KR102682637B1 - System and method for analyzing road freezing using road environment and weather condition - Google Patents

Abstract

A road ice analysis system and method using road environment and weather conditions are provided. According to embodiments of the present invention, roads vulnerable to thin ice according to icing conditions can be more easily calculated using meteorological observation information for adjacent observation points for each road classified into medium-scale regional type and local-scale regional type. In particular, according to embodiments of the present invention, the main cause of icing for each road in the target area and roads vulnerable to icing according to each icing cause can be more accurately derived by using the road environment and weather conditions.

Description

Road freezing analysis system and method using road environment and weather conditions {SYSTEM AND METHOD FOR ANALYZING ROAD FREEZING USING ROAD ENVIRONMENT AND WEATHER CONDITION}

Embodiments of the present invention relate to technology for preventing road ice accidents in winter by deriving roads vulnerable to icing according to road environment and weather conditions.

Thin road ice is a phenomenon in which a thin film of ice forms on the road surface due to low temperatures in wet road conditions. It refers to a freezing phenomenon that is difficult to discern with the naked eye when combined with road dust and smoke. In Korea, large-scale traffic accidents continue to occur due to thin ice on roads in winter due to snow, rain, frost, etc., and drivers cannot distinguish this icing phenomenon with the naked eye, making it difficult to recognize and respond to it in driving situations.

To prevent such road ice accidents, it is necessary to predict which roads are vulnerable to icing in winter. However, due to the current lack of actual road ice observation data, there are limitations in the analysis of road environment and weather conditions for road ice cases.

Korean Patent Publication No. 10-2438741 (2022.08.26)

Embodiments of the present invention derive cases of road icing using the Korea Meteorological Administration's AWS (Automatic Weather System) data and ASOS (Automated Synoptic Observing System) data for observation points adjacent to the road, and use this to determine the main icing for each road. This is to analyze roads vulnerable to freezing according to the cause and each cause of freezing.

According to an exemplary embodiment, a geographic information collection unit that collects geographic information of a target area; a target area classification unit that classifies the target area into one of a plurality of set mid-scale area types from the collected geographical information, and classifies each section of roads within the target area into one of a plurality of set local-scale area types; a weather observation information collection unit that collects weather observation information for a plurality of observation points set within the target area; The plurality of observation points are classified into the mid-scale area type and the local-scale area type based on the geographical information, and one or more adjacent observation points within a set radius from roads in the target area are selected among the plurality of observation points. an observation point classification unit; A plurality of different road ice determination algorithms set based on weather observation information for the adjacent observation points are applied to each of the adjacent observation points in set time units, and the predicted results of the road ice determination algorithm for the adjacent observation points are calculated. Road environment, including a road icing analysis unit that derives the main cause of icing for each road in the target area and roads vulnerable to icing according to each icing cause by applying it to roads having a regional type corresponding to the regional type of the adjacent observation point. and a road ice analysis system using weather conditions is provided.

The plurality of mid-scale area types include at least two of coastal areas, inland areas, and mountainous areas, and the plurality of local-scale area types include water areas, urbanized areas, road areas, wetland areas, and grasslands. It may include at least two of regions, mountainous regions, agricultural regions, and bare areas.

The weather observation information is weather observation information for the plurality of observation points observed by at least one of AWS (Automatic Weather System) and ASOS (Automated Synoptic Observing System), and includes air temperature and dew point for the plurality of observation points. It includes temperature, relative humidity, precipitation, and wind speed, and the road ice analysis unit can estimate the road surface temperature for the plurality of observation points based on the air temperature included in the weather observation information.

The road icing determination algorithm includes a first road icing determination algorithm that determines road icing due to condensation based on the relative humidity, the dew point temperature, the road surface temperature, and the air temperature, the precipitation amount, the air temperature, and the road surface temperature. It includes a second road icing determination algorithm that determines road icing due to precipitation based on and a third road icing determination algorithm that determines road icing due to wind speed based on the road surface temperature, the dew point temperature, and the wind speed, The road icing analysis unit applies the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm to the adjacent observation point in a set time unit, and the first road icing determination algorithm for the adjacent observation point. 1 For each road within the target area, the prediction results of the road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm are applied to roads having a regional type corresponding to the regional type of the adjacent observation point. It may include a road ice analysis unit that derives the main cause of ice and roads vulnerable to ice according to each cause of ice.

The road icing analysis unit determines that the number or time of road icing being determined to be the highest among the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm during the time unit set for the adjacent observation point. Many road icing determination algorithms can be identified, and the icing cause corresponding to the identified road icing determination algorithm can be determined as the main cause of icing on the road having an area type corresponding to the area type of the adjacent observation point.

According to another exemplary embodiment, collecting geographic information of a target area in a geographic information collection unit; In a target area classification unit, classifying the target area into one of a plurality of set mid-scale area types from the collected geographical information; Classifying, in the target area classification unit, each section of roads within the target area into one of a plurality of set local-scale area types; Collecting, in a weather observation information collection unit, weather observation information for a plurality of observation points set within the target area; In an observation point classification unit, classifying the plurality of observation points into the mid-scale region type and the local-scale region type based on the geographic information; In the observation point classification unit, selecting one or more adjacent observation points that fall within a set radius from roads in the target area among the plurality of observation points; In the road ice analysis unit, applying a plurality of different road ice determination algorithms set based on meteorological observation information for the adjacent observation points to each of the adjacent observation points in a set time unit; And in the road icing analysis unit, the prediction result of the road icing determination algorithm for the adjacent observation point is applied to roads having a region type corresponding to the region type of the adjacent observation point, so that the most important road for each road in the target region A road ice analysis method using road environment and weather conditions is provided, including the step of deriving ice vulnerable roads according to ice causes and each ice cause.

The plurality of mid-scale area types include at least two of coastal areas, inland areas, and mountainous areas, and the plurality of local-scale area types include water areas, urbanized areas, road areas, wetland areas, and grasslands. It may include at least two of regions, mountainous areas, agricultural areas, and bare areas.

The weather observation information is weather observation information for the plurality of observation points observed by at least one of AWS (Automatic Weather System) and ASOS (Automated Synoptic Observing System), and includes air temperature and dew point for the plurality of observation points. The road ice analysis method includes temperature, relative humidity, precipitation, and wind speed, and uses the road environment and weather conditions, prior to the step of applying the plurality of different road ice determination algorithms to each of the adjacent observation points in a set time unit. In the road ice analysis unit, the step of estimating the road surface temperature for the plurality of observation points based on the air temperature included in the weather observation information may be further included.

The road icing determination algorithm includes a first road icing determination algorithm that determines road icing due to condensation based on the relative humidity, the dew point temperature, the road surface temperature, and the air temperature, the precipitation amount, the air temperature, and the road surface temperature. It includes a second road icing determination algorithm that determines road icing due to precipitation based on and a third road icing determination algorithm that determines road icing due to wind speed based on the road surface temperature, the dew point temperature, and the wind speed, The step of applying a plurality of different road icing determination algorithms to each of the adjacent observation points in a set time unit includes applying the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm for a set time unit. It can be applied to each of the adjacent observation points as a unit.

The step of deriving the most important cause of icing for each road in the target area and roads susceptible to icing according to each icing cause includes the first road icing determination algorithm and the second road icing determination during a time unit set for the adjacent observation point. Among the algorithms and the third road icing determination algorithm, identify the road icing determination algorithm with the greatest number or time of road icing determination, and correspond the icing cause corresponding to the identified road icing determination algorithm with the regional type of the adjacent observation point. It can be determined as the main cause of icing on roads with this type of area.

According to embodiments of the present invention, roads vulnerable to thin ice according to icing conditions can be more easily calculated using meteorological observation information for adjacent observation points for each road classified into medium-scale regional type and local-scale regional type. In particular, according to embodiments of the present invention, the main cause of icing for each road in the target area and roads vulnerable to icing according to each icing cause can be more accurately derived by using the road environment and weather conditions.

1 is a block diagram showing the detailed configuration of a road ice analysis system according to an embodiment of the present invention.
Figure 2 is an example showing geographic information according to an embodiment of the present invention
Figure 3 is an example showing the results of classifying the regional type of the target area according to an embodiment of the present invention
Figure 4 is an example showing the results of classifying regional types of observation points within the target area according to an embodiment of the present invention.
Figure 5 is an example showing the results of analyzing weather observation information according to the regional type of the observation point in the target area according to an embodiment of the present invention.
Figure 6 is an example showing the results of deriving major weather factors according to regional type for each road based on weather observation information from adjacent observation points according to an embodiment of the present invention.
Figure 7 is an example showing a first road ice determination algorithm according to an embodiment of the present invention
Figure 8 is an example showing a second road ice determination algorithm according to an embodiment of the present invention.
Figure 9 is an example showing a third road ice determination algorithm according to an embodiment of the present invention
Figure 10 is an example showing the process of estimating the road surface temperature using weather observation information according to an embodiment of the present invention.
Figure 11 is an example showing the results of deriving the main cause of icing for each road and roads vulnerable to icing according to each icing cause according to an embodiment of the present invention.
12 is a flowchart illustrating a road ice analysis method according to an embodiment of the present invention.
13 is a block diagram illustrating and illustrating a computing environment including a computing device suitable for use in example embodiments.

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The detailed description below is provided to facilitate a comprehensive understanding of the methods, devices and/or systems described herein. However, this is only an example and the present invention is not limited thereto.

In describing the embodiments of the present invention, if it is determined that a detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the terms described below are terms defined in consideration of functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, the definition should be made based on the contents throughout this specification. The terminology used in the detailed description is only for describing embodiments of the present invention and should in no way be limiting. Unless explicitly stated otherwise, singular forms include plural meanings. In this description, expressions such as “comprising” or “including” are intended to indicate certain features, numbers, steps, operations, elements, parts or combinations thereof, and one or more than those described. It should not be construed to exclude the existence or possibility of any other characteristic, number, step, operation, element, or part or combination thereof.

Figure 1 is a block diagram showing the detailed configuration of a road ice analysis system 100 according to an embodiment of the present invention. As shown in FIG. 1, the road ice analysis system 100 according to an embodiment of the present invention includes a geographic information collection unit 102, a target area classification unit 104, a weather observation information collection unit 106, and an observation It includes a point classification unit 108 and a road ice analysis unit 110.

The geographic information collection unit 102 collects geographic information of the target area. In these embodiments, the target area is an area subject to analysis and includes two or more roads. Here, the road may be a highway, a national road, etc., but the type of road is not particularly limited. For example, the geographic information collection unit 102 may collect geographic information of the target area from a spatial information system (GIS, Geographic Information System). The geographical information is geographical or topographical information about a random point or road in the target area, and may be, for example, distance from the coastline, slope length, slope location, slope, land cover, road standard node link, etc.

Figure 2 is an example showing geographic information according to an embodiment of the present invention.

Referring to FIG. 2, the geographic information collection unit 102 may collect geographic information such as distance from the coastline, slope length, land cover, road standard node link, etc. for any point or road in the target area. You can.

The target area classification unit 104 classifies the regional type of the target area from the collected geographical information. In these embodiments, regional types can be divided into mid-scale regional types and local-scale regional types. The medium-scale regional type is a regional type for classifying the target region into one of coastal regions, inland regions, and mountainous regions. In addition, the local-scale regional type is a regional type for classifying roads within the target area into one of the following: water area, urbanized area, road area, wetland area, grassland area, mountainous area, agricultural area, and bare area.

The target area classification unit 104 may classify the target area into one of a plurality of medium-scale area types according to a known medium-scale area classification algorithm. As an example, if the distance from the coast of a specific area within the target area is within a threshold, the target area classification unit 104 may classify the medium-scale area type for the specific area within the target area as a coastal type area.

Additionally, the target area classification unit 104 may classify each section of roads within the target area into one of a plurality of local-scale area types according to a known local-scale area classification algorithm. For example, the target area classification unit 104 classifies each section of roads in the target area into water area, urbanized area, road area, wetland area, grassland area, mountainous area, etc. based on the land cover map to which the roads in the target area belong. It can be classified into either an agricultural area or a bare area. As an example, if a mountainous area has the largest share within a set radius from a specific section of the road, the target area classification unit 104 may classify the local-scale area type for the specific section of the road as a mountainous area.

Figure 3 is an example showing the results of classifying the regional type of the target area according to an embodiment of the present invention.

Referring to FIG. 3, the target area classification unit 104 can classify the target area, Jeollabuk-do, into a coastal area (left), an inland area (center), and a mountainous area (right). In addition, the target area classification unit 104 divides sections of roads within the target area, for example, Highway 12, Highway 15, Highway 20, Highway 25, Highway 27, and Highway 35 into a water area, It can be classified into any of the following: urbanized area, road area, wetland area, grassland area, mountain area, agricultural area, and bare land area.

The weather observation information collection unit 106 collects weather observation information for a plurality of observation points set within the target area. In the present embodiments, the weather observation information is weather observation information for the plurality of observation points observed by at least one of AWS (Automatic Weather System) and ASOS (Automated Synoptic Observing System), and is provided at the plurality of observation points. It can include atmospheric temperature, dew point temperature, relative humidity, precipitation, and wind speed. The weather observation information collection unit 106 may collect weather observation information for a plurality of observation points set within the target area from the Korea Meteorological Administration server.

The observation point classification unit 108 classifies the plurality of observation points into a mid-scale region type and a local-scale region type. The observation point classification unit 108 may classify the plurality of observation points into a mid-scale area type and a local-scale area type in the same manner as the method in which the target area classification unit 104 classified the regional type of the target area. That is, the observation point classification unit 108 may classify the plurality of observation points into a mid-scale region type and a local-scale region type based on geographic information of the area to which the plurality of observation points belong.

Figure 4 is an example showing the results of classifying the regional types of observation points within the target area according to an embodiment of the present invention.

Referring to Figure 4, in the case of the Jeollabuk-do region, according to the mid-scale regional type classification of the observation point classification unit 108, there is an inland region (southern inland region), a coastal region (southern coastal region), and a mountainous region (southern mountainous region). It can be classified as: In addition, each medium-scale region will be classified into a water area, an urbanized area, a road area, a wetland area, a grassland area, a mountainous area, an agricultural area, a bare area, etc. according to the local-scale area type classification of the observation point classification unit 108. You can.

Accordingly, the 20 observation points in the southern inland can be classified into 2 urbanized areas, 7 mountainous areas, 10 agricultural areas, and 1 bare area. Additionally, the 14 observation points within the southern coast can be classified into 5 water regions, 2 mountain regions, and 7 agricultural regions, respectively. In addition, the eight observation points in the southern mountain area can be classified into one urbanized area, six mountainous areas, and one agricultural area. Afterwards, by analyzing the meteorological observation data for each regional type, it is possible to identify meteorological characteristics and major meteorological factors according to the terrain.

Figure 5 is an example showing the results of analyzing weather observation information according to the regional type of the observation point in the target area according to an embodiment of the present invention.

Referring to Figure 5, the average temperature of the medium-scale region type was 1.76℃ in the southern inland, 3.22℃ in the southern coast, and 1.24℃ in the southern mountainous region, with the coastal region being the highest and the mountainous region being the lowest. In addition, in the local-scale regional type, the temperature in the urbanized area was relatively high, the temperature in the mountainous area was relatively low, and the temperature deviation was the lowest in the water area. In addition, the urbanized areas in the southern inland region had the lowest relative humidity, and the water areas in the southern coastal region had the highest wind speeds. In this way, the observation point classification unit 108 classifies the plurality of observation points into mid-scale regional types and local-scale regional types, and analyzes meteorological observation information according to the regional type of each observation point to determine meteorological characteristics and major factors according to the terrain. Meteorological factors can be identified.

Additionally, the observation point classification unit 108 may select one or more adjacent observation points that fall within a set radius from roads in the target area among the plurality of observation points. In order to analyze and predict road icing on roads within the target area, weather information for the area to which the road belongs is required. Accordingly, in embodiments of the present invention, one or more adjacent observation points within a set radius from the roads in the target area are selected, and then the weather observation information for the selected adjacent observation points is used to determine the roads for the roads in the target area. Freezing could be analyzed.

As an example, the observation point classification unit 108 may select an adjacent observation point within 2 km from roads in the target area among a plurality of observation points. Additionally, the observation point classification unit 108 may compare the regional type of the selected adjacent observation point with the regional type of the road in the target area closest to the adjacent observation point. If the regional type of the selected adjacent observation point and the regional type of the road in the target area closest to the adjacent observation point correspond to each other (i.e., the medium-scale regional type and local-scale regional type of the selected adjacent observation point are adjacent to the adjacent observation point) (if the same as the mid-scale regional type and local-scale regional type of the road in the target area closest to the observation point), meteorological observation data for the selected adjacent observation point can be used as meteorological data for the road in the corresponding target area. .

Figure 6 is an example showing the results of deriving major weather factors according to regional types for each road based on weather observation information from adjacent observation points according to an embodiment of the present invention.

Referring to Figure 6, in the case of Highway 35, the average temperature was lowest in the mountainous area, and in the case of Highway 27, the average temperature was highest in the urbanized area. In addition, relative humidity was found to be relatively high in agricultural areas along Highway 12 and mountainous areas for each road. As described above, the observation point classification unit 108 can analyze weather observation information according to the regional type of each observation point to identify weather characteristics and major weather factors according to the terrain.

The road ice analysis unit 110 applies a plurality of different road ice determination algorithms set based on meteorological observation information for the adjacent observation points to each of the adjacent observation points in a set time unit, thereby Derive the main causes of icing and roads vulnerable to icing according to each icing cause.

In the present embodiments, the road icing determination algorithm may include a first road icing determination algorithm, a second road icing determination algorithm, and a third road icing determination algorithm. Here, the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm are different algorithms used to determine the main cause of road icing. The first road icing determination algorithm is an algorithm that determines that the main cause of road icing is condensation based on the relative humidity, dew point temperature, road surface temperature, and air temperature at the observation point. In addition, the second road icing determination algorithm is an algorithm that determines the main cause of road icing to be precipitation based on the precipitation amount, air temperature, and road surface temperature at the observation point. In addition, the third road icing determination algorithm is an algorithm that determines the main cause of road icing to be wind speed based on the road surface temperature, dew point temperature, and wind speed at the observation point. In this way, the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm are used to predict road icing according to weather conditions, and the specific prediction method is described with reference to FIGS. 7 to 9. Explain.

Figure 7 is an example showing a first road ice determination algorithm according to an embodiment of the present invention.

Referring to FIG. 7, the first road icing determination algorithm determines that the relative humidity at the observation point is 65% or more, the road surface temperature (T _r ) at the observation point is below the dew point temperature (T _d ) + 5°C, and the road surface at the observation point is If the temperature (T _r ) is below the air temperature (T), the air temperature (T) at the observation point is 5℃ or below, and the road surface temperature (T _r ) at the observation point is below 0℃, road icing due to condensation may occur. It is predicted that In addition, the first road icing determination algorithm determines that the relative humidity at the observation point is 65% or more, the road surface temperature (T _r ) at the observation point is below the dew point temperature (T _d ) + 5°C, and the road surface temperature at the observation point (T _r ) is below the air temperature (T), the air temperature (T) at the observation point is below 5℃, the road surface temperature (T _r ) at the observation point is between 0℃ and 1℃, and the hourly road surface temperature (T _r ) is If the change is -1℃/hr, it can be predicted that road icing will occur due to condensation.

Figure 8 is an example showing a second road ice determination algorithm according to an embodiment of the present invention.

Referring to Figure 8, the second road ice determination algorithm determines that the precipitation at the observation point is greater than 1 mm, the air temperature (T) at the observation point within 3 hours after precipitation is 0°C or less, and the observation point within 3 hours after precipitation is If the road surface temperature (T _r ) is below 0℃, it can be predicted that road icing will occur due to precipitation.

Figure 9 is an example showing a third road icing determination algorithm according to an embodiment of the present invention.

Referring to FIG. 9, the third road icing determination algorithm determines that the road surface temperature (T _r ) at the observation point is 0℃ or less, and the dew point temperature (T _d ) - road surface temperature (T _r ) at the observation point is greater than 0.5℃, If the wind speed at the observation point is greater than 2 m/s, it can be predicted that road icing will occur due to the wind speed.

As such, the first road icing determination algorithm is configured to determine road icing due to condensation based on the relative humidity, dew point temperature, road surface temperature, and air temperature at the observation point, and the second road icing determination algorithm is configured to determine road icing due to condensation, precipitation at the observation point, It is configured to determine road icing due to precipitation based on air temperature and road surface temperature, and the third road icing determination algorithm is configured to determine road icing due to wind speed based on the road surface temperature, dew point temperature, and wind speed at the observation point. All meteorological factors used in this road ice determination algorithm are included in the above-mentioned meteorological observation data except road surface temperature. However, since the road surface temperature is not included in the weather observation data, the road ice analysis unit 110 estimates the road surface temperature for the observation point based on the air temperature included in the weather observation data, and then The icing determination algorithm or the third road icing determination algorithm can be applied to the adjacent observation point.

Figure 10 is an example showing the process of estimating the road surface temperature using weather observation information according to an embodiment of the present invention.

Referring to FIG. 10, the air temperature and road surface temperature at the observation point can be expressed as Equation 1 below. Here, the air temperature means the air temperature 2m above the ground surface.

[Equation 1]

Road surface temperature = 1.6487

Equation 1 above was calculated through correlation analysis based on winter data (December 2018 to February 2019, December 2019 to February 2020) from the RWIS timber station, which is the road observation data of the National Institute of Meteorological Research.

Referring to FIG. 10, the air temperature and road surface temperature show a high correlation of R ² 0.9 or higher.

The road ice analysis unit 110 can estimate the road surface temperature for the observation point based on the air temperature included in the weather observation data, and the above-described first road ice determination algorithm uses the estimated road surface temperature. , the second road icing determination algorithm and the third road icing determination algorithm can be applied.

Specifically, the road ice analysis unit 110 applies a plurality of different road ice determination algorithms set based on meteorological observation information for the adjacent observation points to each of the adjacent observation points in a set time unit, and By applying the prediction results of the road icing determination algorithm to roads with a regional type corresponding to the regional type of the adjacent observation point, the main cause of icing for each road in the target area and roads vulnerable to icing according to each icing cause are derived. can do.

As an example, the road icing analysis unit 110 may apply the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm to the adjacent observation points in a set time unit. For example, the road ice analysis unit 110 may apply the first road ice determination algorithm, the second road ice determination algorithm, and the third road ice determination algorithm to the adjacent observation points on a 24-hour basis.

Thereafter, the road icing analysis unit 110 calculates the prediction results of the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm for the adjacent observation point into the regional type corresponding to the adjacent observation point. By applying it to roads with regional types, the main cause of freezing for each road in the target area and roads vulnerable to freezing according to each freezing cause can be derived.

Specifically, the road icing analysis unit 110 determines the number or times of road icing among the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm during the time unit set for the adjacent observation point. This most common road icing determination algorithm can be identified, and the icing cause corresponding to the identified road icing determination algorithm can be determined as the main cause of icing on roads having the same area type as the area type of the adjacent observation point.

As an example, when the maximum time for which road icing due to condensation is predicted to occur at an adjacent observation point for 24 hours is 3 hours, the road ice analysis unit 110 determines the region type that is the same as the region type of the adjacent observation point. The main cause of freezing on roads with , can be determined to be condensation.

As another example, when the number of times road icing due to precipitation is predicted to be the highest for an adjacent observation point for 24 hours is 5, the road ice analysis unit 110 determines the region type that is the same as the region type of the adjacent observation point. The main cause of freezing on roads can be determined as precipitation.

In this way, the road ice analysis unit 110 can determine the main cause of ice for each road in the target area, and thereby derive roads vulnerable to ice according to each cause of ice.

Figure 11 is an example showing the results of deriving the main cause of icing for each road and roads vulnerable to icing according to each icing cause according to an embodiment of the present invention.

Referring to FIG. 11, the road ice analysis unit 110 predicts road ice in a set time unit by applying the first road ice determination algorithm to the third road ice determination algorithm to adjacent observation points, and determines the best road ice condition for each road. The main cause of freezing can be determined. In addition, the road ice analysis unit 110 can derive ice-vulnerable roads according to each ice cause based on the predicted road ice and the main ice causes. As shown in Figure 11, Highway 35 was predicted to be most vulnerable to freezing due to condensation, Highway 12 was predicted to be most vulnerable to freezing due to precipitation, and Highway 15 was predicted to be most vulnerable to freezing due to wind speed. predicted to be vulnerable.

The road ice analysis unit 110 can rank roads vulnerable to freezing due to condensation, roads vulnerable to freezing due to precipitation, and roads vulnerable to freezing due to wind speed according to the degree of vulnerability, and can rank these roads vulnerable to freezing due to condensation/precipitation. /The possibility of icing on each road can be predicted by considering the degree of icing vulnerability depending on wind speed. In this way, by ranking and deriving roads vulnerable to freezing according to each cause of freezing, it is possible to more intuitively and easily determine which road is vulnerable to which cause of freezing. These ranked data can be used as very useful data for winter road management and safety accident prevention.

Figure 12 is a flowchart for explaining a road ice analysis method according to an embodiment of the present invention. As shown in FIG. 12, the road ice analysis method according to an embodiment of the present invention largely consists of a data collection step, a region type classification step, and a road ice analysis step. In the illustrated flow chart, the method is divided into a plurality of steps, but at least some of the steps are performed in a different order, combined with other steps, omitted, divided into detailed steps, or not shown. One or more steps may be added and performed.

In step S102, the geographic information collection unit 102 collects geographic information of the target area.

In step S104, the weather observation information collection unit 106 collects weather observation information for a plurality of observation points set within the target area.

In step S106, the target area classification unit 104 classifies the area type of the target area from the collected geographical information. The target area classification unit 104 may classify the target area into one of a plurality of set mid-scale area types, and classify each section of roads within the target area into one of a plurality of set local-scale area types.

In step S108, the observation point classification unit 108 classifies the regional type of the observation point from the collected geographical information. The observation point classification unit 108 may classify the observation point into one of a plurality of set mid-scale area types and classify the observation point into one of a plurality of set local-scale area types.

In step S110, the observation point classification unit 108 selects one or more adjacent observation points that fall within a set radius from roads in the target area among the plurality of observation points.

In step S112, the road ice analysis unit 110 applies a plurality of different road ice determination algorithms set based on weather observation information for the adjacent observation points to each of the adjacent observation points in a set time unit.

In step S114, the road ice analysis unit 110 may estimate the road surface temperature of the adjacent observation point based on the air temperature of the adjacent observation point prior to applying the road ice determination algorithm in step S112.

In step S116, the road ice analysis unit 110 applies the prediction result of the road ice determination algorithm for the adjacent observation point to each road in the target area by applying the prediction result to the road having a region type corresponding to the region type of the adjacent observation point. Derive the most common causes of icing on each road and roads vulnerable to icing according to each icing cause.

13 is a block diagram illustrating and illustrating a computing environment including a computing device suitable for use in example embodiments. In the illustrated embodiment, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those not described below.

The illustrated computing environment 10 includes a computing device 12 . In one embodiment, computing device 12 may be road ice analysis system 100, or one or more components included in road ice analysis system 100.

Computing device 12 includes at least one processor 14, a computer-readable storage medium 16, and a communication bus 18. Processor 14 may cause computing device 12 to operate in accordance with the example embodiments noted above. For example, processor 14 may execute one or more programs stored on computer-readable storage medium 16. The one or more programs may include one or more computer-executable instructions, which, when executed by the processor 14, cause computing device 12 to perform operations according to example embodiments. It can be.

Computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer-readable storage medium 16 includes a set of instructions executable by the processor 14. In one embodiment, computer-readable storage medium 16 includes memory (volatile memory, such as random access memory, non-volatile memory, or an appropriate combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, another form of storage medium that can be accessed by computing device 12 and store desired information, or a suitable combination thereof.

Communication bus 18 interconnects various other components of computing device 12, including processor 14 and computer-readable storage medium 16.

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide an interface for one or more input/output devices 24. The input/output interface 22 and the network communication interface 26 are connected to the communication bus 18. Input/output device 24 may be coupled to other components of computing device 12 through input/output interface 22. Exemplary input/output devices 24 include, but are not limited to, a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touch screen), a voice or sound input device, various types of sensor devices, and/or imaging devices. It may include input devices and/or output devices such as display devices, printers, speakers, and/or network cards. The exemplary input/output device 24 may be included within the computing device 12 as a component constituting the computing device 12, or may be connected to the computing device 12 as a separate device distinct from the computing device 12. It may be possible.

Although the present invention has been described in detail above through representative embodiments, those skilled in the art will recognize that various modifications to the above-described embodiments are possible without departing from the scope of the present invention. You will understand. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined not only by the claims described later but also by equivalents to the claims.

100: Road icing analysis system
102: Geographic information collection department
104: Target area classification unit
106: Weather observation information collection department
108: Observation point classification unit
110: Road ice analysis unit

Claims (10)

Geographic information collection unit that collects geographic information of the target area;
a target area classification unit that classifies the target area into one of a plurality of set mid-scale area types from the collected geographical information, and classifies each section of roads within the target area into one of a plurality of set local-scale area types;
a weather observation information collection unit that collects weather observation information for a plurality of observation points set within the target area;
Classify the plurality of observation points into the mid-scale area type and the local-scale area type based on the geographical information, and select one or more adjacent observation points that fall within a set radius from roads in the target area among the plurality of observation points. an observation point classification unit;
A plurality of different road ice determination algorithms set based on weather observation information for the adjacent observation points are applied to each of the adjacent observation points in set time units, and the predicted results of the road ice determination algorithm for the adjacent observation points are calculated. It includes a road ice analysis unit that derives the main cause of ice for each road in the target area and roads vulnerable to ice according to each ice cause by applying it to roads having a regional type corresponding to the regional type of the adjacent observation point,
The plurality of medium-scale regional types include at least two of coastal regions, inland regions, and mountainous regions,
The plurality of local-scale area types include at least two of a water area, an urbanized area, a road area, a wetland area, a grassland area, a mountain area, an agricultural area, and a bare area, and a road using road environment and weather conditions. Ice analysis system.
delete In claim 1,
The weather observation information is weather observation information for the plurality of observation points observed by at least one of AWS (Automatic Weather System) and ASOS (Automated Synoptic Observing System), and includes air temperature and dew point for the plurality of observation points. Includes temperature, relative humidity, precipitation and wind speed;
A road ice analysis system using road environment and weather conditions, wherein the road ice analysis unit estimates road surface temperatures for the plurality of observation points based on the air temperature included in the weather observation information.
In claim 3,
The road icing determination algorithm includes a first road icing determination algorithm that determines road icing due to condensation based on the relative humidity, the dew point temperature, the road surface temperature, and the air temperature, the precipitation amount, the air temperature, and the road surface temperature. It includes a second road icing determination algorithm that determines road icing due to precipitation based on and a third road icing determination algorithm that determines road icing due to wind speed based on the road surface temperature, the dew point temperature, and the wind speed,
The road icing analysis unit applies the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm to the adjacent observation point in a set time unit, and Each road in the target area by applying the prediction results of the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm to roads having a regional type corresponding to the regional type of the adjacent observation point. A road icing analysis system using road environment and weather conditions, including a road icing analysis unit that derives the most common icing causes and roads vulnerable to icing according to each icing cause.
In claim 4,
The road icing analysis unit determines that the number or time of determining road icing is the highest among the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm during the time unit set for the adjacent observation point. Road environment and meteorological conditions that identify many road icing determination algorithms and determine the icing cause corresponding to the identified road icing determination algorithm as the main cause of icing on the road with an area type corresponding to the area type of the adjacent observation point. Road ice analysis system using .
In the geographic information collection unit, collecting geographic information of the target area;
In a target area classification unit, classifying the target area into one of a plurality of set mid-scale area types from the collected geographical information;
Classifying, in the target area classification unit, each section of roads within the target area into one of a plurality of set local-scale area types;
Collecting, in a weather observation information collection unit, weather observation information for a plurality of observation points set within the target area;
In an observation point classification unit, classifying the plurality of observation points into the mid-scale region type and the local-scale region type based on the geographic information;
In the observation point classification unit, selecting one or more adjacent observation points that fall within a set radius from roads in the target area among the plurality of observation points;
In the road ice analysis unit, applying a plurality of different road ice determination algorithms set based on meteorological observation information for the adjacent observation points to each of the adjacent observation points in a set time unit; and
In the road icing analysis unit, the prediction result of the road icing determination algorithm for the adjacent observation point is applied to roads having a region type corresponding to the region type of the adjacent observation point, thereby reducing the most major icing for each road in the target region. It includes the step of deriving ice-vulnerable roads according to the cause and each cause of ice,
The plurality of medium-scale regional types include at least two of coastal regions, inland regions, and mountainous regions,
The plurality of local-scale area types include at least two of water area, urbanized area, road area, wetland area, grassland area, mountain area, agricultural area, and bare area, and roads using road environment and weather conditions. Freezing analysis method.
delete In claim 6,
The weather observation information is weather observation information for the plurality of observation points observed by at least one of AWS (Automatic Weather System) and ASOS (Automated Synoptic Observing System), and includes air temperature and dew point for the plurality of observation points. Includes temperature, relative humidity, precipitation and wind speed;
Before applying the plurality of different road ice determination algorithms to each of the adjacent observation points in a set time unit,
A road ice analysis method using road environment and weather conditions, further comprising, in the road ice analysis unit, estimating road surface temperatures for the plurality of observation points based on the air temperature included in the weather observation information.
In claim 8,
The road icing determination algorithm includes a first road icing determination algorithm that determines road icing due to condensation based on the relative humidity, the dew point temperature, the road surface temperature, and the air temperature, the precipitation amount, the air temperature, and the road surface temperature. It includes a second road icing determination algorithm that determines road icing due to precipitation based on and a third road icing determination algorithm that determines road icing due to wind speed based on the road surface temperature, the dew point temperature, and the wind speed,
The step of applying the plurality of different road icing determination algorithms to the adjacent observation points in a set time unit includes the first road icing determination algorithm, the second road icing determination algorithm, and the third road icing determination algorithm. A road ice analysis method using road environment and weather conditions, applied to each of the adjacent observation points on a time basis.
In claim 9,
The step of deriving the most important cause of icing for each road in the target area and roads susceptible to icing according to each icing cause includes the first road icing determination algorithm and the second road icing determination during a time unit set for the adjacent observation point. Among the algorithms and the third road icing determination algorithm, identify the road icing determination algorithm with the greatest number or time of road icing determination, and correspond the icing cause corresponding to the identified road icing determination algorithm with the regional type of the adjacent observation point. Road icing analysis method using road environment and weather conditions to determine the main cause of icing on roads with the following regional types.