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

Abstract

The invention discloses a traffic safety risk early warning method and system based on traffic flow characteristic data, and belongs to the technical field of traffic safety. Comprising the following steps: acquiring a traffic safety comprehensive characteristic index, a real-time traffic characteristic index and a traffic risk influence factor of an expressway section, and analyzing to obtain historical risk data of the expressway section based on the real-time traffic characteristic index in combination with real-time environment data of the expressway section, and comprehensively processing the traffic safety comprehensive characteristic index, the traffic risk influence factor and the historical risk data to obtain a traffic safety risk value of the highway section, and executing corresponding traffic safety risk early warning. According to the invention, the traffic safety risk value is obtained by integrating the traffic safety comprehensive characteristic index, the traffic risk influence factor and the traffic risk data, and early warning is carried out, so that the effect of accurately and effectively carrying out early warning on the traffic safety risk of the highway section is achieved; the problem that early warning of traffic safety risks is inaccurate and not timely in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of traffic safety technology, 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 economic development and accelerated urbanization, traffic flow continues to increase, and the frequency and impact of traffic accidents also increase accordingly. Traffic safety issues are an important factor affecting public safety and social stability. Real-time monitoring and analysis of traffic flow data, timely discovery of potential safety risks and early warning are of great significance.

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

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

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, when conducting traffic safety risk warnings, the safety level evaluation index of the road section is determined by obtaining highway data, and then a safety warning is conducted. In reality, the probability of an accident occurring in different environments is different. Only obtaining conventional overall highway data for traffic safety risk warnings cannot fully guarantee the timeliness of highway warnings. Therefore, there is currently a problem of inaccurate and untimely warnings of traffic safety risks. Summary of the invention

[0006] The embodiments of the present application solve the problems of inaccurate and untimely warning of traffic safety risks in the prior art by providing a traffic safety risk warning method and system based on traffic flow characteristic data, and achieve accurate traffic safety risk warning.

[0007] The present application provides a traffic safety risk warning method based on traffic flow characteristic data, comprising the following steps: acquiring historical data of a highway section, and analyzing to obtain a historical traffic safety characteristic value of the highway section; collecting statistics on the real-time data of the ETC gantries of the highway section, and analyzing to obtain a real-time traffic characteristic index of the highway section; obtaining a comprehensive traffic safety characteristic index of the highway section based on the historical traffic safety characteristic value and the real-time traffic characteristic index; acquiring infrastructure data and real-time environmental data of the highway section, and processing to obtain a traffic risk influencing factor of the 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 environmental data of the highway section, thereby comprehensively processing to obtain a traffic safety risk value of the highway section, and executing a corresponding traffic safety risk warning.

[0008] The present application provides a traffic safety risk warning system based on traffic flow characteristic data, including: a historical traffic safety characteristic evaluation module, which is used to obtain historical data of a highway section and analyze it 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 it to obtain the real-time traffic characteristic index of the highway section; a traffic safety comprehensive evaluation module, which is used to obtain 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; a traffic risk impact evaluation module, which is used to obtain the infrastructure data of the highway section and the real-time environmental data for processing 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 environmental data of the highway section, thereby comprehensively processing to obtain the traffic safety risk value of the highway section, and execute corresponding traffic safety risk warnings.

[0009] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The traffic safety risk value of the highway section is obtained through comprehensive analysis, and early warning is issued based on the traffic safety risk value of the highway section, which realizes accurate and effective early warning of traffic safety risks of highway sections, and effectively solves the problem of inaccurate early warning of traffic safety risks in existing technologies.

[0010] 2. By obtaining the historical traffic safety characteristic values ​​and real-time traffic characteristic index of the highway section, the comprehensive traffic safety characteristic index of the highway section is obtained. The traffic safety status of the highway section is comprehensively analyzed based on the traffic safety characteristic index. Based on the historical data and real-time data, the traffic safety status of the highway section is accurately and timely evaluated.

[0011] 3. By acquiring the basic structure data of the highway section and the real-time environmental data and processing them, the traffic risk influencing factors of the highway section are obtained. The basic characteristic influence, environmental influence and historical risk data of the highway section under the historical environment are fully considered, so that accurate and timely early warning of traffic safety risks can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart of a traffic safety risk warning method based on traffic flow characteristic data provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a traffic safety risk warning system based on traffic flow characteristic data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiments of the present application solve the problem of inaccurate and untimely warning of traffic safety risks in the prior art by providing a traffic safety risk warning method and system based on traffic flow characteristic data. By obtaining the comprehensive traffic safety characteristic index of the highway section, the traffic risk influencing factor and the traffic risk data within the historical environmental risk influencing factor interval corresponding to the traffic risk influencing factor, and comprehensively analyzing the traffic safety risk value of the highway section, warning is issued according to the traffic safety risk value of the highway section, thereby achieving accurate and effective warning of traffic safety risks of the highway section, and effectively solving the problem of inaccurate and untimely warning of traffic safety risks in the prior art.

[0014] The technical solution in the embodiment of the present application is to solve the problem of inaccurate and untimely warning of the above-mentioned traffic safety risks. The overall idea is as follows: By obtaining the historical traffic safety values ​​and real-time traffic characteristic index of the highway section, the comprehensive traffic safety characteristic index of the highway section is obtained. The traffic risk influencing factor of the highway section is obtained by processing the infrastructure data and real-time environmental data of the highway section. Then, the comprehensive traffic safety characteristic index, the traffic risk influencing factor and the traffic risk data within the range of historical environmental risk influencing factors corresponding to the traffic risk influencing factor are combined, and a comprehensive analysis is performed to obtain the traffic safety risk value of the highway section. Early warning is issued based on the traffic safety risk value of the highway section, thereby realizing accurate and effective early warning of traffic safety risks of highway sections.

[0015] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0016] like Figure 1 As shown, it is a flow chart of a traffic safety risk warning method based on traffic flow characteristic data provided by an embodiment of the present application, and the method includes the following steps: acquiring historical data of a highway section, and analyzing to obtain a historical traffic safety characteristic value of the highway section; collecting real-time data of the ETC gantries of the highway section, and analyzing to obtain a real-time traffic characteristic index of the highway section; obtaining a comprehensive traffic safety characteristic index of the highway section based on the historical traffic safety characteristic value and the real-time traffic characteristic index; acquiring infrastructure data and real-time environmental data of the highway section, and processing to obtain a traffic risk influencing factor 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, 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 executing corresponding traffic safety risk warnings.

[0017] Furthermore, the historical traffic safety characteristic values ​​of the highway section are obtained through analysis. The specific process is as follows: the historical data of the highway section is obtained, and the historical data of the highway section includes: the historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section, and the historical average daily traffic density of the section; based on the historical data of the highway section, the historical traffic safety characteristic values ​​of the highway section are obtained through analysis, and the historical traffic safety characteristic values ​​are used to measure the historical traffic load and operating conditions of the highway section.

[0018] In this embodiment, the specific method for obtaining the historical traffic safety characteristic value of the highway section is: ; In the formula, It is represented by the historical daily average peak-hour traffic volume, It is represented by the preset historical daily average peak traffic volume comparison value. It is represented by the historical average daily traffic volume of the road section. It is expressed as the preset historical average daily traffic volume comparison value of the road section. It is expressed as the historical average daily traffic density of the road section, It is expressed as the preset historical average daily traffic density comparison value of the road section. It is expressed as the weight factor of the historical daily average peak traffic volume, It is expressed as the weight factor of the historical average daily traffic flow of the road section, It is expressed as a weight factor of the historical average daily traffic density of the road section. The historical average daily peak traffic flow comparison value, the preset road section historical average daily traffic flow comparison value and the preset road section historical average daily traffic density comparison value are obtained from the database.

[0019] In this embodiment, the value range of the weight factors of the historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section and the historical average daily traffic density of the section is between 0 and 1. The historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section and the historical average daily traffic density of the section can be counted, and combined with a pre-defined mapping table to obtain their corresponding weight factors. For example, a mapping table is formed by the weight factors of the historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section, the historical average daily traffic density of the section and the historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section, and the historical average daily traffic density of the section. In specific applications, the real-time historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section and the historical average daily traffic density of the section are input into the mapping table to obtain the corresponding weight factors.

[0020] In this embodiment, the historical average daily peak-time traffic volume represents the average daily number of peak vehicles passing through the highway section during the historical time period, the historical average daily traffic volume of the section represents the average daily number of vehicles passing through the highway section during the historical time period, and the historical average daily traffic density of the section represents the average daily number of vehicles per unit length of the highway section during the historical time period. The historical average daily peak-time traffic volume, the historical average daily traffic volume of the section, and the historical average daily traffic density of the section are obtained through the historical traffic database.

[0021] Furthermore, the real-time traffic characteristic index of the highway section is obtained by analysis. The specific process is: extracting the real-time data of the ETC gantry of the highway section, the real-time data of the ETC gantry of the highway section includes: the density of vehicles passing through during the current real-time sensing period, the average vehicle speed, the standard deviation of vehicle speed and the proportion of heavy vehicles; based on the real-time data of the ETC gantry of the highway section, the real-time traffic characteristic index of the highway section is obtained by analysis. The real-time traffic characteristic index is used to measure the traffic activity of the highway section.

[0022] In the present embodiment, it should be noted that the traffic activity of a highway section refers to the busyness of the highway section, the vehicle density represents the number of vehicles passing through per unit time, the average vehicle speed represents the average speed of vehicles passing through the ETC gantry, the vehicle speed standard deviation represents the dispersion of the vehicle speeds passing through the ETC gantry, and the proportion of heavy vehicles represents the proportion of heavy vehicles (such as trucks, large trucks, etc.) passing through the ETC gantry to all passing vehicles. The vehicle density, average vehicle speed, vehicle speed standard deviation and heavy vehicle proportion are collected in real time through the ETC gantry and sensors and obtained through statistical calculation.

[0023] In this embodiment, the specific method for obtaining the real-time traffic characteristic index of the highway section is: ; In the formula, Expressed as vehicle density, Represented as the preset vehicle density threshold, is the average vehicle speed, Represented as the preset average vehicle speed threshold, Expressed as the standard deviation of vehicle speed, Represented as the preset vehicle speed standard deviation threshold, Expressed as the proportion of heavy vehicles, Represented as the preset heavy vehicle ratio threshold, Expressed as the vehicle density weight factor, Expressed as the average vehicle speed weight factor, Expressed as the vehicle speed standard deviation weight factor, It is expressed as a heavy vehicle proportion weight factor. A preset vehicle density threshold, a preset average vehicle speed threshold, a preset vehicle speed standard deviation threshold, and a preset heavy vehicle proportion threshold are obtained from a database.

[0024] In this embodiment, the weight factors of vehicle density, average vehicle speed, vehicle speed standard deviation and heavy vehicle ratio range from 0 to 1 and satisfy , the vehicle density, average vehicle speed, vehicle speed standard deviation and heavy vehicle proportion can be counted, and combined with the pre-defined mapping table, the corresponding weight factors can be obtained. In a specific application, the real-time vehicle density, average vehicle speed, vehicle speed standard deviation and heavy vehicle proportion are input into the mapping table to obtain the corresponding weight factors. For example, a mapping table formed by vehicle density, average vehicle speed, vehicle speed standard deviation, heavy vehicle proportion and the weight factors of vehicle density, average vehicle speed, vehicle speed standard deviation and heavy vehicle proportion.

[0025] In this embodiment, the specific method for obtaining the comprehensive traffic safety characteristic index of the highway section is: ; In the formula, It is expressed as the comprehensive characteristic index of traffic safety of highway sections, which is used to measure the traffic safety status of highways. Expressed as the real-time traffic characteristic index of the highway section, It is expressed as the historical traffic safety characteristic value of the highway section, Expressed as the weight factor of historical traffic safety characteristic value, Expressed as real-time traffic characteristic index weight factor.

[0026] In this embodiment, and are the numerical values ​​of the influence of the historical traffic safety characteristic value and the real-time traffic characteristic index on the comprehensive traffic safety characteristic index, respectively, and the corresponding relationship is a preset mapping relationship, and the corresponding weight factor is obtained according to the preset mapping relationship, for example, a mapping set formed by the historical traffic safety characteristic value and the weight factor of the historical traffic safety characteristic value, and a mapping set formed by the real-time traffic characteristic index and the real-time traffic characteristic index. In a specific application, the real-time historical traffic safety characteristic value and the real-time traffic characteristic index are input into the mapping set, thereby obtaining the corresponding weight factor. and The value range of is between 0 and 1, and satisfies .

[0027] In this embodiment, the historical traffic safety characteristic value and the real-time traffic characteristic index are combined to evaluate the traffic safety status of the highway section. For the historical traffic safety characteristic value, there is a close correlation between the historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section and the historical average daily traffic density of the section. If the historical average daily peak-hour traffic volume increases, the corresponding historical average traffic volume of the section increases, that is, the historical average daily traffic volume of the section increases. When the traffic volume increases, the number of vehicles on the highway section increases, which leads to an increase in traffic density. The greater the historical average daily peak-hour traffic volume, the historical average daily traffic volume of the section and the historical average daily traffic density of the section, the greater the historical traffic safety characteristic value. For the real-time traffic characteristic index, vehicle density, average vehicle speed, vehicle speed standard deviation and proportion of heavy vehicles are also closely related. If the vehicle density increases, the distance between vehicles on the highway section will decrease, the traffic flow rate will decrease, and frequent braking, acceleration and lane changing will occur, then the average vehicle speed will decrease. At this time, the speed fluctuation on the highway section is large, and the vehicle speed standard deviation will become larger. 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 reduction in the overall average vehicle speed on the highway section may lead to an increase in vehicle density and a decrease in the vehicle speed standard deviation. The greater the vehicle density, average vehicle speed, vehicle speed standard deviation and proportion of heavy vehicles, the greater the real-time traffic characteristic index.

[0028] Furthermore, the basic structural data and real-time environmental data of the highway section are obtained and processed to obtain the traffic risk influencing factors of the highway section. The specific process is: the basic characteristic data of the highway section is obtained and analyzed to obtain the basic risk influencing factors, and the real-time environmental data of the highway section is obtained and analyzed to obtain the environmental risk influencing factors; the basic risk influencing factors and the environmental risk influencing factors are comprehensively processed to obtain the traffic risk influencing factors of the highway section.

[0029] In this embodiment, the specific method for obtaining the traffic risk influencing factor of the highway section is: ; ; ; In the formula, Expressed as the traffic risk influencing factor of the highway section, It is expressed as the basic risk impact factor of the highway section, Expressed as the environmental risk impact factor of the highway section, It is expressed as the average lane width of the road section, It is expressed as the average lane width reference value of the road section. It is expressed as the highest slope of the road section. It is expressed as the maximum slope threshold of the road section. Expressed as the average curvature of the road segment, Expressed as the average curvature threshold of the road segment, Expressed as the number of bends, Expressed as a reference value for the number of curves, Expressed as the average lane width weight factor of the road section, Expressed as the weight factor of the highest slope of the road section, Expressed as the average curvature weight factor of the road segment, Expressed as the weight factor of the number of curves, Expressed as real-time temperature, Expressed as real-time temperature reference value, Expressed as real-time humidity, Expressed as real-time humidity reference value, Expressed as real-time wind speed, Represents the real-time wind speed reference value, Represented as real-time road segment visibility, It is expressed as the real-time road visibility reference value. Expressed as the real-time temperature weight factor, Expressed as the real-time humidity weight factor, Expressed as the real-time wind speed weight factor, It is expressed as a real-time road segment visibility weight factor. The average lane width reference value of the road segment, the highest slope threshold of the road segment, the average curvature threshold of the road segment, the number of curves reference value, the real-time temperature reference value, the real-time humidity reference value, the real-time wind speed reference value and the real-time road segment visibility reference value are obtained from the database.

[0030] In this embodiment, , , and They are the values ​​of the influence of the average lane width, the highest slope, the average curvature and the number of curves on the basic risk influencing factors, and the range of values ​​is , the average lane width of the road section, the highest slope of the road section, the average curvature of the road section and the number of bends can be counted, and combined with the pre-defined mapping table, the corresponding weight factors can be obtained. In a specific application, the real-time average lane width of the road section, the highest slope of the road section, the average curvature of the road section and the number of bends are input into the mapping table to obtain the corresponding weight factors. For example, the average lane width of the road section, the highest slope of the road section, the average curvature of the road section, the number of bends and the weight factors of the average lane width of the road section, the highest slope of the road section, the average curvature of the road section and the number of bends are formed into a mapping table. , , and The value range of is between 0 and 1, and satisfies The real-time temperature weight factor, the real-time humidity weight factor, the real-time wind speed weight factor and the real-time road section visibility weight factor respectively represent the numerical values ​​of the influence degree of the real-time temperature, the real-time humidity, the real-time wind speed and the real-time road section visibility on the environmental risk influencing factors. The real-time temperature, the real-time humidity, the real-time wind speed and the real-time road section visibility can be counted and combined with the pre-defined mapping table to obtain the corresponding weight factors. After the real-time temperature, the real-time humidity, the real-time wind speed and the real-time road section visibility are input into the mapping table, the corresponding weight factors can be obtained. For example, a mapping table formed by the real-time temperature, the real-time humidity, the real-time wind speed, the real-time road section visibility and the weight factors of the real-time temperature, the real-time humidity, the real-time wind speed and the real-time road section visibility.

[0031] In this embodiment, the average width of the lanes of the road section, the highest slope of the road section, the average curvature of the road section and the number of curves are closely related. If the highest slope of the road section is larger, a wider lane is needed to keep the vehicle stable. The larger the average curvature of the road section, the sharper the curve, and the wider lane is needed to provide more turning space. The more curves there are, the wider lanes are needed to improve the traffic capacity. That is, the larger the average curvature of the road section, the highest slope of the road section and the number of curves, the larger the average width of the road section is required. The road section with a larger average curvature of the road section usually has more curves. If the highest slope of the road section is larger, the average curvature of the road section is larger, and the larger number of curves will bring a higher accident risk, then the basic risk influencing factor of the highway section will be larger. The real-time temperature and real-time humidity are closely related. Rising temperature is often accompanied by rising humidity. When the real-time wind speed is high, it can reduce the accumulation of haze and dust, thereby increasing 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 higher the real-time humidity, the more likely it is that 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, the greater the environmental risk influencing factor of the highway section, the road condition and the driver's comfort will be affected, and the risk of traffic accidents will be greater.

[0032] Furthermore, 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 environmental data include: real-time temperature, real-time humidity, real-time wind speed and real-time visibility of the section.

[0033] In this embodiment, the average lane width of the road section represents the average width of all lanes on the highway section, the highest slope of the road section represents the steepest slope on the highway section, the average curvature of the road section represents the curvature of the road, and the number of curves represents the total number of curves appearing in the highway section. The average lane width of the road section, the highest 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 section, the real-time humidity represents the content of water vapor in the air above the highway section, the real-time wind speed represents the wind flow rate per unit time on the highway section, and the real-time section visibility represents the farthest distance that can be clearly seen on the highway section. The real-time temperature, real-time humidity, real-time wind speed and real-time section visibility are obtained by placing corresponding sensors on the ETC gantry, specifically: temperature sensor, humidity sensor, wind speed sensor and visibility sensor.

[0034] Furthermore, in combination with the real-time environmental data of the highway section, the historical risk data of the highway section is analyzed and obtained. The specific process is as follows: based on the real-time traffic characteristic index of the highway section, the historical environmental risk impact factor interval adjustment value corresponding to each real-time traffic characteristic index interval pre-set in the database is matched to obtain the historical environmental risk impact factor interval adjustment value corresponding to the interval in which the real-time traffic characteristic index of the highway section is located, and recorded 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 combined 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, the traffic risk data of the highway section whose historical environmental risk impact factor is within the specified historical environmental risk impact factor interval is counted, and marked as the historical risk data of the highway section, including the cumulative number of traffic accidents, the maximum congested path length, the average vehicle driving speed and the maximum traffic density.

[0035] In this embodiment, it should be noted that based on the real-time traffic characteristic index of the highway section, the specified historical environmental risk impact factor interval adjustment value is matched to construct the specified historical environmental risk impact factor interval. 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 is more in line with the current actual environment. The real-time traffic characteristic index is a comprehensive indicator calculated based on traffic parameters such as vehicle density, average vehicle speed, vehicle speed standard deviation and heavy vehicle proportion, which 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 to allow more historical risk data to be included in the analysis, thereby excluding historical risk data that is not related to the current characteristics, focusing the calculation on the historical records of high-risk conditions, avoiding processing a large amount of irrelevant or low-relevance historical data, and thus optimizing the calculation efficiency. At the same time, by introducing real-time data and dynamically adjusting the interval range, the problem of historical data not matching the current environment can be effectively reduced.

[0036] In this embodiment, it should be noted that the specific process of collecting statistics on traffic risk data of highway sections whose historical environmental risk impact factors are 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 based on the historical environmental data, match the historical environmental risk impact factor with the specified historical environmental risk impact factor interval, and obtain statistics on the traffic risk data corresponding to the historical environment of the highway section within the specified historical environmental risk impact factor interval, and mark it 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 specified historical environmental risk impact factor interval adjustment value. For example, assuming that the obtained specified historical environmental risk impact factor interval adjustment value is , then the specified historical environmental risk impact factor interval is By matching the environmental risk impact factor with the specified historical environmental risk impact factor interval, the historical risk data of the highway section under similar environmental conditions to the current one can be obtained.

[0037] 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 speed and the highest traffic density. The cumulative number of traffic accidents represents the total number of traffic accidents that occurred on the highway section in the historical period, the maximum congestion path length represents the longest section length of traffic congestion that occurred on the highway section in the historical period, the average vehicle speed represents the average vehicle speed of the highway section in the historical period, and the highest traffic density represents the highest traffic density of the highway section in the historical period. The cumulative number of traffic accidents, the maximum congestion path length, the average vehicle speed and the highest traffic density are obtained from the historical records in the traffic monitoring system.

[0038] Furthermore, the traffic safety risk value of the highway section is obtained through comprehensive processing. The specific process is: extracting the comprehensive characteristic index of traffic safety of the highway section, extracting the traffic risk influencing factors of the highway section and the historical risk data of the highway section, and obtaining the traffic safety risk value of the highway section through comprehensive processing.

[0039] Furthermore, the specific process of obtaining the traffic safety risk value of the highway section is as follows: ; In the formula, It is expressed as the traffic safety risk value of the highway section. It is expressed as the comprehensive characteristic index of traffic safety of the highway section. Expressed as the traffic risk influencing factor of the highway section, It is expressed as the cumulative number of traffic accidents. It is represented by the preset cumulative traffic accident number comparison value. is represented as the maximum congested path length, It is represented by the preset maximum congestion path length comparison value, Expressed as the average vehicle speed, Expressed as the average vehicle speed threshold, is the maximum traffic density, Represents the maximum traffic density threshold, Expressed as the weight factor of the comprehensive characteristic index of traffic safety, Expressed as the weight factor of traffic risk influencing factors, Expressed as the weight factor of the cumulative number of traffic accidents, Expressed as the maximum congested path length weight factor, Expressed as the average vehicle speed weight factor, Expressed as the maximum traffic density weight factor.

[0040] In this embodiment, the cumulative traffic accident number comparison value, the maximum congestion path length comparison value, the average vehicle speed threshold and the maximum traffic density threshold are obtained from the database.

[0041] In this embodiment, the traffic safety risk of the highway section is evaluated by combining the traffic safety comprehensive characteristic index, traffic risk influencing factor and historical risk data. The traffic safety comprehensive characteristic index, traffic risk influencing factor and historical risk data are interrelated. If the average vehicle speed and the maximum traffic density increase, the cumulative traffic accidents will increase. If the maximum traffic density increases, the maximum congestion length will increase. The greater the traffic risk influencing factor of the highway section, the greater the traffic risk of the highway section, and the greater the traffic safety comprehensive characteristic index of the highway section. The greater the historical risk data, the higher the historical traffic safety risk of the highway section, and the higher the traffic risk influencing factor and traffic safety comprehensive characteristic index may be. The greater the traffic safety comprehensive characteristic index, traffic risk influencing factor, cumulative number of traffic accidents, maximum congestion path length, average vehicle speed and maximum traffic density, the greater the possibility of accidents, and the greater the traffic safety risk value.

[0042] In this embodiment, , , , , and They are the preset traffic safety comprehensive characteristic index, traffic risk influencing 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 congestion 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 congestion path length, average vehicle speed and maximum traffic density can be obtained from the pre-defined mapping table, for example, the traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congestion path length, average vehicle speed, maximum traffic density and the weight factors of the traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congestion path length, average vehicle speed and maximum traffic density can be formed into a mapping table. In a specific application, the real-time traffic safety comprehensive characteristic index, traffic risk impact factor, cumulative number of traffic accidents, maximum congestion path length, average vehicle speed and maximum traffic density are input into the mapping table to obtain the corresponding weight factor.

[0043] Furthermore, the corresponding traffic safety risk warning is executed. The specific process is: 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, and when the traffic safety risk value is greater than the traffic safety risk threshold, a safety risk warning is issued.

[0044] In this embodiment, a preset traffic safety risk value threshold is obtained, and the traffic safety risk value is compared 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 vehicles on the highway section to remind drivers to drive carefully.

[0045] like Figure 2As shown, it is a structural schematic diagram of a traffic safety risk warning system based on traffic flow characteristic data provided in an embodiment of the present application. The traffic safety risk warning system based on traffic flow characteristic data provided in an embodiment of the present application includes: a historical traffic safety characteristic evaluation module, which is used to obtain historical data of a highway section and analyze it 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 it 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 infrastructure data of the highway section and the real-time environmental data for processing 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 environmental data of the highway section, thereby comprehensively processing to obtain the traffic safety risk value of the highway section, and execute corresponding traffic safety risk warning.

[0046] In summary, the embodiments of the present application obtain the comprehensive traffic safety characteristic index of the highway section, the traffic risk influencing factor, and the traffic risk data within the historical environmental risk influencing factor interval corresponding to the traffic risk influencing factor, and comprehensively analyze to obtain the traffic safety risk value of the highway section, and issue an early warning based on the traffic safety risk value of the highway section, thereby achieving accurate and effective early warning of traffic safety risks of the highway section, and effectively solving the problem of inaccurate and untimely early warning of traffic safety risks in the prior art.

[0047] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0051] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0052] 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 equivalents, 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: The following steps are involved: Obtain historical data of expressway sections and analyze them to obtain historical traffic safety characteristic values ​​of the expressway sections; Collect the real-time data of ETC gantries on expressway sections and analyze them to obtain the real-time traffic characteristic index of expressway sections; The comprehensive traffic safety characteristic index of the expressway section is obtained based on the historical traffic safety characteristic value and the real-time traffic characteristic index; Obtain the infrastructure data of the expressway section and the real-time environmental data and process them to obtain the traffic risk influencing factors of the expressway section; Based on the real-time traffic characteristic index and combined with the real-time environmental data of the highway section, the historical risk data of the highway section is analyzed, and the traffic safety risk value of the highway section is obtained through comprehensive processing, and the corresponding traffic safety risk warning is executed.

2. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 1, characterized in that: The analysis obtains the historical traffic safety characteristic value of the expressway section, and the specific process is as follows: Acquire historical data of a highway section, wherein the historical data of the highway section includes: historical average daily peak-hour traffic volume, historical average daily traffic volume of the section, and historical average daily traffic density of the section; Based on the historical data of the expressway section, the historical traffic safety characteristic value of the expressway section is analyzed and obtained, and the historical traffic safety characteristic value is used to measure the historical traffic load and operation status of the expressway section.

3. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 1, characterized in that: The analysis obtains the real-time traffic characteristic index of the highway section, and the specific process is as follows: Extracting the real-time data of the ETC gantry of the expressway section, wherein the real-time data of the ETC gantry of the expressway section includes: the density of vehicles passing through during the current real-time sensing period, the average vehicle speed, the standard deviation of vehicle speed, and the proportion of heavy vehicles; Based on the real-time data of the ETC gantries on the expressway section, the real-time traffic characteristic index of the expressway section is analyzed and obtained, and the real-time traffic characteristic index is used to measure the traffic activity status of the expressway section.

4. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 1, characterized in that: The infrastructure data of the expressway section and the real-time environment data are obtained and processed to obtain the traffic risk influencing factor of the expressway section. The specific process is as follows: Obtain basic characteristic data of highway sections for analysis to obtain basic risk impact factors, and obtain real-time environmental data of highway sections for analysis to obtain environmental risk impact factors; The traffic risk influencing factors of the expressway section are obtained by comprehensively processing the basic risk influencing factors and environmental risk influencing factors.

5. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 4, characterized in that: The basic characteristic data of the expressway 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 environmental data includes: real-time temperature, real-time humidity, real-time wind speed and real-time road visibility.

6. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 4, characterized in that: The real-time environmental data of the highway section is combined to analyze the historical risk data of the highway section. The specific process is as follows: Based on the real-time traffic characteristic index of the expressway section, the historical environmental risk impact factor interval adjustment value corresponding to each real-time traffic characteristic index interval pre-set in the database is matched to obtain the historical environmental risk impact factor interval adjustment value corresponding to the interval where the real-time traffic characteristic index of the expressway section is located, which is recorded as the designated historical environmental risk impact factor interval adjustment value; The environmental risk impact factors obtained by analyzing the real-time environmental data are combined with the specified historical environmental risk impact factor interval adjustment value to construct the specified historical environmental risk impact factor interval; Based on the environmental risk impact factor interval, the traffic risk data of the highway sections whose historical environmental risk impact factors are within the specified historical environmental risk impact factor interval are counted and marked as the historical risk data of the highway sections.

7. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 6, characterized in that: The historical risk data of the expressway section include the cumulative number of traffic accidents, the length of the maximum congested path, the average vehicle speed and the maximum traffic density.

8. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 1, characterized in that: The comprehensive processing obtains the traffic safety risk value of the expressway section, and the specific process is as follows: The comprehensive characteristic index of traffic safety of the highway section is extracted, the traffic risk influencing factors of the highway section and the historical risk data of the highway section are extracted, and the traffic safety risk value of the highway section is obtained through comprehensive processing.

9. The traffic safety risk early warning method based on traffic flow characteristic data as claimed in claim 1, characterized in that: The specific process of executing 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 performed, when the traffic safety risk value is greater than the traffic safety risk threshold, a safety risk warning is performed.

10. A system using the traffic safety risk early warning method based on traffic flow characteristic data as claimed in any one of claims 1 to 9, characterized in that: include: The historical traffic safety characteristic evaluation module is used to obtain the historical data of the highway section and analyze it to obtain the historical traffic safety characteristic value of the highway section; The real-time traffic characteristics evaluation module is used to collect statistics on the real-time data of the ETC gantries on the expressway section and analyze them to obtain the real-time traffic characteristics index of the expressway section; A traffic safety comprehensive evaluation module is used to obtain a comprehensive traffic safety characteristic index of a highway section based on historical traffic safety characteristic values ​​and real-time traffic characteristic index; Traffic risk impact assessment module, used to obtain infrastructure data of highway sections and real-time environmental data for processing to obtain traffic risk impact factors of highway sections; The traffic safety risk warning module is used to analyze the historical risk data of the highway section based on the real-time traffic characteristic index and combined with the real-time environmental data of the highway section, thereby comprehensively processing to obtain the traffic safety risk value of the highway section and execute corresponding traffic safety risk warnings.

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