CN117912232A - Traffic accident data integration and analysis method and system - Google Patents
Traffic accident data integration and analysis method and system 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- 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
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
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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
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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Abstract
The invention discloses a traffic accident data integration and analysis method and a system, wherein the method comprises the following steps: acquiring historical traffic accident data, and dividing the historical traffic accident data into a plurality of periods according to time; respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data; and when the accident data value is greater than or equal to a preset accident occurrence threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, sending out traffic accident occurrence early warning information.
Description
Technical Field
The invention belongs to the technical field of traffic accident data integration, and particularly relates to a traffic accident data integration and analysis method and system.
Background
The following is a general case of highway traffic accident data integration:
Data sources are diversified: highway traffic accident data is typically from a variety of sources, including traffic police records, insurance company data, medical records, government traffic agency data, internet platforms, GPS data, and the like. These data sources are inconsistent and may take different formats and standards.
Data integration challenges: integrating data from different sources and formats together is a complex challenge. The data may include information about the date, time, location, participant, vehicle type, casualty, weather conditions, road type, etc. of the incident. Integrating these data typically requires data cleansing, normalization, and conversion.
Data quality: data quality is a critical issue. Data from different sources may have errors, inconsistencies, and missing values. Ensuring the accuracy and integrity of the data is critical.
Geographic Information System (GIS): the GIS technology plays an important role in highway accident data integration. It may help correlate incident data with geographic locations for geospatial analysis, such as hotspot analysis and geographic visualization.
However, in the prior art, no technical scheme can effectively integrate traffic accident data and analyze the traffic accident data according to the integrated traffic accident data so as to provide early warning information of traffic accidents.
Disclosure of Invention
In order to solve the technical problems, the invention provides a traffic accident data integration and analysis method, which comprises the following steps:
Acquiring historical traffic accident data, and dividing the historical traffic accident data into a plurality of periods according to time;
Respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
And when the accident data value is greater than or equal to a preset accident occurrence threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, sending out traffic accident occurrence early warning information.
Further, the traffic accident trend prediction model includes:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
Further, the traffic accident frequency model includes:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
Further, the traffic accident influence model includes:
Wherein, impact is a traffic accident Impact value, α 'is a traffic accident Impact adjustment factor, β' i is the weight of the ith kernel function, x 'is the eigenvector of the current accident data, x i is the ith eigenvector of the historical accident data, θ is the phase, γ' is the amplitude, and σ is the width of the kernel function.
Further, the kth exogenous variable X k,t at the time t includes: visibility, traffic flow, and road smoothness.
The invention also provides a traffic accident data integration and analysis system, which comprises:
The data acquisition module is used for acquiring historical traffic accident data and dividing the historical traffic accident data into a plurality of periods according to time;
the calculation module is used for respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
The early warning module is used for sending out traffic accident occurrence early warning information when the accident data value is greater than or equal to a preset accident occurrence threshold value, and/or sending out traffic accident occurrence early warning information when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold value, and/or sending out traffic accident occurrence early warning information when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold value.
Further, the traffic accident trend prediction model includes:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
Further, the traffic accident frequency model includes:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
Further, the traffic accident influence model includes:
Wherein Impct is a traffic accident impact value, α 'is a traffic accident impact adjustment factor, β' i is a weight of an ith kernel function, x 'is a feature vector of current accident data, x i is an ith feature vector of historical accident data, θ is a phase, γ' is an amplitude, and σ is a width of the kernel function.
Further, the kth exogenous variable X k,t at the time t includes: visibility, traffic flow, and road smoothness.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
The method comprises the steps of obtaining historical traffic accident data and dividing the historical traffic accident data into a plurality of periods according to time; respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data; and when the accident data value is greater than or equal to a preset accident occurrence threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, sending out traffic accident occurrence early warning information. According to the technical scheme, the related model can be set according to the historical traffic accident data, and the traffic accidents possibly occurring on the expressway can be accurately early warned.
Drawings
FIG. 1 is a flow chart of example 1;
FIG. 2 is a system configuration diagram of embodiment 2;
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access memory (Random Access Memory, RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
All subscripts in the formula of the invention are only used for distinguishing parameters and have no practical meaning.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 1, the present invention provides a traffic accident data integrating and analyzing method, which includes:
Step 101, acquiring historical traffic accident data, and dividing the historical traffic accident data into a plurality of periods according to time;
102, respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
Specifically, the traffic accident trend prediction model includes:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
Specifically, the traffic accident frequency model includes:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
Specifically, the traffic accident impact model includes:
Wherein, impact is a traffic accident Impact value, α 'is a traffic accident Impact adjustment factor, β' i is the weight of the ith kernel function, x 'is the eigenvector of the current accident data, x i is the ith eigenvector of the historical accident data, θ is the phase, γ' is the amplitude, and σ is the width of the kernel function.
Specifically, the kth exogenous variable X k,t at the time t includes: visibility, traffic flow, and road smoothness.
Step 103, when the accident data value is greater than or equal to a preset accident occurrence threshold, traffic accident occurrence early warning information is sent out, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, traffic accident occurrence early warning information is sent out, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, traffic accident occurrence early warning information is sent out.
Example 2
As shown in fig. 2, the present invention further provides a system for integrating and analyzing traffic accident data, including:
The data acquisition module is used for acquiring historical traffic accident data and dividing the historical traffic accident data into a plurality of periods according to time;
the calculation module is used for respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
Specifically, the traffic accident trend prediction model includes:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
Specifically, the traffic accident frequency model includes:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β is visibility adjustment factor, and W is visibility.
Specifically, the traffic accident impact model includes:
Wherein, impact is a traffic accident Impact value, α 'is a traffic accident Impact adjustment factor, β' i is the weight of the ith kernel function, x 'is the eigenvector of the current accident data, x i is the ith eigenvector of the historical accident data, θ is the phase, γ' is the amplitude, and σ is the width of the kernel function.
Specifically, the kth exogenous variable X k,t at the time t includes: visibility, traffic flow, and road smoothness.
The early warning module is used for sending out traffic accident occurrence early warning information when the accident data value is greater than or equal to a preset accident occurrence threshold value, and/or sending out traffic accident occurrence early warning information when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold value, and/or sending out traffic accident occurrence early warning information when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold value.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the traffic accident data integration and analysis method.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: step 101, acquiring historical traffic accident data, and dividing the historical traffic accident data into a plurality of periods according to time;
102, respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
Specifically, the traffic accident trend prediction model includes:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
Specifically, the traffic accident frequency model includes:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
Specifically, the traffic accident impact model includes:
Wherein, impact is a traffic accident Impact value, α 'is a traffic accident Impact adjustment factor, β' i is the weight of the ith kernel function, x 'is the eigenvector of the current accident data, x i is the ith eigenvector of the historical accident data, θ is the phase, γ' is the amplitude, and σ is the width of the kernel function.
Specifically, the kth exogenous variable X k,t at the time t includes: visibility, traffic flow, and road smoothness.
Step 103, when the accident data value is greater than or equal to a preset accident occurrence threshold, traffic accident occurrence early warning information is sent out, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, traffic accident occurrence early warning information is sent out, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, traffic accident occurrence early warning information is sent out.
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a traffic accident data integration and analysis method.
Specifically, the electronic device of the present embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
The storage medium may be used for storing software programs and modules, such as a traffic accident data integration and analysis method in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, thereby implementing the traffic accident data integration and analysis method. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may invoke the information stored in the storage medium and the application program via the transmission system to perform the steps of: step 101, acquiring historical traffic accident data, and dividing the historical traffic accident data into a plurality of periods according to time;
102, respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
Specifically, the traffic accident trend prediction model includes:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
Specifically, the traffic accident frequency model includes:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
Specifically, the traffic accident impact model includes:
Wherein Impct is a traffic accident impact value, α 'is a traffic accident impact adjustment factor, β' i is a weight of an ith kernel function, x 'is a feature vector of current accident data, x i is an ith feature vector of historical accident data, θ is a phase, γ' is an amplitude, and σ is a width of the kernel function.
Specifically, the kth exogenous variable X k,t at the time t includes: visibility, traffic flow, and road smoothness.
Step 103, when the accident data value is greater than or equal to a preset accident occurrence threshold, traffic accident occurrence early warning information is sent out, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, traffic accident occurrence early warning information is sent out, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, traffic accident occurrence early warning information is sent out.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a read-only memory (ROM), a random-access memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, etc., which can store program codes.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (10)
1. The traffic accident data integrating and analyzing method is characterized by comprising the following steps:
Acquiring historical traffic accident data, and dividing the historical traffic accident data into a plurality of periods according to time;
Respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
And when the accident data value is greater than or equal to a preset accident occurrence threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, sending out traffic accident occurrence early warning information, and/or when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold, sending out traffic accident occurrence early warning information.
2. The traffic accident data integration and analysis method according to claim 1, wherein the traffic accident trend prediction model comprises:
Wherein Y t is the accident data value at time T, σ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
3. The traffic accident data integration and analysis method according to claim 1, wherein the traffic accident frequency model comprises:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
4. The traffic accident data integration and analysis method according to claim 1, wherein the traffic accident impact model comprises:
Wherein, impact is a traffic accident Impact value, α 'is a traffic accident Impact adjustment factor, β' i is the weight of the ith kernel function, x 'is the eigenvector of the current accident data, x i is the ith eigenvector of the historical accident data, θ is the phase, γ' is the amplitude, and σ is the width of the kernel function.
5. The traffic accident data integrating and analyzing method according to claim 1, wherein the kth exogenous variable X k,t at the time t comprises: visibility, traffic flow, and road smoothness.
6. A traffic accident data integration and analysis system, comprising:
The data acquisition module is used for acquiring historical traffic accident data and dividing the historical traffic accident data into a plurality of periods according to time;
the calculation module is used for respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident influence model, and respectively calculating an accident data value, a traffic accident frequency and a traffic accident influence value according to the historical traffic accident data;
The early warning module is used for sending out traffic accident occurrence early warning information when the accident data value is greater than or equal to a preset accident occurrence threshold value, and/or sending out traffic accident occurrence early warning information when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold value, and/or sending out traffic accident occurrence early warning information when the traffic accident influence value is greater than or equal to a preset accident occurrence influence threshold value.
7. The traffic accident data integration and analysis system according to claim 6, wherein the traffic accident trend prediction model comprises:
wherein Y t is the accident data value at time T, μ is the mean of the time series, p is the number of autoregressive coefficients, β "i is the ith autoregressive coefficient, Y t-i is the accident data value at time T-i, σ' is the volatility adjustment factor, B (H, T) is the fractal brownian motion with the hurst index H at time T, s is the number of cycles, γ j is the weight of the jth cycle, T is the cycle length, q is the number of exogenous variables, δ k is the weight of the kth exogenous variable, X k,t is the kth exogenous variable at time T, and ε t is the residual value at time T.
8. The traffic accident data integration and analysis system according to claim 6, wherein the traffic accident frequency model comprises:
Wherein AF is traffic accident frequency, λ is basic accident rate of traffic accident, α is time weight, β 1 is time adjustment factor, T is cycle length, γ is distance weight, β 2 is distance adjustment factor, L is distance from nearest toll station when accident occurs, δ is road wet skid weight, β 3 is road wet skid adjustment factor, RT is road wet skid, φ is visibility weight, β 4 is visibility adjustment factor, and W is visibility.
9. The traffic accident data integration and analysis system according to claim 6, wherein the traffic accident impact model comprises:
Wherein, impact is a traffic accident Impact value, α 'is a traffic accident Impact adjustment factor, β' i is the weight of the ith kernel function, x 'is the eigenvector of the current accident data, x i is the ith eigenvector of the historical accident data, θ is the phase, γ' is the amplitude, and σ is the width of the kernel function.
10. The traffic accident data integration and analysis system according to claim 6, wherein the kth exogenous variable X k,t at time t comprises: visibility, traffic flow, and road smoothness.
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