CN115640997A - Traffic accident risk dynamic identification method and identification model construction method - Google Patents
Traffic accident risk dynamic identification method and identification model construction method Download PDFInfo
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Abstract
The embodiment of the invention discloses a dynamic identification method and an identification model construction method for a traffic accident risk. The dynamic identification method for the traffic accident risk comprises the following steps: acquiring real-time vehicle track data and real-time meteorological data of a section to be identified of the expressway; inputting the real-time vehicle track data and the real-time meteorological data into a trained random forest model, and identifying vehicle track categories in real time, wherein the track categories comprise normal tracks and abnormal tracks; if the track type is a normal track, determining that no real-time traffic accident risk exists; and if the track type is an abnormal track, inputting the real-time vehicle track data and the real-time meteorological data into a trained support vector machine model, and identifying the risk level of the traffic accident in real time. The embodiment dynamically identifies the traffic accident risk of the expressway in real time.
Description
Technical Field
The embodiment of the invention relates to the field of intelligent traffic, in particular to a dynamic identification method and an identification model construction method for traffic accident risks.
Background
In the field of traffic engineering, traffic risk identification has gradually become one of the important researches in traffic safety. The traffic risk identification is quantitative risk assessment of complex traffic behaviors, is a basic function of the intelligent expressway, and is beneficial to accident prevention of the expressway network.
The traffic accident risk model in the prior art usually needs a large amount of analysis and calculation to predict the risk at a certain time in the future, and a data processing method which is simple in structure and rapid in operation is not available, so that the traffic accident risk of the expressway can be predicted in real time.
Disclosure of Invention
The embodiment of the invention provides a dynamic identification method and an identification model construction method for traffic accident risks, which are used for dynamically identifying the traffic accident risks of a highway in real time.
In a first aspect, an embodiment of the present invention provides a method for dynamically identifying a risk of a traffic accident, including:
acquiring real-time vehicle track data and real-time meteorological data of a section to be identified of the expressway;
inputting the real-time vehicle track data and the real-time meteorological data into a trained random forest model, and identifying vehicle track categories in real time, wherein the track categories comprise normal tracks and abnormal tracks;
if the track type is a normal track, determining that no real-time traffic accident risk exists;
and if the track type is an abnormal track, inputting the real-time vehicle track data and the real-time meteorological data into a trained support vector machine model, and identifying the risk level of the traffic accident in real time.
In a second aspect, an embodiment of the present invention provides a method for constructing a traffic accident risk identification model, including:
obtaining historical vehicle track data and historical meteorological data of a highway section to form a sample set, wherein each sample comprises vehicle track data and meteorological data of a section at a historical moment;
according to the historical meteorological data and the historical traffic accident data, labeling track categories of all samples, wherein the track categories comprise: a normal trajectory and an abnormal trajectory, wherein the abnormal trajectory comprises: a first abnormal trajectory influenced by abnormal weather alone, a second abnormal trajectory influenced by traffic accidents alone, and a third abnormal trajectory comprehensively influenced by abnormal weather and traffic accidents;
inputting each sample into a random forest model for training, and enabling the output of the random forest model to continuously approach to the track type marked by each sample;
marking the traffic accident risk level of each abnormal track sample according to the historical traffic accident data and the track category of each sample;
and inputting the abnormal trajectory samples into a support vector machine model for training, so that the output of the support vector machine model is continuously close to the risk level marked by the abnormal trajectory samples.
In a second aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned traffic accident risk dynamic identification method or the traffic accident risk identification model construction method.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the above-mentioned traffic accident risk dynamic identification method or traffic accident risk identification model building method.
The embodiment of the invention analyzes the vehicle track data, extracts the track characteristics as the identification basis of the traffic accident risk, and realizes the dynamic identification of the traffic accident risk through the dynamic characteristics of the track data; meanwhile, the random forest model and the support vector machine model which are simple in structure are adopted, so that the rapid and real-time response of calculation is guaranteed, real-time data support is provided for the evolution analysis of the vehicle traffic state on the highway, the risk implementation early warning is facilitated, and the accident influence is effectively reduced. In particular, the influence of meteorological data on the risk of the traffic accident is considered, and the accuracy of the identification result is improved. .
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a traffic accident risk dynamic identification method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for constructing a traffic accident risk identification model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a random forest model according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a support vector machine model according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the disclosed embodiments are merely exemplary of the invention, and are not intended to be exhaustive or exhaustive. 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.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Fig. 1 is a flowchart of a traffic accident risk dynamic identification method according to an embodiment of the present invention. The method is suitable for dynamically identifying the traffic accident risk in real time through vehicle track data and meteorological data on the highway. The method is executed by an electronic device, and specifically includes the following steps, as shown in fig. 1.
And S110, acquiring real-time vehicle track data and real-time meteorological data of the section to be identified of the expressway.
The section to be identified is a certain road section on the highway, and the embodiment takes the real-time vehicle data and the real-time meteorological data of the road section as input to identify the traffic accident risk of the road section in real time.
Wherein the real-time vehicle trajectory data of the segment comprises: the number of real-time tracks, the real-time average speed and the real-time braking number of the sections. More specifically, the number of tracks represents the number of vehicles, the average vehicle speed represents the average speed of all vehicles, and the braking data represents the number of braked vehicles. Optionally, the acquiring real-time vehicle track data of the section to be identified on the highway includes: acquiring real-time vehicle information of a section to be identified by using expressway radar equipment; converting the real-time vehicle information into real-time track data of each vehicle, wherein the real-time track data of any vehicle comprises the real-time position, the real-time lane and the real-time speed of the vehicle; and obtaining the real-time track number, the real-time average speed and the real-time braking number of the section to be identified according to the real-time track data of each vehicle.
The real-time meteorological data includes: real-time weather conditions, real-time precipitation, and real-time air temperature. Optionally, the real-time weather data is acquired from a highway weather station in real time.
And S120, inputting the real-time vehicle track data and the real-time meteorological data into a trained random forest model, and identifying vehicle track types in real time, wherein the track types comprise normal tracks and abnormal tracks.
The trained random forest model comprises a plurality of branches, and each branch corresponds to different vehicle track categories including normal tracks and abnormal tracks. Optionally, the abnormal trajectory includes: the abnormal weather detection method comprises the steps of obtaining a first abnormal track independently influenced by abnormal weather, a second abnormal track independently influenced by traffic accidents, and a third abnormal track comprehensively influenced by the abnormal weather and the traffic accidents. Different trajectory categories are used to identify different traffic accident risk situations. It is worth mentioning that the vehicle trajectory category is changed in real time since both the vehicle trajectory data and the weather data are changed in real time.
S130, if the track type is a normal track, determining that no real-time traffic accident risk exists; and if the track type is an abnormal track, inputting the real-time vehicle track data and the real-time meteorological data into a trained support vector machine model, and identifying the risk level of the traffic accident in real time.
And if the track category at a certain moment is a normal track, determining that the road section to be identified has no traffic accident risk at the moment. And if the track category at a certain moment is an abnormal track, further inputting the vehicle track data and the meteorological data at the moment into the trained support vector machine model, and identifying the traffic accident risk level at the moment. Specifically, the accident risk level is related to the abnormal track level, the specific track data and the meteorological data, and the support vector machine model learns the related relation through training, so that the traffic accident risk level is identified.
The embodiment analyzes the vehicle track data, extracts track characteristics as the identification basis of the traffic accident risk, and realizes the dynamic identification of the traffic accident risk through the dynamic characteristics of the track data; meanwhile, the random forest model and the support vector machine model which are simple in structure are adopted, so that the rapid and real-time response of calculation is guaranteed, real-time data support is provided for the evolution analysis of the traffic state of the highway vehicles, the risk implementation early warning is facilitated, and the accident influence is effectively reduced. In particular, the influence of meteorological data on the risk of the traffic accident is considered, and the accuracy of the identification result is improved.
Fig. 2 is a flowchart of a method for constructing a traffic accident risk recognition model according to an embodiment of the present invention, where the method is used to train a traffic accident risk recognition model formed by a random forest model and a support vector machine model in the above embodiment. As shown in fig. 2, the method specifically includes the following steps:
s210, historical vehicle track data and historical meteorological data of the expressway sections are obtained to form a sample set, wherein each sample comprises vehicle track data and meteorological data of one section at a historical moment.
Specifically, historical vehicle information on a highway is acquired by utilizing highway radar equipment; dividing the expressway into different sections according to the distribution position of the radar equipment; converting historical vehicle information of each section into historical track data of each vehicle in each section, wherein the historical track data of any vehicle comprises the position, the lane and the speed of the vehicle at each historical moment; a sample set is generated by forming a sample by historical meteorological data of the same section and the same historical moment and historical track data of each vehicle.
S220, according to the historical meteorological data and the historical traffic accident data, marking track categories of all samples, wherein the track categories comprise: a normal trajectory and an abnormal trajectory, wherein the abnormal trajectory comprises: the abnormal weather detection method comprises the steps of obtaining a first abnormal track independently influenced by abnormal weather, a second abnormal track independently influenced by traffic accidents, and a third abnormal track comprehensively influenced by the abnormal weather and the traffic accidents.
Specifically, historical traffic accident data is acquired from a highway traffic accident platform, and the historical traffic accident data comprises time intervals, places and accident severity of historical traffic accidents; according to the historical traffic accident data, marking the track type of the positive sample without the traffic accident as track normal (class = 0), and marking the track type of the negative sample with the traffic accident as track abnormal; determining a first negative sample meeting a preset weather abnormal condition according to the meteorological data; determining a second negative sample meeting a preset traffic accident abnormal condition according to the traffic accident data; labeling the trajectory category of the intersection sample of the first negative sample and the second negative sample as: a third unusual trajectory (class = 3) which is synthetically affected by unusual weather and traffic accidents; labeling the track category of the first negative sample outside the intersection as: a first anomaly track (class = 1) affected by the anomalous weather alone; labeling the track category of the second negative sample outside the intersection as: a second abnormal trajectory (class = 2) affected by the traffic accident alone.
And S230, inputting each sample into a random forest model for training, and enabling the output of the random forest model to continuously approach to the track type labeled by each sample.
Fig. 3 is a schematic diagram of a random forest model according to an embodiment of the present invention. Where each box represents one sample data. It can be seen that the samples can be classified in multiple stages by the random forest model until the branches output from the last layer meet the required categories. The number of layers of the model, the number of branches and the category of the branches on each layer, and the above weather abnormal conditions and traffic accident abnormal conditions may be specifically set according to actual needs, and this embodiment is not limited.
And S240, marking the traffic accident risk level of each abnormal track sample according to the historical traffic accident data and the track category of each sample.
Optionally, according to the accident severity in the historical traffic accident data, marking the traffic accident risk level of each abnormal track sample; and adjusting the traffic accident risk level of the intersection sample to be higher than the traffic accident risk levels of the first negative sample and the second negative sample.
And S250, inputting the abnormal trajectory samples into a support vector machine model for training, so that the output of the support vector machine model is continuously close to the risk level marked by the abnormal trajectory samples.
Fig. 4 is a schematic diagram of a support vector machine model according to an embodiment of the present invention. Where each box represents one sample data. It can be seen that the samples can be classified by the support vector machine model, and both the classification number and the classification features can be specifically set according to actual needs, which is not limited in this embodiment.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device includes a processor 50, a memory 51, an input device 52, and an output device 53; the number of processors 50 in the device may be one or more, and one processor 50 is taken as an example in fig. 5; the processor 50, the memory 51, the input device 52 and the output device 53 in the apparatus may be connected by a bus or other means, which is exemplified in fig. 5.
The memory 51 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method, device, and storage medium for determining parameters and predicting concentration of orthotropic plates of steel box girders in the embodiments of the present invention. The processor 50 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 51, namely, the method, the device and the storage medium for determining the steel box girder orthotropic plate parameters and predicting the concentration are realized.
The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 51 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 51 may further include memory located remotely from the processor 50, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 52 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 53 may include a display device such as a display screen.
The embodiment of the invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method, the equipment and the storage medium for determining the steel box girder orthotropic plate parameters and predicting the concentrations of the steel box girder orthotropic plate parameters are realized.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the technical solutions of the embodiments of the present invention.
Claims (10)
1. A traffic accident risk dynamic identification method is characterized by comprising the following steps:
acquiring real-time vehicle track data and real-time meteorological data of a section to be identified of the expressway;
inputting the real-time vehicle track data and the real-time meteorological data into a trained random forest model, and identifying vehicle track categories in real time, wherein the track categories comprise normal tracks and abnormal tracks;
if the track type is a normal track, determining that no real-time traffic accident risk exists;
and if the track type is an abnormal track, inputting the real-time vehicle track data and the real-time meteorological data into a trained support vector machine model, and identifying the risk level of the traffic accident in real time.
2. The method of claim 1, wherein the real-time vehicle trajectory data for a segment comprises: the number of real-time tracks, the real-time average speed and the real-time braking number of the sections.
3. The method of claim 1, wherein the obtaining real-time vehicle trajectory data for the section of the highway to be identified comprises:
acquiring real-time vehicle information of a section to be identified by using expressway radar equipment;
converting the real-time vehicle information into real-time track data of each vehicle, wherein the real-time track data of any vehicle comprises the real-time position, the real-time lane and the real-time speed of the vehicle;
and obtaining the real-time track number, the real-time average speed and the real-time braking number of the section to be identified according to the real-time track data of each vehicle.
4. The method of claim 1, wherein the meteorological data comprises: weather conditions, precipitation, and air temperature.
5. A construction method of a traffic accident risk identification model is characterized by comprising the following steps:
obtaining historical vehicle track data and historical meteorological data of a highway section to form a sample set, wherein each sample comprises vehicle track data and meteorological data of a section at a historical moment;
according to the historical meteorological data and the historical traffic accident data, marking the track category of each sample, wherein the track category comprises: a normal trajectory and an abnormal trajectory, wherein the abnormal trajectory comprises: a first abnormal trajectory influenced by abnormal weather alone, a second abnormal trajectory influenced by traffic accidents alone, and a third abnormal trajectory comprehensively influenced by abnormal weather and traffic accidents; inputting each sample into a random forest model for training, and enabling the output of the random forest model to continuously approach to the track type marked by each sample;
marking the traffic accident risk level of each abnormal track sample according to the historical traffic accident data and the track category of each sample;
and inputting the abnormal trajectory samples into a support vector machine model for training, so that the output of the support vector machine model is continuously close to the risk level marked by the abnormal trajectory samples.
6. The method of claim 5, wherein the obtaining historical vehicle trajectory data and historical weather data for the highway segment comprises forming a sample set comprising:
acquiring historical vehicle information on a highway by utilizing highway radar equipment;
dividing the expressway into different sections according to the distribution position of the radar equipment;
converting historical vehicle information of each section into historical track data of each vehicle in each section, wherein the historical track data of any vehicle comprises the position, the lane and the speed of the vehicle at each historical moment;
a sample set is generated by forming a sample by historical meteorological data of the same section and the same historical moment and historical track data of each vehicle.
7. The method of claim 5, wherein said labeling the trajectory category of each sample based on said historical weather data and corresponding historical traffic accident data comprises:
obtaining historical traffic accident data from a highway traffic accident platform, wherein the historical traffic accident data comprises time intervals, places and accident severity of historical traffic accidents;
according to the historical traffic accident data, marking the track type of the positive sample without the traffic accident as normal track, and marking the track type of the negative sample with the traffic accident as abnormal track;
determining a first negative sample meeting a preset weather abnormal condition according to the meteorological data;
determining a second negative sample meeting a preset traffic accident abnormal condition according to the traffic accident data;
labeling the track category of the intersection sample of the first negative sample and the second negative sample as: a third abnormal trajectory comprehensively affected by abnormal weather and traffic accidents;
labeling the track category of the first negative sample outside the intersection as: a first anomalous trajectory affected by anomalous weather alone; labeling the track category of the second negative sample outside the intersection as: a second abnormal trajectory that is affected by the traffic accident alone.
8. The method according to claim 7, wherein the labeling the traffic accident risk level of each abnormal track sample according to the track category of each sample comprises:
marking the traffic accident risk level of each abnormal track sample according to the accident severity;
adjusting the traffic accident risk level of the intersection sample to be higher than the traffic accident risk level of the first negative sample and the second negative sample.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the dynamic traffic accident risk identification method according to any one of claims 1-4, or the traffic accident risk identification model construction method according to any one of claims 5-8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for dynamic identification of risk of a traffic accident according to any one of claims 1 to 4, or a method for construction of a model for identification of risk of a traffic accident according to any one of claims 5 to 8.
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