CN113129549A - Road icing prediction method and system - Google Patents
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Abstract
The invention discloses a road icing prediction method level system, which adopts a correlation analysis method to determine the relation between meteorological factors and road icing, establishes a road icing influence index equation by combining the influence of a road vehicle running speed verification factor on the road icing, calculates national road icing influence index values based on the road icing influence index equation and the values of all parameters in the determined road icing influence index equation, further determines the prediction level of the national road icing influence index corresponding to the national road icing influence index values, and predicts the national road icing influence degree by combining the forecasted meteorological factor information and road actual conditions according to the prediction level of the national road icing influence index. The invention comprehensively considers meteorological factors, road factors and real-time vehicle factor data, realizes the prediction of road icing based on multi-dimensional data, greatly improves the accuracy and confidence of the road icing prediction method, and does not need to arrange sensors on the road surface.
Description
Technical Field
The invention relates to the technical field of weather forecasting, in particular to a road icing prediction method and system.
Background
Road icing refers to snow accumulation or icing phenomenon caused by rain, snow, freezing rain or fog drops falling to the ground with the temperature lower than 0 ℃ in meteorology, and therefore the condition that the temperature of the road ground is less than or equal to 0 ℃ at a certain time in the whole country in a certain time period is defined as the road icing. Often including frozen snow residues, rough ice ruts, snow melt water or road water from other causes, a hard ice layer that forms during cold seasons. Under the condition of road icing, the friction coefficient between the vehicle tire and the road surface is greatly reduced, so that the vehicle body is easy to slip, and road traffic safety accidents are caused.
At present, one prediction method for road icing is as follows: and predicting road icing based on a C4.5 decision tree algorithm. The method specifically comprises the following steps: the method comprises the steps of taking meteorological elements such as daily accumulated snow depth, daily minimum air temperature and daily precipitation which influence road icing as input of a road icing model, taking whether the road icing is iced as a target variable of the road icing model, and carrying out classification analysis by means of a C4.5 decision tree algorithm in data mining to obtain a forecast road icing rule set convenient for a forecaster to use. When the road icing model is constructed, a road icing data sample of a certain city is selected to learn the classification rule, so that a forecast road icing rule set suitable for the certain city is obtained, and the accuracy of the road icing forecast model is verified by using meteorological data which does not participate in road icing prediction.
Because the road is frozen except being influenced by meteorological factors, still is influenced by other factors, for example, the influence of vehicle speed, consequently, only judge road frozen state through meteorological factors, the degree of accuracy can't guarantee. In addition, on a road surface with a known icy road, if the road vehicle is too many, the vehicle speed is too fast, and other factors affect the icy road, the icy road state cannot be maintained for a long time, or the icy road strength value is reduced, so that the accuracy of the road icing prediction method cannot be further ensured.
In the prior art, another prediction method for road icing is as follows: detecting the road surface temperature of the current road section in real time by adopting a road surface ice condensation sensor; when the first road surface temperature is detected to be less than or equal to the early warning temperature at the first time, the road surface ice condensation sensor carries out ice condensation monitoring; and detecting the actual road surface ice condensation temperature and the second road surface temperature at the second time, uploading the actual road surface ice condensation temperature and the second road surface temperature to the information processing system, and calculating by the information processing system to obtain the icing early warning time. Although the road icing prediction method can accurately predict the road icing temperature, the accuracy and the real-time performance are high, the technical scheme needs to arrange a large number of sensors near the road according to the known frequency, the method is not suitable for road icing prediction in the national range and is only suitable for road icing prediction in part of specified area ranges, and the factors such as the sensor arrangement density and the sensor software and hardware cost influence the large-scale deployment and application of the scheme.
Disclosure of Invention
In view of this, the invention discloses a road icing prediction method level system to realize road icing prediction based on multi-dimensional data.
A method of road icing prediction comprising:
acquiring historical meteorological information of regions throughout the country, wherein meteorological factors in the historical meteorological information comprise: precipitation, snow and temperature, wherein, the temperature includes: the daily minimum air temperature and the daily average air temperature;
determining the relation between meteorological factors in the historical meteorological information and road icing by adopting a correlation analysis method;
and integrating the influence of the meteorological factors and the road vehicle running speed verification factors on road icing, and establishing a road icing influence index equation, wherein the road vehicle running speed verification factors are as follows: the applicable motor vehicle passing speed is determined according to the national highway and urban road grade division standard and the defined different highways and road grades;
calculating a national road icing influence index value according to the road icing influence index equation and the determined value of each parameter in the road icing influence index equation;
determining the forecast level of the national road icing influence index corresponding to the national road icing influence index value from the preset corresponding relation between the road icing influence index value and the forecast level of the road icing influence index;
according to the forecast level of the national road icing influence index, forecasting the national road icing influence degree by combining forecasted meteorological factor information and road actual conditions, wherein the road actual conditions comprise: the road vehicle running speed verification factor and the shady mountain road adjustment index.
Optionally, the expression of the road icing influence index equation is as follows:
in the formula IRIThe road icing influence index is represented by case (i), X (case (i)) represents the road icing index corresponding to the road icing factor influencing condition, case (1) represents the precipitation condition of the day and the previous day, case (2) represents the snow depth condition of the day, case (3) represents the lowest air temperature condition of the day, case (4) represents the average air temperature condition of the day, case (5) represents the road vehicle running speed condition, and case (6) represents the shady mountain road condition.
Optionally, values of each parameter in the road icing influence index equation are as follows:
x (case (1)) is a road icing index corresponding to precipitation conditions on the current day and the previous day, the index assignment is determined by the phase state of precipitation, when rainfall exists on the day and/or the previous day, the index assignment is larger than that on a day without precipitation, and when snowfall exists on the day or the previous day, the index assignment is larger than that on a rainy day;
x (case (2)) is a road icing index corresponding to the situation of the depth of accumulated snow on the day, the size of the index assignment is determined by the amount of accumulated snow, the index assignment is lower than the preset index assignment when the accumulated snow is absent, and the index assignment is higher when the accumulated snow depth is higher;
x (case (3)) is a road icing index corresponding to the condition of the lowest temperature at the current day, and the lower the value of the temperature value of the lowest temperature at the current day is, the higher the index assignment is;
x (case (4)) is a road icing index corresponding to the daily average air temperature, and the index assignment is higher when the daily average air temperature value is lower;
x (case (5)) is a road icing index corresponding to the road vehicle running speed verification factor, the lower the vehicle speed is, the higher the index assignment is, and when the vehicle speed exceeds a preset threshold value, the index assignment is changed from a positive value to a negative value;
x (case (6)) is the road icing index corresponding to the shady mountain road, and the higher the shady mountain road adjustment index is, the higher the assignment is.
Optionally, the road vehicle running speed verification factor X (case (5)) is obtained as follows:
calculating the vehicle speed v of each minute in each k minutes of the road section by adopting the formula (2)nAverage vehicle speed of
Equation (2) is as follows:
in the formula, vnVehicle speed data vehicle speed, n ═ 1,2 ….. k, updated for the nth minute;
according to the minute-level road speed data v by adopting a formula (3)0And vehicle speed v per minutenThe standard deviation σ of the vehicle speed at k minutes is calculated, and equation (3) is as follows:
wherein, the minute-level road speed data v0Updating once every minute;
the road vehicle travel speed verification factor X (case (5)) is calculated using equation (4), equation (4) being as follows:
in the formula, X (case (5)) is an integer, the value of X (case (5)) is valid only when X (case (1)) has an assignment, and if the assignment of X (case (1)) is zero, X (case (5)) does not assignWhen the vehicle running speed is higher than the driving speed under the icing state of the expected road, X (case (5)) is a negative value, when the vehicle running speed is higher than the icing state of the expected road, m is a congestion coefficient, m is more than 0 and less than 1, and p is a one-time standard deviation range [ v0-σ,v0+σ]Number of vehicle speed data, VsThe lower value of the current road grade in the standard driving speed and the speed limit speed in the normal driving environment.
Optionally, the classification manner of the forecast level of the road icing impact index includes:
level 1: road icing which has no influence on traffic in 24 hours in the future;
level 2: there are roads that have a slight impact on traffic for 12 hours in the future;
level 3: road icing which affects traffic is available for 12 hours in the future;
level 4: roads with large influence on traffic are frozen in the future for 12 hours;
level 5: roads that have a large impact on traffic freeze in 6 hours in the future.
A road icing prediction system comprising:
the acquiring unit is used for acquiring historical meteorological information of regions across the country, and meteorological factors in the historical meteorological information comprise: precipitation, snow and temperature, wherein, the temperature includes: the daily minimum air temperature and the daily average air temperature;
the first determining unit is used for determining the relation between meteorological factors in the historical meteorological information and road icing by adopting a correlation analysis method;
the equation establishing unit is used for integrating the influence of the meteorological factors and the road vehicle running speed verification factors on road icing and establishing a road icing influence index equation, wherein the road vehicle running speed verification factors are as follows: the applicable motor vehicle passing speed is determined according to the national highway and urban road grade division standard and the defined different highways and road grades;
the calculation unit is used for calculating the national road icing influence index value according to the road icing influence index equation and the determined value of each parameter in the road icing influence index equation;
the second determining unit is used for determining the national road icing influence index forecast level corresponding to the national road icing influence index value from the preset corresponding relation between the road icing influence index value and the road icing influence index forecast level;
the prediction unit is used for predicting the national road icing influence degree according to the forecast level of the national road icing influence index by combining forecasted meteorological factor information and road actual conditions, wherein the road actual conditions comprise: the road vehicle running speed verification factor and the shady mountain road adjustment index.
Optionally, the expression of the road icing influence index equation is as follows:
in the formula IRIThe road icing influence index is represented by case (i), X (case (i)) represents the road icing index corresponding to the road icing factor influencing condition, case (1) represents the precipitation condition of the day and the previous day, case (2) represents the snow depth condition of the day, case (3) represents the lowest air temperature condition of the day, case (4) represents the average air temperature condition of the day, case (5) represents the road vehicle running speed condition, and case (6) represents the shady mountain road condition.
Optionally, values of each parameter in the road icing influence index equation are as follows:
x (case (1)) is a road icing index corresponding to precipitation conditions on the current day and the previous day, the index assignment is determined by the phase state of precipitation, when rainfall exists on the day and/or the previous day, the index assignment is larger than that on a day without precipitation, and when snowfall exists on the day or the previous day, the index assignment is larger than that on a rainy day;
x (case (2)) is a road icing index corresponding to the situation of the depth of accumulated snow on the day, the size of the index assignment is determined by the amount of accumulated snow, the index assignment is lower than the preset index assignment when the accumulated snow is absent, and the index assignment is higher when the accumulated snow depth is higher;
x (case (3)) is a road icing index corresponding to the condition of the lowest temperature at the current day, and the lower the value of the temperature value of the lowest temperature at the current day is, the higher the index assignment is;
x (case (4)) is a road icing index corresponding to the daily average air temperature, and the index assignment is higher when the daily average air temperature value is lower;
x (case (5)) is a road icing index corresponding to the road vehicle running speed verification factor, the lower the vehicle speed is, the higher the index assignment is, and when the vehicle speed exceeds a preset threshold value, the index assignment is changed from a positive value to a negative value;
x (case (6)) is the road icing index corresponding to the shady mountain road, and the higher the shady mountain road adjustment index is, the higher the assignment is.
Optionally, the road vehicle running speed verification factor X (case (5)) is obtained as follows:
calculating the vehicle speed v of each minute in each k minutes of the road section by adopting the formula (2)nAverage vehicle speed of
Equation (2) is as follows:
in the formula, vnVehicle speed data vehicle speed, n ═ 1,2 ….. k, updated for the nth minute;
according to the minute-level road speed data v by adopting a formula (3)0And vehicle speed v per minutenThe standard deviation σ of the vehicle speed at k minutes is calculated, and equation (3) is as follows:
wherein, the minute-level road speed data v0Updating once every minute;
the road vehicle travel speed verification factor X (case (5)) is calculated using equation (4), equation (4) being as follows:
wherein X (case (5)) is an integer, X (case (5)) is effective only when X (case (1)) is assigned, X (case (5)) is not assigned if X (case (1)) is zero, X (case (5)) is a negative value when the vehicle running speed is higher than the driving speed under the expected road icing condition, m is a congestion coefficient when the vehicle running speed is higher than the expected road icing condition, 0 < m < 1, and p is a one-time standard deviation range [ v0-σ,v0+σ]Number of vehicle speed data, VsThe lower value of the current road grade in the standard driving speed and the speed limit speed in the normal driving environment.
Optionally, the classification manner of the forecast level of the road icing impact index includes:
level 1: road icing which has no influence on traffic in 24 hours in the future;
level 2: there are roads that have a slight impact on traffic for 12 hours in the future;
level 3: road icing which affects traffic is available for 12 hours in the future;
level 4: roads with large influence on traffic are frozen in the future for 12 hours;
level 5: roads that have a large impact on traffic freeze in 6 hours in the future. The technical scheme includes that the invention discloses a road icing prediction method level system, a correlation analysis method is adopted to determine the relation between meteorological factors in acquired historical meteorological information and road icing, the influence of the meteorological factors and road vehicle running speed verification factors on road icing is combined, specifically, the influence of rainfall property, accumulated snow, daily minimum air temperature and daily average air temperature are combined, a road icing influence index equation is established, the national road icing influence index value is calculated based on the road icing influence index equation and the value of each parameter in the determined road icing influence index equation, the prediction level of the national road icing influence index corresponding to the national road icing influence index value is determined from the corresponding relation between the road icing influence index value and the prediction level of the road icing influence index, and the prediction level of the national road icing influence index is determined according to the prediction level of the national road icing influence index, forecasting the icing influence degree of the national roads by combining forecasted meteorological factor information and road conditions, wherein the road conditions comprise: a road vehicle running speed verification factor and a shady mountain road adjustment index. Therefore, the meteorological factor, the road factor and the real-time vehicle factor data are comprehensively considered, the road icing is predicted based on the multidimensional data, compared with the traditional scheme that the road icing state is judged only through the meteorological factor, the accuracy and the confidence coefficient of the road icing prediction method are greatly improved, and a sensor does not need to be arranged on the road surface.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the disclosed drawings without creative efforts.
FIG. 1 is a flow chart of a road icing prediction method disclosed in an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a road icing prediction system disclosed in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a road icing prediction method level system, which adopts a correlation analysis method to determine the relation between meteorological factors in acquired historical meteorological information and road icing, combines the influence of the meteorological factors and road vehicle running speed verification factors on the road icing, specifically combines precipitation property, accumulated snow, daily minimum air temperature and daily average air temperature to establish a road icing influence index equation, calculates national road icing influence index values based on the road icing influence index equation and the values of all parameters in the determined road icing influence index equation, determines the prediction level of the national road icing influence index corresponding to the national road icing influence index values from the corresponding relation between the road icing influence index values and the prediction level of the road icing influence index, combines the predicted meteorological factor information and road actual conditions according to the prediction level of the national road icing influence index, predicting a national road icing impact level, wherein the road condition comprises: a road vehicle running speed verification factor and a shady mountain road adjustment index. Therefore, the meteorological factor, the road factor and the real-time vehicle factor data are comprehensively considered, the road icing is predicted based on the multidimensional data, compared with the traditional scheme that the road icing state is judged only through the meteorological factor, the accuracy and the confidence coefficient of the road icing prediction method are greatly improved, and a sensor does not need to be arranged on the road surface.
Referring to fig. 1, a flowchart of a road icing prediction method disclosed in an embodiment of the present invention includes the steps of:
s101, acquiring historical meteorological information of regions all over the country;
in practical application, historical weather information of regions throughout the country within a preset time period, such as about 10 years, can be acquired.
In this embodiment, the weather factors in the historical weather information include: precipitation, snow and temperature, wherein, the temperature includes: the daily minimum air temperature and the daily average air temperature.
Specifically, the historical weather information includes: the road pavement ground temperature, daily minimum air temperature, daily average air temperature, precipitation property and snow accumulation depth of meteorological stations in all regions of the country.
When historical weather information statistics is performed, statistics is performed according to seasons defined by meteorology, for example, the winter of the year is 12 months in the current year to 2 months in the next year. The monthly average temperature means: the temperature average value of the preset time interval of the preset place is 10 years.
S102, determining the relation between meteorological factors in the historical meteorological information and road icing by adopting a correlation analysis method;
correlation analysis is a statistical analysis method for studying the correlation between two or more equally positioned random variables. For example, the correlation between the relative humidity in the air and the rainfall is a problem for the correlation analysis research.
The invention analyzes meteorological factors causing road icing, and determines the meteorological factors by utilizing a correlation analysis method commonly used in statistics, wherein the method comprises the following steps: precipitation, accumulated snow, the daily minimum air temperature and the daily average air temperature have a large relationship with road icing, that is, precipitation, accumulated snow, the daily minimum air temperature and the daily average air temperature have a large influence on road icing.
S103, integrating the influence of the meteorological factors and the road vehicle running speed verification factors on road icing, and establishing a road icing influence index equation;
the applicable motor vehicle passing speed determined according to the grade division standards of national highways and urban roads and defined different highways and road grades is used as a road vehicle running speed verification factor, and the degree of influence of road freezing meteorological disasters on vehicles in a road section is judged according to the vehicle speed verification factor.
In practical application, the relationship between meteorological factors and road icing can be verified by using road data (such as road grade) and actual traffic road condition data, so as to obtain a comprehensive road icing prediction index.
Studies have shown that road icing requires two basic conditions: in the first condition, precipitation exists; and secondly, the temperature of the underlying surface of the road is less than or equal to 0 ℃.
The road icing meteorological characteristics are analyzed, namely the time length of the road with the ground temperature less than or equal to 0 ℃ in one day is combined with data such as snowfall, and whether the road icing occurs in the day or not and the influence degree of the road icing on traffic can be judged.
Through analysis, the frequency of road icing is higher under the snowing and snowing weather conditions, and the frequency of road icing is lower under the rainfall weather conditions.
In order to objectively reflect the influence of meteorological factors such as rainfall property, snow cover, lowest air temperature, average air temperature and the like on road icing, the invention defines a road icing influence index IRIRoad icing impact index IRIThe expression of the equation is shown in formula (1), and formula (1) is as follows:
in the formula, case (i) represents the occurrence condition of the road icing factor, X (case (i)) represents the road icing index corresponding to the occurrence condition of different factors, case (1) represents the precipitation condition on the current day and the previous day, case (2) represents the snow depth condition on the current day, case (3) represents the lowest air temperature condition on the current day, case (4) represents the average air temperature condition on the current day, case (5) represents the road vehicle running speed condition, and case (6) represents the shady mountain road condition.
Step S104, calculating national road icing influence index values according to the road icing influence index equation and the determined values of all parameters in the road icing influence index equation;
specifically, according to experience, each item in the formula (1) needs to be assigned respectively, and all assignments are integer values according to the design idea of the road icing influence index.
The values of all parameters in the determined road icing influence index equation are as follows:
x (case (1)) is a road icing index corresponding to precipitation conditions on the current day and the previous day, the index assignment is determined by the phase state of precipitation, when rainfall exists on the day and/or the previous day, the index assignment is larger than that on a day without precipitation, and when snowfall exists on the day or the previous day, the index assignment is larger than that on a rainy day;
x (case (2)) is a road icing index corresponding to the situation of the depth of accumulated snow on the day, the size of the index assignment is determined by the amount of accumulated snow, the index assignment is lower than the preset index assignment when the accumulated snow is absent, and the index assignment is higher when the accumulated snow depth is higher;
x (case (3)) is a road icing index corresponding to the condition of the lowest temperature at the current day, and the lower the value of the temperature value of the lowest temperature at the current day is, the higher the index assignment is;
x (case (4)) is a road icing index corresponding to the daily average air temperature, and the index assignment is higher when the daily average air temperature value is lower;
x (case (5)) is a road icing index corresponding to the road vehicle running speed verification factor, the lower the vehicle speed is, the higher the index assignment is, and when the vehicle speed exceeds a preset threshold value, the index assignment is changed from a positive value to a negative value;
x (case (6)) is the road icing index corresponding to the shady mountain road, and the higher the shady mountain road adjustment index is, the higher the assignment is.
Calculating the vehicle speed v of each minute in each k minutes of the road section by adopting the formula (2)nAverage vehicle speed of
Equation (2) is as follows:
in the formula, vnThe vehicle speed is the vehicle speed data updated at the nth minute, and n is 1,2 ….
According to the minute-level road speed data v by adopting a formula (3)0And vehicle speed v per minutenThe standard deviation σ of the vehicle speed at k minutes is calculated, and equation (3) is as follows:
wherein, the minute-level road speed data v0Updated once per minute.
Obtaining one time standard deviation range v0-σ,v0+σ]Calculating the average speed of all the vehicles within one time standard deviation range, and setting one time standardThe number of the vehicle speed data in the range of the standard deviation is p, and the average vehicle speed of all the vehicle speeds in the range of one time of the standard deviation can be obtained.
According to the road grade, defining the standard driving speed of the current road grade in the normal driving environment, and selecting the lower value of the defined standard driving speed and speed-limiting speed for the speed-limiting road section (such as the road section with severe environment, the curve, the ramp and the like) as the standard driving speed V of the normal driving environment of the current roads。
According to the congestion index data of the road at different moments, a congestion coefficient m (0 < m < 1) is defined, and the assignment value of n is lower when the congestion coefficient of the preset road section at the preset time is higher. On the contrary, when the congestion coefficient of the preset road section at the preset time is lower, that is, the real-time road condition of the road is less congested, the assignment of n is higher.
The road vehicle travel speed verification factor X (case (5)) is calculated using equation (4), equation (4) being as follows:
in the formula, X (case (5)) is an integer, the value of X (case (5)) is valid only when X (case (1)) has an assignment, and X (case (5)) is not assigned if the assignment of X (case (1)) is zero. X (case (5)) is a negative value when the vehicle running speed is higher than the driving speed in the expected road icing condition, and when the vehicle running speed is higher than the expected road icing condition.
In practical application, the road icing influence index value I of all samples with the over-ground temperature less than or equal to 0 ℃ occurring nationwide in a preset time period can be calculated according to the road icing influence index equationRI。
Step S105, determining the national road icing influence index forecast level corresponding to the national road icing influence index value from the preset corresponding relationship between the road icing influence index value and the road icing influence index forecast level;
specifically, the national road icing influence index value is forecasted and graded according to the preset grading requirement in advance to obtain the road icing influence index grade;
the invention influences the road icing according to the traffic influence generated by the road icing influence index and the regulation of the China weather bureau on the road icing warning and early warning signalRIAnd (4) grading.
The higher the road icing influence index value is, the higher the possibility of road icing is, the higher the intensity of road icing is, and the greater the intensity of influence on traffic is. According to the road icing influence value, a grading standard suitable for road icing forecasting can be determined.
The national road icing influence index forecasting classification can adopt the following classification modes:
1) level 1: road icing which has no influence on traffic in 24 hours in the future;
2) level 2: there are roads that have a slight impact on traffic for 12 hours in the future;
3) level 3: road icing which affects traffic is available for 12 hours in the future;
4) level 4: roads with large influence on traffic are frozen in the future for 12 hours;
5) level 5: roads that have a large impact on traffic freeze in 6 hours in the future.
It should be noted that, in this embodiment, the value of the road icing influence index value interval corresponding to each level may be determined according to actual needs, and the present invention is not limited herein.
And S106, predicting the national road icing influence degree according to the forecast grade of the national road icing influence index by combining the forecasted meteorological factor information and the road actual condition.
Wherein, the forecasted weather factor information comprises: forecast precipitation property, whether snow is accumulated, the lowest daily temperature and the average daily temperature.
The road condition includes: a road vehicle running speed verification factor and a shady mountain road adjustment index.
In practical application, the comprehensive road icing prediction index can be obtained according to the forecast level of the road icing influence index and by combining the forecast meteorological factor information and the road actual condition, so that the influence degree of national road icing is predicted.
To sum up, the road icing prediction method disclosed by the invention adopts a correlation analysis method to determine the relation between meteorological factors in acquired historical meteorological information and road icing, combines the influence of the meteorological factors and a road vehicle running speed verification factor on the road icing, specifically combines precipitation property, accumulated snow, daily minimum air temperature and daily average air temperature to establish a road icing influence index equation, calculates national road icing influence index values based on the road icing influence index equation and the values of all parameters in the determined road icing influence index equation, determines the forecast level of the national road icing influence index corresponding to the national road icing influence index values from the corresponding relation between the road icing influence index values and the forecast levels of the road icing influence index, combines the forecasted meteorological factor information and the road actual conditions according to the forecast level of the national road icing influence index, predicting a national road icing impact level, wherein the road condition comprises: a road vehicle running speed verification factor and a shady mountain road adjustment index. Therefore, the meteorological factor, the road factor and the real-time vehicle factor data are comprehensively considered, the road icing is predicted based on the multidimensional data, compared with the traditional scheme that the road icing state is judged only through the meteorological factor, the accuracy and the confidence coefficient of the road icing prediction method are greatly improved, and a sensor does not need to be arranged on the road surface.
In addition, the road icing prediction method disclosed by the invention has the advantages that the logic and the implementation mode are simple and easy to calculate, compared with a machine learning mode or a deep neural network data learning mode, the logic implementation mode and the interpretability are clearer, and the multidimensional influence factors enable the prediction algorithm to have higher accuracy.
The method is suitable for road icing algorithm prediction based on high-precision weather information, map information and road information, data fusion and information verification are carried out based on high-precision weather forecast data, map data, road information and vehicle running information, and high-precision national road icing state forecast which is not lower than the weather data granularity can be realized.
It should be noted that, in practical application, prediction and early warning of vehicle danger levels for different vehicle speeds under different road grades can be provided according to road icing index prediction, and effective help can be brought to road safety early warning and vehicle driving safety under severe weather conditions.
Corresponding to the embodiment of the method, the invention also discloses a road icing prediction system.
Referring to fig. 2, a schematic structural diagram of a road icing prediction system disclosed in an embodiment of the present invention includes:
an acquiring unit 201, configured to acquire historical weather information of regions across the country;
weather factors in the historical weather information include: precipitation, snow and temperature, wherein, the temperature includes: the daily minimum air temperature and the daily average air temperature.
Specifically, the historical weather information includes: the road pavement ground temperature, daily minimum air temperature, daily average air temperature, precipitation property and snow accumulation depth of meteorological stations in all regions of the country.
A first determining unit 202, configured to determine a relationship between a meteorological factor in the historical meteorological information and road icing by using a correlation analysis method;
the invention analyzes meteorological factors causing road icing, and determines the meteorological factors by utilizing a correlation analysis method commonly used in statistics, wherein the method comprises the following steps: precipitation, accumulated snow, the daily minimum air temperature and the daily average air temperature have a large relationship with road icing, that is, precipitation, accumulated snow, the daily minimum air temperature and the daily average air temperature have a large influence on road icing.
The equation establishing unit 203 is configured to synthesize the influence of the meteorological factor and the road vehicle running speed verification factor on road icing, and establish a road icing influence index equation, where the road vehicle running speed verification factor is: the applicable motor vehicle passing speed is determined according to the national highway and urban road grade division standard and the defined different highways and road grades;
the applicable motor vehicle passing speed determined according to the grade division standards of national highways and urban roads and defined different highways and road grades is used as a road vehicle running speed verification factor, and the degree of influence of road freezing meteorological disasters on vehicles in a road section is judged according to the vehicle speed verification factor.
In practical application, the relationship between meteorological factors and road icing can be verified by using road data (such as road grade) and actual traffic road condition data, so as to obtain a comprehensive road icing prediction index.
Studies have shown that road icing requires two basic conditions: in the first condition, precipitation exists; and secondly, the temperature of the underlying surface of the road is less than or equal to 0 ℃.
The road icing meteorological characteristics are analyzed, namely the time length of the road with the ground temperature less than or equal to 0 ℃ in one day is combined with data such as snowfall, and whether the road icing occurs in the day or not and the influence degree of the road icing on traffic can be judged.
Through analysis, the frequency of road icing is higher under the snowing and snowing weather conditions, and the frequency of road icing is lower under the rainfall weather conditions.
In order to objectively reflect the influence of meteorological factors such as rainfall property, snow cover, lowest air temperature, average air temperature and the like on road icing, the invention defines a road icing influence index IRIRoad icing impact index IRIThe expression of the equation is shown in formula (1), and formula (1) is as follows:
in the formula, case (i) represents the occurrence condition of the road icing factor, X (case (i)) represents the road icing index corresponding to the occurrence condition of different factors, case (1) represents the precipitation condition on the current day and the previous day, case (2) represents the snow depth condition on the current day, case (3) represents the lowest air temperature condition on the current day, case (4) represents the average air temperature condition on the current day, case (5) represents the road vehicle running speed condition, and case (6) represents the shady mountain road condition.
The calculation unit 204 is configured to calculate a national road icing influence index value according to the road icing influence index equation and the determined values of the parameters in the road icing influence index equation;
specifically, according to experience, each item in the formula (1) needs to be assigned respectively, and all assignments are integer values according to the design idea of the road icing influence index.
The values of all parameters in the determined road icing influence index equation are as follows:
x (case (1)) is a road icing index corresponding to precipitation conditions on the current day and the previous day, the index assignment is determined by the phase state of precipitation, when rainfall exists on the day and/or the previous day, the index assignment is larger than that on a day without precipitation, and when snowfall exists on the day or the previous day, the index assignment is larger than that on a rainy day;
x (case (2)) is a road icing index corresponding to the situation of the depth of accumulated snow on the day, the size of the index assignment is determined by the amount of accumulated snow, the index assignment is lower than the preset index assignment when the accumulated snow is absent, and the index assignment is higher when the accumulated snow depth is higher;
x (case (3)) is a road icing index corresponding to the condition of the lowest temperature at the current day, and the lower the value of the temperature value of the lowest temperature at the current day is, the higher the index assignment is;
x (case (4)) is a road icing index corresponding to the daily average air temperature, and the index assignment is higher when the daily average air temperature value is lower;
x (case (5)) is a road icing index corresponding to the road vehicle running speed verification factor, the lower the vehicle speed is, the higher the index assignment is, and when the vehicle speed exceeds a preset threshold value, the index assignment is changed from a positive value to a negative value;
x (case (6)) is the road icing index corresponding to the shady mountain road, and the higher the shady mountain road adjustment index is, the higher the assignment is.
Calculating the vehicle speed v of each minute in each k minutes of the road section by adopting the formula (2)nAverage vehicle speed of
Equation (2) is as follows:
in the formula, vnThe vehicle speed is the vehicle speed data updated at the nth minute, and n is 1,2 ….
According to the minute-level road speed data v by adopting a formula (3)0And vehicle speed v per minutenThe standard deviation σ of the vehicle speed at k minutes is calculated, and equation (3) is as follows:
wherein, the minute-level road speed data v0Updated once per minute.
Obtaining one time standard deviation range v0-σ,v0+σ]And calculating the average speed of all the speeds in the range of one time of standard deviation, and setting the number of the speed data in the range of one time of standard deviation as p to obtain the average speed of all the speeds in the range of one time of standard deviation.
According to the road grade, defining the standard driving speed of the current road grade in the normal driving environment, and selecting the lower value of the defined standard driving speed and speed-limiting speed for the speed-limiting road section (such as the road section with severe environment, the curve, the ramp and the like) as the standard driving speed V of the normal driving environment of the current roads。
According to the congestion index data of the road at different moments, a congestion coefficient m (0 < m < 1) is defined, and the assignment value of n is lower when the congestion coefficient of the preset road section at the preset time is higher. On the contrary, when the congestion coefficient of the preset road section at the preset time is lower, that is, the real-time road condition of the road is less congested, the assignment of n is higher.
The road vehicle travel speed verification factor X (case (5)) is calculated using equation (4), equation (4) being as follows:
in the formula, X (case (5)) is an integer, the value of X (case (5)) is valid only when X (case (1)) has an assignment, and X (case (5)) is not assigned if the assignment of X (case (1)) is zero. X (case (5)) is a negative value when the vehicle running speed is higher than the driving speed in the expected road icing condition, and when the vehicle running speed is higher than the expected road icing condition.
In practical application, the road icing influence index value I of all samples with the over-ground temperature less than or equal to 0 ℃ occurring nationwide in a preset time period can be calculated according to the road icing influence index equationRI。
A second determining unit 205, configured to determine, from a preset correspondence relationship between the road icing influence index value and a forecast level of the road icing influence index, a forecast level of a national road icing influence index corresponding to the national road icing influence index value;
specifically, the national road icing influence index value is forecasted and graded according to the preset grading requirement in advance to obtain the road icing influence index grade;
the invention influences the road icing according to the traffic influence generated by the road icing influence index and the regulation of the China weather bureau on the road icing warning and early warning signalRIAnd (4) grading.
The higher the road icing influence index value is, the higher the possibility of road icing is, the higher the intensity of road icing is, and the greater the intensity of influence on traffic is. According to the road icing influence value, a grading standard suitable for road icing forecasting can be determined.
The national road icing influence index forecasting classification can adopt the following classification modes:
1) level 1: road icing which has no influence on traffic in 24 hours in the future;
2) level 2: there are roads that have a slight impact on traffic for 12 hours in the future;
3) level 3: road icing which affects traffic is available for 12 hours in the future;
4) level 4: roads with large influence on traffic are frozen in the future for 12 hours;
5) level 5: roads that have a large impact on traffic freeze in 6 hours in the future.
It should be noted that, in this embodiment, the value of the road icing influence index value interval corresponding to each level may be determined according to actual needs, and the present invention is not limited herein.
And the prediction unit 206 is used for predicting the national road icing influence degree according to the forecast level of the national road icing influence index and by combining the forecasted meteorological factor information and the road live condition.
Wherein, the forecasted weather factor information comprises: forecast precipitation property, whether snow is accumulated, the lowest daily temperature and the average daily temperature.
The road condition includes: a road vehicle running speed verification factor and a shady mountain road adjustment index.
In practical application, the comprehensive road icing prediction index can be obtained according to the forecast level of the road icing influence index and by combining the forecast meteorological factor information and the road actual condition, so that the influence degree of national road icing is predicted.
To sum up, the road icing prediction system disclosed by the invention adopts a correlation analysis method to determine the relation between meteorological factors in acquired historical meteorological information and road icing, combines the influence of the meteorological factors and road vehicle running speed verification factors on the road icing, specifically combines precipitation property, accumulated snow, daily minimum air temperature and daily average air temperature to establish a road icing influence index equation, calculates national road icing influence index values based on the road icing influence index equation and the values of all parameters in the determined road icing influence index equation, determines the forecast level of the national road icing influence index corresponding to the national road icing influence index values from the corresponding relation between the road icing influence index values and the forecast levels of the road icing influence index, combines the forecasted meteorological factor information and the road actual conditions according to the forecast level of the national road icing influence index, predicting a national road icing impact level, wherein the road condition comprises: a road vehicle running speed verification factor and a shady mountain road adjustment index. Therefore, the meteorological factor, the road factor and the real-time vehicle factor data are comprehensively considered, the road icing is predicted based on the multidimensional data, compared with the traditional scheme that the road icing state is judged only through the meteorological factor, the accuracy and the confidence coefficient of the road icing prediction method are greatly improved, and a sensor does not need to be arranged on the road surface.
In addition, the road icing prediction method disclosed by the invention has the advantages that the logic and the implementation mode are simple and easy to calculate, compared with a machine learning mode or a deep neural network data learning mode, the logic implementation mode and the interpretability are clearer, and the multidimensional influence factors enable the prediction algorithm to have higher accuracy.
The method is suitable for road icing algorithm prediction based on high-precision weather information, map information and road information, data fusion and information verification are carried out based on high-precision weather forecast data, map data, road information and vehicle running information, and high-precision national road icing state forecast which is not lower than the weather data granularity can be realized.
It should be noted that specific working principles of each component in the system embodiment may refer to corresponding parts of the method embodiment, which are not described herein again.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of predicting road icing, comprising:
acquiring historical meteorological information of regions throughout the country, wherein meteorological factors in the historical meteorological information comprise: precipitation, snow and temperature, wherein, the temperature includes: the daily minimum air temperature and the daily average air temperature;
determining the relation between meteorological factors in the historical meteorological information and road icing by adopting a correlation analysis method;
and integrating the influence of the meteorological factors and the road vehicle running speed verification factors on road icing, and establishing a road icing influence index equation, wherein the road vehicle running speed verification factors are as follows: the applicable motor vehicle passing speed is determined according to the national highway and urban road grade division standard and the defined different highways and road grades;
calculating a national road icing influence index value according to the road icing influence index equation and the determined value of each parameter in the road icing influence index equation;
determining the forecast level of the national road icing influence index corresponding to the national road icing influence index value from the preset corresponding relation between the road icing influence index value and the forecast level of the road icing influence index;
according to the forecast level of the national road icing influence index, forecasting the national road icing influence degree by combining forecasted meteorological factor information and road actual conditions, wherein the road actual conditions comprise: the road vehicle running speed verification factor and the shady mountain road adjustment index.
2. The road icing prediction method of claim 1, wherein the expression of the road icing impact index equation is as follows:
in the formula IRIThe road icing influence index is represented by case (i), X (case (i)) represents the road icing index corresponding to the road icing factor influencing condition, case (1) represents the precipitation condition of the day and the previous day, case (2) represents the snow depth condition of the day, case (3) represents the lowest air temperature condition of the day, case (4) represents the average air temperature condition of the day, case (5) represents the road vehicle running speed condition, and case (6) represents the shady mountain road condition.
3. The road icing prediction method according to claim 2, wherein the values of the parameters in the road icing impact index equation are as follows:
x (case (1)) is a road icing index corresponding to precipitation conditions on the current day and the previous day, the index assignment is determined by the phase state of precipitation, when rainfall exists on the day and/or the previous day, the index assignment is larger than that on a day without precipitation, and when snowfall exists on the day or the previous day, the index assignment is larger than that on a rainy day;
x (case (2)) is a road icing index corresponding to the situation of the depth of accumulated snow on the day, the size of the index assignment is determined by the amount of accumulated snow, the index assignment is lower than the preset index assignment when the accumulated snow is absent, and the index assignment is higher when the accumulated snow depth is higher;
x (case (3)) is a road icing index corresponding to the condition of the lowest temperature at the current day, and the lower the value of the temperature value of the lowest temperature at the current day is, the higher the index assignment is;
x (case (4)) is a road icing index corresponding to the daily average air temperature, and the index assignment is higher when the daily average air temperature value is lower;
x (case (5)) is a road icing index corresponding to the road vehicle running speed verification factor, the lower the vehicle speed is, the higher the index assignment is, and when the vehicle speed exceeds a preset threshold value, the index assignment is changed from a positive value to a negative value;
x (case (6)) is the road icing index corresponding to the shady mountain road, and the higher the shady mountain road adjustment index is, the higher the assignment is.
4. The road icing prediction method according to claim 3, characterized in that the road vehicle travel speed verification factor X (case (5)) is obtained as follows:
calculating the vehicle speed v of each minute in each k minutes of the road section by adopting the formula (2)nAverage vehicle speed of
Equation (2) is as follows:
in the formula, vnVehicle speed data vehicle speed, n ═ 1,2 ….. k, updated for the nth minute;
according to the minute-level road speed data v by adopting a formula (3)0And vehicle speed v per minutenThe standard deviation σ of the vehicle speed at k minutes is calculated, and equation (3) is as follows:
wherein, the minute-level road speed data v0Updating once every minute;
the road vehicle travel speed verification factor X (case (5)) is calculated using equation (4), equation (4) being as follows:
wherein X (case (5)) is an integer, X (case (5)) is effective only when X (case (1)) is assigned, X (case (5)) is not assigned if X (case (1)) is zero, X (case (5)) is a negative value when the vehicle running speed is higher than the driving speed under the expected road icing condition, m is a congestion coefficient when the vehicle running speed is higher than the expected road icing condition, 0 < m < 1, and p is a one-time standard deviation range [ v0-σ,v0+σ]Number of vehicle speed data, VsThe lower value of the current road grade in the standard driving speed and the speed limit speed in the normal driving environment.
5. The method of claim 1, wherein the grading of the forecast level of the road icing impact index comprises:
level 1: road icing which has no influence on traffic in 24 hours in the future;
level 2: there are roads that have a slight impact on traffic for 12 hours in the future;
level 3: road icing which affects traffic is available for 12 hours in the future;
level 4: roads with large influence on traffic are frozen in the future for 12 hours;
level 5: roads that have a large impact on traffic freeze in 6 hours in the future.
6. A road icing prediction system, comprising:
the acquiring unit is used for acquiring historical meteorological information of regions across the country, and meteorological factors in the historical meteorological information comprise: precipitation, snow and temperature, wherein, the temperature includes: the daily minimum air temperature and the daily average air temperature;
the first determining unit is used for determining the relation between meteorological factors in the historical meteorological information and road icing by adopting a correlation analysis method;
the equation establishing unit is used for integrating the influence of the meteorological factors and the road vehicle running speed verification factors on road icing and establishing a road icing influence index equation, wherein the road vehicle running speed verification factors are as follows: the applicable motor vehicle passing speed is determined according to the national highway and urban road grade division standard and the defined different highways and road grades;
the calculation unit is used for calculating the national road icing influence index value according to the road icing influence index equation and the determined value of each parameter in the road icing influence index equation;
the second determining unit is used for determining the national road icing influence index forecast level corresponding to the national road icing influence index value from the preset corresponding relation between the road icing influence index value and the road icing influence index forecast level;
the prediction unit is used for predicting the national road icing influence degree according to the forecast level of the national road icing influence index by combining forecasted meteorological factor information and road actual conditions, wherein the road actual conditions comprise: a road vehicle running speed verification factor and a shady mountain road adjustment index.
7. The road icing prediction system of claim 6, wherein the expression of the road icing impact index equation is as follows:
in the formula IRIThe road icing influence index is represented by case (i), X (case (i)) represents the road icing index corresponding to the road icing factor influencing condition, case (1) represents the precipitation condition of the day and the previous day, case (2) represents the snow depth condition of the day, case (3) represents the lowest air temperature condition of the day, case (4) represents the average air temperature condition of the day, case (5) represents the road vehicle running speed condition, and case (6) represents the shady mountain road condition.
8. The road icing prediction system of claim 7, wherein the values of the parameters in the road icing impact index equation are as follows:
x (case (1)) is a road icing index corresponding to precipitation conditions on the current day and the previous day, the index assignment is determined by the phase state of precipitation, when rainfall exists on the day and/or the previous day, the index assignment is larger than that on a day without precipitation, and when snowfall exists on the day or the previous day, the index assignment is larger than that on a rainy day;
x (case (2)) is a road icing index corresponding to the situation of the depth of accumulated snow on the day, the index assignment is determined by the accumulated snow amount, the assignment is lower than a preset assignment when the accumulated snow is absent, and the assignment is higher when the accumulated snow depth is higher;
x (case (3)) is a road icing index corresponding to the condition of the lowest air temperature on the day, and the lower the value of the temperature value of the lowest air temperature on the day is, the higher the value is assigned;
x (case (4)) is a road icing index corresponding to the daily average air temperature, and the lower the daily average air temperature value is, the higher the value is assigned;
x (case (5)) is a road icing index corresponding to the road vehicle running speed verification factor, the lower the vehicle speed is, the higher the value is assigned, and when the vehicle speed exceeds a preset threshold value, the value is changed from a positive value to a negative value;
x (case (6)) is the road icing index corresponding to the shady mountain road, and the higher the shady mountain road adjustment index is, the higher the assignment is.
9. The road icing prediction system according to claim 8, wherein the road vehicle travel speed verification factor X (case (5)) is obtained as follows:
calculating the vehicle speed v of each minute in each k minutes of the road section by adopting the formula (2)nAverage vehicle speed of
Equation (2) is as follows:
in the formula, vnVehicle speed data vehicle speed, n ═ 1,2 ….. k, updated for the nth minute;
according to the minute-level road speed data v by adopting a formula (3)0And vehicle speed v per minutenThe standard deviation σ of the vehicle speed at k minutes is calculated, and equation (3) is as follows:
wherein, the minute-level road speed data v0Updating once every minute;
the road vehicle travel speed verification factor X (case (5)) is calculated using equation (4), equation (4) being as follows:
wherein X (case (5)) is an integer, X (case (5)) is effective only when X (case (1)) is assigned, X (case (5)) is not assigned if X (case (1)) is zero, X (case (5)) is a negative value when the vehicle running speed is higher than the driving speed under the expected road icing condition, m is a congestion coefficient when the vehicle running speed is higher than the expected road icing condition, 0 < m < 1, and p is a one-time standard deviation range [ v0-σ,v0+σ]Number of vehicle speed data, VsThe lower value of the current road grade in the standard driving speed and the speed limit speed in the normal driving environment.
10. The system of claim 6, wherein the grading of the forecast level of the road icing impact index comprises:
level 1: road icing which has no influence on traffic in 24 hours in the future;
level 2: there are roads that have a slight impact on traffic for 12 hours in the future;
level 3: road icing which affects traffic is available for 12 hours in the future;
level 4: roads with large influence on traffic are frozen in the future for 12 hours;
level 5: roads that have a large impact on traffic freeze in 6 hours in the future.
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