Stack risk calculation program and information processing device

Abstract

To provide a stack risk rate calculation program and an information processing device for predicting a stack risk rate.SOLUTION: An information processing device 1 includes: snow ice state prediction means 102 for calculating snow ice state prediction data in a road surface snow ice layer based on a thermal balance model and an ice-water-air balance model of the road surface snow ice layer, using prediction data on a weather condition and a traffic condition, and determining a road surface sliding friction coefficient based on the calculated snow ice state prediction data; and stuck risk rate calculation means 103 for calculating a stuck risk rate of a vehicle in the road surface snow ice layer determined by a linear combination of a first function with a variable being the road surface snow ice thickness among the snow and ice condition prediction data, a second function with a variable being a road surface snow ice water content, and a third function with a variable being a road surface sliding friction coefficient.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a stuck risk calculation program and an information processing device. [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 slip friction coefficient based on the calculated snow and ice condition prediction data, but it has the problem of not being able to predict the possibility of a vehicle's tires spinning on that road surface, becoming unable to move forward and becoming stuck on the road, in other words, the possibility of the vehicle becoming stuck (hereinafter referred to as the "stuck risk rate").If multiple vehicles become stuck on a road, there is a risk that even vehicles that are not stuck will be unable to move, so predicting the stuck risk rate is extremely important.

[0006] An object of the present invention is to provide a stuck risk calculation program and an information processing device for predicting a stuck risk. [Means for solving the problem]

[0007] In order to achieve the above object, one aspect of the present invention provides the following stuck risk calculation program and information processing device.

[0008] [1] Computer, a snow and ice condition prediction means for calculating prediction data for snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and for determining a road surface slip friction coefficient based on the calculated prediction data for snow and ice conditions; A stuck risk calculation program that functions as a calculation means for calculating the stuck risk rate of a vehicle in the road surface snow and ice layer determined by a linear combination of a first function in the snow and ice condition prediction data that has the road surface snow and ice thickness as a variable, a second function in the snow and ice condition prediction data that has the road surface snow and ice moisture content as a variable, and a third function in the road surface sliding friction coefficient as a variable. [2] The calculation means has, as the linear combination, a first coefficient that correlates with temperature, a second coefficient that correlates with traffic congestion, and a third coefficient that correlates with snowfall, and multiplies the first function by the first coefficient, the second coefficient, and the third coefficient, multiplies the second function by the first coefficient and the second coefficient, and multiplies the third function by the negative correlation coefficient of the first coefficient and the second coefficient. [3] Computer, a snow and ice condition prediction means for calculating prediction data for snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and for determining a road surface slip friction coefficient based on the calculated prediction data for snow and ice conditions; a stuck risk calculation program that functions as a calculation means for determining a stuck risk based on a first table that previously associates the road surface sliding friction coefficient with a stuck risk when the air temperature is below a predetermined value; determining a stuck risk based on a second table that previously associates the road surface snow and ice thickness, snowfall, and stuck risk when the air temperature is above the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is below a predetermined value; and determining a stuck risk based on a third table that previously associates the road surface snow and ice thickness and stuck risk when the air temperature is above the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is above a predetermined value. [4] The stuck risk calculation program described in [3], wherein the calculation means uses, as the first table, information in which the road surface sliding friction coefficient and the stuck risk rate are in a negative proportional relationship, as the second table, information in which the road surface snow / ice thickness and the amount of snowfall and the stuck risk rate are in a directly proportional relationship, and as the third table, information in which the road surface snow / ice thickness and the stuck risk rate are in a directly proportional relationship. [5] A snow and ice condition prediction means for calculating prediction data on snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and determining the road surface slip friction coefficient based on the calculated prediction data on snow and ice conditions; An information processing device having a calculation means for calculating the risk of a vehicle getting stuck in the snow and ice layer on the road surface, which is determined by a linear combination of a first function having the road surface snow and ice thickness as a variable from the snow and ice condition prediction data, a second function having the road surface snow and ice moisture content as a variable from the snow and ice condition prediction data, and a third function having the road surface sliding friction coefficient as a variable. [6] A snow and ice condition prediction means for calculating prediction data on snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice-water-air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and determining a road surface slip friction coefficient based on the calculated prediction data on snow and ice conditions; an information processing device having a calculation means for determining a risk of getting stuck based on a first table that previously associates the road surface sliding friction coefficient with a risk of getting stuck when the air temperature is below a predetermined value; determining a risk of getting stuck based on a second table that previously associates the road surface snow and ice thickness, snowfall amount, and risk of getting stuck when the air temperature is above the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is below a predetermined value; and determining a risk of getting stuck based on a third table that previously associates the road surface snow and ice thickness and risk of getting stuck when the air temperature is above the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is above a predetermined value. [Effects of the Invention]

[0009] According to the inventions set forth in claims 1 to 6, it is possible to predict the risk of getting stuck. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of a configuration of an information processing device according to an embodiment. [Figure 2]FIG. 2 is a diagram showing the model concept for analyzing the state of snow and ice layers. [Figure 3] 3(a) and 3(b) are a schematic diagram and a flowchart for explaining the stuck determination operation. [Figure 4] FIG. 4 is a flowchart for explaining the operation of predicting the snow and ice condition of the road surface and the operation of calculating the stuck risk rate. [Figure 5] FIG. 5 is a diagram showing an example of displaying the road surface snow / ice condition and the risk of getting stuck. [Figure 6] 6(a) to 6(c) are diagrams showing an example of the structure of a table that associates snow and ice condition information with stuck risk rates in a modified example of this embodiment. [Figure 7] FIG. 7 is a flowchart for explaining the operation of predicting the snow and ice condition of the road surface and the operation of calculating the stuck risk rate. DETAILED DESCRIPTION OF THE INVENTION

[0011] [Embodiment Mode] (Configuration of information processing device) FIG. 1 is a schematic diagram illustrating an example of a configuration of an information processing device according to an embodiment.

[0012] This information processing device 1 processes the information provided to it and calculates the snow and ice conditions at the predicted location and the possibility of the vehicle getting stuck on the snow and ice layer (hereinafter referred to as the "stuck risk rate").It is a server-type information processing device that operates in response to requests input from 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.It should be noted 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.

[0013] The information processing device 1 comprises a control unit 10 which is composed of a CPU and the like and controls each unit and executes various programs, a storage 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.

[0014] The control unit 10 executes the stuck risk rate calculation program 110 described later, thereby functioning as a weather information acquisition means 100, a setting means 101, a snow and ice condition prediction means 102, a stuck risk rate calculation means 103, an output means 104, a suggestion means 105, etc.

[0015] 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.

[0016] 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.

[0017] The snow and ice condition prediction means 102 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 that accompany temperature changes based on a heat balance model and an ice-water-air balance model for the road surface snow and ice layer, thereby calculating the snow and ice temperature, snow and ice thickness, and the mixture ratio of water, ice, and air in the snow and ice layer, predicting these snow and ice conditions and their time transitions, and generating snow and ice condition information 113. The snow and ice conditions include, 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. Note that the method for calculating the snow and ice condition information 113 and the method for calculating the road surface sliding friction coefficient, which will be described below, are based on the contents of JP 2008-102006 A.

[0018] The stuck risk calculation means 103 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. 3(a) and 3(b)), and stores the stuck risk information 114 in the storage unit 11. The method of calculating the stuck risk will be described in detail later.

[0019] The output means 104 outputs the snow and ice condition information 113 predicted by the snow and ice condition prediction means 102 and the stuck risk rate information 114 calculated by the stuck risk rate calculation means 103 to other devices.

[0020] The suggestion means 105 outputs suggestion information 115 that suggests the timing to issue a warning, the timing to spray antifreeze, the timing to perform snow removal, etc. based on the snow and ice condition information 113 and the stuck risk rate information 114.

[0021] The memory unit 11 stores a stuck risk rate calculation program 110 that 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, and proposal information 115, etc.

[0022] As an example, the information processing device 1 receives weather information such as temperature, wind speed, and humidity and / or weather information including weather forecast values for these from the outside in response to user operations on a connected external terminal, and predicts snow and ice conditions and road surface slip friction coefficients based on the received information and setting value information such as calculation conditions and initial conditions input by the user. The information processing device 1 also calculates a stuck vehicle risk rate based on this information, and, as necessary, suggests to the user the timing to issue a warning, spray antifreeze, or perform snow removal based on the stuck vehicle risk rate. Each component will be described in detail below.

[0023] (Operation of information processing device) Next, the operation of this embodiment will be explained by dividing it into (1) basic principle, (2) operation for predicting snow and ice conditions on the road surface, (3) basic principle for determining whether a vehicle is stuck, and (4) operation for calculating the risk of the vehicle being stuck.

[0024] (1) Basic principle FIG. 2 is a diagram showing the model concept for analyzing the state of snow and ice layers.

[0025] First, we will explain the model that serves as the basis for predicting the state of snow and ice layers. Figure 2 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 and becomes a compacted road surface, mainly due to vehicle traffic. 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.

[0026] In addition to these heat balance components due to anthropogenic factors, natural factors are also considered to be contributing to the heat balance. Figure 2 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 solar radiation (including absorption by the snow and ice layer, reflection by the atmosphere, and transmission through the pavement surface) and latent heat due to 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 either 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.

[0027] 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.

[0028] (2) Road snow and ice condition prediction FIG. 4 is a flowchart for explaining the operation of predicting the snow and ice condition of the road surface and the operation of calculating the stuck risk rate.

[0029] First, 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 (S10). 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.

[0030] Next, the snow and ice condition prediction means 102 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 setting value information 112 based on the above-mentioned "(1) Basic Principle" (S11), and 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 (S12). The specific method for calculating the road surface sliding friction coefficient μ is as follows. First, the correlation between the volumetric proportions of water, ice, and air in the snow and ice layer (volume water content θw, volume ice content θi, and volume air 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 from the setting table based on the calculated snow and ice condition prediction data, and the sliding friction coefficient is determined.

[0031] Next, the stuck danger rate calculation means 103 calculates the stuck danger rate R (S13), but before calculating the stuck danger rate, the basic principle of stuck judgment will be explained.

[0032] (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.

[0033] 3(a) and 3(b) are a schematic diagram and a flowchart for explaining the stuck determination operation.

[0034] First, as shown in Figure 3(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.

[0035] 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.

[0036] 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.

[0037] The above determination flow will be explained in detail below with reference to FIG.

[0038] 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).

[0039] 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).

[0040] 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).

[0041] 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).

[0042] 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).

[0043] 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).

[0044] 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).

[0045] 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.

[0046] 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 with which the tire sinks, and road surface friction coefficient μ affects the ease with which the tire spins, 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 103 calculates the rate.

[0047] (4) Stack risk calculation operation

[0048] The stuck risk calculation means 103 uses the road surface snow and ice thickness Hs, road surface snow and ice moisture 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 calculated stuck risk rate R in the memory unit 11 as stuck risk rate information 114 (S13).

number

[0049] 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 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).

[0050] 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

[0051] Next, the output means 104 outputs the stuck risk rate information 114 to a terminal used by an administrator who manages the road or a user who uses the road (S14). Each terminal receives the stuck risk rate information 114 and displays a screen such as that shown in Fig. 5 on its display unit. The output means 104 may also upload the stuck risk rate information 114 to a cloud or a server other than the terminal, making the uploaded information accessible from an external terminal.

[0052] FIG. 5 is a diagram showing an example of displaying the road surface snow / ice condition and the risk of getting stuck.

[0053] The screen 104a has display fields 1040 to 1046 for displaying various information, and a setting field 1047 for inputting initial conditions.

[0054] A display field 1040 displays the road surface slip friction coefficient μ (solid line) predicted by the snow and ice condition prediction means 102 and the time change of the stuck risk rate R (dashed line) calculated by the stuck risk rate calculation means 103.

[0055] The display field 1041 displays the height of water contained in the snow, the height of ice contained in the snow, the height of air contained in the snow, and the total height of snow and ice on the road surface as a change over time, as predicted by the snow and ice condition prediction means 102.

[0056] A display field 1042 displays the temperature, wind speed, relative temperature, snow temperature, and road surface temperature over time based on the weather information 111 as the first weather condition.

[0057] A display field 1043 displays the amount of precipitation and the amount of snowfall over time based on the weather information 111 as the second weather condition.

[0058] A display field 1044 displays hourly traffic volume and time-dependent changes in travel speed as traffic conditions.

[0059] Display field 1045 displays the water mass and ice mass balance on the road surface.

[0060] A display field 1046 displays the heat balance of the snow and ice layer on the road surface.

[0061] Next, the suggestion means 105 outputs suggestion information 115 that suggests the timing to issue a warning, the timing to spray antifreeze, the timing to perform snow removal, etc. based on the snow and ice condition information 113 and the stuck vehicle risk information 114. Specifically, the suggestion means 105 suggests spraying antifreeze so as not to decrease the road surface sliding friction coefficient μ at the time when the stuck vehicle risk exceeds a predetermined threshold, or a predetermined time before that time, or suggests snow removal so as not to increase the thickness of snow and ice on the road surface, or suggests a road closure if these measures are not sufficient.

[0062] (Effects of the embodiment) According to the above-described embodiment, based on the basic principles of stuck-car detection, the road surface snow and ice thickness Hs, the road surface snow and ice moisture content Θs, and the road surface sliding friction coefficient μ among the predicted snow and ice condition information are each considered to contribute to the risk of getting stuck, and the contribution of each item is determined based on the air temperature and hourly snowfall to calculate the risk of getting stuck.As a result, the risk of getting stuck can be predicted from the predicted state of the snow and ice layer.

[0063] Moreover, the time change in the stuck risk rate is presented by the output means 104, so that the user can easily grasp the transition of the stuck risk rate. Furthermore, the time change in the stuck risk rate is presented together with the time change in each item of the weather information 111 and the snow and ice condition information 113, so that the relationship between each type of information and the stuck risk rate can be grasped, and material for more detailed consideration can be provided.

[0064] Furthermore, the timing for issuing a warning, the timing for spraying antifreeze, the timing for snow removal, etc. are suggested based on the snow and ice condition information 113 and the stuck risk rate information 114, so even an inexperienced person in charge who is unable to take subsequent action based on the snow and ice condition information 113 and the stuck risk rate information 114 alone can take appropriate countermeasures.

[0065] [Variations] In the above embodiment, a method for calculating and quantifying the risk of a vehicle getting stuck from information obtained on the snow and ice condition of the road surface has been described, but any other method that can indicate the risk of a vehicle getting stuck may be used, and for example, as described below, the table used may be changed based on information obtained on the snow and ice condition of the road surface to determine the risk of a vehicle getting stuck. Note that unless otherwise specified, the same configuration as in the above embodiment is assumed.

[0066] 6(a) to 6(c) are diagrams showing an example of the structure of a table that associates snow and ice condition information with stuck risk rates in a modified example of this embodiment.

[0067] The tables 103a to 103c are used by the stuck risk calculation means 103 when calculating the stuck risk, and one of them is selected based on the conditions described below.

[0068] Table 103a shown in FIG. 6(a) is a table for cases where the coefficient of sliding friction decreases and a vehicle becomes stuck, and associates the coefficient of sliding friction of the road surface out of the snow and ice condition information 113 with the risk of getting stuck.

[0069] Table 103b shown in Figure 6(b) is a table for when a vehicle becomes stuck when the moisture content of the snow and ice is low, and it associates the thickness of the snow and ice on the road surface, the amount of snowfall per hour, and the risk of getting stuck, which are included in the snow and ice condition information 113.

[0070] Table 103c shown in Figure 6(c) is a table for when getting stuck occurs when the moisture content of snow and ice is high, and it associates the thickness of snow and ice on the road surface, which is included in snow and ice condition information 113, with the risk of getting stuck.

[0071] FIG. 7 is a flowchart for explaining the operation of predicting the snow and ice condition of the road surface and the operation of calculating the stuck risk rate.

[0072] First, the weather information acquisition means 100 acquires weather information from an external source and stores it in the memory unit 11 as weather information 111. In addition, the setting means 101 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 (S20).

[0073] Next, the snow and ice condition prediction means 102 performs a heat and material balance analysis of the road surface snow and ice and a heat balance analysis of the pavement and ground based on the weather information 111 and the setting value information 112 (S21), and outputs the road surface snow and ice thickness Hs, the road surface snow and ice water content Θs, and the road surface sliding friction coefficient μ as the results (S22).

[0074] Next, the stuck risk rate calculation means 103 selects (S23 to S27) tables 103a to 103c for calculating the stuck risk rate R based on the snow and ice condition information 113. First, if the air temperature is −5° C. or lower (S23; Yes), the stuck risk rate calculation means 103 determines that the stuck condition is a reduced slip friction coefficient type stuck condition in which the road surface slip friction coefficient is the main cause of the stuck condition, and adopts table 103a shown in FIG. 6(a) (S24).

[0075] In addition, if the air temperature is higher than -5°C (S23; No) and the moisture content of the snow and ice is less than 25% (S25; Yes), the stuck risk calculation means 103 determines that the stuck condition is a low moisture content, thick, packed snow type stuck, where the thickness of the snow and ice on the road surface and the amount of snowfall per hour are the main causes of stuckness, and adopts table 103b shown in Figure 6(b) (S26).

[0076] Furthermore, if the moisture content of the snow and ice is 25% or more (S25; No), it is determined that the thickness of the snow and ice on the road surface is the main cause of the stack, and a high moisture content, thick, packed snow stack is formed, and table 103c shown in Figure 6(c) is adopted (S27).

[0077] Next, the stuck risk calculation means 103 uses the necessary information from among the road surface snow and ice thickness Hs, road surface snow and ice water content Θs, and road surface sliding friction coefficient μ obtained in step S22 to calculate the stuck risk rate R based on the adopted tables 103a to 103c, and stores it in the memory unit 11 as stuck risk rate information 114 (S28).

[0078] Next, the output means 104 outputs the stuck risk rate information 114 to a terminal used by an administrator who manages the road or a user who uses the road (S29).

[0079] (Effects of Modification) According to the above-described modified example, the table to be used is changed by conditional branching based on the temperature and moisture content, and each table corresponds one or two parameters with the risk of getting stuck, so that the same effect as that of the embodiment can be achieved with less calculation cost.

[0080] [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.

[0081] For example, in the above embodiment and modified examples, the numerical values of the snow and ice condition prediction means 102 and the stuck risk calculation means 103 are calculated based on predetermined formulas, but they may also be obtained using a multivariate analysis technique, or may be obtained by learning using a technique such as a decision tree or neural network and using the learning results.

[0082] In the above embodiment, the functions of the means 100 to 105 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 stored on a recording medium such as a CD-ROM and provided. 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]

[0083] 1: Information processing equipment 10: Control section 11: Storage section 12: Communications Department 100:Method of obtaining weather information 101: Setting method 102: Snow and ice condition prediction method 103: Stack risk calculation means 104: Output means 105: Proposal means 110: Stack risk calculation program 111: Weather information 112: Setting value information 113: Snow and ice condition information 114: Stack risk information 115: Proposal information

Claims

1. Computer, a snow and ice condition prediction means for calculating prediction data for snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and for determining a road surface slip friction coefficient based on the calculated prediction data for snow and ice conditions; A stuck risk calculation program that functions as a calculation means for calculating the stuck risk rate of a vehicle in the snow and ice layer on the road surface, which is determined by a linear combination of a first function in the snow and ice condition prediction data that has the road surface snow and ice thickness as a variable, a second function in the snow and ice condition prediction data that has the road surface snow and ice moisture content as a variable, and a third function in the road surface sliding friction coefficient as a variable.

2. 2. The stack risk calculation program according to claim 1, wherein the calculation means has, as the linear combination, a first coefficient that correlates with temperature, a second coefficient that correlates with traffic congestion, and a third coefficient that correlates with snowfall, and multiplies the first function by the first coefficient, the second coefficient, and the third coefficient, multiplies the second function by the first coefficient and the second coefficient, and multiplies the third function by a negative correlation coefficient of the first coefficient and the second coefficient.

3. Computer, a snow and ice condition prediction means for calculating prediction data for snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and for determining a road surface slip friction coefficient based on the calculated prediction data for snow and ice conditions; a stuck risk calculation program that functions as a calculation means for determining a stuck risk based on a first table that previously associates the road surface sliding friction coefficient with a stuck risk when the air temperature is below a predetermined value; determining a stuck risk based on a second table that previously associates the road surface snow and ice thickness, snowfall amount, and stuck risk when the air temperature is greater than the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is less than a predetermined value; and determining a stuck risk based on a third table that previously associates the road surface snow and ice thickness and stuck risk when the air temperature is greater than the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is equal to or greater than a predetermined value.

4. 4. The stuck risk calculation program according to claim 3, wherein the calculation means uses, as the first table, information in which the road surface sliding friction coefficient and the stuck risk rate are in a negative proportional relationship, as the second table, information in which the road surface snow / ice thickness and the amount of snowfall and the stuck risk rate are in a directly proportional relationship, and as the third table, information in which the road surface snow / ice thickness and the stuck risk rate are in a directly proportional relationship.

5. a snow and ice condition prediction means for calculating prediction data for snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and for determining a road surface slip friction coefficient based on the calculated prediction data for snow and ice conditions; An information processing device having a calculation means for calculating the risk of a vehicle getting stuck in the snow and ice layer on the road surface, which is determined by a linear combination of a first function having the road surface snow and ice thickness as a variable from the snow and ice condition prediction data, a second function having the road surface snow and ice moisture content as a variable from the snow and ice condition prediction data, and a third function having the road surface sliding friction coefficient as a variable.

6. a snow and ice condition prediction means for calculating prediction data for snow and ice conditions in the snow and ice layer on the road surface based on a heat balance model and an ice / water / air balance model of the snow and ice layer on the road surface using prediction data on meteorological conditions and traffic conditions, and for determining a road surface slip friction coefficient based on the calculated prediction data for snow and ice conditions; an information processing device having a calculation means for determining the risk of getting stuck based on a first table that previously associates the road surface sliding friction coefficient with the risk of getting stuck when the air temperature is below a predetermined value; determining the risk of getting stuck based on a second table that previously associates the road surface snow and ice thickness, snowfall amount, and risk of getting stuck when the air temperature is above the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is below a predetermined value; and determining the risk of getting stuck based on a third table that previously associates the road surface snow and ice thickness and risk of getting stuck when the air temperature is above the predetermined value and the road surface snow and ice moisture content in the snow and ice condition prediction data is above a predetermined value.

Images