A quantitative assessment method for the impact degree of geological disasters on highway traffic

Abstract

The present invention belongs to the field of data processing, and provides a quantitative evaluation method for the influence degree of geological disasters on highway traffic. The preset highway is segmented according to the gantry position; the traffic flow information of each segment in each time slot is obtained; geological disaster data is collected; nodes are determined, and the causal relationship between the nodes is constructed. Geological disasters affect the traffic flow, and the mutual influence of the traffic flow of adjacent segments is considered; the collected data is sorted into a matrix form; the conditional probability table of each node is calculated; the Bayesian inference method is used, combined with the current geological disaster data, to infer the forward inference of the influence degree of the traffic flow of each segment of the highway in the current time slot; the quantitative evaluation value of the influence of geological disasters on highway traffic is calculated. The above solution can quantitatively evaluate the influence degree of geological disasters on highway traffic.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and particularly relates to a quantitative evaluation method for the impact degree of geological disasters on highway traffic. Background Art

[0002] With the rapid development of modern society, the importance of highway traffic in economic and social activities has become increasingly prominent. However, geological disasters such as landslides, debris flows, earthquakes, etc. occur frequently, posing a serious threat to the highway traffic system. These geological disasters may not only cause traffic jams and a decrease in traffic flow, but also trigger traffic accidents, endangering people's lives and property safety. Therefore, how to effectively evaluate the impact of geological disasters on highway traffic has become the focus of attention of traffic management departments and related research institutions.

[0003] Traditional methods for evaluating the impact of geological disasters are mostly based on experience and qualitative analysis, lacking systematic quantitative evaluation means, and it is difficult to accurately predict the specific impact of geological disasters on traffic. Summary of the Invention

[0004] To solve the problems in the prior art, the present invention provides a quantitative evaluation method for the impact degree of geological disasters on highway traffic, and the method includes the following steps:

[0005] Step 1, data collection and preprocessing;

[0006] 1.1 Segment the preset highway according to the gantry position;

[0007] 1.2 Use the cameras on the gantries to obtain the traffic flow information of each segment at each time slot;

[0008] 1.3 Collect the occurrence time, type and intensity data of geological disasters;

[0009] 1.4 Perform preprocessing on the traffic flow information and geological disaster information;

[0010] Step 2, construct a Bayesian network structure;

[0011] 2.1 Determine the nodes, including the traffic flow node representing the traffic flow of the i-th segment of the highway at the t-th time slot and the geological disaster intensity node representing the geological disaster intensity of the i-th segment of the highway at the t-th time slot;

[0012] 2.2 Construct the causal relationship between the nodes. Geological disasters affect the traffic flow, and the traffic flows of adjacent segments are considered to affect each other;

[0013] Step 3, parameter learning;

[0014] 3.1 Construct a data matrix and organize the collected data into a matrix form;

[0015] 3.2 Calculate the conditional probability table for each node according to the data matrix;

[0016] Step 4, Inference and evaluation;

[0017] 4.2 Use the Bayesian inference method and combine with the current geological disaster data to perform forward inference on the influence degree of traffic flow on each section of the road in the current time slot;

[0018] 4.3 Calculate the quantitative evaluation value of the impact of geological disasters on road traffic through the inference results..

[0019] Through the above technical solutions, the present invention can produce the following beneficial effects:

[0020] The present invention systematically quantifies the impact of geological disasters on road traffic through a Bayesian network model, provides quantitative evaluation results, overcomes the limitations of traditional methods, and makes the evaluation more scientific and accurate.

[0021] Utilize the forward inference of the Bayesian network to predict the impact of geological disasters on traffic flow, provide a comprehensive analysis perspective, and improve the application breadth of the model.

[0022] By analyzing historical data and real-time data, the present invention can timely reflect the actual impact of geological disasters on traffic, provide data-driven decision support for traffic management departments, and improve the efficiency and effectiveness of emergency response. And the calculation process of the entire solution is simple and convenient, and can be quickly implemented through a computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0024] Figure 1 is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, in combination with the drawings and specific embodiments, a preferred description of the invention will be made.

[0026] This embodiment solves the above problems through the following steps:

[0027] In one embodiment, referring to Figure 1 , the present invention provides a method for quantitatively evaluating the impact degree of geological disasters on road traffic.

[0028] Geological disasters refer to disasters caused by natural geological processes, including landslides, debris flows, earthquakes, subsidence, etc., which may cause damage or interference to the environment and human activities.

[0029] Highway traffic refers to the vehicle flow and transportation activities carried out through highways, including indicators such as traffic volume, traffic speed, and traffic density.

[0030] The degree of impact refers to the specific impact size and scope of geological disasters on highway traffic, usually measured by quantitative indicators, such as changes in traffic volume, reduction in traffic capacity, increase in accident frequency, etc.

[0031] In this implementation, quantitative assessment refers to objectively and accurately measuring and evaluating the impact of geological disasters through quantitative indicators and mathematical models, using data analysis and statistical methods to score the degree of impact.

[0032] Step 1: Data collection and preprocessing.

[0033] To conduct a quantitative assessment of the impact of geological disasters on highway traffic, relevant data needs to be collected first. This includes information such as the occurrence time, location, type, and intensity of geological disasters, as well as data on highway traffic volume, traffic speed, and traffic accidents.

[0034] 1.1 Segment the preset highway according to the gantry position.

[0035] A gantry is a structure installed above a highway to support cameras, sensors, and other monitoring devices. These devices are used to collect traffic data, such as traffic volume, vehicle speed, license plate recognition, etc.

[0036] The highway is divided according to the position of the gantry, and each segment is a continuous road section. The purpose of segmentation is to facilitate the precise collection and analysis of traffic data.

[0037] Segmenting the preset highway according to the gantry position means dividing the entire highway into several segments based on the position of the gantry installed above the highway. This segmentation method is mainly to facilitate the precise collection and analysis of traffic data, so as to realize the monitoring and evaluation of traffic conditions on different road sections.

[0038] Suppose there are multiple gantries on a highway, and these gantries are installed at positions such as 5 kilometers, 10 kilometers, 15 kilometers, etc. according to the kilometer number. According to the positions of the gantries, this highway can be divided into three segments:

[0039] Segment 1 (S1): From 0 kilometers to 5 kilometers;

[0040] Segment 2 (S2): From 5 kilometers to 10 kilometers;

[0041] Segment 3 (S3): From 10 km to 15 km.

[0042] 1.2 Use the cameras on the gantry to obtain the traffic flow information for each segment and each time slot.

[0043] Traffic flow information refers to the number of vehicles passing through a certain highway segment within a certain period of time. Traffic flow information is important data for traffic flow analysis, used to evaluate road usage and traffic load.

[0044] A time slot refers to a preset time interval used to divide the data collection period. For example, traffic data can be recorded at minute, hour, or shorter time intervals.

[0045] The camera continuously captures images and videos of the highway segment and uses image processing algorithms to detect and record the number of passing vehicles in real time.

[0046] Divide the data collection time into several fixed time slots (such as every minute, every hour), and count the traffic flow information within each time slot.

[0047] Preprocess the collected traffic flow data, including denoising, duplicate removal, data format conversion, etc., to ensure the accuracy and consistency of the data.

[0048] 1.3 Collect the occurrence time, type, and intensity data of geological disasters.

[0049] Record the specific time point or time period when the geological disaster actually occurred, used to record the time information of the disaster event for subsequent analysis and response.

[0050] The specific category or form of geological disasters, such as landslides, mudslides, earthquakes, etc. Different types of geological disasters have different causes, characteristics, and influence ranges.

[0051] The destructive power or influence degree of geological disasters, usually expressed by quantitative indicators, such as the volume of landslides, the flow rate of mudslides, the magnitude of earthquakes, etc., used to evaluate the severity of the disaster.

[0052] Geological disaster data can be collected in real time through monitoring devices installed in geological disaster-prone areas, such as seismographs, rain gauges, landslide monitors, etc. Use satellite remote sensing technology to monitor the occurrence and development of geological disasters through high-resolution images. After a disaster occurs, obtain first-hand geological disaster information through on-site investigations, including the type, influence range, and intensity of the disaster. Consult historical materials and documents to collect the occurrence time, type, and intensity data of past geological disasters.

[0053] Exemplarily, assume that a geological disaster monitoring station collects the following geological disaster data through installed monitoring devices and on-site investigations:

[0054] 2024-01-15 10:30 Landslide volume 5000 cubic meters

[0055] 2024-02-20 14:00 Debris flow rate 3000 cubic meters / hour

[0056] 2024-03-05 08:15 Earthquake magnitude 5.6

[0057] 1.4 Pre-process the traffic flow information and geological disaster information.

[0058] Ensure the accuracy, consistency and availability of the data by cleaning, converting and organizing the raw data. The purpose of this step is to transform the collected raw data into high-quality data suitable for subsequent analysis and modeling. Specifically:

[0059] Choose an appropriate missing value processing method based on the characteristics of the data set and analysis requirements. Common methods include deleting records with a large number of missing values, filling missing values ​​with the mean or median, and estimating missing values ​​using interpolation or regression methods.

[0060] Depending on the nature of the outlier, you can choose to delete, modify, or replace it. For example, for an abnormal peak in traffic flow data, you can combine the context data to determine whether it is an erroneous record and take corresponding measures.

[0061] Detect whether there are duplicate records in the data set, especially when merging data from multiple sources, as duplicate data will affect the accuracy of the analysis results. Identify and remove duplicate data through unique identifiers (such as timestamps, road segment numbers, etc.).

[0062] Convert data from different sources into a unified unit. For example, convert traffic flow data in different units (such as vehicles per hour and vehicles per minute) into vehicles per hour.

[0063] Ensure that all data types are consistent, such as converting string-formatted timestamps to date-time format for time series analysis. For example, converting traffic flow data from string format (such as "1000 cars / hour") to numeric format (such as 1000) for subsequent statistical analysis.

[0064] Convert data of different dimensions to the same scale range to eliminate the impact of dimension differences on the analysis results. Common methods include minimum-maximum normalization and Z-score normalization. For example, normalize traffic flow data and geological disaster intensity data to scale their value range to between 0 and 1.

[0065] Ensure that data from different data sources are aligned in time and space to guarantee data consistency and comparability. For example, align the timestamps of traffic flow data and geological disaster data to ensure that the traffic flow at the same time point is consistent with the corresponding geological disaster intensity.

[0066] Step 2: Construct a Bayesian network structure.

[0067] A Bayesian network is a probabilistic graphical model composed of nodes and edges, used to represent causal relationships and conditional probabilities between variables. Bayesian networks can effectively handle uncertainties and dependencies in complex systems.

[0068] In this embodiment, a Bayesian network model will be established to describe and quantify the complex dependencies between geological disasters and traffic flow. By determining the nodes and edges of the network and calculating the conditional probability table, an effective quantitative assessment of the impact of geological disasters on highway traffic can be carried out.

[0069] 2.1 Determine the nodes, including the traffic flow of the i-th section of the road at the t-th time slot and the geological disaster intensity of the i-th section of the road at the t-th time slot.

[0070] Nodes are the basic units in a Bayesian network, representing random variables. In the assessment of the impact of geological disasters on highway traffic, nodes can represent specific traffic and environmental variables, specifically traffic flow and geological disaster intensity in this embodiment.

[0071] Traffic flow node C i,t : Represents the traffic flow of the i-th section of the road at the t-th time slot. Traffic flow is a key traffic indicator, reflecting the traffic conditions of a specific section at a specific time.

[0072] Example: Suppose a certain road is divided into three sections (S1, S2, S3), and the traffic flow is recorded once per hour for each section. Then C 1,1 represents the traffic flow of section S1 in the first hour, C 2，2 represents the traffic flow of section S2 in the second hour, and so on.

[0073] Geological disaster intensity node G i,t , represents the geological disaster intensity of the i-th section of the road at the t-th time slot. Geological disaster intensity is a key indicator for assessing the impact of geological disasters on highway traffic.

[0074] Example: Suppose the geological disaster intensities at different times and different sections are recorded in geological disaster monitoring. Then G 1,1 represents the geological disaster intensity of section S1 in the first hour, G 2,2 represents the geological disaster intensity of section S2 in the second hour, and so on.

[0075] 2.2 Construct the causal relationships between nodes. Geological disasters affect traffic flow, and the traffic flows of adjacent segments are considered to influence each other.

[0076] In a Bayesian network, nodes are connected by directed edges, representing the causal influence or dependence relationship of one variable (node) on another variable (node). For example, the influence of the intensity of geological disasters on traffic flow can be represented by a directed edge. The traffic flows of adjacent segments on a road may influence each other. For instance, traffic congestion in the upstream segment may lead to a decrease in traffic flow or a reduction in vehicle speed in the downstream segment.

[0077] In a Bayesian network, connecting nodes with directed edges represents the causal relationships and dependence relationships between different variables. In particular, it is necessary to clarify the influence of the intensity of geological disasters on traffic flow and consider the possible mutual influence of traffic flows between adjacent road segments. In this way, the impact of geological disasters on road traffic can be described and quantified more accurately.

[0078] The occurrence and intensity of geological disasters have a direct causal relationship with traffic flow. For example, when a landslide occurs, it will cause partial or complete blockage of the road, making it impossible for vehicles to pass, thus significantly reducing traffic flow.

[0079] In a Bayesian network, a directed edge is used to connect from the geological disaster intensity node G i,t to the traffic flow node C i,t , representing the causal influence of geological disasters on traffic flow.

[0080] The traffic conditions in the upstream segment will affect the traffic flow in the downstream segment. For example, if there is congestion in the upstream segment, vehicles will be detained upstream, and the traffic flow in the downstream segment may decrease.

[0081] In a Bayesian network, directed edges are used to connect the traffic flow nodes of adjacent segments, from C i,t to C i+1,t , representing the influence of the traffic flow of the i-th road segment on the traffic flow of the (i + 1)-th road segment.

[0082] Step 3: Parameter learning.

[0083] In a Bayesian network, parameter learning refers to using the collected data to estimate the conditional probability distributions of the nodes in the network. Through parameter learning, the conditional probability table (CPT) in the Bayesian network can be determined, enabling the network to accurately perform inference and prediction.

[0084] 3.1 Construct a data matrix and organize the collected data into matrix form.

[0085] In this step, it is first necessary to clean, transform, and structure the original traffic flow and geological disaster data, and finally form a matrix format that is convenient for analysis. Each row in the data matrix represents an observation record within a time slot, and each column represents a variable (such as traffic flow, geological disaster intensity, etc.). In this way, the data can be systematically organized, facilitating subsequent statistical analysis and parameter estimation.

[0086] 3.2 Calculate the conditional probability table for each node based on the data matrix.

[0087] In this step, using the constructed data matrix, through statistical analysis and calculation, determine the conditional probability distribution of each node in the Bayesian network. Specifically, by statistically analyzing the observation records in the data matrix, calculate the occurrence probability of each node under different conditions of its parent nodes, thereby constructing the conditional probability table (CPT) for each node in the Bayesian network. These conditional probability tables are used to describe the dependence relationship between variables and play a key role in network inference and prediction.

[0088] First, count the frequencies and define the joint frequency table: Count the traffic flow C i,t under different geological disaster intensities G i,t For example, count the frequencies of traffic flows 100, 150, and 200 when the geological disaster intensity is 2.

[0089] Example joint frequency table:

[0090] <![CDATA[G i,t > <![CDATA[C i,t (low)]]> <![CDATA[C i,t (in Chinese)]]> <![CDATA[C i,t (high)]]> 1 10 20 5 2 15 25 10 3 5 10 3 4 8 12 2

[0091] Define the marginal frequency table: Count the total frequencies under each geological disaster intensity G i,t For example, count the total frequency of all traffic flows when the geological disaster intensity is 2.

[0092] Example marginal frequency table:

[0093] <![CDATA[G i,t > Total frequency 1 35 2 50 3 18 4 22

[0094] Definition of conditional probability: The conditional probability P(C i,t |G i,t ) is the probability distribution of the target node C i,t under different conditions of the parent node G i,t .

[0095] Conditional probability formula:

[0096]

[0097] where c j represents the traffic flow and g k represents the geological disaster parameter.

[0098] Based on the joint frequency table and marginal frequency table, calculate the conditional probability values under each condition. For example, calculate the conditional probability that the traffic flow is 100 when the geological disaster intensity is 2.

[0099] Example calculation:

[0100] <![CDATA[G i,t > <![CDATA[C i,t (low)]]> <![CDATA[C i,t (Chinese)]]> <![CDATA[C i,t (high)]]> 1 10 / 35＝0.286 20 / 35＝0.571 5 / 35＝0.143 2 15 / 50＝0.300 25 / 50＝0.500 10 / 50＝0.200 3 5 / 18＝0.278 10 / 18＝0.556 3 / 18＝0.167 4 8 / 22＝0.364 12 / 22＝0.545 2 / 22＝0.091

[0101] Construct a conditional probability table (CPT)

[0102] Fill the calculated conditional probability values into the conditional probability table. The sum of all target node probability values under each parent node condition should be 1.

[0103] Example conditional probability table:

[0104] <![CDATA[G i,t > <![CDATA[C i,t (low)]]> <![CDATA[C i,t (Chinese)]]> <![CDATA[C i,t (high)]]> 1 0.286 0.571 0.143 2 0.300 0.500 0.200 3 0.278 0.556 0.167 4 0.364 0.545 0.091

[0105] Step 4, Inference and evaluation;

[0106] 4.1 Use the Bayesian inference method, combined with the current geological disaster data, to perform forward inference on the impact degree of traffic flow on each section of the road in the current time slot.

[0107] Utilize the Bayesian network and Bayesian inference method. By analyzing the currently collected geological disaster data, calculate and predict the traffic flow changes of each road section in the current time slot. Specifically, through forward inference, the probability distribution of traffic flow for each section can be predicted based on the known geological disaster intensity; through backward inference, the probability distribution of geological disaster intensity can be inferred based on the known traffic flow.

[0108] Through geological monitoring equipment and on-site investigations, obtain the geological disaster information at the current time point, including the occurrence time, location, type, and intensity. Organize the geological disaster data into a standard format for subsequent analysis and processing.

[0109] For the forward inference part, define the forward inference target:

[0110] Known condition: The current geological disaster intensity G i,t

[0111] Inference target: The traffic flow C i,t Probability distribution.

[0112] Use the conditional probability table: Based on the known geological disaster intensity G i,t Use the conditional probability table P(C i,t |G i,t ) to calculate the probability distribution of traffic flow.

[0113] Example: Assume that the current geological disaster intensity is 2, and use the conditional probability table to calculate the probabilities of low, medium, and high traffic flows.

[0114] P(C i,t |G i,t =2)=[P(C i,t =low|G i,t =2), P(C i,t =medium|G i,t =2), P(C i,t =high|G i,t =2)]

[0115] Based on the calculation results, obtain the probability distribution of traffic flow for each segment under the current geological disaster intensity, and evaluate the impact degree of geological disasters on traffic flow.

[0116] 4.2 Calculate the quantitative evaluation value of the impact of geological disasters on highway traffic through the inference results.

[0117] According to the probability distribution of traffic flow, calculate the expected value E(C i,t )

[0118]

[0119] where c j is the different value of traffic flow, and P(C i,t =c j |G i,t ) is the corresponding conditional probability.

[0120] Exemplarily, assume that: the conditional probability distribution of traffic flow when the current geological disaster intensity G i,t =2 is

[0121] P(C i,t =low|G i,t =2)=0.300

[0122] P(C i,t =medium|G i,t =2)=0.500

[0123] P(C i,t =high|G i,t =2)=0.200

[0124] Expected value of traffic flow:

[0125] E(C i,t )=(low × 0.300)+(medium × 0.500)+(high × 0.200)

[0126] Calculate the impact of geological disasters on traffic flow

[0127] The impact of geological disasters on traffic flow can be expressed as a decrease or increase in traffic flow. The impact value I of geological disasters on traffic flow i,t is defined as the difference between the current expected traffic flow value and the expected traffic flow value without geological disasters.

[0128] I i,t = E(C i,t ) 当前 - E(C i,t ) 无地质灾害

[0129] Exemplarily

[0130] Expected traffic flow value without geological disasters: Assume that the expected traffic flow value without geological disasters is E(C i,t ) 无地质灾害 = 15

[0131] Current expected traffic flow value: According to the foregoing calculations, assume that the current expected traffic flow value is E(C i,t ) 当前 = 120

[0132] Impact value of geological disasters on traffic flow:

[0133] I i,t = 120 - 150 = -30

[0134] Finally, it should be noted that: The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

[0135] For the part of the module structure not specifically defined in the present invention, it shall be subject to the content recorded in the prior art. The prior art mentioned in the foregoing background art part and the specific embodiment part of the present invention can be used as a part of the present invention to understand the meaning of some technical features or parameters. The protection scope of the present invention shall be subject to the content actually recorded in the claims.

Claims

1. A quantitative assessment method for the impact of geological disasters on highway traffic, characterized in that The method comprises the following steps: Step 1: data collection and preprocessing; 1.1 Divide the preset highway into sections according to the position of the gantry; 1.2 Use the camera on the gantry to obtain the traffic flow information of each segment and each time slot; 1.3 Collect data on the occurrence time, type and intensity of geological disasters; 1.4 Pre-process the traffic flow information and geological disaster information; Step 2: construct the Bayesian network structure; 2.1 Determine nodes, including a node representing the traffic flow of the i-th section of highway at the t-th time slot and a node representing the geological disaster intensity of the i-th section of highway at the t-th time slot; 2.2 Construct the causal relationship between nodes, geological disasters affect the traffic flow, and consider the mutual influence of traffic flow in adjacent segments; Step 3: parameter learning; 3.1 Construct a data matrix and organize the collected data into a matrix form; 3.2 Calculate the conditional probability table of each node based on the data matrix; Step 4: Reasoning and evaluation; 4.2 Using the Bayesian reasoning method, combined with the current geological disaster data, the forward reasoning of the impact of traffic flow on each section of highway at the current time slot is inferred; 4.3 Calculate the quantitative assessment value of the impact of geological disasters on highway traffic through the inference results; Based on the inference results, the quantitative evaluation value of the impact of geological disasters on highway traffic is calculated, including: according to the probability distribution of traffic flow, the expected value of traffic flow E(C i,t ) where c j For different values ​​of traffic flow, P(C i,t =c j |G i,t ) is the corresponding conditional probability; Calculate the impact of geological hazards on traffic flow I i,t =E(C i,t ) 当前 -E(C i,t ) 无地质灾害 Among them I i,t is the impact of geological disasters on highway traffic, E(C i,t ) 当前 is the expected value of the current traffic flow, E(C i,t ) 无地质灾害 is the expected value of traffic flow when there is no disaster; Calculating the conditional probability table for each node includes: Traffic statistics C i,t Under different geological disaster intensity G i,t The frequency of occurrence of the following; Statistics of the intensity of each geological disaster G i,t The total frequency of the following; Conditional probability P(C i,t |G i,t ) is in the parent node G i,t Under different conditions, the target node C i,t The probability distribution of Where cj represents the traffic volume, g k Indicates geological hazard parameters; According to the joint frequency table and marginal frequency table, calculate the conditional probability value under each condition; fill the calculated conditional probability value into the conditional probability table, and the sum of all target node probability values ​​under each parent node condition should be 1; Using the Bayesian reasoning method, combined with the current geological disaster data, the forward reasoning to infer the impact of each section of the highway on the traffic flow in the current time slot includes: based on the known geological disaster intensity G i,t Using the conditional probability table P(C i,t |G i,t ) Calculate the probability distribution of traffic flow.

2. A quantitative assessment method for the impact of geological disasters on highway traffic according to claim 1, characterized in that The traffic flow information refers to the number of vehicles passing through a certain highway segment in a time slot.

3. A quantitative assessment method for the impact of geological disasters on highway traffic according to claim 2, characterized in that The one time slot is 1 minute.

4. The quantitative evaluation method of the impact of geological disasters on highway traffic according to claim 1 is characterized in that The preprocessing of the traffic flow information and geological disaster information includes: cleaning, converting and organizing the original data to ensure the accuracy, consistency and availability of the data.

5. The quantitative evaluation method of the impact of geological disasters on highway traffic according to claim 1 is characterized in that : The traffic flow node is C i,t , represents the traffic volume of the i-th section of highway at the t-th time slot; the geological disaster intensity node is G i,t , which represents the geological disaster intensity of the i-th section of highway at the t-th time slot.

6. A quantitative assessment method for the impact of geological disasters on highway traffic according to claim 5, characterized in that The causal relationship between nodes is constructed, geological disasters affect the traffic flow, and the mutual influence of traffic flow in adjacent segments is considered, including: using directed edges from the geological disaster intensity node G i,t Pointing to traffic flow node C i,t , indicating the causal impact of geological disasters on traffic flow; using directed edges to connect the traffic flow nodes of adjacent segments, from C i,t Point to C i+1,t , which represents the impact of the traffic flow of the i-th section of highway on the traffic flow of the i+1-th section of highway.

7. A quantitative evaluation method for the impact of geological disasters on highway traffic according to claim 6, characterized in that The process of arranging the collected data into a matrix form includes: The original traffic flow and geological disaster data need to be cleaned, converted and structured to form a matrix format that is easy to analyze; each row in the data matrix represents an observation record within a time slot, and each column represents a variable, including traffic flow and geological disaster intensity.

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