Rural highway network security situation awareness method
By constructing a multi-source data collection and fusion analysis system, and combining the analytic hierarchy process (AHP) and fuzzy evaluation method, the problem of scattered rural road infrastructure distribution was solved. This enabled dynamic safety situation awareness of the rural road network and the identification and optimization of vulnerable road sections, thereby improving the safety and service level of rural roads.
Patent Information
- Application Number
- CN202511720124.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-02-17
AI Technical Summary
Rural road safety facilities are scattered and operate independently, making it difficult to achieve overall road network situational awareness and coordinated management. They also lack the ability to predict the spread of traffic hazards and the mechanism for coordinated management, which affects the improvement of traffic safety levels.
By constructing a multi-source data collection and fusion analysis system, and using the analytic hierarchy process (AHP) and fuzzy evaluation method, combined with meteorological, traffic, and highway condition data, a road network safety situation assessment is conducted to identify vulnerable road sections and perform virtual optimization, thereby achieving dynamic safety situation awareness of the rural road network.
It enables low-cost dynamic safety situation awareness of rural road networks, provides accurate safety situation assessment basis, identifies and optimizes vulnerable road sections, and improves the operational efficiency of safety facilities and the overall service level of rural roads.
Smart Images
Figure CN121545353A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of road safety situation awareness technology, and in particular to a method for safety situation awareness of rural road networks. Background Technology
[0002] In recent years, significant progress has been made in the construction of safety facilities on rural roads. Various single-point monitoring and safety early warning systems have been deployed, such as highway video surveillance, curve passing warning systems, and safety sentry systems. These facilities use sensors to collect information such as vehicle numbers and speeds, enabling real-time warnings of dangerous driving behaviors on specific road sections, thus improving traffic safety on rural roads to a certain extent.
[0003] However, with the development of the rural economy and the increase in motorization, rural road traffic conditions are becoming increasingly complex. Existing safety facilities suffer from problems such as scattered distribution, independent operation, and limited monitoring range, making it impossible to achieve overall situational awareness of the road network. At the same time, due to the lack of predictive capabilities for the spread of traffic hazards and the lack of collaborative management mechanisms, timely early warning and guidance are difficult, hindering the improvement of the overall service level of the rural road network. Summary of the Invention
[0004] In order to overcome the shortcomings of the existing technology, the purpose of this invention is to provide a method for safety situation perception of rural road networks, which solves the problems of lack of system linkage of rural road safety facilities, difficulty in realizing overall situation perception and collaborative management of the road network, and the limitations of scattered distribution and independent operation of rural road safety facilities.
[0005] To achieve the above objectives, the present invention provides the following solution: A method for assessing the security situation of rural road networks includes: Collect traffic status data at the target location; Collect road condition data for the target locations; Collect weather information in the areas where rural roads are located to obtain meteorological data; An original data base map is constructed based on the highway condition data, the meteorological data, and the traffic status data. The blank road segments in the original data base map are filled by iterative interpolation using the average value of adjacent road segments, and the ratio of average speed difference between adjacent road segments and the speed difference between road segments are calculated to obtain the basic data base map. Based on the meteorological data, a single-factor analysis is performed on each road segment to obtain the single-factor warning level. The single-factor warning level with the lowest warning level is determined as the road network warning level. If the road network warning level is Level I, then the road network safety situation is the safety situation corresponding to Level I. When the weather factors for each road segment within the base data map are greater than or equal to the preset Level II single-factor warning level, the road segment warning level is obtained by using the analytic hierarchy process (AHP) and fuzzy evaluation method to determine the judgment matrix, solve the weight matrix, perform consistency checks, determine the membership matrix, conduct multi-level fuzzy evaluation, and determine the safety status level for each road segment based on the base data map. By selecting road segments whose warning level is greater than or equal to the preset Level II safety warning level, a road network connectivity sub-graph is obtained. Based on the road network connectivity sub-graph, the size of the largest connectivity sub-graph and the proportion of the rural road network map are calculated to obtain the overall road network safety status index. One by one, road segments whose warning level is lower than the preset Level II safety warning level are selected to obtain the road segments to be detected. The road segments to be detected are virtually optimized, and the overall road network safety status index is recalculated. If the current overall road network safety status index is greater than the overall road network safety status index before virtual optimization, the road segments to be detected are identified as key road segments. The frequency of each critical road segment within a preset time period is counted, and the top 20% of the critical road segments in terms of frequency are identified as vulnerable road segments. The statistical results of the vulnerable road segments are then output.
[0006] Preferably, the traffic status data collected at the target location includes: Collect raw video data from the target location; The traffic flow, average vehicle speed, and proportion of non-passenger vehicles are extracted from the original video data to obtain the traffic status data of the target location.
[0007] Preferably, collecting road condition data at the target location includes: By using a map interface to capture the road network, we can obtain a GIS map of each road segment and elevation data of the two endpoints of the road segment. The horizontal curve radius and longitudinal slope of each road segment are calculated based on the GIS map and the elevation data, and the road surface condition index of each road segment is determined based on the collected annual inspection data of road condition monitoring, thus obtaining the highway condition data.
[0008] Preferably, the meteorological data includes: visibility, wind speed, rainfall, and snowfall.
[0009] Preferably, an original data base map is constructed based on the highway condition data, the meteorological data, and the traffic state data, including: Based on the GIS map, the endpoints and road segments of each road segment are set as nodes and edges respectively, thus obtaining a rural road network map; The traffic status data, the road condition data, and the meteorological data are mapped onto the rural road network map to obtain the original data base map.
[0010] The present invention discloses the following technical effects: This invention provides a method for safety situation awareness of rural road networks. By constructing a multi-source data acquisition and fusion analysis system, it overcomes the limitations of traditional single-point monitoring and achieves low-cost dynamic safety situation awareness of rural road networks. By establishing a multi-level evaluation index system and scientific evaluation methods, it solves the problem of the lack of evaluation basis in traditional technologies and achieves accurate provision of safety situation assessment basis. Through the safety situation assessment basis, it solves the defect of the lack of proactive control closed loop in traditional technologies and achieves the identification and optimization of vulnerable road sections. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a schematic diagram of the rural road network security situation awareness process provided in an embodiment of the present invention; Figure 2 This is a structural layout diagram of the platform functional modules provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the information acquisition process structure of the data acquisition module provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the data processing module workflow provided in an embodiment of the present invention; Figure 5 This is a flowchart illustrating the modeling and evaluation process of the situation assessment module provided in this embodiment of the invention. Figure 6 This is a schematic diagram of the workflow of the result output module provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the influencing factor index system for fuzzy evaluation modeling of the situation assessment module provided in this embodiment of the invention. Detailed Implementation
[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0014] The purpose of this invention is to provide a method for safety situation awareness of rural road networks, which solves the problem of lack of system linkage of rural road safety facilities and difficulty in realizing overall situation awareness and collaborative management of the road network.
[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0016] Figure 1 This is a schematic diagram of the rural road network security situation awareness process provided in an embodiment of the present invention, such as... Figure 1 As shown, the present invention provides a method for assessing the safety situation of rural road networks, comprising: Step 100: Collect traffic status data at the target location; Step 200: Collect road condition data for the target location; Step 300: Collect weather information for the area where the rural road is located to obtain meteorological data; Step 400: Construct an original data base map based on the highway condition data, the meteorological data, and the traffic status data; Step 500: Iteratively interpolate and fill the blank road segments in the original data base map using the average values of adjacent road segments, and calculate the ratio of average speed difference between adjacent road segments and the speed difference between road segments to obtain the basic data base map; Step 600: Perform single-factor analysis on each road segment based on the meteorological data to obtain the single-factor warning level. Determine the single-factor warning level with the lowest warning level as the road network warning level. If the road network warning level is Level I, then the safety status of the road network is the safety status corresponding to Level I. Step 700: When the weather factors of each road segment in the base data map are greater than or equal to the preset Level II single-factor warning level, the judgment matrix, weight matrix solution, consistency test, membership matrix determination, multi-level fuzzy evaluation, and safety status level determination are performed on each road segment according to the base data map using the analytic hierarchy process and fuzzy evaluation method to obtain the road segment warning level. Step 800: Filter the road segments whose warning level is greater than or equal to the preset Level II safety warning level to obtain the road network connectivity sub-graph. Calculate the size of the largest connectivity sub-graph and the proportion of the rural road network map based on the road network connectivity sub-graph to obtain the overall road network safety status index. Step 900: Select road segments whose warning level is lower than the preset Level II safety warning level one by one to obtain the road segments to be detected. Perform virtual optimization on the road segments to be detected and perform a second calculation on the overall road network safety status index. If the current overall road network safety status index is greater than the overall road network safety status index before virtual optimization, then the road segments to be detected are identified as key road segments. Step 1000: Calculate the frequency of occurrence of each key road segment within a preset time period, identify the top 20% of the key road segments in frequency order as vulnerable road segments, and output the statistical results of the vulnerable road segments.
[0017] Furthermore, traffic status data at the target locations is collected, including: Collect raw video data from the target location; The traffic flow, average vehicle speed, and proportion of non-passenger vehicles are extracted from the original video data to obtain the traffic status data of the target location.
[0018] Specifically, collecting road condition data for the target locations includes: By using a GIS map interface to acquire road network data, geographic information data such as the alignment, length, elevation of road segment endpoints, and latitude and longitude of each road segment can be obtained. The horizontal curve radius and longitudinal slope of each road segment are calculated based on the geographic information data. The average pavement condition index of the relevant road segments is calculated as the pavement condition index of the road segments based on the collected annual road condition inspection data of the rural road network, thus obtaining the road condition data.
[0019] Preferably, the meteorological data includes: visibility, wind speed, rainfall, and snowfall.
[0020] Furthermore, an original data base map is constructed based on the highway condition data, the meteorological data, and the traffic state data, including: Based on the GIS map, the endpoints and road segments of each road segment are set as nodes and edges respectively, thus obtaining a rural road network map; The traffic status data, the road condition data, and the meteorological data are mapped onto the rural road network map to obtain the original data base map.
[0021] Preferably, video data of the target location is collected, and vehicle flow, average vehicle speed, and proportion of non-passenger vehicles are extracted from the original video data to obtain traffic status data of the road segment where the target location is located, including: Set two longitudinal highway lines that are separated by a preset distance; Based on the triggering conditions of the two longitudinal lines of the highway, vehicles are identified, counted, and their speeds are calculated to obtain the original traffic state data. Specifically, road network data is captured through a GIS map interface to obtain geographic information data such as the length, alignment, latitude and longitude of endpoints, and elevation data of each road segment. The horizontal curve radius and longitudinal slope are calculated, and the road surface condition index (PCI) of the road segment is obtained based on the annual inspection of rural roads by the local transportation department (calculated by averaging the measurement results of the road segment's mileage). This yields the road condition data for the target road segment, including: Use the GIS map interface to capture road network data and collect highway infrastructure data such as PCI.
[0022] Specifically, weather conditions such as rainfall and snowfall are obtained from the internet (e.g., https: / / tools.yeyupiaoling.cn / doc / weather-query), including: Import weather information from the internet for a given time period, focusing on visibility, wind speed, snowfall, and rainfall that affect traffic and driving conditions.
[0023] Furthermore, based on the geographic information data of road segments, a rural road network map is constructed with endpoints as nodes and road segments as edges. Traffic status data, road condition data, and meteorological data are mapped onto the rural road network map to obtain a base map, including: The endpoints of road segments in the geographic information data are processed into nodes, nodes at the same intersection are merged into the same node, and the road segments between nodes are processed into edges to obtain the basic road network. The original video data and the data collected by the existing equipment are mapped onto the edges of the basic road network to obtain the basic data base map.
[0024] Furthermore, iterative interpolation is performed on blank road segments in the base data map that lack data (taking the median value of neighboring road segments), and the ratio of average speed difference between adjacent road segments and the speed difference between road segments are calculated to obtain a complete base data map, including: For missing traffic status data, highway condition data, and meteorological data in each direction of the road network, the average value is calculated by collecting data from neighboring road segments and assigning it to the missing road segments. This process is repeated multiple times to obtain complete road network data. For traffic status data of each road segment, calculate the average speed, the difference between the maximum and minimum speeds of the road segment, and the speed ratio of adjacent road segments within a given evaluation time, and then map the calculated data back to the base map network.
[0025] Furthermore, based on weather data, single-factor analysis is performed to determine the highway safety warning level under single-factor conditions. The warning level is determined when certain factors are present. Level II: The lowest warning level among multiple single factors is selected for direct warning intervention. After single-factor safety situation assessment, if the warning levels of all weather single factors fall within the range of "Level II, III, IV, or V," the established comprehensive traffic safety situation assessment model is used to determine the corresponding level of traffic safety status for the road segment. Warning Level Reference Table 1: Classification of Meteorological Factor Warning Levels: Table 1
[0026] Furthermore, the determination of the safety status level of a road segment is mainly based on the analytic hierarchy process (AHP) for determining the judgment matrix, solving the weight matrix, and performing consistency checks. Fuzzy evaluation is then used to determine the membership matrix, perform multi-level fuzzy evaluation, and determine the safety evaluation level, resulting in a comprehensive traffic safety index for the road segment, including: Based on the overall situation map, traffic condition influencing factors are extracted. These factors include: traffic status, road conditions, and weather conditions. Traffic status includes: relative vehicle speed difference between road segments, vehicle speed difference between adjacent road segments, and the proportion of non-passenger vehicles. Road conditions include: road service level, pavement skid resistance, horizontal curve radius, longitudinal slope, and curvature-slope comprehensive coefficient. Weather conditions include: visibility, rainfall, snowfall, and wind force. The judgment matrix is constructed based on the aforementioned traffic condition influencing factors; the expression of the judgment matrix is: ;in, =1 / ; The judgment matrix is as described above; To determine the influencing factors based on traffic conditions, road conditions, and weather conditions respectively. ,index The judgment elements are determined by combining the AHP (Analytic Hierarchy Process) and scaling theory. The importance of each factor is compared pairwise, and the final effective judgment matrix is obtained after consistency testing. The judgment matrix is then normalized sequentially to obtain a normalized matrix; Based on the normalized matrix, eigenvalues and eigenvectors are calculated for the judgment matrix. Then, a consistency check is performed using the consistency formula based on the calculated eigenvalues and eigenvectors to obtain a consistency ratio. If the consistency ratio is less than a preset ratio, the check is considered passed; otherwise, the process returns to the step "Construct the judgment matrix based on the traffic condition influencing factors." The expression for the consistency formula is: ; ; The consistency ratio; As a consistency indicator; The average random consistency index; The order of the matrix; To determine the matrix eigenvalues.
[0027] Preferably, the safety status level of a road segment is determined primarily based on the analytic hierarchy process (AHP) to assign weights to various indicators across different weight sets. Then, fuzzy evaluation is used to determine the membership matrix, perform multi-level fuzzy evaluation, and determine the safety evaluation level, resulting in a comprehensive traffic safety index for the road segment, including: Construct a fuzzy set based on the normalized matrix; The fuzzy set determines the primary indicator set, the secondary indicator set, the first weight set corresponding to the primary indicator set, and the second weight set corresponding to the secondary indicator set. Define an evaluation set; the evaluation set includes: the first to the fifth warning levels.
[0028] Specifically, the determination of the safety status level of a road segment mainly relies on the analytic hierarchy process (AHP) to determine the judgment matrix, solve the weight matrix, and perform consistency checks. Furthermore, the fuzzy evaluation method is used to determine the membership matrix, perform multi-level fuzzy evaluation, and determine the safety evaluation level, resulting in a comprehensive traffic safety index for the road segment, including: A single-factor evaluation is performed on the secondary indicator sets corresponding to each primary indicator set to obtain the membership matrix; The initial membership vector is obtained by weighting and merging the second weight set corresponding to the first weight set with the membership matrix. Construct a fuzzy evaluation matrix using the initial membership vector; The first weight set and the fuzzy evaluation matrix are weighted and merged to obtain the overall membership vector. The traffic safety situation comprehensive index is obtained by taking a weighted average of the elements in the overall membership vector.
[0029] Furthermore, the comprehensive index of road segments is extrapolated to the road network. The proportion of the largest sub-connected cluster formed by road segments with high safety levels in the road network is used as the overall road network safety status indicator. For dynamic information data, if virtually upgrading road segments with lower safety warning levels can improve the road network safety status indicator, they are identified as critical road segments of the road network. For critical road segments that frequently appear (top 20%) within a given observation period, they are identified as vulnerable road segments, including: Extending from the comprehensive index of road segments to the road network, the proportion of the largest sub-connected cluster formed by road segments with high safety levels within the road network is used as an indicator of the overall road network safety warning level. The calculation formula is as follows: In the formula, This indicates the percentage of road segments included in the largest connected region after failure. This represents the total number of segments in the largest connected subcluster under safe conditions. This represents the total number of road segments within the overall road network. Based on dynamic information data, road segments with a comprehensive index consistently below a fixed safety value K are identified as potentially vulnerable safety segments. If virtually increasing the safety level of such a road segment improves the road network safety status index, it is then identified as a vulnerable road segment of the road network. In the formula, This indicates the percentage of road segments included in the maximum connected region after virtual enhancement. This represents the total number of segments in the largest sub-connected cluster under a safe state after virtual enhancement.
[0030] The key road segments in the road segment identification results are virtually enhanced to output the vulnerable road segment identification results.
[0031] Specifically, this embodiment proposes a rural road network safety situation awareness system, which includes a data acquisition module, a data processing module, a situation assessment module, and a result output module. This platform utilizes the data acquisition module to integrate existing facilities, comprehensively utilize and upgrade existing cameras, and directly record vehicle flow, speed, and vehicle type through video processing devices. It also uses the internet to collect weather data and road maintenance conditions during the observation window. The data processing module collects GIS base map data, transforms the overall map into a graph theory, adds the collected rural road data to the edges of the road network, imports traffic flow data into each road network node, and combines and upgrades the original scattered data to make it more intuitive. Addressing the lack of data for other parts of the road network, the data processing module combines the traffic flow conditions around the road and performs iterative interpolation simulation to draw a complete traffic flow map. The situation assessment module, by acquiring existing weather conditions, road conditions, and traffic flow conditions, uses existing traffic flow data and a fuzzy evaluation system based on the Analytic Hierarchy Process (AHP) to calculate relevant judgment matrices and generate a comprehensive road safety index for each road segment. The output module first calculates the proportion of the largest sub-connected cluster formed by road segments with high safety levels in the road network. Through dynamic data analysis, it identifies vulnerable road segments with consistently low comprehensive safety indices. Finally, it verifies the optimization plan by virtual enhancement to determine the key road segments, and transmits and feeds back the warning results to the terminal facilities of each road segment node. Ultimately, this improves the overall safety and robustness of rural road traffic, and enables the coordinated utilization and upgrading of rural road traffic safety facilities.
[0032] Furthermore, the various functional modules of this platform, such as Figure 2 As shown. The functional modules in this platform include a data acquisition module, a data processing module, a situation assessment module, and a result output module.
[0033] refer to Figure 3The data acquisition module consists of three parts: meteorological condition data, road condition data, and traffic status data. Meteorological condition data is primarily obtained through the API of internet weather query websites and includes four components: visibility, rainfall, snowfall, and wind speed. Road condition data includes road service level, pavement skid resistance, horizontal curve radius, longitudinal slope, and curvature-slope composite coefficient. Road service level and pavement skid resistance are calculated from the previous year's rural road inspection data obtained from the local traffic management department. The remaining indicators are calculated one by one by processing a GIS map containing information on road sections within the jurisdiction. Traffic status data is acquired by modifying existing road video surveillance equipment. The modified traffic status data acquisition equipment consists of road cameras deployed at key locations on rural roads (such as curves, intersections, and accident-prone sections), a Raspberry Pi edge computing unit, and a wireless transmission module. Its main workflow involves adding Raspberry Pi hardware to existing highway camera infrastructure to perform edge processing calculations on the collected video data. Finally, it connects to the data processing module via a wireless transmission link to upload the identification data in real time. Its primary objective is to collect calculated data such as vehicle flow and speed (the camera interface can be enabled on the Raspberry Pi, and image processing can be performed using OpenCV. During this process, two longitudinal road lines approximately 2 meters apart are added. Based on the simultaneous triggering of both road markings, vehicle identification is performed, and vehicle counts are recorded to determine the total traffic flow per unit time. The average vehicle speed is calculated based on the time difference between the triggering of the road markings; non-passenger vehicle traffic flow is identified based on vehicle height). Based on the above online processing results, vehicles with excessively fast flow and large relative speed differences can be prioritized for identification. Under actual operating conditions, an improved image processing module can be added based on the video data to identify the vehicle type of passing vehicles, such as large vehicles or special engineering vehicles, further enhancing the perception of hazardous elements. Due to the excellent scalability of the Raspberry Pi, GPS modules and solar power systems can also be added as needed to enhance the overall ecosystem functionality.
[0034] refer to Figure 4 The data processing module, comprising a GIS base map engine and a data weighting simulation algorithm module, is deployed on the system server. It receives real-time data from the data acquisition module and shares the fused data with the security situation assessment module. The specific process is as follows: Using the Python programming language, road network data is obtained from the Gaode Map API. Multi-source data (traffic status data, road condition data, and weather condition data) are used as weights for edges in the network to construct a weighted rural road network based on complex network theory. For data gaps, the average of relevant data from all adjacent road segments is taken as the result for the data gap road segments, and this process is iterated multiple times to complete all information of the road network and obtain the basic data base map.
[0035] refer to Figure 5 The safety situation assessment module includes three parts: meteorological single-factor analysis, road section safety situation assessment, and road network safety situation assessment.
[0036] Based on the meteorological data, a single-factor analysis is performed on each road segment to obtain the single-factor warning level. The single-factor warning level with the lowest warning level is determined as the road network warning level. If the road network warning level is Level I, then the road network safety status is the safety status corresponding to Level I. When the weather factors of each road segment in the base data map are less than the preset Level II single-factor warning level, the safety status corresponding to the road network safety status warning level is directly output. The road safety situation assessment adopts the AHP-fuzzy evaluation system model, which is deployed on the system server. The AHP method performs subjective comparison and matrix weighting of factors affecting road traffic safety to obtain the local and overall influence weights of various influencing factors. Furthermore, the fuzzy analysis method evaluates the safety level of rural road sections by integrating the above weight matrix with the evaluation matrix (membership matrix) and comprehensively evaluating the value range.
[0037] refer to Figure 6 The road network safety situation assessment employs a cascading failure model. It determines the overall road network safety situation by calculating the proportion of failed sub-graphs within the entire road network and further transmits the results to the output module. The output module primarily outputs the overall road network safety situation, critical road sections, and vulnerable road sections. The overall road network safety situation is calculated by the safety situation assessment module. Critical road sections are identified by virtually upgrading each failed road section with a safety warning level lower than the preset Level II. Further, the critical road sections are statistically ranked based on the number of failures within a certain period, and the top 20% are selected as vulnerable road sections. The overall results are then output.
[0038] refer to Figure 7 In the process of the Analytic Hierarchy Process (AHP), the overall objective of the influencing factors is first determined and then subdivided into various evaluation index systems. A judgment matrix is then constructed, and the weight matrix is obtained by solving it. If the consistency test is successful, the weight matrix is valid and serves as the weight vector for the fuzzy evaluation. Based on the AHP-fuzzy evaluation system, an evaluation index system is constructed from three aspects: traffic conditions, highway conditions, and meteorological conditions, to conduct a hierarchical analysis of the road network traffic safety situation μ. The influencing factor X1 based on traffic conditions can be decomposed into relative vehicle speed difference between road segments X11, vehicle speed difference between adjacent road segments X12, and the proportion of non-passenger vehicles X13. The influencing factor X2 based on road conditions can be decomposed into road service level X21, pavement skid resistance X22, horizontal curve radius X23, longitudinal slope X24, and curvature-slope comprehensive coefficient X25. The influencing factor X3 based on meteorological conditions can be decomposed into visibility X31, rainfall X32, snowfall X33, and wind force level X34.
[0039] Furthermore, based on the above influencing factors, a pairwise comparison judgment matrix is constructed to analyze the relative importance of each factor. The weight of each indicator is then calculated using the determined judgment matrix, as follows: In the formula: A is the overall judgment matrix; k is the matrix order; This indicates that pairwise comparisons are performed using the 1-9 scale based on scaling theory, relative to the secondary factors. Time, factors contrast The relative importance, A value greater than 1 indicates positive importance, with larger values indicating greater importance; conversely, a smaller value indicates a lower importance. The numerical value is its reciprocal; the smaller the value, the less important it is. =1 (i=j), =1 / .
[0040] Normalize each column of the judgment matrix A, and then sum the elements of each row of A. , and its normalized matrix : From the known data, the eigenvalues and eigenvectors can be calculated for consistency testing.
[0041] In the formula, CI is the consistency index, K is the matrix order, and the average random consistency index RI can be obtained from the K value, which is used to calculate the consistency ratio. The results are shown in Table 2.
[0042] Table 2
[0043] In the formula, CR is the consistency ratio. When CR < 0.1, the consistency test is passed, that is, the weights obtained above are valid; otherwise, A is readjusted.
[0044] Furthermore, in the fuzzy evaluation process, based on the obtained weight matrix, a fuzzy set is first established to determine the first-level indicator set and its corresponding weights: In the formula, (i=1,2,...,k) represents the first-level index set The proportion of (i=1, 2, ..., k) in X, therefore .
[0045] Similarly, determine the set of secondary indicators and their corresponding weights: In the formula, (s=1,2,...,n) represents the set of secondary indicators. (s=1, 2, ..., n) in The proportion of, therefore .
[0046] Based on the road traffic safety situation, define the evaluation set: The values q and V1 represent the evaluation levels of each indicator. In this embodiment, the safety situation is divided into five levels for different traffic conditions, so q=5. V1 to V5 represent safe, moderate, relatively serious, serious, and very serious, respectively. Please refer to Table 3 for details. Table 3
[0047] By performing single-factor evaluations on each factor in the evaluation factor set, a single-factor evaluation set (r) can be obtained. i1 r i2 r i3 r i4 r i5 ), where r ij (j=1, 2, 3, 4, 5) represents the factors. For the judgment set The membership degree is thus a fuzzy mapping from X to V. Similarly, for each primary indicator X... k The corresponding secondary indicators are then evaluated using single-factor assessments, which can be used to construct the evaluation matrix R. k This is called the membership matrix.
[0048] Assuming the traffic safety situation assessment factor is O, and its membership function is u(O), then the membership degree of this factor to the evaluation set is u1(O), u2(O), ..., u L+1 (O). The calculation formula is as follows: In the formula, , ,..., It is the membership degree of the evaluation set; , ,..., It is the dividing point for evaluation levels.
[0049] Starting from the secondary indicator layer, the weight vector and the single-factor fuzzy evaluation matrix can be combined using fuzzy matrix operations to obtain the primary indicators. The comprehensive evaluation vector is, that is For the membership vector of the evaluation set : Then use the obtained first-level indicator evaluation vector Construct a single-factor fuzzy evaluation matrix R for the first-level indicator layer: The comprehensive evaluation result can be obtained by merging the vector weights W and R of the first-level indicator layer: Finally, the membership vector B is obtained. Taking into account all influencing factors, a weighted average is calculated from the elements in set B, resulting in... As the final evaluation level: In the formula, This represents the score for each evaluation set. After comprehensively considering various factors, the score is determined based on the results. Assess the traffic safety situation and provide feedback to the transmission and early warning module. Refer to Table 4 for the correspondence between the comprehensive traffic safety situation index and safety levels.
[0050] Table 4
[0051] In actual operation, the weighting rules of the membership vector B can be changed, or the element with the maximum value can be selected for direct output, in order to adapt to the different safety situation analysis needs under different traffic conditions.
[0052] Specifically, the road network safety situation assessment is conducted based on the segment safety situation assessment. Based on this index assessment, a comprehensive situational awareness road network is constructed. On this road network base map, segments with meteorological conditions of level II or higher in the single-factor meteorological analysis are deleted, resulting in a safety situation failure map. This map describes the remaining road network that can function normally under a certain failure condition. The largest sub-connectivity graph is found within the failure map, representing the largest area of the local road network that can operate normally under failure conditions. This is used to quantitatively assess the overall road network safety situation. The metric is the percentage of road segments included in the largest connected area after failure. A higher percentage of this indicator indicates a smaller area of road network paralysis, better connectivity within the safety domain, less impact from failures, and a superior safety situation. When the percentage is less than 50%, the road network is fragmented into multiple isolated areas, resulting in a high risk of overall collapse and a poor safety situation. In this case, it is necessary to issue overload warnings for the entire road network and improve traffic control and intervention measures. This cascading failure model is used to assess the safety status of a road network. Its core logic starts with local road segment safety indicators and presents the overall risk through quantifiable indicators of the largest connected sub-graph. The calculation formula is as follows: In the formula, This indicates the percentage of road segments included in the largest connected region after failure. This represents the total number of segments in the largest connected subcluster under safe conditions. This represents the total number of road segments within the overall road network. Further summarizing the patterns of sub-connected cluster proportions, the correspondence between their values and safety levels is shown in Table 5.
[0053] Table 5
[0054] Preferably, while conducting security situation awareness, key road segments favorable to the security situation can be identified. The edge weights of the deleted road segments are virtually increased, and the post-increase weights are then re-evaluated. Value, set to If the result is and If there is no difference, then discard that node. If the result... This potentially vulnerable node is then considered a critical road segment. Identifying critical road segments through virtual enhancement can more effectively identify segments within the network that are crucial to overall road network security, offering benefits for proactive security prevention.
[0055] Preferably, in the dynamic security situation analysis within a selected period (e.g., 7 days), the frequency of occurrence of key road segments is statistically analyzed. If a road segment ranks in the top 20% in frequency during the overall statistical process, it is considered a vulnerable road segment in the overall road network. Further, using comprehensive evaluation and modeling methods such as entropy weight TOPSIS, improvement schemes for vulnerable nodes are evaluated and ranked in order, leading to more optimized virtual improvement schemes. The overall process, by screening the connectivity graph of safe road segments in the remaining road network, not only achieves a comprehensive security situation assessment of the entire road network through the comprehensive security index of each road segment, but also identifies vulnerable nodes in the road network through dynamic data, further providing solutions for optimizing the road network structure and improving the robustness and resilience of the complex network.
[0056] Preferably, in real-world applications, given the predictability and unpredictability of weather conditions, the safety situation assessment module can, based on the actual situation, link the day's weather conditions and assess road conditions according to the maintenance status of local road maintenance units, conduct specific modeling analysis, and perform fuzzy evaluation. If weather conditions are not readily available or road conditions are difficult to reference, the influence weights of the target layer in the hierarchical analysis can be further modified, thereby making the resulting judgment set closer to the actual traffic flow impact, and making the model judgment more convenient or accurate.
[0057] The beneficial effects of this invention are as follows: This invention combines existing rural road infrastructure and safety facilities, enabling traffic data collection on rural roads through simple modifications. It fully utilizes dispersed road facilities within the road network, improving the overall operational efficiency of safety facilities. Based on existing road condition data and road condition detection data from rural road management, it constructs a road segment situation assessment system using modeling methods such as AHP-fuzzy evaluation, proposing a low-cost, dynamic safety situation awareness approach to improve the actual service level and safety of rural roads. Through traffic safety situation assessment based on complex network theory, it constructs a road network safety perception system and, combined with virtual enhancement strategies, identifies relevant vulnerable nodes, improving the overall road network's ability to perceive vulnerable nodes and effectively improving network safety resilience. These advantages stem from the data acquisition and situation assessment modules. By monitoring traffic conditions and perceiving road and weather conditions, it quantitatively assesses the overall road network safety and the vulnerability of certain nodes, effectively compensating for the deficiencies in rural road safety situation awareness and realizing a low-cost rural road safety situation assessment method.
[0058] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0059] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A rural road network safety situation awareness method, characterized in that, The method comprises the following steps: Collecting traffic state data of target points; Collecting road condition data of the target points; Collecting weather conditions of the area where the rural road is located to obtain meteorological data; Constructing an original data base map according to the road condition data, the meteorological data and the traffic state data; Iteratively interpolating and filling the blank road sections of the original data base map by using the average values of adjacent road sections, and calculating the average speed difference ratio and the road section speed difference of adjacent road sections to obtain a basic data base map; Performing single-factor analysis on each road section according to the meteorological data to obtain a single-factor warning level, and determining the single-factor warning level with the smallest warning level as the road network warning level, and if the road network warning level is level I, the safety situation of the road network is the safety situation corresponding to level I; When the weather factor of each road section in the basic data base map is greater than or equal to a preset level II single-factor warning level, determining, weight matrix solving, consistency test, membership matrix determination, multi-level fuzzy evaluation and safety situation level determination of each road section by using the analytic hierarchy process and the fuzzy evaluation method according to the basic data base map to obtain a road section warning level; Selecting road sections with a road section warning level greater than or equal to a preset level II safety warning level to obtain a road network connected subgraph, and calculating the size of the maximum connected subgraph and the proportion of the rural road network graph to obtain an overall road network safety situation index; Selecting road sections one by one with a road section warning level less than a preset level II safety warning level to obtain a to-be-detected road section, virtually optimizing the to-be-detected road section, and performing secondary calculation on the overall road network safety situation index, and if the current overall road network safety situation index is greater than the overall road network safety situation index before virtual optimization, the to-be-detected road section is determined as a key road section; Counting the frequency of each key road section appearing in a preset time period, and determining the top 20% of the key road sections in the frequency order as vulnerable road sections, and outputting the statistical results of the vulnerable road sections.
2. The rural road network safety situation awareness method according to claim 1, characterized in that, Collecting traffic state data of target points, comprising: Collecting original video data of target points; Extracting vehicle flow, average speed and non-passenger vehicle proportion in the original video data to obtain the traffic state data of the target points.
3. The rural road network safety situation awareness method according to claim 1, characterized in that, Collecting road condition data of the target points, comprising: Performing road network grabbing through a map interface to obtain GIS maps of each road section and elevation data of two endpoints of the road section; Calculating the horizontal curve radius and longitudinal slope gradient of each road section according to the GIS maps and the elevation data, and determining the pavement condition index of each road section according to the collected road condition detection annual inspection data to obtain the road condition data.
4. The rural road network safety situation awareness method of claim 1, wherein, The meteorological data comprises visibility, wind force level, rainfall and snowfall.
5. The rural road network safety situation awareness method according to claim 3, characterized in that, Constructing an original data base map according to the road condition data, the meteorological data and the traffic state data, comprising: Setting the endpoints and road sections of each road section as nodes and edges respectively according to the GIS maps to obtain a rural road network graph; Map the traffic state data, the highway condition data, the meteorological data to the rural highway network diagram, and obtain the original data base map.