Traffic safety risk warning method and system based on traffic flow characteristic data

Abstract

The present invention discloses a traffic safety risk warning method and system based on traffic flow characteristic data, belonging to the technical field of traffic safety. The method includes the following steps: obtaining the comprehensive traffic safety characteristic index, real-time traffic characteristic index and traffic risk impact factor of a highway section, analyzing to obtain the historical risk data of the highway section based on the real-time traffic characteristic index and in combination with the real-time environment data of the highway section, comprehensively processing the comprehensive traffic safety characteristic index, traffic risk impact factor and historical risk data to obtain the traffic safety risk value of the highway section, and performing corresponding traffic safety risk warnings. By comprehensively obtaining the traffic safety risk value from the comprehensive traffic safety characteristic index, traffic risk impact factor and traffic risk data and giving warnings, the present invention achieves the effect of accurately and effectively warning the traffic safety risks of highway sections, and solves the problems of inaccurate and untimely warnings of traffic safety risks in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic safety, and in particular, to a traffic safety risk early warning method and system based on traffic flow characteristic data. Background Art

[0002] With the rapid development of the economy and the acceleration of the urbanization process, the traffic flow has been increasing continuously, and the frequency and impact of traffic accidents have also increased accordingly. Traffic safety issues are important factors affecting public safety and social stability. It is of great significance to monitor and analyze traffic flow data in real time, discover potential safety risks in a timely manner and give early warnings.

[0003] For example, the highway safety risk perception model training method, system, application method and system disclosed in the patent application with the publication number: CN116341894A include: constructing a highway safety risk perception model based on a convolutional neural network; obtaining high-speed data; determining the road characteristic risk indicators and traffic flow characteristic risk indicators of the upstream road section unit and the downstream road section unit according to the high-speed data; determining the safety level evaluation indicators for each group of road section units; training the highway safety risk perception model according to the road characteristic risk indicators, traffic flow characteristic risk indicators and corresponding safety level evaluation indicators of each group of road section units to obtain a trained highway safety risk perception model.

[0004] For example, a short-term prediction method, system, terminal and readable storage medium for the safety level of intersections based on traffic flow characteristics disclosed in the invention patent announcement with the announcement number: CN113095584B include: collecting the driving video data and traffic flow characteristic values of vehicles on the intersection road section; obtaining the vehicle trajectory data of vehicles from the driving video data of vehicles; determining conflicts based on the vehicle trajectory data and taking the proportion of dangerous conflicts in the total number of conflicts and the location where the conflicts occur as the risk conflict level of the intersection; constructing a training sample set by using the traffic flow characteristic values of the intersection road section and the risk conflict level of the intersection; building and training a prediction model by applying the random forest algorithm based on the training sample set to obtain a short-term prediction model for the risk conflicts at the intersection; predicting the risk conflicts in the short term in the future at the intersection by using the short-term prediction model for the risk conflicts at the intersection and the traffic flow characteristic values of the intersection road section.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0006] In the prior art, when conducting traffic safety risk early warning, the safety level evaluation index of a road section is determined by obtaining high-speed data, so as to conduct safety early warning. In reality, the probability of accidents occurring in different environments is different. Only obtaining conventional overall high-speed data for traffic safety risk early warning cannot fully guarantee the timeliness of early warning for expressways. Therefore, there are currently problems of inaccurate and untimely early warning of traffic safety risks. Summary of the Invention

[0007] By providing a traffic safety risk early warning method and system based on traffic flow characteristic data in the embodiments of the present application, the problems of inaccurate and untimely early warning of traffic safety risks in the prior art are solved, and accurate traffic safety risk early warning is realized.

[0008] The present application provides a traffic safety risk early warning method based on traffic flow characteristic data, including the following steps: obtaining historical data of an expressway road section, and analyzing to obtain the historical traffic safety characteristic value of the expressway road section; counting the real-time data of ETC gantries of the expressway road section, and analyzing to obtain the real-time traffic characteristic index of the expressway road section; obtaining the comprehensive traffic safety characteristic index of the expressway road section according to the historical traffic safety characteristic value and the real-time traffic characteristic index; obtaining the basic structure data and real-time environment data of the expressway road section, and processing to obtain the traffic risk impact factor of the expressway road section; based on the real-time traffic characteristic index, and combining with the real-time environment data of the expressway road section, analyzing to obtain the historical risk data of the expressway road section, thereby comprehensively processing to obtain the traffic safety risk value of the expressway road section, and performing corresponding traffic safety risk early warning.

[0009] The present application provides a traffic safety risk early warning system based on traffic flow characteristic data, including: a historical traffic safety characteristic evaluation module, configured to obtain historical data of an expressway road section, and analyze to obtain the historical traffic safety characteristic value of the expressway road section; a real-time traffic characteristic evaluation module, configured to count the real-time data of ETC gantries of the expressway road section, and analyze to obtain the real-time traffic characteristic index of the expressway road section; a traffic safety comprehensive evaluation module, configured to obtain the comprehensive traffic safety characteristic index of the expressway road section according to the historical traffic safety characteristic value and the real-time traffic characteristic index; a traffic risk impact evaluation module, configured to obtain the basic structure data and real-time environment data of the expressway road section, and process to obtain the traffic risk impact factor of the expressway road section; a traffic safety risk early warning module, configured to analyze based on the real-time traffic characteristic index, and combine with the real-time environment data of the expressway road section, analyze to obtain the historical risk data of the expressway road section, thereby comprehensively processing to obtain the traffic safety risk value of the expressway road section, and performing corresponding traffic safety risk early warning.

[0010] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0011] 1. By comprehensively analyzing, the traffic safety risk value of the highway section is obtained, and early warnings are issued according to the traffic safety risk value of the highway section, realizing accurate and effective early warnings for the traffic safety risks of the highway section, and effectively solving the problem of inaccurate early warnings of traffic safety risks in the prior art.

[0012] 2. By obtaining the historical traffic safety characteristic values and real-time traffic characteristic indexes of the highway section, the comprehensive traffic safety characteristic index of the highway section is obtained, the traffic safety condition of the highway section is comprehensively analyzed according to the traffic safety characteristic index, and the traffic safety condition of the highway section is accurately and timely evaluated according to historical data and real-time data.

[0013] 3. By obtaining the basic structure data and real-time environment data of the highway section and processing them to obtain the traffic risk impact factors of the highway section, comprehensively considering the basic characteristic impact, environmental impact and historical risk data in the historical environment of the highway section, accurate and timely early warnings for traffic safety risks can be realized. Description of the Drawings

[0014] Figure 1 It is a flowchart of the traffic safety risk early warning method based on traffic flow characteristic data provided by an embodiment of the present application;

[0015] Figure 2 It is a schematic structural diagram of the traffic safety risk early warning system based on traffic flow characteristic data provided by an embodiment of the present application. Detailed Embodiments

[0016] In the embodiments of the present application, by providing a traffic safety risk early warning method and system based on traffic flow characteristic data, the problems of inaccurate and untimely early warnings of traffic safety risks in the prior art are solved. By obtaining the comprehensive traffic safety characteristic index of the highway section, the traffic risk impact factors, and the traffic risk data within the interval of the historical environmental risk impact factors corresponding to the traffic risk impact factors, and comprehensively analyzing to obtain the traffic safety risk value of the highway section, and issuing early warnings according to the traffic safety risk value of the highway section, accurate and effective early warnings for the traffic safety risks of the highway section are realized, and the problems of inaccurate and untimely early warnings of traffic safety risks in the prior art are effectively solved.

[0017] The technical solutions in the embodiments of the present application are to solve the problems of inaccurate and untimely early warnings of the above-mentioned traffic safety risks, and the general idea is as follows:

[0018] The comprehensive traffic safety characteristic index of a highway section is obtained by acquiring the historical traffic safety value and the real-time traffic characteristic index of the highway section. The traffic risk impact factor of the highway section is obtained by processing the basic structure data and the real-time environment data of the highway section. Then, in combination with the comprehensive traffic safety characteristic index, the traffic risk impact factor, and the traffic risk data within the historical environmental risk impact factor range corresponding to the traffic risk impact factor, and through comprehensive analysis, the traffic safety risk value of the highway section is obtained. Early warnings are issued based on the traffic safety risk value of the highway section, achieving accurate and effective early warnings for the traffic safety risks of highway sections.

[0019] To better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific implementation manners.

[0020] As Figure 1 shown, it is a flowchart of a traffic safety risk warning method based on traffic flow characteristic data provided by an embodiment of the present application. The method includes the following steps: acquiring historical data of a highway section and analyzing to obtain the historical traffic safety characteristic value of the highway section; counting the real-time data of ETC gantries of the highway section and analyzing to obtain the real-time traffic characteristic index of the highway section; obtaining the comprehensive traffic safety characteristic index of the highway section according to the historical traffic safety characteristic value and the real-time traffic characteristic index; acquiring the basic structure data and the real-time environment data of the highway section and processing to obtain the traffic risk impact factor of the highway section; based on the real-time traffic characteristic index, and in combination with the real-time environment data of the highway section, analyzing to obtain the historical risk data of the highway section, thereby comprehensively processing to obtain the traffic safety risk value of the highway section, and performing corresponding traffic safety risk warnings.

[0021] Furthermore, the process of analyzing to obtain the historical traffic safety characteristic value of the highway section is as follows: acquiring historical data of the highway section, where the historical data of the highway section includes: historical daily average peak-hour traffic volume, historical daily average traffic volume of the section, and historical daily average traffic density of the section; based on the historical data of the highway section, analyzing to obtain the historical traffic safety characteristic value of the highway section, and the historical traffic safety characteristic value is used to measure the historical traffic load and operating conditions of the highway section.

[0022] In this embodiment, the specific method for obtaining the historical traffic safety characteristic value of the highway section is:

[0023] ;

[0024] In the formula, represents the historical daily average peak-hour traffic volume, represents the preset historical daily average peak-hour traffic volume reference value, is represented as the historical daily average traffic volume of the road section, is represented as the preset control value of the historical daily average traffic volume of the road section, is represented as the historical daily average traffic flow density of the road section, is represented as the preset control value of the historical daily average traffic flow density of the road section, is represented as the weight factor of the historical daily average traffic volume during the peak period, is represented as the weight factor of the historical daily average traffic volume of the road section, is represented as the weight factor of the historical daily average traffic flow density of the road section. The control value of the historical daily average traffic volume during the peak period, the preset control value of the historical daily average traffic volume of the road section, and the preset control value of the historical daily average traffic flow density of the road section are obtained from the database.

[0025] In this embodiment, the value ranges of the weight factors of the historical daily average traffic volume during the peak period, the historical daily average traffic volume of the road section, and the historical daily average traffic flow density of the road section are between 0 and 1. The historical daily average traffic volume during the peak period, the historical daily average traffic volume of the road section, and the historical daily average traffic flow density of the road section can be counted, and combined with the pre-defined mapping table, the corresponding weight factors can be obtained. For example, the mapping table formed by the historical daily average traffic volume during the peak period, the historical daily average traffic volume of the road section, the historical daily average traffic flow density of the road section, and the weight factors of the historical daily average traffic volume during the peak period, the historical daily average traffic volume of the road section, and the historical daily average traffic flow density of the road section. In specific applications, after the real-time historical daily average traffic volume during the peak period, the historical daily average traffic volume of the road section, and the historical daily average traffic flow density of the road section are input into the mapping table, the corresponding weight factors can be obtained.

[0026] In this embodiment, the historical daily average traffic volume during the peak period represents the number of daily peak vehicles passing through the highway road section within the historical time period, the historical daily average traffic volume of the road section represents the number of daily vehicles passing through the highway road section within the historical time period, and the historical daily average traffic flow density of the road section represents the number of daily vehicles per unit length of the highway road section within the historical time period. The historical daily average traffic volume during the peak period, the historical daily average traffic volume of the road section, and the historical daily average traffic flow density of the road section are obtained through the historical traffic database.

[0027] Furthermore, the real-time traffic characteristic index of the highway road section is analyzed. The specific process is as follows: Extract the real-time data of the ETC gantry of the highway road section. The real-time data of the ETC gantry of the highway road section includes: the vehicle density, average vehicle speed, vehicle speed standard deviation, and heavy vehicle ratio passing through within the current real-time sensing period; Based on the real-time data of the ETC gantry of the highway road section, the real-time traffic characteristic index of the highway road section is analyzed. The real-time traffic characteristic index is used to measure the traffic activity status of the highway road section.

[0028] In this embodiment, it should be noted that the traffic activity status of the highway section refers to the busyness degree of the highway section. The vehicle density represents the number of vehicles passing through per unit time. The average vehicle speed represents the average driving speed of the vehicles passing through the ETC gantry. The standard deviation of vehicle speed represents the degree of dispersion of the vehicle speeds of the vehicles passing through the ETC gantry. The heavy vehicle ratio represents the proportion of heavy vehicles (such as trucks, large freight vehicles, etc.) passing through the ETC gantry among all passing vehicles. The vehicle density, average vehicle speed, standard deviation of vehicle speed, and heavy vehicle ratio are collected and statistically calculated in real time through the ETC gantry and sensors.

[0029] In this embodiment, the specific method for obtaining the real-time traffic characteristic index of the highway section is as follows:

[0030] ;

[0031] In the formula, represents the vehicle density, represents the preset vehicle density threshold, represents the average vehicle speed, represents the preset average vehicle speed threshold, represents the standard deviation of vehicle speed, represents the preset standard deviation of vehicle speed threshold, represents the heavy vehicle ratio, represents the preset heavy vehicle ratio threshold, represents the vehicle density weight factor, represents the average vehicle speed weight factor, represents the standard deviation of vehicle speed weight factor, represents the heavy vehicle ratio weight factor. The preset vehicle density threshold, preset average vehicle speed threshold, preset standard deviation of vehicle speed threshold, and preset heavy vehicle ratio threshold are obtained from the database.

[0032] In this embodiment, the value ranges of the weight factors of vehicle density, average vehicle speed, standard deviation of vehicle speed, and heavy vehicle ratio are between 0 and 1, and satisfy , and the vehicle density, average vehicle speed, standard deviation of vehicle speed, and heavy vehicle ratio can be statistically calculated. By combining with the predefined mapping table, the corresponding weight factors can be obtained. In specific applications, after inputting the real-time vehicle density, average vehicle speed, standard deviation of vehicle speed, and heavy vehicle ratio into the mapping table, the corresponding weight factors can be obtained. For example, a mapping table formed by vehicle density, average vehicle speed, standard deviation of vehicle speed, heavy vehicle ratio, and their weight factors of vehicle density, average vehicle speed, standard deviation of vehicle speed, and heavy vehicle ratio.

[0033] In this embodiment, the specific method for obtaining the comprehensive traffic safety characteristic index of a highway section is as follows:

[0034] ;

[0035] In the formula, represents the comprehensive traffic safety characteristic index of the highway section, which is used to measure the traffic safety condition of the highway. represents the real-time traffic characteristic index of the highway section. represents the historical traffic safety characteristic value of the highway section. represents the weight factor of the historical traffic safety characteristic value. represents the weight factor of the real-time traffic characteristic index.

[0036] In this embodiment, and are respectively the numerical values of the influence degrees of the historical traffic safety characteristic value and the real-time traffic characteristic index on the comprehensive traffic safety characteristic index. Their corresponding relationship is a pre-set mapping relationship. According to the pre-set mapping relationship, the corresponding weight factors are obtained. For example, the mapping set formed by the historical traffic safety characteristic value and the weight factor of the historical traffic safety characteristic value, and the mapping set formed by the real-time traffic characteristic index and the real-time traffic characteristic index. In specific applications, after inputting the real-time historical traffic safety characteristic value and the real-time traffic characteristic index into the mapping set, the corresponding weight factors are obtained. and both have a value range between 0 and 1 and satisfy .

[0037] In this embodiment, the historical traffic safety characteristic values and real-time traffic characteristic indices are integrated to evaluate the traffic safety condition of a highway section. Regarding the historical traffic safety characteristic values, there is a close correlation among the historical daily average peak-hour traffic volume, the historical daily average traffic volume of the section, and the historical daily average traffic flow density of the section. If the historical daily average peak-hour traffic volume increases, the corresponding historical average traffic volume of the section increases, that is, the historical daily average traffic volume of the section increases. With the increase in traffic volume, the number of vehicles on the highway section increases, leading to an increase in traffic flow density, and thus the historical daily average traffic flow density of the section rises. The larger the historical daily average peak-hour traffic volume, the historical daily average traffic volume of the section, and the historical daily average traffic flow density, the larger the historical traffic safety characteristic value. Regarding the real-time traffic characteristic index, there is also a close correlation among vehicle density, average vehicle speed, standard deviation of vehicle speed, and proportion of heavy vehicles. If the vehicle density increases, the distance between vehicles on the highway section decreases, the traffic flow velocity will decrease, and behaviors such as frequent braking, accelerating, and lane-changing will occur, resulting in a decrease in the average vehicle speed. At this time, the speed fluctuation of the highway section is relatively large, so the standard deviation of vehicle speed will increase. At the same time, since the speed of heavy vehicles is usually low, if the proportion of heavy vehicles increases, the overall average vehicle speed on the highway section will decrease. The decrease in the overall average vehicle speed on the highway section may lead to an increase in vehicle density and a decrease in the standard deviation of vehicle speed. The larger the vehicle density, average vehicle speed, standard deviation of vehicle speed, and proportion of heavy vehicles, the larger the real-time traffic characteristic index.

[0038] Further, the basic structure data and real-time environment data of the highway section are obtained and processed to obtain the traffic risk impact factor of the highway section. The specific process is as follows: The basic characteristic data of the highway section are obtained and analyzed to obtain the basic risk impact factor, and the real-time environment data of the highway section are obtained and analyzed to obtain the environmental risk impact factor; The basic risk impact factor and the environmental risk impact factor are comprehensively processed to obtain the traffic risk impact factor of the highway section.

[0039] In this embodiment, the specific method for obtaining the traffic risk impact factor of the highway section is as follows:

[0040] ;

[0041] ;

[0042] ;

[0043] wherein, represents the traffic risk impact factor of the highway section, represents the basic risk impact factor of the highway section, represents the environmental risk impact factor of the highway section, Denoted as the average width of the lanes on the road section, Denoted as the reference value of the average width of the lanes on the road section, Denoted as the maximum gradient of the road section, Denoted as the threshold value of the maximum gradient of the road section, Denoted as the average curvature of the road section, Denoted as the threshold value of the average curvature of the road section, Denoted as the number of bends, Denoted as the reference value of the number of bends, Denoted as the weight factor of the average width of the lanes on the road section, Denoted as the weight factor of the maximum gradient of the road section, Denoted as the weight factor of the average curvature of the road section, Denoted as the weight factor of the number of bends, Denoted as the real-time temperature, Denoted as the reference value of the real-time temperature, Denoted as the real-time humidity, Denoted as the reference value of the real-time humidity, Denoted as the real-time wind speed, Denoted as the reference value of the real-time wind speed, Denoted as the real-time visibility of the road section, Denoted as the reference value of the real-time visibility of the road section, Denoted as the weight factor of the real-time temperature, Denoted as the weight factor of the real-time humidity, Denoted as the weight factor of the real-time wind speed, Denoted as the weight factor of the real-time visibility of the road section. The reference value of the average width of the lanes on the road section, the threshold value of the maximum gradient of the road section, the threshold value of the average curvature of the road section, the reference value of the number of bends, the reference value of the real-time temperature, the reference value of the real-time humidity, the reference value of the real-time wind speed, and the reference value of the real-time visibility of the road section are obtained from the database.

[0044] In this embodiment, , , and are respectively the numerical values of the influence degrees of the average width of the lanes on the road section, the maximum gradient of the road section, the average curvature of the road section, and the number of bends on the basic risk influence factor, and the value ranges are all , and it is possible to count the average width of the lanes on the road section, the maximum gradient of the road section, the average curvature of the road section, and the number of bends, and combine the pre-defined mapping table to obtain their corresponding weight factors. In specific applications, after inputting the real-time average width of the lanes on the road section, the maximum gradient of the road section, the average curvature of the road section, and the number of bends into the mapping table, the corresponding weight factors can be obtained. For example, a mapping table formed by the average width of the lanes on the road section, the maximum gradient of the road section, the average curvature of the road section, the number of bends, and the weight factors of the average width of the lanes on the road section, the maximum gradient of the road section, the average curvature of the road section, and the number of bends. , , and all have a value range between 0 and 1, and satisfy . The real-time temperature weight factor, real-time humidity weight factor, real-time wind speed weight factor, and real-time road section visibility weight factor respectively represent the numerical values of the influence degrees of real-time temperature, real-time humidity, real-time wind speed, and real-time road section visibility on the environmental risk impact factor. The real-time temperature, real-time humidity, real-time wind speed, and real-time road section visibility can be statistically analyzed, and combined with a pre-defined mapping table to obtain their corresponding weight factors. After inputting the real-time temperature, real-time humidity, real-time wind speed, and real-time road section visibility into the mapping table, the corresponding weight factors can be obtained. For example, a mapping table formed by real-time temperature, real-time humidity, real-time wind speed, real-time road section visibility, and their corresponding weight factors of real-time temperature, real-time humidity, real-time wind speed, and real-time road section visibility.

[0045] In this embodiment, the average lane width of the road section, the maximum slope of the road section, the average curvature of the road section, and the number of curves are closely related. If the maximum slope of the road section is large, a wider lane is required to keep the vehicle stable. If the average curvature of the road section is larger, the curve is sharper, and a wider lane is required to provide more turning space. If the number of curves is larger, a wider lane is required to improve the traffic capacity. That is, the larger the average curvature, the maximum slope, and the number of curves of the road section, the larger the average width of the road section required. The road sections with a larger average curvature usually have a larger number of curves. If the maximum slope of the road section is large and the average curvature of the road section is large, a large number of curves will bring a higher accident risk at the same time, and the basic risk impact factor of the highway road section will be larger. The real-time temperature and real-time humidity are closely related. When the temperature rises, the humidity often rises. When the real-time wind speed is high, it can reduce the accumulation of haze and dust, thereby increasing the visibility. The higher the real-time temperature, the greater the possibility of affecting the performance of the vehicle, which may increase the tire pressure of the vehicle or cause the road surface to expand. The greater the real-time humidity, the more likely the road surface will be slippery, increasing the risk of skidding. The higher the real-time temperature, real-time humidity, and real-time wind speed, the lower the real-time road section visibility, and the greater the environmental risk impact factor of the highway road section, which will affect the road surface condition and the comfort level of the driver, and the risk of traffic accidents will be greater.

[0046] Furthermore, the basic characteristic data of the highway road section include: the average lane width of the road section, the maximum slope of the road section, the average curvature of the road section, and the number of curves; the real-time environmental data include: real-time temperature, real-time humidity, real-time wind speed, and real-time road section visibility.

[0047] In this embodiment, the average lane width of the road section represents the average width of all lanes on the highway road section, the maximum slope of the road section represents the steepest slope on the highway road section, the average curvature of the road section represents the degree of curvature of the road, the number of curves represents the total number of curves appearing in the highway road section. The average lane width of the road section, the maximum slope of the road section, the average curvature of the road section, and the number of curves can be obtained through GIS (Geographic Information System) software. The real-time temperature represents the temperature on the highway road section, the real-time humidity represents the content of water vapor in the air above the highway road section, the real-time wind speed represents the flow rate of the wind per unit time on the highway road section, and the real-time visibility of the road section represents the farthest distance that can be clearly seen on the highway road section. The real-time temperature, real-time humidity, real-time wind speed, and real-time visibility of the road section are obtained by placing corresponding sensors on the ETC gantry, specifically: temperature sensors, humidity sensors, wind speed sensors, and visibility sensors.

[0048] Furthermore, by combining the real-time environmental data of the highway road section, historical risk data of the highway road section is analyzed. The specific process is as follows: Based on the real-time traffic characteristic index of the highway road section, it is matched with the adjustment value of the historical environmental risk impact factor interval corresponding to each real-time traffic characteristic index interval preset in the database to obtain the adjustment value of the historical environmental risk impact factor interval corresponding to the interval where the real-time traffic characteristic index of the highway road section is located, denoted as the specified historical environmental risk impact factor interval adjustment value; Based on the environmental risk impact factor obtained by analyzing the real-time environmental data, and in combination with the specified historical environmental risk impact factor interval adjustment value, a specified historical environmental risk impact factor interval is constructed; Based on the specified historical environmental risk impact factor interval, traffic risk data where the historical environmental risk impact factor of the highway road section is within the specified historical environmental risk impact factor interval is statistically analyzed and marked as the historical risk data of the highway road section, including the cumulative number of traffic accidents, the maximum congestion path length, the average vehicle driving speed, and the highest traffic flow density.

[0049] In this embodiment, it should be noted that based on the real-time traffic characteristic index of the highway section, the adjustment value for the specified historical environmental risk impact factor interval is obtained through matching, and then the specified historical environmental risk impact factor interval is constructed. The purpose is to adjust the range of the specified historical environmental risk impact factor interval according to the real-time traffic characteristic index, so that the historical risk data can better fit the current actual environment. The real-time traffic characteristic index is a comprehensive index calculated based on traffic parameters such as vehicle density, average vehicle speed, vehicle speed standard deviation, and heavy vehicle proportion, and is used to quantify the traffic conditions of the current section. The higher the real-time traffic characteristic index, the higher the current traffic congestion and potential traffic risk factors. At this time, the specified historical environmental risk impact factor interval corresponding to the current high-risk environment can be relaxed, so that more historical risk data can be included in the analysis, realizing the exclusion of historical risk data that is not relevant to the current characteristics, making the calculation focus on the historical records of high-risk conditions, and avoiding dealing with a large amount of irrelevant or low-correlation historical data, thereby optimizing the calculation efficiency. At the same time, by introducing real-time data and dynamically adjusting the interval range, the problem of mismatch between historical data and the current environment can be effectively reduced.

[0050] In this embodiment, it should be noted that the specific process of counting the traffic risk data of the historical environmental risk impact factor of the highway section within the specified historical environmental risk impact factor interval is as follows: it is necessary to obtain the historical environmental data of the highway section, and obtain the historical environmental risk impact factor according to the historical environmental data. The historical environmental risk impact factor is matched with the specified historical environmental risk impact factor interval, and the traffic risk data corresponding to the historical environment of the highway section within the specified historical environmental risk impact factor interval is counted and marked as the historical risk data of the highway section. In addition, the specified historical environmental risk impact factor interval is constructed by combining the environmental risk impact factor with the adjustment value of the specified historical environmental risk impact factor interval. For example, assuming that the obtained adjustment value of the specified historical environmental risk impact factor interval is , then the specified historical environmental risk impact factor interval is . Matching the environmental risk impact factor with the specified historical environmental risk impact factor interval can obtain the historical risk data of the highway section under similar current environmental conditions.

[0051] In this embodiment, the historical risk data of the highway section specifically includes the cumulative number of traffic accidents, the maximum congestion path length, the average vehicle driving speed, and the highest traffic flow density. The cumulative number of traffic accidents represents the total number of traffic accidents that occurred on the highway section during the historical period. The maximum congestion path length represents the length of the longest traffic congestion section on the highway section during the historical period. The average vehicle driving speed represents the average vehicle driving speed on the highway section during the historical period. The highest traffic flow density represents the highest traffic flow density on the highway section during the historical period. The cumulative number of traffic accidents, the maximum congestion path length, the average vehicle driving speed, and the highest traffic flow density are obtained from the historical records in the traffic monitoring system.

[0052] Furthermore, the traffic safety risk value of the highway section is obtained through comprehensive processing. The specific process is as follows: Extract the traffic safety comprehensive characteristic index of the highway section, extract the traffic risk impact factor of the highway section and the historical risk data of the highway section, and obtain the traffic safety risk value of the highway section through comprehensive processing.

[0053] Furthermore, the specific process for obtaining the traffic safety risk value of the highway section is as follows:

[0054] ;

[0055] In the formula, represents the traffic safety risk value of the highway section, represents the traffic safety comprehensive characteristic index of the highway section, represents the traffic risk impact factor of the highway section, represents the cumulative number of traffic accidents, represents the preset reference value for the cumulative number of traffic accidents, represents the maximum congestion path length, represents the preset reference value for the maximum congestion path length, represents the average vehicle driving speed, represents the average vehicle driving speed threshold, represents the highest traffic flow density, represents the highest traffic flow density threshold, represents the weight factor of the traffic safety comprehensive characteristic index, represents the weight factor of the traffic risk impact factor, represents the weight factor of the cumulative number of traffic accidents, represents the weight factor of the maximum congestion path length, represents the weight factor of the average vehicle driving speed, represents the weight factor of the highest traffic flow density.

[0056] In this embodiment, the cumulative traffic accident number comparison value, the maximum congested path length comparison value, the average vehicle speed threshold value, and the maximum traffic flow density threshold value are obtained from a database.

[0057] In this embodiment, the traffic safety risk of a highway section is assessed by combining a comprehensive traffic safety characteristic index, a traffic risk impact factor, and historical risk data. The comprehensive traffic safety characteristic index, traffic risk impact factor, and historical risk data are interrelated. Increases in average vehicle speed and peak traffic density lead to an increase in cumulative traffic accidents. Increases in peak traffic density increase the maximum congestion length. A greater traffic risk impact factor for a highway section indicates a greater traffic risk for that highway section, and thus a greater comprehensive traffic safety characteristic index for that highway section. A greater historical risk data indicates a higher historical traffic safety risk for that highway section, and thus a higher traffic risk impact factor and comprehensive traffic safety characteristic index are likely to be present. A greater comprehensive traffic safety characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congestion path length, average vehicle speed, and peak traffic density indicates a greater likelihood of an accident and a greater traffic safety risk value.

[0058] In this embodiment, 、 、 、 、 and They are the preset traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congestion path length, average vehicle speed and influence weight factor of maximum traffic density obtained from the preset database, and the value range is , the traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congested path length, average vehicle speed, and maximum traffic density can be counted, and combined with a pre-defined mapping table, the weight factors corresponding to the traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congested path length, average vehicle speed, and maximum traffic density can be obtained from the pre-defined mapping table. For example, a mapping table is formed by the traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congested path length, average vehicle speed, and maximum traffic density and their weight factors. In a specific application, the real-time traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congested path length, average vehicle speed, and maximum traffic density are input into the mapping table to obtain the corresponding weight factors.

[0059] Further, corresponding traffic safety risk warnings are executed. The specific process is as follows: Obtain the traffic safety risk threshold of the highway section, compare the traffic safety risk value of the highway section with the traffic safety risk threshold. When the traffic safety risk value is less than or equal to the traffic safety risk threshold, no warning is executed. When the traffic safety risk value is greater than the traffic safety risk threshold, a safety risk warning is issued.

[0060] In this embodiment, obtain the preset traffic safety risk value threshold, compare the traffic safety risk value with the traffic safety risk value threshold. If the traffic safety risk value is less than or equal to the traffic safety risk value threshold, no warning is executed. If the traffic safety risk value is greater than the traffic safety risk value threshold, a safety risk warning is issued, and the actual situation is notified to the relevant traffic management departments so that the relevant departments can prepare corresponding solutions in advance, and the warning is synchronized to the vehicles on the highway section to remind the drivers to drive carefully.

[0061] As Figure 2 shown, it is a schematic structural diagram of the traffic safety risk warning system based on traffic flow characteristic data provided by the embodiment of the present application. The traffic safety risk warning system based on traffic flow characteristic data provided by the embodiment of the present application includes: a historical traffic safety characteristic evaluation module, which is used to obtain the historical data of the highway section and analyze to obtain the historical traffic safety characteristic value of the highway section; a real-time traffic characteristic evaluation module, which is used to count the real-time data of the ETC gantry of the highway section and analyze to obtain the real-time traffic characteristic index of the highway section; a traffic safety comprehensive evaluation module, which is used to obtain the traffic safety comprehensive characteristic index of the highway section according to the historical traffic safety characteristic value and the real-time traffic characteristic index; a traffic risk impact evaluation module, which is used to obtain the basic structure data and real-time environment data of the highway section and process them to obtain the traffic risk impact factor of the highway section; a traffic safety risk warning module, which is used to analyze the historical risk data of the highway section based on the real-time traffic characteristic index and in combination with the real-time environment data of the highway section, thereby comprehensively process to obtain the traffic safety risk value of the highway section and execute the corresponding traffic safety risk warning.

[0062] In summary, the embodiment of the present application obtains the traffic safety comprehensive characteristic index, traffic risk impact factor and traffic risk data within the historical environmental risk impact factor range corresponding to the traffic risk impact factor of the highway section, and comprehensively analyzes to obtain the traffic safety risk value of the highway section. Warnings are issued according to the traffic safety risk value of the highway section, realizing accurate and effective warning of the traffic safety risks of the highway section, and effectively solving the problems of inaccurate and untimely warning of traffic safety risks in the prior art.

[0063] Those skilled in the art will understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0064] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0067] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A traffic safety risk early warning method based on traffic flow characteristic data, characterized in that It includes the following steps: Obtain the historical data of the highway section, and analyze it to obtain the historical traffic safety characteristic values of the highway section; Statistically analyze the real-time data of the ETC gantries on the highway section, and analyze it to obtain the real-time traffic characteristic index of the highway section; Obtain the comprehensive traffic safety characteristic index of the highway section based on the historical traffic safety characteristic values and the real-time traffic characteristic index; Obtain the basic structure data and real-time environmental data of the highway section, and process them to obtain the traffic risk impact factors of the highway section; Based on the real-time traffic characteristic index, and in combination with the real-time environmental data of the highway section, analyze to obtain the historical risk data of the highway section, thereby comprehensively process to obtain the traffic safety risk value of the highway section, and execute the corresponding traffic safety risk warning; The specific process of analyzing to obtain the historical risk data of the highway section by combining with the real-time environmental data of the highway section is as follows: Based on the real-time traffic characteristic index of the highway section, match it with the adjustment value of the historical environmental risk impact factor interval corresponding to each preset real-time traffic characteristic index interval in the database, and obtain the adjustment value of the historical environmental risk impact factor interval corresponding to the interval where the real-time traffic characteristic index of the highway section is located, denoted as the specified historical environmental risk impact factor interval adjustment value; Based on the environmental risk impact factor obtained by analyzing the real-time environmental data, and in combination with the specified historical environmental risk impact factor interval adjustment value, construct the specified historical environmental risk impact factor interval; Based on the specified historical environmental risk impact factor interval, statistically analyze the traffic risk data where the historical environmental risk impact factor of the highway section is within the specified historical environmental risk impact factor interval, and mark it as the historical risk data of the highway section; The specific process of comprehensively processing to obtain the traffic safety risk value of the highway section is as follows: Extract the comprehensive traffic safety characteristic index of the highway section, extract the traffic risk impact factors of the highway section and the historical risk data of the highway section, and comprehensively process to obtain the traffic safety risk value of the highway section.

2. The traffic safety risk warning method based on traffic flow characteristic data according to claim 1, wherein The specific process of analyzing to obtain the historical traffic safety characteristic values of the highway section is as follows: Obtain the historical data of the highway section, and the historical data of the highway section includes: historical average daily peak-hour traffic volume, section historical average daily traffic volume, and section historical average daily traffic density; Based on the historical data of the highway section, analyze to obtain the historical traffic safety characteristic values of the highway section, and the historical traffic safety characteristic values are used to measure the historical traffic load and operating conditions of the highway section.

3. The traffic safety risk warning method based on traffic flow characteristic data according to claim 1, wherein The specific process of analyzing to obtain the real-time traffic characteristic index of the highway section is as follows: Extract the real-time data of the ETC gantries on the highway section, and the real-time data of the ETC gantries on the highway section includes: vehicle density, average vehicle speed, vehicle speed standard deviation, and heavy vehicle ratio passing within the current real-time sensing period; Based on the real-time data of the ETC gantries on the highway section, analyze to obtain the real-time traffic characteristic index of the highway section, and the real-time traffic characteristic index is used to measure the traffic activity status of the highway section.

4. The traffic safety risk warning method based on traffic flow characteristic data according to claim 1, wherein The process of obtaining the basic structure data and real-time environment data of a highway section, and processing them to obtain the traffic risk impact factors of the highway section is as follows: Obtain the basic characteristic data of the highway section for analysis to obtain the basic risk impact factors, and obtain the real-time environment data of the highway section for analysis to obtain the environmental risk impact factors; Comprehensively process the basic risk impact factors and environmental risk impact factors to obtain the traffic risk impact factors of the highway section.

5. The traffic safety risk warning method based on traffic flow characteristic data according to claim 4, wherein, The basic characteristic data of the highway section include: average lane width of the section, maximum slope of the section, average curvature of the section, and number of curves; The real-time environment data include: real-time temperature, real-time humidity, real-time wind speed, and real-time visibility of the section.

6. The traffic safety risk warning method based on traffic flow characteristic data according to claim 1, wherein The historical risk data of the highway section include the cumulative number of traffic accidents, the length of the maximum congestion path, the average vehicle driving speed, and the highest traffic flow density.

7. The traffic safety risk warning method based on traffic flow characteristic data according to claim 1, wherein The process of performing the corresponding traffic safety risk warning is as follows: Obtain the traffic safety risk threshold of the highway section, compare the traffic safety risk value of the highway section with the traffic safety risk threshold. When the traffic safety risk value is less than or equal to the traffic safety risk threshold, no warning is executed. When the traffic safety risk value is greater than the traffic safety risk threshold, a safety risk warning is issued.

8. A system for applying the traffic safety risk warning method based on traffic flow characteristic data according to any one of claims 1-7, characterized in that, It includes: A historical traffic safety feature evaluation module, which is used to obtain the historical data of the highway section and analyze it to obtain the historical traffic safety feature value of the highway section; A real-time traffic feature evaluation module, which is used to count the real-time data of the ETC gantry of the highway section and analyze it to obtain the real-time traffic feature index of the highway section; A traffic safety comprehensive evaluation module, which is used to obtain the traffic safety comprehensive feature index of the highway section according to the historical traffic safety feature value and the real-time traffic feature index; A traffic risk impact evaluation module, which is used to obtain the basic structure data and real-time environment data of the highway section and process them to obtain the traffic risk impact factors of the highway section; A traffic safety risk warning module, which is used to analyze the historical risk data of the highway section based on the real-time traffic feature index and combined with the real-time environment data of the highway section, and thus comprehensively process to obtain the traffic safety risk value of the highway section and perform the corresponding traffic safety risk warning.

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