Road surface icy and snowy condition prediction program, information processing device, and road surface icy and snowy condition prediction system

Abstract

To provide a road surface ice snow state prediction program, an information processing device, and a road surface ice snow state prediction system which predict a state of a snow ice layer taking into account an operating status of snow removal vehicles.SOLUTION: An information processing device 1 includes: snow ice state prediction means 103 for calculating snow ice state prediction data in a road surface snow ice layer at each time point based on a thermal balance model and an ice-water-air balance model of the road surface snow ice layer using a history and a plan of snow removal at each point and prediction data related to a weather condition and a traffic condition; and display information generation means 105 for generating display information in which the snow ice state prediction data at each point calculated by the snow ice state prediction means 103 is displayed on a map for each time point.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a road surface icy and snowy condition prediction program, an information processing device, and a road surface icy and snowy condition prediction system. [Background technology]

[0002] As a conventional technology, a snow and ice condition prediction program has been proposed that models the snow and ice layer on the road surface using a heat balance model and an ice-water-air balance model, and then performs a quantitative evaluation through simultaneous coupled analysis of each model to predict the coefficient of sliding friction on the road surface (see, for example, Patent Document 1).

[0003] The snow and ice condition prediction program disclosed in Patent Document 1 acquires prediction data on meteorological and traffic conditions, calculates the heat balance based on a heat balance model for the snow and ice layer, and calculates the ice, water, and air balance based on an ice, water, and air balance model for the snow and ice layer, and calculates snow and ice condition prediction data such as whether the road surface is snow-covered, packed snow, frozen, or slush, and the thickness of the snow and ice layer, and predicts the road surface's sliding friction coefficient based on the calculated snow and ice condition prediction data.Whether the road surface is snow-covered, packed snow, frozen, or slush is determined by the volume ratio of ice, water, and air. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-102006 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the snow and ice condition prediction program in Patent Document 1 mentioned above acquires prediction data on weather and traffic conditions, calculates snow and ice condition prediction data, and predicts the road surface's sliding friction coefficient based on the calculated snow and ice condition prediction data, but it does not predict the road surface's snow and ice condition and sliding friction coefficient taking into account the operating status of snowplows, and the predicted values ​​are for a certain point, not an overview of a certain area.

[0006] An object of the present invention is to provide a road surface icy and snowy condition prediction program, an information processing device, and a road surface icy and snowy condition prediction system that predict the state of a snow and ice layer taking into account the operating status of a snowplow. [Means for solving the problem]

[0007] In order to achieve the above object, one aspect of the present invention provides the following road surface icy and snowy condition prediction program, information processing device, and road surface icy and snowy condition prediction system.

[0008] [1] Computer, snow and ice condition prediction means for calculating predicted data on the state of snow and ice in the snow and ice layer on the road surface at each time based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface, using predicted data on the history and schedule of snow removal at each point, meteorological conditions, and traffic conditions; a road surface icy and snowy condition prediction program that functions as a display information generation means for generating display information that displays the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means on a map for each time period; [2] The road surface snow and ice condition prediction program described in [1] further functions as a snow removal schedule prediction means that determines the snow removal schedule based on the performance of the snow removal vehicle and the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means. [3] A road surface snow and ice condition prediction program according to [1] or [2], which further functions as a stuck risk calculation means for calculating a road surface slip friction coefficient from the snow and ice condition prediction data for each location and each time, and calculating the risk rate of a vehicle getting stuck based on the road surface slip friction coefficient. [4] The road surface snow and ice condition prediction program described in [3], wherein the display information generation means generates display information indicating the necessity or schedule of the next snow removal for a point where the calculated stuck risk rate exceeds a predetermined threshold value. [5] The road surface snow and ice condition prediction program described in [3], wherein the display information generation means generates display information indicating the necessity or schedule of a road closure for a point where the calculated stuck risk rate exceeds a predetermined threshold. [6] A road surface snow and ice condition prediction program described in any of [1] to [5], wherein the display information generation means generates display information indicating the need for or schedule for spraying antifreeze at a point where the road surface sliding friction coefficient calculated from the snow and ice condition prediction data is smaller than a predetermined threshold value. [7] A road surface snow and ice condition prediction program according to any of [1] to [6], wherein the display information generation means generates display information that displays traffic congestion information based on at least one of the snow and ice condition prediction data, the road surface slip friction coefficient calculated from the snow and ice condition prediction data, and the stuck risk rate calculated from the snow and ice condition prediction data and the road surface slip friction coefficient. [8] A snow and ice condition prediction means for calculating predicted data on the snow and ice condition in the snow and ice layer on the road surface at each time based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface, using predicted data on the history and schedule of snow removal at each point, meteorological conditions, and traffic conditions; an information processing device having a display information generating means for generating display information that displays the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means on a map for each time period. [9] A snow and ice condition prediction means for calculating predicted data on the snow and ice condition in the snow and ice layer on the road surface at each time based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface, using predicted data on the history and schedule of snow removal at each point, meteorological conditions, and traffic conditions; an information processing device having a display information generating means for generating display information that displays the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means on a map for each time period; A road surface snow and ice condition prediction system comprising a terminal that displays the display information. [Effects of the Invention]

[0009] According to the inventions set forth in claims 1, 8 and 9, the state of the snow and ice layer can be predicted taking into account the operating status of the snowplow. According to the invention of claim 2, the snow removal schedule can be determined based on the performance of the snow removal vehicle and the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means. According to the invention of claim 3, the road surface slip friction coefficient can be calculated from snow and ice condition prediction data for each location and each time, and the risk rate of a vehicle getting stuck can be calculated based on the road surface slip friction coefficient. According to the invention of claim 4, if there is a point where the calculated stuck risk rate exceeds a predetermined threshold, display information can be generated that displays the necessity or schedule for the next snow removal at that point. According to the invention of claim 5, if there is a point where the calculated stuck risk rate exceeds a predetermined threshold, display information can be generated that indicates the necessity or schedule of a road closure for that point. According to the invention of claim 6, if there is a point where the road surface sliding friction coefficient calculated from snow and ice condition prediction data is smaller than a predetermined threshold value, display information can be generated that indicates the need for or schedule for spraying antifreeze at that point. According to the invention of claim 7, it is possible to generate display information that displays traffic congestion information based on at least one of snow and ice condition prediction data, road surface slip friction coefficient calculated from the snow and ice condition prediction data, and stuck risk rate calculated from the snow and ice condition prediction data and road surface slip friction coefficient. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram showing an example of the configuration of a snow and ice condition prediction system according to an embodiment. [Figure 2] FIG. 2 is a schematic diagram illustrating an example of a configuration of an information processing device according to an embodiment. [Figure 3] FIG. 3 is a diagram showing the model concept for analyzing the state of snow and ice layers. [Figure 4] 4(a) and 4(b) are a schematic diagram and a flowchart for explaining the stuck determination operation. [Figure 5] FIG. 5 is a diagram showing a display example of the display information. [Figure 6] FIG. 6 is a flowchart for explaining the operation of predicting the snow and ice condition of the road surface, the operation of calculating the stuck risk rate, and the operation of generating display information. DETAILED DESCRIPTION OF THE INVENTION

[0011] [Embodiment Mode] (Configuration of snow and ice condition prediction system) FIG. 1 is a schematic diagram showing an example of the configuration of a snow and ice condition prediction system according to an embodiment.

[0012] The snow and ice condition prediction system is composed of an information processing device 1 that predicts the snow and ice conditions on the road surface of a road 40 and outputs the prediction results, terminals 2 and 3 that display the information output by the information processing device 1, and a snowplow 4 that removes accumulated snow 41 on the road 40, all connected via a network 5. Terminal 2 is operated by an administrator 6 of the snowplow 4 and road 40, and terminal 3 is operated by a user 7 of the road 40. The snowplow 4 is equipped with a location information transmission and reception system such as a GPS (Global Positioning System) and periodically transmits its own location to the information processing device 1.

[0013] (Configuration of information processing device) FIG. 2 is a schematic diagram illustrating an example of a configuration of an information processing device according to an embodiment.

[0014] This information processing device 1 processes the information provided to it and calculates the snow and ice conditions and the risk of getting stuck at the predicted location. It is a server-type information processing device that operates in response to requests input from the outside and is equipped with electronic components such as a CPU (Central Processing Unit) and flash memory that have the function of processing information within its main body. Note that "snow and ice conditions" here includes at least the snow and ice temperature, snow and ice thickness, and the mixture ratio of ice, water, and air in the snow and ice layer on the road surface.

[0015] The information processing device 1 comprises a control unit 10 which is composed of a CPU and the like and controls each part and executes various programs, a memory unit 11 which is composed of a storage medium such as an HDD (Hard Disk Drive) or flash memory and stores information, and a communication unit 12 which communicates with the outside via a network 5.

[0016] The control unit 10 executes the road surface ice and snow condition prediction program 110 described later, thereby functioning as a weather information acquisition means 100, a setting means 101, a snow removal schedule prediction means 102, a snow and ice condition prediction means 103, a stuck risk rate calculation means 104, a display information generation means 105, an output means 106, etc.

[0017] The weather information acquisition means 100 acquires weather information from the outside via the communication unit 12 and stores it in the storage unit 11 as weather information 111. The weather information 111 includes actual measured values ​​and forecast values ​​of temperature, relative humidity, wind speed, solar radiation, atmospheric radiation, and precipitation, but atmospheric radiation may be estimated from temperature, relative humidity, vapor pressure, and cloud cover.

[0018] The setting means 101 sets calculation conditions and initial conditions for predicting snow and ice conditions, and stores them in the storage unit 11 as set value information 112. The contents of the set value information 112 will be described later. The setting means 101 also acquires prediction data on traffic conditions from the outside and stores it in the storage unit 11 as set value information 112.

[0019] The snow removal schedule prediction means 102 analyzes snow removal information 116, which includes past GPS location information of the snow removal vehicle 4, and determines the capacity of the snow removal vehicle 4 based on the standard work capacity of the snow removal vehicle 4, the snow and ice conditions and road conditions (number of lanes and lane width), traffic conditions (traffic volume and driving speed), weather conditions (precipitation, wind speed, temperature), snow removal speed and efficiency relative to the time (visibility), snow removal delay factors such as obstacles and parked vehicles, and records the determined capacity in the snow removal information 116. The snow removal schedule prediction means 102 also plans a snow removal schedule based on the capacity of the snow removal vehicle 4 so that the road 40 will not be closed to traffic, and records the plan in the snow removal information 116. The decision to close the road is made based on the stuck risk rate calculated by the stuck risk rate calculation means 104.

[0020] The snow and ice condition prediction means 103 uses meteorological information 111 and setting value information 112 to calculate the volumetric changes of water, ice, and air in the snow and ice layer due to temperature changes based on a heat balance model and an ice-water-air balance model for the road surface snow and ice layer. This allows the snow and ice temperature, snow and ice thickness, and the mixture ratio of water, ice, and air in the snow and ice layer to be calculated. The snow and ice condition prediction means 103 predicts these snow and ice conditions and their time transitions, generating snow and ice condition information 113. The snow and ice condition includes, for example, at least the thickness of the snow and ice layer and the mixture ratio of ice, water, and air in the road surface snow and ice layer. Details of the calculation method for the snow and ice condition information 113 and the calculation method for the road surface sliding friction coefficient, which will be described below, are based on the contents of JP 2008-102006 A. The snow and ice condition is predicted for each of multiple locations. The snow and ice condition prediction means 103 also references snow removal information 116 and takes into account snow removal history when generating the snow and ice condition information 113.

[0021] Furthermore, snow and ice condition prediction means 103 further refers to snow removal information 116 and predicts snow and ice condition information 113 taking into account the snow and ice conditions when snow is removed by snowplow 4. Specifically, as an example, it is assumed that the amount of snow accumulation decreases according to the capacity of snowplow 4. Furthermore, snow and ice condition prediction means 103 may further take into account the spraying of antifreeze, the operating status of snow melting equipment, etc.

[0022] The stuck risk calculation means 104 calculates the road surface slip friction coefficient based on the set value information 112 and the snow and ice condition information 113, and then calculates the stuck risk based on these values ​​and the basic principles of stuck determination (FIGS. 4(a) and 4(b)), and stores the result as stuck risk information 114 in the storage unit 11. The method of calculating the stuck risk will be described in detail later.

[0023] The display information generating means 105 generates display information 115 that displays information included in the snow removal schedule predicted by the snow removal schedule predicting means 102 (and set by the manager 6), the snow and ice condition information 113 predicted by the snow and ice condition predicting means 103, and the stuck risk rate information 114 calculated by the stuck risk rate calculating means 104 on a map for each time period, and stores the display information 115 in the storage unit 11. Furthermore, the display information generating means 105 can further include in the display information 115 the timing of snow removal, the timing of road closures, etc. Note that the display information 115 may contain different information for the user 7 and the manager 6. For example, the snow removal schedule may be displayed for the user 7, while the time when snow removal should be performed (to prevent road closures) may be displayed for the manager 6.

[0024] The output means 106 outputs display information 115 to the terminals 2 and 3. In addition to the display information 115, the output means 106 may also output to the terminals 2 and 3 the snow removal schedule predicted by the snow removal schedule prediction means 102, snow and ice condition information 113 predicted by the snow and ice condition prediction means 103, and stuck risk rate information 114 calculated by the stuck risk rate calculation means 104.

[0025] The memory unit 11 stores a road surface ice and snow condition prediction program 110, which causes the control unit 10 to operate as each of the above-mentioned means 100-105, weather information 111, setting value information 112, snow and ice condition information 113, stuck risk rate information 114, display information 115, etc.

[0026] (Operation of information processing device) Next, the operation of this embodiment will be explained by dividing it into (1) basic principle, (2) road surface snow and ice condition prediction operation, (3) stack determination basic principle, (4) stack risk rate calculation operation, and (5) display information generation operation.

[0027] (1) Basic principle FIG. 3 is a diagram showing the model concept for analyzing the state of snow and ice layers.

[0028] First, we will explain the model that serves as the basis for predicting the state of snow and ice layers. Figure 3 is an explanatory diagram illustrating the heat balance components that affect road surface snow and ice layers. First, a snow and ice layer is formed on a paved road surface due to snow accumulation, resulting in a snow-covered road surface. The formed snow and ice layer is compacted mainly by vehicle traffic, becoming a compacted snow road surface. However, vehicle traffic is thought to generate heat balance components such as radiant heat from the bottom of the vehicle, frictional heat from the tires, and sensible heat due to wind induced by the passing of vehicles (vehicle heat). The compacted snow and ice layer then melts in the areas where vehicles pass through. Furthermore, vehicle traffic causes snow and ice on the road surface to fly away.

[0029] In addition to these heat balance components due to anthropogenic factors, natural factors are also considered to be contributing to the heat balance. Figure 3 shows that the following components are considered to be generated: pavement heat transferred from the pavement surface to the snow and ice layer; sensible wind heat; sensible heat from rainfall and snow removal; latent heat from solidification due to freezing; and long-wave radiation heat emitted from the snow and ice layer (and radiant heat emitted from the sky to the snow and ice layer). Melted water on the surface of the snow and ice layer solidifies (freezes), forming a frozen road surface, primarily at night. During the day, solar heat from sunlight (including absorption by the snow and ice layer, reflection by the atmosphere, and transmission through the pavement surface) and latent heat from evaporation and condensation are thought to be generated, causing the entire snow and ice layer to melt, turning into a slush-like, loosened surface. As a result of melting, water seeps into the pavement surface or drains out of the snow and ice layer. Antifreeze agents are also sprayed to prevent the snow and ice layer from freezing.

[0030] By performing simultaneous coupled analysis based on a road surface snow and ice condition model that combines the heat balance model and ice, water, and air balance model described above, it becomes possible to calculate the volumetric changes (melting and freezing) of water, ice, and air in the snow and ice layer due to temperature changes (see JP 2008-102006 A for details of the calculations), and the snow and ice thickness and the mixture ratio (volume or mass) of water, ice, and air in the snow and ice layer can be calculated. The snow and ice condition can be expressed by this mixture ratio. For example, a volume ratio of water:ice:air = 1:0:0 indicates a wet state, water:ice:air = 0.3:0.7:0 indicates a slush state, and water:ice:air = 0:0.7:0.3 indicates a packed snow state.

[0031] (2) Road snow and ice condition prediction FIG. 6 is a flowchart for explaining the operation of predicting the snow and ice condition of the road surface, the operation of calculating the stuck risk rate, and the operation of generating display information.

[0032] First, the time to be predicted is set to t=t0 (for example, the current time) (S10), and the weather information acquisition means 100 acquires weather information from an external source and stores it as weather information 111 in the memory unit 11. The setting means 101 also accepts calculation conditions and initial conditions for predicting snow and ice conditions from an external terminal and stores them in the memory unit 11 as setting value information 112 (S11). The setting value information 112 includes, for example, the degree of traffic congestion, the thickness of snow and ice on the road surface as an initial value, the moisture content of snow and ice on the road surface, etc.

[0033] Next, the snow and ice condition prediction means 103 considers the snow removal information 116 based on the above-mentioned "(1) Basic Principles" and performs a heat and mass balance analysis of the road surface snow and ice and a heat balance analysis of the pavement and ground from the weather information 111 and the set value information 112 (S12). As a result, it calculates and outputs the snow and ice temperature, road surface snow and ice thickness Hs, road surface snow and ice water content Θs, ​​and from these, the road surface sliding friction coefficient μ (S13). Note that the snow and ice condition prediction means 103 executes the above steps S12 and S13 by referring to the snow removal information 116 and taking into account the snow removal history. The specific method for calculating the road surface sliding friction coefficient μ is as follows. First, the correlation between the volume fractions of water, ice, and air in the snow and ice layer (volume water content θw, volume ice content θi, and volume void content θa) and the sliding friction coefficient μ is determined in advance, and these values ​​are registered in a corresponding setting table. Next, the corresponding sliding friction coefficient is read out from the setting table based on the calculated snow and ice condition prediction data, and the sliding friction coefficient is determined.

[0034] Next, the stuck danger rate calculation means 104 calculates the stuck danger rate R (S14), but before calculating the stuck danger rate, the basic principle of stuck judgment will be explained.

[0035] (3) Basic principles of stack determination First, packed snow roads with depressions and unevenness are thought to form in the following way: (a) During heavy snowfall, poor visibility due to blowing snow, a reduction in the effective road width due to accumulated snow, and worsening road surface conditions gradually reduce driving speeds and cause traffic jams. (b) When a vehicle stops, as shown in the stopping test, heat transfer from the tires and wheel load cause the tires to sink into the packed snow, reducing the coefficient of friction. Furthermore, depending on the accelerator pedal when starting, the tires may spin, which accelerates the changes in the packed snow described above. (c) If the tires of a following vehicle get stuck in a depression created in this way, the tires will spin each time, further deepening the depression. Once a depression forms in this way, it tends to grow, forming a deeper packed snow depression. (d) Packed snow depressions further deteriorate driving performance and worsen vehicle congestion. The longer and over which vehicles are stuck, the larger the area in which they occur. (E) If heavy snowfall occurs during snow accumulation, snow will accumulate only in areas where there are no vehicles, making the thickness of the packed snow on the road surface increasingly uneven, and creating a rough, wavy, and uneven packed snow road surface. Furthermore, heat transfer from the tires to the packed snow and radiant heat from the bottom of the vehicle are thought to melt and soften the packed snow, contributing to the formation of an uneven packed snow road surface.

[0036] 4(a) and 4(b) are a schematic diagram and a flowchart for explaining the stuck determination operation.

[0037] First, as shown in Figure 4(a), when a vehicle tire falls into a depression in the packed snow and the vehicle tries to move forward, a force is generated to rotate the tire. At the contact surface between the tire and the depression in the packed snow, a driving force acts to drive the tire, a frictional force acts on the tire, and a climbing resistance force due to the wheel load acts.

[0038] If the frictional force is smaller than the driving force, the tires will spin and the vehicle will become stuck. Also, even if the driving force is equal to or smaller than the frictional force, if the driving force is smaller than the resistance force to climbing a slope, the tires will spin and the vehicle will become stuck.

[0039] On the other hand, when the driving force is equal to or less than the friction force and is greater than the resistance force to climbing a slope, the tires grip the road and the vehicle moves forward.

[0040] The above determination flow will be explained in detail below with reference to FIG. 4(b).

[0041] When a vehicle tire falls into a depression in packed snow (0), the tire's wheel load acts on the packed snow (i), and tire heat is transferred (ii). Furthermore, when the accelerator is pressed, the tire rotates (iii). Taking these factors into consideration, we first compare the driving force and friction force from the tire's rotation (iv). Friction force is calculated from the normal force and the tire's material, pattern, etc., and when the accelerator is pressed hard, the driving force becomes greater than the friction force (iv; Yes), causing the tire to spin (b-vi).

[0042] On the other hand, if the accelerator is pressed slowly and the driving force becomes equal to or less than the friction force (iv; No), the driving force is compared with the climbing resistance (v), and if the driving force is greater than the climbing resistance (v; Yes), the vehicle will move forward (a-vi), but if the driving force is equal to or less than the climbing resistance (v; No), the tires will spin (b-vi).

[0043] Next, when the tire spins (b-vi), friction occurs between the tire and the packed snow (b-vii), and the tire heats up (b-viii). As a result, the packed snow melts (b-ix), and the tire sinks (bx).

[0044] On the other hand, tire spin and melting of packed snow cause the surface of the packed snow to become smooth (b-xiv) and the coefficient of sliding friction to decrease (b-xv), which ultimately leads to a decrease in frictional force (b-xvi).

[0045] When the tire sinks (bx), the slope angle of the packed snow surface increases (b-xi), which reduces normal resistance (b-xii), resulting in a decrease in friction (b-xvi). In addition, an increase in the slope angle of the packed snow surface (b-xi) increases climbing resistance (b-xiii).

[0046] The decrease in friction force (b-xvi) affects the comparison between driving force and friction force (iv), and the increase in climbing resistance force (b-xiii) affects the comparison between driving force and climbing resistance force (v).

[0047] Also, if the vehicle moves forward (a-vi) and escapes from the depression in the packed snow (a-vii; Yes), it will avoid getting stuck (a-viii).

[0048] Furthermore, if the vehicle moves forward (a-vi) but does not escape from the depression (a-vii; No), or after the tire gets stuck in the depression as described above (0), the vehicle is in a "stuck state" until it moves forward and escapes from the depression.

[0049] Based on the basic principles of stuck vehicle detection, it can be seen that road surface snow and ice thickness Hs affects the depth to which the tire sinks, road surface snow and ice moisture content Θs affects the ease of sinking, and road surface friction coefficient μ affects the ease of tire spin, and that these factors collectively contribute to the degree of risk of getting stuck. Furthermore, the degree of contribution of road surface snow and ice thickness Hs varies depending on temperature, traffic congestion, and snowfall, the degree of contribution of road surface snow and ice moisture content Θs varies depending on temperature and traffic congestion, and the degree of contribution of road surface friction coefficient μ varies depending on temperature and traffic congestion. Furthermore, the degree of risk of getting stuck is expressed as the stuck risk rate, and a mathematical formula (described below) that reflects these parameters is defined, and the stuck risk rate calculation means 104 calculates the rate.

[0050] (4) Stack risk calculation operation

[0051] The stuck risk calculation means 104 uses the road surface snow / ice thickness Hs, road surface snow / ice water content Θs, ​​and road surface sliding friction coefficient μ obtained in step S12 to calculate the stuck risk rate R based on a calculation formula defined by a linear combination of the first to third functions as shown below, and stores the stuck risk rate information 114 in the memory unit 11 (S14).

number

[0052] In the above, J1=F1(T a ) and the temperature T a is a function with a positive correlation with (T a Increase → J1 increase, first coefficient. J2=F2(T r ) and the traffic congestion level T r is a function with a positive correlation with (T r Increase → J2 increase, second coefficient. J3=F3(I s ) and the hourly snowfall amount I s is a function with a positive correlation with s Increase → J3 increase, the third coefficient. R1=F4(H s ) and the thickness of snow and ice on the road surface H s is a function with a positive correlation with (H s Increase → R1 increase, the first function. R2=F5(Θ s ) and the water content of the snow and ice on the road surface Θ s is a function that varies depending on (the second function). It is expressed as R3 = F6(μ), and is a function that has a negative correlation with the road surface slip friction coefficient μ (a decrease in μ → an increase in R3, the third function). These functions can be determined in advance through preliminary experiments or the like.

[0053] The variables used above are explained below: J i : Weight variable that changes between 0 and 1.0 (i=1, 2, 3, 4) R i : The stack risk rate (i=1, 2, 3, 4) varies between 0 and 1.0 depending on each explanatory variable. T a :Temperature (℃) T r : Traffic congestion I s :Hourly snowfall (mm / h) H s :Road snow and ice thickness (mm) Θ s :Road surface snow and ice moisture content

[0054] (5) Display information generation operation Next, as explained above in "(2) Road surface snow and ice condition prediction operation" and "(4) Stuck risk rate calculation operation," steps S11 to S14 are executed for each time (S10, S21, S22), road closures and snow removal schedules are created based on the obtained snow and ice condition information 113 and stuck risk rate information 114, and display information 115 is generated that shows this information on a map. The method for generating display information 115 will be explained below.

[0055] First, the display information generating means 105 checks whether the stuck risk rate R is equal to or less than a predetermined value (S15), and if it is equal to or less than the predetermined value (S15; Yes), it determines that no road closure is necessary (S19). If it is greater than the predetermined value (S15; No), the snow removal schedule predicting means 102 creates the next snow removal schedule (including snow removal operating conditions) based on the snow removal capacity of the snow removal vehicle 4 and the snow and ice condition information 113 to make the stuck risk rate equal to or less than the predetermined value (S16).

[0056] The snow removal schedule may be included and displayed in the display information 115, or may be included and displayed in the display information 115 that can only be viewed by the administrator 6, or may be notified to the administrator 6 directly.

[0057] When snow removal is to be performed based on the presented snow removal schedule, the manager 6 registers the snow removal time (at least the start time of snow removal) and the operating conditions such as the snow removal area (or snow removal route) in the snow removal information 116 of the information processing device 1 via the terminal 2. When the snow removal time is set by the manager 6 (S17; Yes), the snow removal information 116 is taken into consideration and the snow and ice condition on the road surface and the risk of getting stuck are calculated in the case where snow removal is performed at the set snow removal time (S12 to S14).

[0058] Furthermore, if the manager 6 cannot set the snow removal time (S17; No), such as when there is insufficient snow removal capacity or when the amount of accumulated snow exceeds the snow removal capacity, a road closure becomes necessary because there is a high possibility of people getting stuck (S18). When a road closure is set by the manager 6, the display information 115 indicates that there is a possibility of a road closure, and if a road closure has been set by the manager 6, it indicates that the road closure has been decided.

[0059] The display information generating means 105 generates display information 115 that shows the obtained snow and ice condition information 113, stuck risk rate information 114, set snow removal schedule, road closure and possibility of road closure, etc. on a map (S20). The above-mentioned operations S11 to S20 are performed at the set time t=t end This is performed at predetermined times (for example, every hour) until the end of the period (for example, 18 hours later) (S21, S22).

[0060] The output means 106 outputs display information 115 to the terminal 2 or 3, and the terminal 2 or 3 displays the received display information on a display unit, as shown in FIG. 5 below, as an example.

[0061] FIG. 5 is a diagram showing a display example of the display information.

[0062] Screen 105a is an example of a screen displayed on the display unit of terminal 2 or terminal 3, and includes a legend 105a1 showing the operation history and schedule of snowplow 4 and the condition of road 40, a time axis slider 105a2 for changing the displayed time, buttons 105a3 for zooming in and out of the map, and a road surface snow and ice map 105a4 in which the lines and patterns of the legend (actually a color-coded display) are superimposed on the map.

[0063] Legend 105a1 displays "Routes traveled by snowplows" which is the snow removal history, "Routes scheduled for snowplow travel" which is the snow removal schedule, and "Road conditions" which is the state of snow on the roads in snow removal information 116. "Road conditions" include the presence or absence of snow, the state of the snow (frozen, slush, packed snow), the risk of getting stuck, and scheduled road closures.

[0064] The road surface ice and snow map 105a4 may display the above-mentioned precedent 105a1 on the road on the map, and may further display the current (past and future) position of the snowplow 4, etc.

[0065] (Effects of the embodiment) According to the above-described embodiment, the snow removal schedule is predicted taking into account the capacity of the snowplow 4 so that the stuck risk rate does not exceed a predetermined value, and when a snow removal schedule is set, the snow and ice conditions are predicted taking the snow removal schedule into account, and these operations are performed at multiple times at each location, so that the state of the snow and ice layer can be predicted taking into account the operating status of the snowplow 4. In addition, display information 115 is generated and the above-described information is displayed on a map based on display information 115, so the information at each location can be checked at a bird's-eye view.

[0066] In addition, if snow removal is not enough, road closures are predicted, and the planned and confirmed road closures are displayed on the map. Routes that snowplows have passed and routes that snowplows are scheduled to pass are also displayed, allowing users to see even more information about snow accumulation at a glance.

[0067] [Other embodiments] The present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit of the present invention.

[0068] For example, the display information generating means 105 may predict traffic congestion based on at least one of the road surface slip friction coefficient, snow and ice condition prediction data, and the risk of getting stuck, and display this as traffic congestion information on the display information 115. The traffic information prediction method may involve learning, for example, from at least one of the past road surface slip friction coefficient, snow and ice condition prediction data, and the risk of getting stuck, and the probability of congestion occurring.

[0069] In addition, if there is a point where the road surface sliding friction coefficient calculated from the snow and ice condition prediction data is smaller than a predetermined threshold value, the display information generation means 105 may generate display information 115 that displays to the manager 6 the need to spray antifreeze at that point, or displays a schedule for spraying antifreeze set by the manager 6.

[0070] Furthermore, for example, in the above embodiment and modified examples, the stuck risk calculation means 103 calculates the stuck risk based on a predetermined formula, but other methods may be used. That is, the stuck risk may be compared with a predetermined threshold, and based on the comparison results, the necessity or schedule of the next snow removal, road closure, or antifreeze spraying can be determined, or traffic congestion information can be displayed. As a method for satisfying the condition, for example, in addition to the method of calculating the "stuck state" described in the basic principles of stuck detection from a formula, a method of calculating the probability of occurrence under certain conditions can be used. Examples of methods for calculating the probability of occurrence include the following: That is, the probability may be calculated using a multivariate analysis technique, or learning may be performed using a decision tree, neural network, or other technique, and the learning results may be used to calculate the probability.

[0071] In the above embodiment, the functions of the means 100 to 106 of the control unit 10 are realized by a program, but all or part of the means may be realized by hardware such as an ASIC. The program used in the above embodiment may also be provided by storing it on a recording medium such as a CD-ROM. The steps described in the above embodiment may be replaced, deleted, or added without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0072] 1: Information processing equipment 2, 3: Terminal 4: Snowplow 5: Network 6:Administrator 7: User 10: Control section 11: Storage section 12: Communications Department 40: Road 41: Snowfall 100:Method of obtaining weather information 101: Setting method 102: Snow removal schedule forecasting method 103: Snow and ice condition forecasting method 104: Stack risk calculation means 105:Display information generation means 106: Output means 110: Road ice and snow condition prediction program 111: Weather information 112: Setting value information 113: Snow and ice condition information 114: Stack risk information 115:Display information

Claims

1. Computer, a snow and ice condition prediction means for calculating predicted data on the state of snow and ice in the snow and ice layer on the road surface at each time based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface, using the history of snow removal at each point, including past location information of snow removal vehicles, schedules for snow removal by snow removal vehicles, and predicted data on weather and traffic conditions; A road surface snow and ice condition prediction program that functions as a display information generation means for generating display information that displays the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means, the snow removal history, and the snow removal schedule on a map by time.

2. The road surface ice and snow condition prediction program according to claim 1, further functioning as a snow removal schedule prediction means for determining the snow removal schedule based on the performance of the snow removal vehicle and the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means.

3. 3. A road surface snow and ice condition prediction program as described in claim 1 or 2, further functioning as a stuck risk calculation means that calculates a road surface slip friction coefficient from the snow and ice condition prediction data for each location and each time, and calculates the risk rate of a vehicle getting stuck based on the road surface slip friction coefficient.

4. A computer, snow and ice condition prediction means for calculating predicted data on the state of snow and ice in the snow and ice layer on the road surface at each time based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface, using predicted data on the history and schedule of snow removal at each point, weather conditions, and traffic conditions; a display information generating means for generating display information that displays the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means on a map for each time period; a road surface icy and snowy condition prediction program that functions as stuck risk calculation means that calculates a road surface slip friction coefficient from the snow and ice condition prediction data for each point and each time, and calculates a risk rate of a vehicle getting stuck based on the road surface slip friction coefficient, The display information generation means is a road surface snow and ice condition prediction program that generates display information indicating the necessity or schedule of the next snow removal for a point where the calculated stuck risk rate exceeds a predetermined threshold.

5. The road surface snow and ice condition prediction program according to claim 3, wherein the display information generation means generates display information indicating the necessity or schedule of a road closure for a point where the calculated stuck risk rate exceeds a predetermined threshold value.

6. A road surface snow and ice condition prediction program as described in any one of claims 1 to 5, wherein the display information generation means generates display information indicating the need for or a schedule for spraying antifreeze at a point where the road surface sliding friction coefficient calculated from the snow and ice condition prediction data is smaller than a predetermined threshold value.

7. A computer, snow and ice condition prediction means for calculating predicted data on the state of snow and ice in the snow and ice layer on the road surface at each time based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface, using predicted data on the history and schedule of snow removal at each point, weather conditions, and traffic conditions; a road surface icy and snowy condition prediction program that causes the program to function as display information generation means that generates display information that displays the snow and ice condition prediction data for each point calculated by the snow and ice condition prediction means on a map for each time period, The display information generation means is a road surface snow and ice condition prediction program that generates display information that displays traffic congestion information based on at least one of the snow and ice condition prediction data, the road surface slip friction coefficient calculated from the snow and ice condition prediction data, and the stuck risk rate calculated from the snow and ice condition prediction data and the road surface slip friction coefficient.

8. A snow and ice condition prediction means for calculating predicted data on the snow and ice condition in the snow and ice layer on the road surface at each time based on a heat balance model and an ice, water, and air balance model of the snow and ice layer on the road surface, using the history of snow removal at each point including past location information of snow removal vehicles, schedules for snow removal by snow removal vehicles, and predicted data on weather and traffic conditions; An information processing device having a display information generation means for generating display information that displays the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means, the snow removal history, and the snow removal schedule on a map by time.

9. A snow and ice condition prediction means for calculating predicted data on the snow and ice condition in the snow and ice layer on the road surface at each time based on a heat balance model and an ice, water, and air balance model of the snow and ice layer on the road surface, using the history of snow removal at each point including past location information of snow removal vehicles, schedules for snow removal by snow removal vehicles, and predicted data on weather and traffic conditions; an information processing device having a display information generating means for generating display information in which the snow and ice condition prediction data for each location calculated by the snow and ice condition prediction means is displayed on a map for each of the snow removal history and each of the scheduled snow removal times; A road surface snow and ice condition prediction system comprising a terminal that displays the display information.

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