CN113256969A - Traffic accident early warning method, device and medium for expressway - Google Patents
Traffic accident early warning method, device and medium for expressway Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- 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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The application discloses a traffic accident early warning method, equipment and medium for an expressway, comprising the following steps: acquiring terminal information positioned in communication equipment, and trying to acquire traffic flow information of a current road section through vehicle monitoring equipment; if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information to obtain the average speed of the current road section; if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, obtaining the vehicle flow and the vehicle density through the terminal information and the road section information of the current road section; and inputting the average speed, the vehicle flow and the vehicle density into an accident prediction model which is trained in advance, and performing accident prediction on the current road section. And a more accurate accident prediction means is provided, and a more accurate accident prediction result is obtained.
Description
Technical Field
The application relates to the field of traffic early warning, in particular to a traffic accident early warning method, equipment and medium for an expressway
Background
The highway plays an extremely important role in the national highway network, is called as the economic aorta, can powerfully drive and promote the development of regional economy, and has good economic and social benefits. With the continuous improvement of the living standard of the masses, the automobile holding amount is in a rapidly increasing trend, and a series of problems are brought while higher benefits are brought to the expressway.
Many highways are very congested in daily rush hours and holidays, the congestion of many road sections becomes a normality, and the incidence rate of traffic accidents is increasing. Based on the above situation, it is difficult for the road administration department and the high-speed traffic police department to take timely, effective and even advance precaution against the traffic accidents on the highway.
Disclosure of Invention
In order to solve the above problems, that is, the current frequent traffic accidents on the highway, and the staff are difficult to take timely, effective and even advanced precaution against the traffic accidents, the application provides a traffic accident early warning method, device and medium for the highway, comprising:
on one hand, the application provides a traffic accident early warning method for a highway, which comprises the following steps: acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway; if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing; obtaining the average speed of the current road section according to the result of the speed estimation; if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section; and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
In one example, the average vehicle speed, the vehicle flow and the vehicle density are input into a pre-trained accident prediction model, and after the accident prediction is performed on the current road section, the method further includes: acquiring the distance of the current road section and a prediction result of the accident prediction, wherein the prediction result at least comprises at least one of the following: accident frequency prediction results and accident type prediction results; obtaining accident prediction distribution points according to the road section distance and the accident frequency prediction result; and aiming at any one of the accident prediction distribution points, generating an accident simulation processing video of the accident prediction distribution point according to the accident type prediction result so as to simulate and demonstrate a processing means of the traffic accident.
In one example, the average vehicle speed, the vehicle flow and the vehicle density are input into a pre-trained accident prediction model, and before the accident prediction is performed on the current road section, the method further includes: training an accident prediction model; the training process of the accident prediction model comprises the following steps: dividing the expressway into a plurality of road sections according to the road sections with the multiple historical traffic accidents, the positions of the vehicle monitoring devices and the distance of the longest road section; for any one of the road segments, obtaining historical data of the road segment, wherein the historical data at least comprises one of the following data: historical average vehicle speed, historical vehicle flow, historical vehicle density, historical accident frequency and historical accident type; processing the historical average vehicle speed, the historical vehicle flow, the historical vehicle density, the historical accident frequency and the historical accident type to obtain characteristic numerical values; selecting a numerical value larger than a preset threshold value from a plurality of characteristic numerical values as a representative characteristic numerical value, and taking historical data of a road section corresponding to the representative characteristic numerical value as a training sample; aiming at any one of a plurality of training samples, inputting historical average vehicle speed, historical vehicle flow and historical vehicle density contained in the training sample to an input layer of a fuzzy neural network, inputting corresponding historical accident frequency and historical accident type to an output layer of the fuzzy neural network, and performing supervision training to obtain an initial accident prediction model; and carrying out precision detection on the plurality of initial accident prediction models, and selecting the initial accident prediction models with the highest precision as the accident prediction models.
In one example, if the traffic flow information is not collected, obtaining the vehicle flow and the vehicle density through the terminal information and the link information of the current link specifically includes: acquiring the number of vehicles on the current road section according to the terminal information, and determining the vehicle flow on the current road section according to the number of vehicles and a preset time interval; and acquiring the road width of the current road section, and determining the vehicle density of the current road section according to the number of the vehicles and the road width.
In one example, the average vehicle speed, the vehicle flow and the vehicle density are input into a pre-trained accident prediction model, and after the accident prediction is performed on the current road section, the method further includes: determining that the vehicle density is greater than a congestion threshold; generating a first control instruction and sending the first control instruction to an information issuing unit arranged on the expressway, wherein the first control instruction comprises: the speed limit value of the vehicle is reduced, and the distance value between the vehicles is improved.
In one example, after generating a first control instruction and transmitting the first control instruction to an information issuing unit provided on the expressway, the method further includes: accessing a vehicle dynamic identification unit to a violation monitoring camera in the current road section, identifying the congestion position of the current road section by rotating the violation monitoring camera, and aligning the violation monitoring camera to the congestion position; and carrying out video analysis on the real-time video to determine a starting point for generating congestion.
In one example, the average vehicle speed, the vehicle flow and the vehicle density are input into a pre-trained accident prediction model, and after the accident prediction is performed on the current road section, the method further includes: calling a corresponding accident case in a case library according to a prediction result corresponding to the accident prediction; analyzing the accident case through a case analysis model to generate an accident handling scheme, wherein the accident handling scheme at least comprises the following steps: accident handling measures, accident prevention measures.
In one example, after the accident case is analyzed by the case analysis model and the accident handling plan is generated, the method further includes: determining a plurality of communication devices corresponding to the current road section through the terminal signal acquisition module; and sending the accident prevention measures to a plurality of communication devices corresponding to the current road section through short message editing.
On the other hand, the application provides a traffic accident early warning equipment of highway, includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway; if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing; obtaining the average speed of the current road section according to the result of the speed estimation; if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section; and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
In another aspect, the present application provides a non-volatile computer storage medium for highway traffic accident warning, storing computer-executable instructions configured to: acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway; if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing; obtaining the average speed of the current road section according to the result of the speed estimation; if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section; and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
The traffic accident early warning method, the traffic accident early warning equipment and the traffic accident early warning medium for the expressway provided by the application can bring the following beneficial effects: by adopting different modes to acquire the vehicle information of the highway, the current road section can acquire the vehicle information in real time, the vehicle information is processed in real time, accident early warning is carried out, and related personnel can react in time or in advance. Meanwhile, compared with the prior art, the early warning method more accurate is provided in the technical scheme, the more accurate early warning result is generated, and the corresponding method is generated aiming at the prediction result from multiple dimensions, so that the accident occurrence frequency is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a traffic accident warning method for an expressway in an embodiment of the present application;
fig. 2 is a schematic diagram of a traffic accident warning device for an expressway in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and 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 application.
The traffic accident early warning method for the expressway is stored in a corresponding system or server, a user can log in through a terminal to enter the system or server, so that a bank can schedule data, the terminal can be a hardware device with corresponding functions, such as a smart phone, a tablet personal computer and a personal computer, the terminal is pre-installed with a corresponding system or APP, and the user can log in the system or server where the traffic accident early warning method for the expressway is located through the corresponding system, the APP or a WEB page and other forms.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a traffic accident early warning method for an expressway provided in an embodiment of the present application includes:
s101, acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a running vehicle on a current road section; and attempting to acquire traffic flow information of the current section through a plurality of vehicle monitoring devices disposed on the highway.
It should be noted that, in order to implement early warning on a traffic accident of an expressway, the expressway in the embodiment of the application has been divided into a plurality of road sections in advance, and early warning is performed on different road sections respectively, so that compared with the early warning on the whole expressway, the early warning effect of the method is more accurate and more targeted.
Specifically, the system is based on a communication equipment activity data technology to realize the acquisition of terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section. The communication device is a terminal with a communication function, including but not limited to: a mobile phone, a tablet personal computer with a communication function, a notebook personal computer with a communication function, and the like. In the embodiment of the present application, the communication device is explained by taking a mobile phone as an example, and the number of the vehicles is determined by analyzing the switching of the wireless communication network during the process that the mobile phone in the vehicle runs along the road, and by acquiring the number of the mobile phones performing data exchange with the wireless communication network in the wireless communication network corresponding to the current road section.
In addition, a plurality of vehicle monitoring devices are disposed on the highway, including but not limited to: a monitoring camera, a vehicle speed sensor, a microwave traffic detector and the like. Because the number of vehicle monitoring devices in the highway is small, the distribution range is wide, and seamless real-time monitoring cannot be performed on vehicles, the situation that the vehicle monitoring devices are not arranged on the current road section may exist.
Further, in this embodiment of the application, the system attempts to acquire traffic flow information of the current road segment through a plurality of or all vehicle monitoring devices arranged on the highway by issuing an instruction, if the vehicle monitoring devices exist in the current road segment, the traffic flow information of the current road segment can be successfully acquired, and if the vehicle monitoring devices do not exist in the current road segment, the vehicle monitoring devices located in other road segments may generate feedback data that the traffic flow information of the current road segment cannot be acquired.
Traffic flow information includes, but is not limited to: the speed of any vehicle in the current road section, the vehicle flow of the current road section, the vehicle density of the current road section and the like. The vehicle flow and the vehicle density may be obtained by statistically processing data in a preset time period, and the vehicle density may be an instantaneous density of a current road section.
S102, if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; and if the traffic flow information is acquired, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the consistency processing.
Specifically, if no vehicle monitoring device is arranged in the current road section, the traffic flow information cannot be acquired, and only the terminal information of the communication device in the vehicle running in the current road section can be acquired. And when the traffic flow information is not acquired, directly estimating the speed of the vehicles in the current road section through terminal information, wherein the terminal information comprises the respective speeds of all the vehicles in the current road section.
Further, if the current road section is provided with the vehicle monitoring device, the system can acquire the traffic flow information of the current road section through the vehicle monitoring device, and can also acquire the terminal information of the current road section. In the collected terminal information, the terminal information may not be accurate enough due to the influence of the mobile phone wireless network switching section length and the mobile phone wireless network switching sample. In the embodiment of the application, errors can be reduced to the greatest extent by fusing the terminal information and the traffic flow information, and the accuracy of vehicle speed estimation is improved.
Furthermore, the collected terminal information and traffic flow information are subjected to consistency processing from three dimensions of time, space and semantics, time consistency can be that the collected two kinds of information are calculated at the same time interval, space consistency can be that the collected two kinds of information are calculated at the same space interval, and semantic consistency is that the collected two kinds of information are processed according to two kinds of information with different formats to obtain information in the same format representation form. And then, the vehicle speed estimation is carried out on the data after the consistency processing.
And S103, obtaining the average speed of the current road section according to the result of the speed estimation.
Specifically, the calculation is performed according to the number of vehicles in the current road segment and the vehicle speed of each vehicle, which are included in the terminal information or the data after the unification processing, to obtain the average vehicle speed of the current road segment.
S104, if the traffic flow information is collected, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; and if the traffic flow information is not acquired, obtaining the vehicle flow and the vehicle density through the terminal information and the road section information of the current road section.
If the vehicle monitoring equipment is arranged in the current road section, the system can acquire the traffic flow information of the current road section through the vehicle monitoring equipment, and the vehicle flow and the vehicle density of the current road section can be obtained according to the traffic flow information. If the current road section is not provided with the vehicle monitoring equipment, the system cannot acquire traffic flow information, only the terminal information of the communication equipment in the running vehicles in the current road section can be acquired at the moment, and the number of the vehicles and the density of the vehicles are obtained through the terminal information and the road section information of the current road section.
Specifically, the system can obtain the vehicle quantity of the current road section in real time according to various data contained in the terminal information, and perform statistical calculation on the vehicle quantity through a preset time interval prestored in the system so as to determine the vehicle flow of the current road section. Meanwhile, the system acquires the road width of the current road section, and according to the number of vehicles on the current road section acquired in real time, the vehicle density of the current road section is determined through calculation of data of the road width.
Further, it should be noted that when a tidal road is provided in an expressway or a road repair situation occurs, the road width of the current link may vary. Therefore, before the road width of the current road section is obtained, the system can obtain the road change information of the current road section, and if the road width has a change in the near term, the road width of the current road section can be updated in real time through the road change information.
And S105, inputting the average speed, the vehicle flow and the vehicle density into an accident prediction model which is trained in advance, and performing accident prediction on the current road section.
Specifically, the system inputs the average vehicle speed, the vehicle flow rate, and the vehicle density obtained in the foregoing into an accident prediction model trained in advance to realize accident prediction on the current road segment, where the prediction result of the accident prediction includes but is not limited to: accident frequency prediction results and accident type prediction results.
The accident prediction model can perform linear regression processing on three parameters, namely average vehicle speed, vehicle flow and vehicle density to obtain a characteristic value, and performs accident prediction by judging the correlation between the characteristic value and an accident prediction result.
In one embodiment, after the accident prediction is performed on the current road segment through the accident prediction model, the road segment distance of the current road segment and the prediction result of the accident prediction may also be obtained, including: accident frequency prediction results and accident type prediction results.
And carrying out accident prediction analysis according to the distance of the road sections and the accident frequency prediction result to obtain an accident prediction distribution point. For example, in a certain road segment with a length of 3KM, five accidents are predicted to occur in a specific time, and the five accidents are not evenly distributed in the 3KM road segment and do not necessarily occur at the same place at the same time. Therefore, the system can adopt four probability distribution modes of Poisson distribution, negative binomial distribution, zero-pile Poisson distribution and zero-pile negative binomial distribution to fit the road section distance and the accident frequency prediction result so as to obtain the total number of accidents including the accident frequency prediction result, wherein the distribution point of the maximum probability is in the road section.
For any one of the accident prediction distribution points, the accident type can be fitted according to the accident type prediction result to obtain the maximum probability accident prediction of different types for different distribution points, and an accident simulation processing video of the accident prediction distribution point is generated to simulate and demonstrate a processing means of the traffic accident.
In one embodiment, the system may also train the accident prediction model before performing the accident prediction on the current road segment through the accident prediction model. The training process of the accident prediction model specifically comprises the following steps:
the system first needs to divide the highway into multiple segments by multiple criteria including, but not limited to: the road section with multiple historical traffic accidents, the position of the vehicle monitoring equipment and the longest distance of the road section. The database can be recorded with an accident hotspot distribution diagram, a plurality of accident-prone road sections can be marked through the distribution diagram, and the accident-prone road sections are divided into the same road section as much as possible, so that the accident pertinence prediction is realized. Meanwhile, the vehicle monitoring devices are distributed at different positions of the expressway, so that the vehicle monitoring devices are divided into each road section as much as possible. In addition, the system is pre-stored with the longest distance of the road section, which is the longest distance that the divided road section can not exceed, and the numerical value can be flexibly set according to the actual situation, and is not specifically limited herein.
After the system divides the expressway into a plurality of road sections according to the road sections with multiple historical traffic accidents, the positions of the vehicle monitoring devices and the distance of the longest road section, historical data in the road sections can be inquired according to any one of the road sections, wherein the historical data comprises but is not limited to: historical average vehicle speed, historical vehicle flow, historical vehicle density, historical accident frequency, and historical accident type.
The historical average vehicle speed, the historical vehicle flow, the historical vehicle density, the historical accident frequency and the historical accident type are processed, and the processing mode can refer to the data uniformization processing in the above to obtain characteristic numerical values capable of integrating five items of data. The characteristic value is used for displaying the representativeness of the road section, and when the characteristic value is high, at least one item in the five items of data in the current road section is more prominent than other road sections.
And selecting a value which is greater than a preset threshold value from the plurality of characteristic values as a representative characteristic value, wherein the preset threshold value can be flexibly set according to the actual situation, and the specific value is not limited herein. And taking historical data representing the road sections corresponding to the characteristic numerical values as training samples. By the method, the representative characteristic numerical value can be ensured to have stronger characteristic, the fuzzy relation can be established between the input data and the output data more easily when the representative characteristic numerical value is used as a training sample, and the precision of a training result is ensured.
At this time, the system obtains a plurality of training samples, and each training sample corresponds to the historical data of a road section. According to any one of a plurality of training samples, inputting historical average vehicle speed, historical vehicle flow and historical vehicle density contained in the training sample to an input layer of a fuzzy neural network, inputting corresponding historical accident frequency and historical accident type to an output layer of the fuzzy neural network, performing supervision training, training to obtain a fuzzy relation between input data and output data through a neural network fitting relation in the fuzzy neural network, and further obtaining a plurality of initial accident prediction models.
The system can perform precision detection on a plurality of accident prediction models, for example, historical data which is not selected as a representative characteristic value and corresponds to the representative characteristic value is used as a test sample, historical average vehicle speed, historical vehicle flow and historical vehicle density in the test sample are input into an input layer of a plurality of initial accident prediction models to obtain a plurality of output results, the output results are compared with historical accident frequency and historical accident types, the initial accident prediction model with the highest accuracy is used as the model with the highest precision, and the model with the highest precision is selected as the accident prediction model.
In one embodiment, after the system performs the accident prediction on the current road segment through the accident prediction model, a congestion critical value of the current road segment may be further obtained, where the congestion critical value refers to whether the vehicle density is greater than a density threshold value and/or the vehicle flow rate is less than a flow rate threshold value in unit time based on the road width of the current road segment.
The system may determine whether the vehicle density of the current road segment is greater than the congestion critical value, and if it is determined that the vehicle density is greater than the congestion critical value, may generate a first control instruction, and send the first control instruction to an information issuing unit disposed on the highway, where the first control instruction includes, but is not limited to: the speed limit value of the vehicle is reduced, and the distance value between the vehicles is improved. The first control instruction is preferentially sent to the information issuing unit of the previous road section corresponding to the current road, so that the time for a vehicle of the previous road section not entering the current road section can be reduced, the current road section is prevented from being blocked, and the probability of traffic accidents is reduced.
Meanwhile, the system may further generate a second control instruction, and send the second control instruction to the information issue unit of the next road segment corresponding to the current road, where the second control instruction may include: and the vehicle speed limit value is improved. The vehicle which just leaves the jam position in the current road section can be ensured to quickly drive away from the current road section, and the jam of the current road section is further slowed down. In addition, in the expressway, the first reason for the accident is not the vehicle speed but the inter-vehicle distance, so that the second control command does not include the reduction of the inter-vehicle distance value, and the accident caused by too small inter-vehicle distance is prevented.
In one embodiment, after the first control instruction is issued, the vehicle dynamic identification unit can be further connected to a violation monitoring camera in the current road section. The vehicle dynamic recognition unit has an image recognition function and can dynamically recognize an image. The violation monitoring camera is controlled to rotate, the vehicle dynamic identification unit is used for enabling the violation monitoring camera to identify the congestion position of the current road section, and after identification, the violation monitoring camera is aligned to the congestion position and does not rotate any more.
The system can determine the starting point of the congestion by carrying out video analysis on the real-time video at the current congestion position. By determining the starting point of the jam, the reaction speed of the road administration personnel can be increased, and the road administration personnel can reach the starting point as soon as possible to evacuate the jam.
In one embodiment, after the accident prediction is performed on the current road section through the accident prediction model, the corresponding accident case in the case base can be called according to the prediction result corresponding to the accident prediction, and the accident case can be consistent with the accident type prediction result in the prediction result. Analyzing the accident case through a pre-trained case analysis model to generate an accident response scheme, wherein the accident response scheme at least comprises the following steps: accident handling measures, accident prevention measures.
In one embodiment, after the system generates the accident handling scheme, the terminal signal acquisition module may further determine a plurality of communication devices corresponding to the current road segment, where the communication devices reflect vehicles traveling on the current road. And the accident precautionary measures are sent to a plurality of communication devices corresponding to the current road section through short message editing, so that the driver of the vehicle can take precaution in advance, and the accident is further avoided.
In one embodiment, as shown in fig. 2, the present application provides a traffic accident early warning apparatus for a highway, including: at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform instructions for:
acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway;
if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing;
obtaining the average speed of the current road section according to the result of the speed estimation;
if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section;
and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
In one embodiment, the present application provides a non-transitory computer storage medium for highway traffic accident warning, storing computer-executable instructions configured to:
acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway;
if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing;
obtaining the average speed of the current road section according to the result of the speed estimation;
if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section;
and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and media embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for relevant points.
The device and the medium provided by the embodiment of the application correspond to the method one to one, so the device and the medium also have the similar beneficial technical effects as the corresponding method, and the beneficial technical effects of the method are explained in detail above, so the beneficial technical effects of the device and the medium are not repeated herein.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A traffic accident early warning method for an expressway is characterized by comprising the following steps:
acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway;
if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing;
obtaining the average speed of the current road section according to the result of the speed estimation;
if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section;
and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
2. The method as claimed in claim 1, wherein the average vehicle speed, the vehicle flow rate and the vehicle density are input into a pre-trained accident prediction model, and after an accident prediction is performed on the current road segment, the method further comprises:
acquiring the distance of the current road section and a prediction result of the accident prediction, wherein the prediction result at least comprises at least one of the following: accident frequency prediction results and accident type prediction results;
obtaining accident prediction distribution points according to the road section distance and the accident frequency prediction result;
and aiming at any one of the accident prediction distribution points, generating an accident simulation processing video of the accident prediction distribution point according to the accident type prediction result so as to simulate and demonstrate a processing means of the traffic accident.
3. The method as claimed in claim 1, wherein the average vehicle speed, the vehicle flow rate and the vehicle density are input into a pre-trained accident prediction model, and before predicting an accident on the current road section, the method further comprises: training an accident prediction model;
the training process of the accident prediction model comprises the following steps:
dividing the expressway into a plurality of road sections according to the road sections with the multiple historical traffic accidents, the positions of the vehicle monitoring devices and the distance of the longest road section;
for any one of the road segments, obtaining historical data of the road segment, wherein the historical data at least comprises one of the following data: historical average vehicle speed, historical vehicle flow, historical vehicle density, historical accident frequency and historical accident type;
processing the historical average vehicle speed, the historical vehicle flow, the historical vehicle density, the historical accident frequency and the historical accident type to obtain characteristic numerical values;
selecting a numerical value larger than a preset threshold value from a plurality of characteristic numerical values as a representative characteristic numerical value, and taking historical data of a road section corresponding to the representative characteristic numerical value as a training sample;
aiming at any one of a plurality of training samples, inputting historical average vehicle speed, historical vehicle flow and historical vehicle density contained in the training sample to an input layer of a fuzzy neural network, inputting corresponding historical accident frequency and historical accident type to an output layer of the fuzzy neural network, and performing supervision training to obtain an initial accident prediction model;
and carrying out precision detection on the plurality of initial accident prediction models, and selecting the initial accident prediction models with the highest precision as the accident prediction models.
4. The method according to claim 1, wherein if the traffic flow information is not collected, the vehicle flow and the vehicle density are obtained through the terminal information and the section information of the current section, and specifically comprises:
acquiring the number of vehicles on the current road section according to the terminal information, and determining the vehicle flow on the current road section according to the number of vehicles and a preset time interval;
and acquiring the road width of the current road section, and determining the vehicle density of the current road section according to the number of the vehicles and the road width.
5. The method as claimed in claim 1, wherein the average vehicle speed, the vehicle flow rate and the vehicle density are input into a pre-trained accident prediction model, and after an accident prediction is performed on the current road segment, the method further comprises:
determining that the vehicle density is greater than a congestion threshold;
generating a first control instruction and sending the first control instruction to an information issuing unit arranged on the expressway, wherein the first control instruction comprises: the speed limit value of the vehicle is reduced, and the distance value between the vehicles is improved.
6. The traffic accident early warning method for the expressway according to claim 5, wherein after generating a first control command and transmitting the first control command to an information issuing unit provided on the expressway, the method further comprises:
accessing a vehicle dynamic identification unit to a violation monitoring camera in the current road section, identifying the congestion position of the current road section by rotating the violation monitoring camera, and aligning the violation monitoring camera to the congestion position;
and carrying out video analysis on the real-time video to determine a starting point for generating congestion.
7. The method as claimed in claim 1, wherein the average vehicle speed, the vehicle flow rate and the vehicle density are input into a pre-trained accident prediction model, and after an accident prediction is performed on the current road segment, the method further comprises:
calling a corresponding accident case in a case library according to a prediction result corresponding to the accident prediction;
analyzing the accident case through a case analysis model to generate an accident handling scheme, wherein the accident handling scheme at least comprises the following steps: accident handling measures, accident prevention measures.
8. The method as claimed in claim 7, wherein the accident case is analyzed by a case analysis model, and after an accident handling plan is generated, the method further comprises:
determining a plurality of communication devices corresponding to the current road section through the terminal signal acquisition module;
and sending the accident prevention measures to a plurality of communication devices corresponding to the current road section through short message editing.
9. A traffic accident early warning apparatus for a highway, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform instructions for:
acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway;
if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing;
obtaining the average speed of the current road section according to the result of the speed estimation;
if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section;
and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
10. A non-transitory computer storage medium for highway traffic accident warning, the medium storing computer-executable instructions configured to:
acquiring terminal information of communication equipment, wherein the communication equipment is positioned in a vehicle running on a current road section; attempting to acquire traffic flow information of the current road section through a plurality of vehicle monitoring devices arranged on the highway;
if the traffic flow information is not acquired, estimating the speed of the running vehicle through the terminal information; if the traffic flow information is collected, carrying out data consistency processing on the terminal information and the traffic flow information, and carrying out vehicle speed estimation on the data after the data consistency processing;
obtaining the average speed of the current road section according to the result of the speed estimation;
if the traffic flow information is acquired, obtaining the vehicle flow and the vehicle density of the current road section according to the traffic flow information; if the traffic flow information is not acquired, the vehicle flow and the vehicle density are obtained through the terminal information and the road section information of the current road section;
and inputting the average speed, the vehicle flow and the vehicle density into a pre-trained accident prediction model, and performing accident prediction on the current road section.
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