CN117912232A - Traffic accident data integration and analysis method and system - Google Patents

Abstract

The present invention discloses a traffic accident data integration and analysis method and system, the method comprising: acquiring historical traffic accident data, and dividing the historical traffic accident data into multiple periods according to time; respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model, and respectively calculating the accident data value, the traffic accident frequency and the traffic accident impact value according to the historical traffic accident data; when the accident data value is greater than or equal to a preset accident occurrence threshold, issuing a traffic accident occurrence warning message, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, issuing a traffic accident occurrence warning message, and/or when the traffic accident impact value is greater than or equal to a preset accident occurrence impact threshold, issuing a traffic accident occurrence warning message.

Description

Traffic accident data integration and analysis method and system

Technical Field

The present invention belongs to the technical field of traffic accident data integration, and more specifically, relates to a traffic accident data integration and analysis method and system.

Background technique

The following is a general description of highway traffic accident data integration:

Diversified data sources: Highway traffic accident data usually comes from various sources, including traffic police records, insurance company data, medical records, government transportation department data, Internet platforms, GPS data, etc. These data sources are inconsistent and may use different formats and standards.

Data integration challenges: Integrating data from different sources and formats is a complex challenge. The data may include information about the date, time, location, participants, vehicle type, casualties, weather conditions, road type, etc. Integrating this data usually requires data cleaning, standardization, and transformation.

Data quality: Data quality is a critical issue. Data from different sources may contain errors, inconsistencies, and missing values. Ensuring the accuracy and completeness of data is critical.

Geographic Information System (GIS): GIS technology plays an important role in the integration of highway accident data. It can help associate accident data with geographic locations for geospatial analysis, such as hot spot analysis and geographic visualization.

However, there is no technical solution in the prior art that can effectively integrate traffic accident data and analyze the integrated traffic accident data to provide early warning information of traffic accidents.

Summary of the invention

In order to solve the above technical problems, the present invention proposes a traffic accident data integration and analysis method, comprising:

Acquire historical traffic accident data, and divide the historical traffic accident data into multiple periods according to time;

A traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model are respectively set, and based on the historical traffic accident data, accident data values, traffic accident frequencies and traffic accident impact values are respectively calculated;

When the accident data value is greater than or equal to a preset accident occurrence threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident impact value is greater than or equal to a preset accident occurrence impact threshold, a traffic accident occurrence warning message is issued.

Furthermore, the traffic accident trend prediction model includes:

Where _Yt 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 i-th autoregressive coefficient, _Yti is the accident data value at time ti, σ′ is the volatility adjustment factor, B(H,t) is the fractal Brownian motion with Hurst index H at time t, s is the number of cycles, _γj is the weight of the j-th cycle, T is the cycle length, q is the number of exogenous variables, _δk is the weight of the k-th exogenous variable, Xk _,t is the k-th exogenous variable at time t, and _εt is the residual value at time t.

Furthermore, the traffic accident frequency model includes:

Among them, AF is the traffic accident frequency, λ is the basic accident rate of traffic accidents, α is the time weight, _β1 is the time adjustment factor, T is the cycle length, γ is the distance weight, _β2 is the distance adjustment factor, L is the distance to the nearest toll station when the accident occurs, δ is the road slipperiness weight, _β3 is the road slipperiness adjustment factor, RT is the road slipperiness, φ is the visibility weight, _β4 is the visibility adjustment factor, and W is the visibility.

Furthermore, the traffic accident impact model includes:

Among them, Impact is the impact value of the traffic accident, α′ is the traffic accident impact adjustment factor, _β′i is the weight of the i-th kernel function, x _′ is the eigenvector of the current accident data, xi is the i-th eigenvector of the historical accident data, θ is the phase, γ′ is the amplitude, and σ is the width of the kernel function.

Furthermore, the kth exogenous variable X _k, t at time t includes: visibility, traffic flow and road slipperiness.

The present invention also proposes a traffic accident data integration and analysis system, comprising:

A data acquisition module is used to acquire historical traffic accident data and divide the historical traffic accident data into multiple periods according to time;

A calculation module, used to respectively set a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model, and respectively calculate the accident data value, the traffic accident frequency and the traffic accident impact value based on the historical traffic accident data;

The early warning module is used to issue a traffic accident early warning message when the accident data value is greater than or equal to a preset accident occurrence threshold, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, and/or when the traffic accident impact value is greater than or equal to a preset accident occurrence impact threshold, issue a traffic accident early warning message.

Furthermore, the traffic accident trend prediction model includes:

Where _Yt 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 i-th autoregressive coefficient, _Yti is the accident data value at time ti, σ′ is the volatility adjustment factor, B(H,t) is the fractal Brownian motion with Hurst index H at time t, s is the number of cycles, _γj is the weight of the j-th cycle, T is the cycle length, q is the number of exogenous variables, _δk is the weight of the k-th exogenous variable, Xk _,t is the k-th exogenous variable at time t, and _εt is the residual value at time t.

Furthermore, the traffic accident frequency model includes:

Among them, AF is the traffic accident frequency, λ is the basic accident rate of traffic accidents, α is the time weight, _β1 is the time adjustment factor, T is the cycle length, γ is the distance weight, _β2 is the distance adjustment factor, L is the distance to the nearest toll station when the accident occurs, δ is the road slipperiness weight, _β3 is the road slipperiness adjustment factor, RT is the road slipperiness, φ is the visibility weight, _β4 is the visibility adjustment factor, and W is the visibility.

Furthermore, the traffic accident impact model includes:

Among them, Impct is the impact value of the traffic accident, α′ is the traffic accident impact adjustment factor, _β′i is the weight of the i-th kernel function, x _′ is the eigenvector of the current accident data, xi is the i-th eigenvector of the historical accident data, θ is the phase, γ′ is the amplitude, and σ is the width of the kernel function.

Furthermore, the kth exogenous variable X _k, t at time t includes: visibility, traffic flow and road slipperiness.

Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

The present invention obtains historical traffic accident data, and divides the historical traffic accident data into multiple periods according to time; respectively sets a traffic accident trend prediction model, a traffic accident frequency model, and a traffic accident impact model, and respectively calculates the accident data value, the traffic accident frequency, and the traffic accident impact value according to the historical traffic accident data; when the accident data value is greater than or equal to a preset accident occurrence threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident impact value is greater than or equal to a preset accident occurrence impact threshold, a traffic accident occurrence warning message is issued. Through the above technical solution, the present invention can set relevant models according to historical traffic accident data to accurately warn of traffic accidents that may occur on highways.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a flow chart of Embodiment 1;

FIG2 is a system structure diagram of Example 2;

Detailed ways

In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

The display screen is used to display the user interface of each application.

All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

In addition, those skilled in the art will appreciate that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

Example 1

As shown in FIG1 , the present invention proposes a method for integrating and analyzing traffic accident data, including:

Step 101, obtaining historical traffic accident data, and dividing the historical traffic accident data into multiple periods according to time;

Step 102, respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model, and respectively calculating the accident data value, the traffic accident frequency and the traffic accident impact value based on the historical traffic accident data;

Specifically, the traffic accident trend prediction model includes:

Where _Yt 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 i-th autoregressive coefficient, _Yti is the accident data value at time ti, σ′ is the volatility adjustment factor, B(H,t) is the fractal Brownian motion with Hurst index H at time t, s is the number of cycles, _γj is the weight of the j-th cycle, T is the cycle length, q is the number of exogenous variables, _δk is the weight of the k-th exogenous variable, Xk _,t is the k-th exogenous variable at time t, and _εt is the residual value at time t.

Specifically, the traffic accident frequency model includes:

Among them, AF is the traffic accident frequency, λ is the basic accident rate of traffic accidents, α is the time weight, _β1 is the time adjustment factor, T is the cycle length, γ is the distance weight, _β2 is the distance adjustment factor, L is the distance to the nearest toll station when the accident occurs, δ is the road slipperiness weight, _β3 is the road slipperiness adjustment factor, RT is the road slipperiness, φ is the visibility weight, _β4 is the visibility adjustment factor, and W is the visibility.

Specifically, the traffic accident impact model includes:

Among them, Impact is the impact value of the traffic accident, α′ is the traffic accident impact adjustment factor, _β′i is the weight of the i-th kernel function, x _′ is the eigenvector of the current accident data, xi is the i-th 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 time t includes: visibility, traffic flow and road slipperiness.

Step 103, when the accident data value is greater than or equal to the preset accident occurrence threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident frequency is greater than or equal to the preset accident occurrence frequency threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident impact value is greater than or equal to the preset accident occurrence impact threshold, a traffic accident occurrence warning message is issued.

Example 2

As shown in FIG2 , the present invention further proposes a traffic accident data integration and analysis system, comprising:

A data acquisition module is used to acquire historical traffic accident data and divide the historical traffic accident data into multiple periods according to time;

A calculation module, used to respectively set a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model, and respectively calculate the accident data value, the traffic accident frequency and the traffic accident impact value based on the historical traffic accident data;

Specifically, the traffic accident trend prediction model includes:

Where _Yt 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 i-th autoregressive coefficient, _Yti is the accident data value at time ti, σ′ is the volatility adjustment factor, B(H,t) is the fractal Brownian motion with Hurst index H at time t, s is the number of cycles, _γj is the weight of the j-th cycle, T is the cycle length, q is the number of exogenous variables, _δk is the weight of the k-th exogenous variable, Xk _,t is the k-th exogenous variable at time t, and _εt is the residual value at time t.

Specifically, the traffic accident frequency model includes:

Among them, AF is the traffic accident frequency, λ is the basic accident rate of traffic accidents, α is the time weight, _β1 is the time adjustment factor, T is the cycle length, γ is the distance weight, _β2 is the distance adjustment factor, L is the distance to the nearest toll station when the accident occurs, δ is the road slipperiness weight, _β3 is the road slipperiness adjustment factor, RT is the road slipperiness, φ is the visibility weight, _β4 is the visibility adjustment factor, and W is the visibility.

Specifically, the traffic accident impact model includes:

Among them, Impact is the impact value of the traffic accident, α′ is the traffic accident impact adjustment factor, _β′i is the weight of the i-th kernel function, x _′ is the eigenvector of the current accident data, xi is the i-th 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 time t includes: visibility, traffic flow and road slipperiness.

The early warning module is used to issue a traffic accident early warning message when the accident data value is greater than or equal to a preset accident occurrence threshold, and/or when the traffic accident frequency is greater than or equal to a preset accident occurrence frequency threshold, and/or when the traffic accident impact value is greater than or equal to a preset accident occurrence impact threshold, issue a traffic accident early warning message.

Example 3

The embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the traffic accident data integration and analysis method.

Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, acquiring historical traffic accident data, and dividing the historical traffic accident data into multiple periods according to time;

Step 102, respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model, and respectively calculating the accident data value, the traffic accident frequency and the traffic accident impact value based on the historical traffic accident data;

Specifically, the traffic accident trend prediction model includes:

Where _Yt 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 i-th autoregressive coefficient, _Yti is the accident data value at time ti, σ′ is the volatility adjustment factor, B(H,t) is the fractal Brownian motion with Hurst index H at time t, s is the number of cycles, _γj is the weight of the j-th cycle, T is the cycle length, q is the number of exogenous variables, _δk is the weight of the k-th exogenous variable, Xk _,t is the k-th exogenous variable at time t, and _εt is the residual value at time t.

Specifically, the traffic accident frequency model includes:

Among them, AF is the traffic accident frequency, λ is the basic accident rate of traffic accidents, α is the time weight, _β1 is the time adjustment factor, T is the cycle length, γ is the distance weight, _β2 is the distance adjustment factor, L is the distance to the nearest toll station when the accident occurs, δ is the road slipperiness weight, _β3 is the road slipperiness adjustment factor, RT is the road slipperiness, φ is the visibility weight, _β4 is the visibility adjustment factor, and W is the visibility.

Specifically, the traffic accident impact model includes:

Among them, Impact is the impact value of the traffic accident, α′ is the traffic accident impact adjustment factor, _β′i is the weight of the i-th kernel function, x _′ is the eigenvector of the current accident data, xi is the i-th 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 time t includes: visibility, traffic flow and road slipperiness.

Step 103, when the accident data value is greater than or equal to the preset accident occurrence threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident frequency is greater than or equal to the preset accident occurrence frequency threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident impact value is greater than or equal to the preset accident occurrence impact threshold, a traffic accident occurrence warning message is issued.

Example 4

An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to 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 this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

Among them, the storage medium can be used to store software programs and modules, such as a traffic accident data integration and analysis method in an embodiment of the present invention, and the corresponding program instructions/modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned traffic accident data integration and analysis method. The storage medium may include a high-speed random 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 media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The processor may call the information and application program stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining historical traffic accident data, and dividing the historical traffic accident data into multiple periods according to time;

Step 102, respectively setting a traffic accident trend prediction model, a traffic accident frequency model and a traffic accident impact model, and respectively calculating the accident data value, the traffic accident frequency and the traffic accident impact value based on the historical traffic accident data;

Specifically, the traffic accident trend prediction model includes:

Where _Yt 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 i-th autoregressive coefficient, _Yti is the accident data value at time ti, σ′ is the volatility adjustment factor, B(H,t) is the fractal Brownian motion with Hurst index H at time t, s is the number of cycles, _γj is the weight of the j-th cycle, T is the cycle length, q is the number of exogenous variables, _δk is the weight of the k-th exogenous variable, Xk _,t is the k-th exogenous variable at time t, and _εt is the residual value at time t.

Specifically, the traffic accident frequency model includes:

Among them, AF is the traffic accident frequency, λ is the basic accident rate of traffic accidents, α is the time weight, _β1 is the time adjustment factor, T is the cycle length, γ is the distance weight, _β2 is the distance adjustment factor, L is the distance to the nearest toll station when the accident occurs, δ is the road slipperiness weight, _β3 is the road slipperiness adjustment factor, RT is the road slipperiness, φ is the visibility weight, _β4 is the visibility adjustment factor, and W is the visibility.

Specifically, the traffic accident impact model includes:

Among them, Impct is the impact value of the traffic accident, α′ is the traffic accident impact adjustment factor, _β′i is the weight of the i-th kernel function, x _′ is the eigenvector of the current accident data, xi is the i-th 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 time t includes: visibility, traffic flow and road slipperiness.

Step 103, when the accident data value is greater than or equal to the preset accident occurrence threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident frequency is greater than or equal to the preset accident occurrence frequency threshold, a traffic accident occurrence warning message is issued, and/or when the traffic accident impact value is greater than or equal to the preset accident occurrence impact threshold, a traffic accident occurrence warning message is issued.

The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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 each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present 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.