic jam identification indexes of the electric police data source can comprise the average speed of a road section, the passing proportion of upstream vehicles, the queuing length and the flow.
a) The link average speed is calculated using formula (5).
The equation (5) is:
wherein the content of the first and second substances,
average speed (m/s) for the section of road i over a time period t; l is the length (meter) of the road section L, and n is the number of vehicles matched at the upstream and downstream intersections of the road section L in the time period t; t is tk,tIn order to eliminate the noise, the travel time of the vehicle k passing through the section l in the time period t is determined.
b) Upstream vehicle passing ratio
Firstly, the flow Q of the entering road section in the time period is countedinThen counting the number Q of matched vehicles in the time periodmaAnd finally, calculating the upstream vehicle passing ratio: rup_cr＝Qin/Qma。
c) Length of queue
And calculating the queuing length of each inlet channel in the time period t at each time interval. This data store will be used as historical traffic data.
d) Flow rate
And counting the number of passing vehicles per entrance lane in the time period t at each time interval, namely the number of data after the repeated data are removed. This data store will be used as historical traffic data.
4) The traffic jam identification index of the calculated high-grade map data source can comprise the average speed of the road section and the jam state of the road section.
a) The link average speed is calculated using equation (6).
The equation (6) is:
wherein the content of the first and second substances,
average speed (m/s) for the section of road i over a time period t; n is the effective data volume of the road section l in the time period t; v. ofk,tIs the speed (m/s) of vehicle k through section i during time period t.
b) Road segment congestion status
The states of the high-grade road sections are divided into a plurality of levels such as smooth road sections, congestion and serious congestion, and the congestion index of the road sections is judged according to the states of the high-grade road sections.
103, determining the mutation condition of the road section to be identified corresponding to each traffic jam identification index according to the traffic jam identification index of the at least one data source of the road section to be identified and the historical data of the traffic jam identification index.
After each traffic jam identification index is obtained, the mutation condition of the road section to be identified is judged according to the historical data corresponding to each traffic jam identification index. The mutation can be divided into suspected mutation events and non-mutation events.
The method specifically comprises the following steps: the method comprises the steps of acquiring historical data of any traffic jam identification index of at least one data source in a preset time period before the current time, determining a first range threshold of the historical data of the traffic jam identification index according to the mean value and variance of the historical data of the traffic jam identification index, determining that the traffic jam identification index has sudden change if the traffic jam identification index is larger than the first range threshold of the historical data, determining whether the traffic jam identification index has sudden change in time intervals larger than a first number threshold in continuous n time intervals, determining that the sudden change situation of a road section to be identified corresponding to the traffic jam identification index is suspected to have an emergency if the traffic jam identification index has the sudden change situation, and determining that the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is not the emergency if the traffic jam identification index has the sudden change situation, wherein n is a positive integer. The first range threshold may be determined from historical data, primarily from the mean and variance of the historical data.
For example, first, a threshold value is calculated based on historical data. Acquiring historical data 15 minutes before the current time, and calculating the mean value of the historical data
Sum variance
And calculate3 sigma range of historical data
If it is not
The history is considered to be out of range.
Then, carrying out mutation judgment on each index of multiple data sources, for example:
1) index of microwave data source
(1) If the indexes (speed, flow and occupancy rate) of the microwave data source obtained at the current moment exceed the historical range, the microwave data source is considered to be mutated;
(2) and if m% of indexes are mutated at n continuous time intervals, determining that the accident is suspected to exist, otherwise, determining that the accident does not exist. n and m are positive integers.
2) Indicators for electrical alarm data sources
a) Sudden change in flow
And step 1, counting the number of passing vehicles per entrance lane in the current time window by taking 5 minutes as a time window and 1 minute as a time interval according to the flow data.
In step 2, since the historical data is 1 minute data, the historical data needs to be converted into 5 minutes data, and a plurality of historical 1 minute flow data are combined into a plurality of 5 minute flow data in a 5 minute time window.
And 3, calculating the mean value and the variance of the 5-minute flow of the historical time windows corresponding to each time interval. If the current flow rate is beyond its historical range, the flow rate is considered to be abrupt.
And if m% of flow is mutated in n continuous time intervals, determining that the emergency is suspected to exist, otherwise, determining that the emergency does not exist.
b) Sudden change of speed
In step 1, the average speed of passing vehicles within a 5-minute time window (configurable) is calculated every time interval (default 1 minute, configurable). I.e. the average speed of each vehicle within a 5 minute time window.
And 2, acquiring historical average speed, determining a historical data acquisition time range according to a historical data acquisition mechanism, acquiring historical speed data intersected with the time range, and calculating the historical average speed and the variance. If the average speed in the current period is beyond the historical range, the speed is considered to have sudden change.
And 3, if m% of speed is suddenly changed in n continuous time intervals, determining that the emergency is suspected to exist, otherwise, determining that the emergency does not exist.
c) Upstream vehicle passing proportion mutation
Step 1, if the upstream vehicle passing proportion exceeds the 3 sigma range of the historical data at the current time point, the upstream vehicle passing proportion of the approach is considered to be suddenly changed.
And 2, continuously carrying out n time intervals, and if m% of upstream vehicles of the entrance lane suddenly change in passing proportion, considering that the corresponding road section of the entrance lane is suspected to have an emergency, otherwise, considering that no emergency exists.
d) Sudden change of queue length
Step 1, if the queue length at the current time point exceeds the 3 sigma range of the historical data, the queue length of the entrance lane is considered to be suddenly changed.
And 2, continuously carrying out n time intervals, and if m% of queuing length changes suddenly, determining that the road section corresponding to the inlet road is suspected to have an emergency.
3) Indicators of a high-end data source
(1) If the speed obtained at the current moment exceeds the historical range, the speed is considered to be suddenly changed;
(2) and if m% of indexes are mutated at n continuous time intervals, determining that the accident is suspected to exist, otherwise, determining that the accident does not exist.
4) Indices of coil data sources
(1) If the data indexes (occupancy and green loss) obtained at the current moment exceed the historical range, the data indexes are considered to be mutated;
(2) and if m% of indexes are mutated at n continuous time intervals, determining that the accident is suspected to exist, otherwise, determining that the accident does not exist.
And 104, evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the evaluation value of the road section to be identified.
When determining the evaluation value of the road section to be identified, the evaluation value can be determined in a scoring manner, specifically:
and scoring the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the total score of the road section to be identified according to the weight of each traffic jam identification index, wherein the total score of the road section to be identified is obtained by accumulating the scores of all traffic jam identification indexes except the intersection index. The total score of the road segment to be identified may be determined as an evaluation value of the road segment to be identified.
The specific scoring process is as follows:
whether the road section to be identified has the emergency or not needs to be scored first on the mutation condition of each traffic jam identification index, wherein when the mutation condition of the road section to be identified corresponding to the traffic jam identification index is suspected to have the emergency, the score is 1; when the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is no emergency, the score is-1; when the traffic jam identification index has no data, the score is 0. See in particular the scoring table shown in table 1.
TABLE 1
α and β in table 1 are weight coefficients. Because the weights corresponding to different data sources are different, the weights of the indexes in the same data source are also different, and the weights corresponding to the same indexes in different data sources are also different. Except for road condition indexes in the data source of the Gaode map, if a certain index considers that a suspected emergency happens, the score is 1; if the index has no data source, the index is scored as 0; if the index is within the historical normal range, the index is scored as-1.
After the scoring is completed, the score of each index and the corresponding weight thereof can be multiplied and then accumulated, so that the total score of the road section is obtained.
It should be noted that the above method for evaluating the sudden change condition of the road segment to be identified corresponding to each traffic congestion identification index is only an example, and other methods that can be used for evaluation can be applied to the embodiment of the present invention.
Step 405, if the evaluation value of the road section to be identified is greater than a first threshold value, determining that the road section to be identified is a road section in which an emergency occurs.
For example, when the evaluation value of the road segment to be identified is the total score of the road segment to be identified, if the total score of the road segment to be identified is greater than the first threshold value, it indicates that the road segment is the emergency occurrence road segment. The first threshold may be set empirically.
And then determining whether the traffic jam is caused according to the road condition index information. If the road condition index is congestion, the road section can be determined to be a traffic sudden congestion road section.
The embodiment shows that traffic data of at least one data source of a road section to be identified is acquired, a traffic congestion identification index of the at least one data source is determined according to the traffic data of the at least one data source, a sudden change condition of the road section to be identified corresponding to each traffic congestion identification index is determined according to the traffic congestion identification index of the at least one data source of the road section to be identified and historical data of the traffic congestion identification index, the sudden change condition of the road section to be identified corresponding to each traffic congestion identification index is evaluated, an evaluation value of the road section to be identified is determined, and if the evaluation value of the road section to be identified is greater than a first threshold value, the road section to be identified is determined to be a road section with an emergency. The traffic state distinguishing technology based on the multi-source big data can distinguish the traffic state by utilizing the traffic data collected by the coil, the electric police, the microwave and the online map, overcomes the inherent limitation of distinguishing the traffic state based on single-source data, ensures the accuracy and the actual applicability of a distinguishing result, carries out weighting processing on the same index according to different data sources, strengthens the distinguishing strength, and ensures that the distinguishing result is more in line with the feeling of a driver.
Based on the same technical concept, fig. 2 exemplarily shows a structure of an emergency event recognition apparatus provided by an embodiment of the present invention, and the apparatus can perform a flow of emergency event recognition.
As shown in fig. 2, the apparatus includes:
an obtaining unit 201, configured to obtain traffic data of at least one data source of a road segment to be identified;
the processing unit 202 is configured to determine a traffic congestion identification indicator of the at least one data source according to the traffic data of the at least one data source; determining mutation conditions of the road sections to be identified corresponding to the traffic jam identification indexes according to the traffic jam identification indexes of the at least one data source of the road sections to be identified and historical data of the traffic jam identification indexes; evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the evaluation value of the road section to be identified; and if the evaluation value of the road section to be identified is greater than a first threshold value, determining that the road section to be identified is the road section with the emergency.
Optionally, the processing unit 202 is specifically configured to:
aiming at any traffic jam identification index of the at least one data source, acquiring historical data in a preset time period before the current time of the traffic jam identification index;
determining a first range threshold of the historical data of the traffic jam identification index according to the mean value and the variance of the historical data of the traffic jam identification index;
if the traffic identification index is larger than a first range threshold of historical data of the traffic identification index, determining that the traffic identification index is mutated;
determining whether the traffic identification index has sudden change in time intervals larger than a first quantity threshold value in n continuous time intervals, if so, determining that the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is suspected to have an emergency, otherwise, determining that the sudden change situation of the road section to be identified corresponding to the traffic identification index is not the emergency, wherein n is a positive integer.
Optionally, the processing unit 202 is specifically configured to:
scoring the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the total score of the road section to be identified according to the weight of each traffic jam identification index; the total score of the road section to be identified is the accumulation of scores of all traffic jam identification indexes except the intersection index;
and determining the total score of the road section to be identified as the evaluation value of the road section to be identified.
Optionally, the at least one data source includes one or any combination of the following data sources:
the system comprises a microwave data source, an electric alarm data source, a coil data source and an online map data source.
Optionally, the processing unit 202 is further configured to:
and after the road section to be identified is determined to be the road section with the emergency, if the intersection index of the road section to be identified is congested, determining that the road section to be identified is in a traffic emergency congestion state.
Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the emergency identification method according to the obtained program.
Based on the same technical concept, embodiments of the present invention also provide a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer-readable instructions are read and executed by a computer, the computer-readable instructions cause the computer to perform the above emergency event identification method.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for incident identification, comprising:
acquiring traffic data of at least one data source of a road section to be identified;
determining a traffic jam identification index of the at least one data source according to the traffic data of the at least one data source;
determining mutation conditions of the road sections to be identified corresponding to the traffic jam identification indexes according to the traffic jam identification indexes of the at least one data source of the road sections to be identified and historical data of the traffic jam identification indexes;
evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the evaluation value of the road section to be identified;
if the evaluation value of the road section to be identified is larger than a first threshold value, determining the road section to be identified as the road section with the emergency;
the determining the mutation condition of the road section to be identified corresponding to each traffic jam identification index according to the traffic jam identification index of the at least one data source of the road section to be identified and the historical data of the traffic jam identification index comprises the following steps:
aiming at any traffic jam identification index of the at least one data source, acquiring historical data in a preset time period before the current time of the traffic jam identification index;
determining a first range threshold of the historical data of the traffic jam identification index according to the mean value and the variance of the historical data of the traffic jam identification index;
if the traffic identification index is larger than a first range threshold of historical data of the traffic identification index, determining that the traffic identification index is mutated;
determining whether the traffic identification index has sudden change in time intervals larger than a first quantity threshold value in n continuous time intervals, if so, determining that the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is suspected to have an emergency, otherwise, determining that the sudden change situation of the road section to be identified corresponding to the traffic identification index is not the emergency, wherein n is a positive integer.
2. The method as claimed in claim 1, wherein the evaluating the sudden change of the road segment to be identified corresponding to each traffic jam recognition index to determine the evaluation value of the road segment to be identified comprises:
scoring the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the total score of the road section to be identified according to the weight of each traffic jam identification index; the total score of the road section to be identified is the accumulation of scores of all traffic jam identification indexes except the intersection index;
and determining the total score of the road section to be identified as the evaluation value of the road section to be identified.
3. The method of claim 1, wherein the at least one data source comprises one or any combination of the following data sources:
the system comprises a microwave data source, an electric alarm data source, a coil data source and an online map data source.
4. The method according to any one of claims 1 to 3, wherein after the determining that the road segment to be identified is a road segment in which an emergency occurs, the method further comprises:
and if the intersection index of the road section to be identified is congestion, determining that the road section to be identified is in a traffic burst congestion state.
5. An emergency event recognition apparatus, comprising:
the acquisition unit is used for acquiring traffic data of at least one data source of the road section to be identified;
the processing unit is used for determining a traffic jam identification index of the at least one data source according to the traffic data of the at least one data source; determining mutation conditions of the road sections to be identified corresponding to the traffic jam identification indexes according to the traffic jam identification indexes of the at least one data source of the road sections to be identified and historical data of the traffic jam identification indexes; evaluating the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the evaluation value of the road section to be identified; if the evaluation value of the road section to be identified is larger than a first threshold value, determining the road section to be identified as the road section with the emergency;
the processing unit is specifically configured to:
aiming at any traffic jam identification index of the at least one data source, acquiring historical data in a preset time period before the current time of the traffic jam identification index;
determining a first range threshold of the historical data of the traffic jam identification index according to the mean value and the variance of the historical data of the traffic jam identification index;
if the traffic identification index is larger than a first range threshold of historical data of the traffic identification index, determining that the traffic identification index is mutated;
determining whether the traffic identification index has sudden change in time intervals larger than a first quantity threshold value in n continuous time intervals, if so, determining that the sudden change situation of the road section to be identified corresponding to the traffic jam identification index is suspected to have an emergency, otherwise, determining that the sudden change situation of the road section to be identified corresponding to the traffic identification index is not the emergency, wherein n is a positive integer.
6. The apparatus as claimed in claim 5, wherein said processing unit is specifically configured to:
scoring the mutation condition of the road section to be identified corresponding to each traffic jam identification index, and determining the total score of the road section to be identified according to the weight of each traffic jam identification index; the total score of the road section to be identified is the accumulation of scores of all traffic jam identification indexes except the intersection index;
and determining the total score of the road section to be identified as the evaluation value of the road section to be identified.
7. The apparatus of claim 5, wherein the at least one data source comprises one or any combination of the following data sources:
the system comprises a microwave data source, an electric alarm data source, a coil data source and an online map data source.
8. The apparatus of any of claims 5 to 7, wherein the processing unit is further to:
and after the road section to be identified is determined to be the road section with the emergency, if the intersection index of the road section to be identified is congested, determining that the road section to be identified is in a traffic emergency congestion state.
9. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the method of any one of claims 1 to 4 in accordance with the obtained program.
10. A computer-readable non-transitory storage medium including computer-readable instructions which, when read and executed by a computer, cause the computer to perform the method of any one of claims 1 to 4.
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| CN111369792B (en) * | 2019-11-22 | 2021-09-10 | 杭州海康威视系统技术有限公司 | Traffic incident analysis method and device and electronic equipment |
| CN111680745B (en) * | 2020-06-08 | 2021-03-16 | 青岛大学 | Burst congestion judging method and system based on multi-source traffic big data fusion |
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| WO2007103180A2 (en) * | 2006-03-03 | 2007-09-13 | Inrix, Inc. | Assessing road traffic conditions using data from mobile data sources |
| CN101540101A (en) * | 2008-03-17 | 2009-09-23 | 上海宝康电子控制工程有限公司 | Method and system for detecting road traffic incidents |
| JP2010230521A (en) * | 2009-03-27 | 2010-10-14 | Aisin Aw Co Ltd | Apparatus, method and program for transmitting traffic congestion degree information, and apparatus for receiving traffic congestion degree |
| CN101739814B (en) * | 2009-11-06 | 2011-11-09 | 吉林大学 | SCATS coil data-based traffic state online quantitative evaluation and prediction method |
| KR101047598B1 (en) * | 2010-04-05 | 2011-07-07 | 서울통신기술 주식회사 | System and method for providing position information of vehicles using dsrc |
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| KR101518894B1 (en) * | 2013-07-11 | 2015-05-11 | 현대자동차 주식회사 | Method for setting warning reference of advanced driver assistance system |
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