CN116403403B - Traffic early warning method, system, equipment and medium based on big data analysis - Google Patents
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- 206010039203 Road traffic accident Diseases 0.000 claims abstract description 134
- 238000004891 communication Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 11
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- 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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Abstract
The invention belongs to the technical field of data processing, and aims to provide a traffic early warning method, system, equipment and medium based on big data analysis. According to the invention, a classification decision theory is introduced, decision problems existing in early warning management are analyzed, various traffic index parameters influencing traffic accident category information are introduced, traffic data are acquired in fact, and early warning of traffic accident categories is realized based on a final accident classification decision model, so that the output precision of early warning signals can be improved, and effective early warning can be realized.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a traffic early warning method, system, equipment and medium based on big data analysis.
Background
With the increasing standard of living and the continuous progress of technology, automobiles become a walking tool for people to go out gradually, and accordingly, traffic accidents are increasing year by year. In the prior art, for example, a highway traffic accident risk assessment system is proposed in chinese patent application with publication number CN109636125a, and the application uses a support vector machine algorithm to input traffic accident influencing factors, including environmental factors and road factors, and predict the number of road traffic accidents.
However, in using the prior art, the inventors found that there are at least the following problems in the prior art:
in the prior art, the traffic safety risk is quantified by predicting the number of road traffic accidents, however, the prior art cannot acquire main factors influencing the traffic accidents, cannot acquire the conditions of traffic accident types and the like, and cannot realize effective early warning.
Disclosure of Invention
The invention aims to solve the technical problems at least to a certain extent, and provides a traffic early warning method, a system, equipment and a medium based on big data analysis.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a traffic early warning method based on big data analysis, including:
acquiring traffic accident data of a target area from a preset road traffic management information database;
according to the traffic accident data, obtaining traffic accident category information of a target area and traffic index parameters corresponding to the traffic accident category information;
acquiring importance degrees of all traffic index parameters corresponding to the traffic accident category information, and selecting the traffic index parameters with the importance degrees ranked N before, so as to obtain a reconstruction index set corresponding to the traffic accident category information; wherein N is a positive integer;
constructing an initial accident classification decision model, and training the initial accident classification decision model according to a reconstruction index set corresponding to traffic accident category information to obtain a final accident classification decision model;
and collecting real-time traffic index parameters matched with the traffic index parameters with the N top importance ranking, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters.
In the implementation process, the invention can improve the output precision of the early warning signal. In the implementation process, firstly, acquiring traffic accident data of a target area from a preset road traffic management information database; acquiring traffic accident category information of a target area and traffic index parameters corresponding to the traffic accident category information according to the traffic accident data; then, the importance degree of all traffic index parameters corresponding to the traffic accident category information is obtained, and the traffic index parameters with the importance degree ranked N before are selected to obtain a reconstruction index set corresponding to the traffic accident category information; then, an initial accident classification decision model is constructed, and training is carried out on the initial accident classification decision model according to a reconstruction index set corresponding to traffic accident category information, so that a final accident classification decision model is obtained; and finally, collecting real-time traffic index parameters matched with the traffic index parameters of N with the highest ranking of importance, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters. In the process, the invention introduces a classification decision theory, analyzes decision problems existing in early warning management, introduces various traffic index parameters influencing traffic accident category information, acquires traffic data in fact, and realizes early warning of traffic accident categories based on a final accident classification decision model, thereby improving the output precision of early warning signals and realizing effective early warning.
In one possible design, the traffic accident category information is corresponding category information of traffic accidents of different grades;
the traffic index parameters corresponding to the traffic accident category information comprise a primary index parameter, a secondary index parameter corresponding to the primary index parameter and a tertiary index parameter corresponding to the secondary index parameter; the first-level index parameters comprise weather index parameters, ground traffic index parameters and driver index parameters, the second-level index parameters corresponding to the weather index parameters comprise rainfall intensity, visibility, ground humidity and/or wind speed, the second-level index parameters corresponding to the ground traffic index parameters comprise vehicle flow, people flow, vehicle density, vehicle speed, road width and/or time, the second-level index parameters corresponding to the driver index parameters comprise driver age, driver gender and/or driver fatigue, and the third-level index parameters are quantized data corresponding to the second-level index parameters.
In one possible design, after obtaining the traffic accident category information of the target area and the traffic index parameter corresponding to the traffic accident category information, the method further includes:
judging whether the traffic index parameter is out of a preset range, if so, judging that the current traffic index parameter is error data, deleting the current traffic index parameter, and entering the next step;
repairing the current traffic index parameter by using the traffic index parameter of the current target area positioned in the adjacent time period of the current traffic index parameter, and then continuously judging the latter traffic index parameter until all traffic index parameters are judged, and then acquiring the importance degree of all traffic index parameters corresponding to the traffic accident category information.
In one possible design, the obtaining the importance of all the traffic index parameters corresponding to the traffic accident category information includes:
acquiring correlation coefficients between every two traffic index parameters, wherein all the correlation coefficients form a binary relation library;
and obtaining the importance degree of all the traffic index parameters according to the binary relation library.
In one possible design, the importance of the ith traffic index parameter with respect to the current traffic accident category information y is as follows:
wherein y is i The traffic accident category information corresponding to the ith traffic index parameter is obtained; n is the number of all traffic index parameters corresponding to the traffic accident category information; a, a ij Is the correlation coefficient of the ith traffic index parameter relative to the jth traffic index parameter.
In one possible design, the initial incident classification decision model is:
y＝a 1 x 1+ a 2 x 2 +……a N x N +b；
wherein y is traffic accident category information; x is x 1 、x 2 、……、x N Respectively isThe top N traffic index parameters of importance ranking; a, a 1 、a 2 、……、a N Respectively the weight coefficients of the traffic index parameters; b is the association parameter.
In one possible design, after the early warning traffic accident category information corresponding to the real-time traffic index parameter is obtained, the method further includes:
and visually displaying the real-time traffic index parameters and/or traffic accident category early warning information corresponding to the real-time traffic index parameters.
In a second aspect, the present invention provides a traffic early warning system based on big data analysis, for implementing the traffic early warning method based on big data analysis as described in any one of the above; the traffic early warning system based on big data analysis comprises:
the data acquisition module is used for acquiring traffic accident data of a target area from a preset road traffic management information database, and acquiring traffic accident category information of the target area and traffic index parameters corresponding to the traffic accident category information according to the traffic accident data;
the index reconstruction module is in communication connection with the data acquisition module and is used for acquiring the importance of all traffic index parameters corresponding to the traffic accident category information, and selecting the traffic index parameters with the importance ranking N to obtain a reconstruction index set corresponding to the traffic accident category information; wherein N is a positive integer;
the model acquisition module is in communication connection with the index reconstruction module and is used for constructing an initial accident classification decision model, training the initial accident classification decision model according to a reconstruction index set corresponding to the traffic accident category information, and obtaining a final accident classification decision model;
the traffic early warning module is in communication connection with the model acquisition module and is used for acquiring real-time traffic index parameters matched with the traffic index parameters with the importance ranking N, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters.
In a third aspect, the present invention provides an electronic device, comprising:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing the computer program instructions to perform the operations of the big data analysis based traffic warning method as set forth in any one of the preceding claims.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer program instructions readable by a computer, the computer program instructions configured to perform the operations of the big data analysis based traffic warning method as defined in any one of the preceding claims when run.
Drawings
FIG. 1 is a flow chart of a traffic early warning method based on big data analysis in an embodiment;
FIG. 2 is a block diagram of a traffic warning system based on big data analysis in an embodiment;
fig. 3 is a block diagram of an electronic device in an embodiment.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
Example 1:
the embodiment discloses a traffic early warning method based on big data analysis, which can be executed by computer equipment or virtual machines with certain computing resources, such as personal computers, smart phones, personal digital assistants or electronic equipment such as wearable equipment, or virtual machines.
As shown in fig. 1, a traffic early warning method based on big data analysis may include, but is not limited to, the following steps:
s1, acquiring traffic accident data of a target area from a preset road traffic management information database; big data has four big characteristics of massive data scale, quick data circulation, various data types and low value density. In the embodiment, the traffic accident data of the target area is obtained from the preset road traffic management information database based on big data analysis, so that the subsequent effective early warning of the traffic accident of the target area can be conveniently realized.
S2, acquiring traffic accident category information of a target area and traffic index parameters corresponding to the traffic accident category information according to the traffic accident data; therefore, effective identification of risks can be realized, in the process, the information characteristics provided by the indexes are compared with the risk accident state, so that the problem that the risk state cannot be comprehensively judged and the alarm is missed due to the fact that the number of dimensions is excessive and the number of the dimensions is too small is prevented.
Specifically, in this embodiment, the traffic accident category information is corresponding category information of traffic accidents of different levels, and the traffic accidents of different levels are classified into no risk, low risk, medium risk, high risk and extremely high risk;
the traffic index parameters corresponding to the traffic accident category information comprise a primary index parameter, a secondary index parameter corresponding to the primary index parameter and a tertiary index parameter corresponding to the secondary index parameter; the first-level index parameters comprise weather index parameters, ground traffic index parameters and driver index parameters, the second-level index parameters corresponding to the weather index parameters comprise rainfall intensity, visibility, ground humidity and/or wind speed, the second-level index parameters corresponding to the ground traffic index parameters comprise vehicle flow, people flow, vehicle density, vehicle speed, road width and/or time, the second-level index parameters corresponding to the driver index parameters comprise driver age, driver gender and/or driver fatigue, and the third-level index parameters are quantized data corresponding to the second-level index parameters.
After the traffic accident category information of the target area and the traffic index parameters corresponding to the traffic accident category information are acquired, the method further comprises the steps of respectively carrying out assignment quantification on the traffic accident category information and the traffic index parameters corresponding to the traffic accident category information to obtain quantified traffic index parameters corresponding to the traffic accident category information so as to carry out subsequent calculation processing on the traffic accident category information and the traffic index parameters;
in this embodiment, after obtaining the traffic accident category information of the target area and the traffic index parameter corresponding to the traffic accident category information, the method further includes:
A1. judging whether the traffic index parameters are out of a preset range, if so, judging the current traffic index parameters as error data, deleting the current traffic index parameters, and entering the next step, if not, continuing to judge the next traffic index parameters until all the traffic index parameters are judged, and acquiring the importance of all the traffic index parameters corresponding to the traffic accident type information;
A2. repairing the current traffic index parameter by using the traffic index parameter of the current target area positioned in the adjacent time period of the current traffic index parameter, and then continuously judging the latter traffic index parameter until all traffic index parameters are judged, and then acquiring the importance degree of all traffic index parameters corresponding to the traffic accident category information. In the step, the traffic index parameter of the current target area positioned in the adjacent time period of the current traffic index parameter is assigned to the current traffic index parameter, so that the problem of inaccurate prediction caused by the lack of the current traffic index parameter is avoided.
S3, acquiring importance degrees of all traffic index parameters corresponding to the traffic accident category information, and selecting the traffic index parameters with the importance degrees ranked N before, so as to obtain a reconstruction index set corresponding to the traffic accident category information; in this embodiment, N is a positive integer greater than or equal to 3, which is not limited herein;
in this embodiment, obtaining importance degrees of all traffic index parameters corresponding to the traffic accident category information includes:
s301, obtaining correlation coefficients between every two traffic index parameters, wherein all the correlation coefficients form a binary relation library; in this embodiment, the ith traffic index parameter A i Relative jth traffic index parameter A j Correlation coefficient a between ij The following are provided:
in the method, in the process of the invention,is the average value of all traffic index parameters;
s302, obtaining importance of all traffic index parameters according to the binary relation library.
Specifically, in this embodiment, among all the traffic index parameters corresponding to the traffic accident category information, the importance of the ith traffic index parameter with respect to the current traffic accident category information y is:
wherein y is i For the ith traffic index parameter A i Corresponding traffic accident category information; n is the number of all traffic index parameters corresponding to the traffic accident category information y; a, a ij For the ith traffic index parameter A i Relative jth traffic index parameter A j Is used for the correlation coefficient of the (c).
It should be noted that the larger the correlation coefficient between any two traffic index parameters, the closer the corresponding traffic accident relationship between the two traffic index parameters is.
In this embodiment, the importance of all traffic index parameters corresponding to the traffic accident category information may also be calculated by a random forest algorithm. For example, the method adopts Chinese patent with publication number of CN109409430A for calculation, wherein the random forest algorithm has high prediction accuracy and high tolerance to abnormal values and noise, can process high-dimensional data, effectively analyze nonlinear data and data with co-linearity and interaction, can give index importance scores while analyzing the data, and is suitable for screening importance index parameters of traffic accident severity. The process of calculating the importance of the traffic index parameter by the random forest algorithm is not described here again.
In this embodiment, the importance of the traffic index parameter is obtained by adopting the correlation coefficient mode, which has the advantages of simpler calculation process and more accurate calculation result.
S4, constructing an initial accident classification decision model, and training the initial accident classification decision model according to a reconstruction index set corresponding to traffic accident category information to obtain a final accident classification decision model;
in this embodiment, the initial accident classification decision model is:
y＝a 1 x 1+ a 2 x 2 +……a N x N +b；
wherein y is traffic accident category information; x is x 1 、x 2 、……、x N N traffic index parameters with the top importance ranking are respectively obtained; a, a 1 、a 2 、……、a N Respectively the weight coefficients of the traffic index parameters; b is the association parameter.
It should be noted that, in this embodiment, after training the initial accident classification decision model according to the reconstructed index set corresponding to the traffic accident category information, the weight coefficient a of each traffic index parameter may be obtained 1 、a 2 、……、a n And the related parameter b, thereby being convenient for obtaining the traffic accident category early warning information corresponding to the real-time traffic index parameter through monitoring the obtained real-time traffic index parameter.
S5, collecting real-time traffic index parameters matched with the traffic index parameters with the importance ranking N, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters.
In this embodiment, after obtaining the early warning traffic accident category information corresponding to the real-time traffic index parameter, the method further includes:
s6, visually displaying the real-time traffic index parameters and/or traffic accident category early warning information corresponding to the real-time traffic index parameters.
It should be noted that, the real-time traffic index parameter and/or the accident type information corresponding to the real-time traffic index parameter may be visually displayed in the corresponding area in the traffic map in the user terminal or the management terminal, and different areas of the traffic map display the corresponding traffic index parameter and/or the accident type information, so that the driver or the traffic management staff can quickly learn the predicted accident situation of each traffic area, and can prepare for the corresponding traffic accident in time.
In the implementation process of the embodiment, the output precision of the early warning signal can be improved. Specifically, in the implementation process of the embodiment, firstly, traffic accident data of a target area is obtained from a preset road traffic management information database; acquiring traffic accident category information of a target area and traffic index parameters corresponding to the traffic accident category information according to the traffic accident data; then, the importance degree of all traffic index parameters corresponding to the traffic accident category information is obtained, and the traffic index parameters with the importance degree ranked N before are selected to obtain a reconstruction index set corresponding to the traffic accident category information; then, an initial accident classification decision model is constructed, and training is carried out on the initial accident classification decision model according to a reconstruction index set corresponding to traffic accident category information, so that a final accident classification decision model is obtained; and finally, collecting real-time traffic index parameters matched with the traffic index parameters of N with the highest ranking of importance, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters. In the process, the embodiment introduces a classification decision theory, analyzes decision problems existing in early warning management, introduces various traffic index parameters influencing traffic accident category information, acquires traffic data in fact, and realizes early warning of traffic accident categories based on a final accident classification decision model, so that the output precision of early warning signals can be improved, and effective early warning can be realized.
Example 2:
the embodiment discloses a traffic early warning system based on big data analysis, which is used for realizing the traffic early warning method based on big data analysis in the embodiment 1; as shown in fig. 2, the traffic early warning system based on big data analysis includes:
the data acquisition module is used for acquiring traffic accident data of a target area from a preset road traffic management information database, and acquiring traffic accident category information of the target area and traffic index parameters corresponding to the traffic accident category information according to the traffic accident data;
the index reconstruction module is in communication connection with the data acquisition module and is used for acquiring the importance of all traffic index parameters corresponding to the traffic accident category information, and selecting the traffic index parameters with the importance ranking N to obtain a reconstruction index set corresponding to the traffic accident category information; wherein N is a positive integer;
the model acquisition module is in communication connection with the index reconstruction module and is used for constructing an initial accident classification decision model, training the initial accident classification decision model according to a reconstruction index set corresponding to the traffic accident category information, and obtaining a final accident classification decision model;
the traffic early warning module is in communication connection with the model acquisition module and is used for acquiring real-time traffic index parameters matched with the traffic index parameters with the importance ranking N, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters.
Example 3:
on the basis of embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like. An electronic device may be referred to as being used for a terminal, a portable terminal, a desktop terminal, etc., as shown in fig. 3, the electronic device includes:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing the computer program instructions to perform the operations of the big data analysis based traffic warning method as described in any one of embodiment 1.
In particular, processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 301 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 301 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen.
Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 302 is used to store at least one instruction for execution by processor 301 to implement the big data analysis based traffic pre-warning method provided by embodiment 1 in the present application.
In some embodiments, the terminal may further optionally include: a communication interface 303, and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. The respective peripheral devices may be connected to the communication interface 303 through a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power supply 306.
The communication interface 303 may be used to connect at least one peripheral device associated with an I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 301, the memory 302, and the communication interface 303 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 304 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 304 communicates with a communication network and other communication devices via electromagnetic signals.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof.
The power supply 306 is used to power the various components in the electronic device.
Example 4:
on the basis of any one of embodiments 1 to 3, this embodiment discloses a computer-readable storage medium for storing computer-readable computer program instructions configured to perform the operations of the traffic early warning method based on big data analysis as described in embodiment 1 when run.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solution of the present invention, and not limiting thereof; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A traffic early warning method based on big data analysis is characterized in that: comprising the following steps:
acquiring traffic accident data of a target area from a preset road traffic management information database;
according to the traffic accident data, obtaining traffic accident category information of a target area and traffic index parameters corresponding to the traffic accident category information;
acquiring importance degrees of all traffic index parameters corresponding to the traffic accident category information, and selecting the traffic index parameters with the importance degrees ranked N before, so as to obtain a reconstruction index set corresponding to the traffic accident category information; wherein N is a positive integer;
constructing an initial accident classification decision model, and training the initial accident classification decision model according to a reconstruction index set corresponding to traffic accident category information to obtain a final accident classification decision model;
collecting real-time traffic index parameters matched with the traffic index parameters of N with the highest importance ranking, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters;
the obtaining the importance degree of all the traffic index parameters corresponding to the traffic accident category information comprises the following steps:
acquiring correlation coefficients between every two traffic index parameters, wherein all the correlation coefficients form a binary relation library;
obtaining importance of all traffic index parameters according to the binary relation library;
among all the traffic index parameters corresponding to the traffic accident category information, the importance of the ith traffic index parameter relative to the current traffic accident category information y is as follows:
wherein y is i The traffic accident category information corresponding to the ith traffic index parameter is obtained; n is the number of all traffic index parameters corresponding to the traffic accident category information; a, a ij The correlation coefficient of the ith traffic index parameter relative to the jth traffic index parameter;
the initial accident classification decision model is as follows:
y＝a 1 x 1+ a 2 x 2 +……a N x N +b；
wherein y is traffic accident category information; x is x 1 、x 2 、……、x N N traffic index parameters with the top importance ranking are respectively obtained; a, a 1 、a 2 、……、a N Respectively the weight coefficients of the traffic index parameters; b is the association parameter.
2. The traffic early warning method based on big data analysis according to claim 1, wherein: the traffic accident category information is corresponding category information of traffic accidents of different levels;
the traffic index parameters corresponding to the traffic accident category information comprise a primary index parameter, a secondary index parameter corresponding to the primary index parameter and a tertiary index parameter corresponding to the secondary index parameter; the first-level index parameters comprise weather index parameters, ground traffic index parameters and driver index parameters, the second-level index parameters corresponding to the weather index parameters comprise rainfall intensity, visibility, ground humidity and/or wind speed, the second-level index parameters corresponding to the ground traffic index parameters comprise vehicle flow, people flow, vehicle density, vehicle speed, road width and/or time, the second-level index parameters corresponding to the driver index parameters comprise driver age, driver gender and/or driver fatigue, and the third-level index parameters are quantized data corresponding to the second-level index parameters.
3. The traffic early warning method based on big data analysis according to claim 1, wherein: after obtaining the traffic accident category information of the target area and the traffic index parameters corresponding to the traffic accident category information, the method further comprises the following steps:
judging whether the traffic index parameter is out of a preset range, if so, judging that the current traffic index parameter is error data, deleting the current traffic index parameter, and entering the next step;
repairing the current traffic index parameter by using the traffic index parameter of the current target area positioned in the adjacent time period of the current traffic index parameter, and then continuously judging the latter traffic index parameter until all traffic index parameters are judged, and then acquiring the importance degree of all traffic index parameters corresponding to the traffic accident category information.
4. The traffic early warning method based on big data analysis according to claim 1, wherein: after the early warning traffic accident category information corresponding to the real-time traffic index parameter is obtained, the method further comprises the following steps:
and visually displaying the real-time traffic index parameters and/or traffic accident category early warning information corresponding to the real-time traffic index parameters.
5. A traffic early warning system based on big data analysis is characterized in that: a traffic early warning method based on big data analysis according to any one of claims 1 to 4; the traffic early warning system based on big data analysis comprises:
the data acquisition module is used for acquiring traffic accident data of a target area from a preset road traffic management information database, and acquiring traffic accident category information of the target area and traffic index parameters corresponding to the traffic accident category information according to the traffic accident data;
the index reconstruction module is in communication connection with the data acquisition module and is used for acquiring the importance of all traffic index parameters corresponding to the traffic accident category information, and selecting the traffic index parameters with the importance ranking N to obtain a reconstruction index set corresponding to the traffic accident category information; wherein N is a positive integer;
the model acquisition module is in communication connection with the index reconstruction module and is used for constructing an initial accident classification decision model, training the initial accident classification decision model according to a reconstruction index set corresponding to the traffic accident category information, and obtaining a final accident classification decision model;
the traffic early warning module is in communication connection with the model acquisition module and is used for acquiring real-time traffic index parameters matched with the traffic index parameters with the importance ranking N, and inputting the real-time traffic index parameters into the final accident classification decision model so as to obtain traffic accident category early warning information corresponding to the real-time traffic index parameters.
6. An electronic device, characterized in that: comprising the following steps:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing the computer program instructions to perform the operations of the big data analysis based traffic warning method of any one of claims 1 to 4.
7. A computer readable storage medium storing computer program instructions readable by a computer, characterized by: the computer program instructions are configured to perform the operations of the big data analysis based traffic warning method of any of claims 1 to 4 when run.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646534A (en) * | 2013-11-22 | 2014-03-19 | 江苏大学 | A road real time traffic accident risk control method |
| CN110796859A (en) * | 2019-10-28 | 2020-02-14 | 长安大学 | Real-time traffic state identification and accident risk early warning method based on traffic flow |
| CN111815986A (en) * | 2020-09-02 | 2020-10-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Traffic accident early warning method and device, terminal equipment and storage medium |
| CN113160564A (en) * | 2021-04-01 | 2021-07-23 | 公安部道路交通安全研究中心 | Traffic safety early warning analysis method and device and computer equipment |
| CN113380033A (en) * | 2021-06-09 | 2021-09-10 | 山东交通学院 | Urban traffic safety early warning method and system based on man-machine hybrid enhanced intelligence |
| CN114186747A (en) * | 2021-12-16 | 2022-03-15 | 安徽达尔智能控制系统股份有限公司 | Big data based accident analysis decision method and system |
| CN114239927A (en) * | 2021-11-25 | 2022-03-25 | 武汉卓尔信息科技有限公司 | Regional traffic accident early warning method and system based on deep learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180299284A1 (en) * | 2014-12-02 | 2018-10-18 | Kevin Sunlin Wang | Method and System For Avoidance of Accidents |
-
2023
- 2023-04-12 CN CN202310415348.6A patent/CN116403403B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646534A (en) * | 2013-11-22 | 2014-03-19 | 江苏大学 | A road real time traffic accident risk control method |
| CN110796859A (en) * | 2019-10-28 | 2020-02-14 | 长安大学 | Real-time traffic state identification and accident risk early warning method based on traffic flow |
| CN111815986A (en) * | 2020-09-02 | 2020-10-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Traffic accident early warning method and device, terminal equipment and storage medium |
| CN113160564A (en) * | 2021-04-01 | 2021-07-23 | 公安部道路交通安全研究中心 | Traffic safety early warning analysis method and device and computer equipment |
| CN113380033A (en) * | 2021-06-09 | 2021-09-10 | 山东交通学院 | Urban traffic safety early warning method and system based on man-machine hybrid enhanced intelligence |
| CN114239927A (en) * | 2021-11-25 | 2022-03-25 | 武汉卓尔信息科技有限公司 | Regional traffic accident early warning method and system based on deep learning |
| CN114186747A (en) * | 2021-12-16 | 2022-03-15 | 安徽达尔智能控制系统股份有限公司 | Big data based accident analysis decision method and system |
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