JP4500602B2 - Road surface temperature prediction system, road surface temperature prediction method, and road surface temperature prediction program - Google Patents

Description

  The present invention relates to a road surface temperature prediction system, a road surface temperature prediction method, and a road surface temperature prediction program for predicting a road surface temperature.

  Conventionally, as a method of predicting the road surface temperature, the road surface information data determined by the type of road surface such as altitude, roughness length, albeto (reflectivity), evaporation rate, and the weather measured near the target point of the road surface freezing prediction, Generally, a prediction method is applied by applying a heat balance model to weather information data such as cloud cover, temperature, atmospheric pressure, wind direction, wind speed, precipitation (snow), and net radiation. By doing in this way, it is possible to acquire road surface temperature, without measuring actual road surface temperature.

Further, as described in Patent Document 1, a method of detecting road surface freezing using an integrated value of the daily solar radiation amount has been devised.
JP 09-114371 A

  However, in the road surface temperature prediction by the conventional method, the prediction accuracy may not be improved. This is because the road surface temperature is influenced not only by road surface information and weather information determined by the type of road surface, but also by the surrounding conditions of the road such as buildings on the side of the road and vehicles traveling on the road.

  Moreover, since the amount of solar radiation cannot be predicted even by the method described in Patent Document 1, the amount of solar radiation cannot be used to predict the road surface temperature.

  The present invention has been made in view of the above problems, and an object of the present invention is to provide a road surface temperature prediction system, a road surface temperature prediction method, and a road surface temperature prediction capable of predicting a road surface temperature in consideration of surrounding conditions of the road. To provide a program.

In order to solve the above problems, a road surface temperature prediction system according to the present invention includes an all-sky camera that is installed on a moving body that moves on a road and captures all-sky images, and is imaged by the all-sky camera The solar radiation shielding amount data acquisition means for acquiring the solar radiation shielding amount data indicating the direct radiation rate of the solar radiation based on the location of the road, and the road based on the direct radiation rate of the solar radiation indicated by the solar radiation shielding amount data seen including a road surface temperature prediction means for predicting surface temperature for each location, a, wherein the imaging data, position data indicating the captured position is associated, said solar shading data acquisition means, the position data The solar orbit is acquired based on the solar radiation amount, and the solar shading amount data is calculated based on the imaging data and the solar orbit.

  By doing so, it is possible to obtain solar radiation shielding amount data indicating the direct rate of solar rays on a specific road surface, and to predict the road surface temperature on the specific road surface based on the solar radiation shielding amount data. The road surface temperature can be predicted in consideration of the surrounding conditions.

In the above surface temperature predicting system, traffic data acquisition means for acquiring traffic data showing the traffic of the road, further comprising a said road surface temperature prediction means, a further road surface temperature based on the traffic data It may be predicted. In this way, the road surface temperature can be further predicted based on the traffic volume data, so that the road surface temperature can be predicted with higher accuracy in consideration of the surrounding conditions of the road.

Further, in the road surface temperature prediction system, the road surface temperature prediction means changes the solar radiation amount, which is a parameter of the heat balance model, in accordance with the direct reach rate of sunlight indicated by the solar radiation shielding amount data, so that the point of the road The road surface temperature may be predicted every time . If it does in this way, solar radiation shielding amount data can be used as a parameter of heat balance calculation.

In addition, the road surface temperature prediction method according to the present invention is a solar shading that indicates the direct reach of sunlight based on imaging data that is installed on a moving body that moves on a road and that is imaged by an all-sky camera that captures an all-sky image. the amount data, seen including a solar radiation-shielding amount data obtaining step of obtaining for each point of the road, and a road surface temperature prediction step of predicting a surface temperature for each point of the road on the basis of the solar radiation-shielding amount data, the The imaging data is associated with position data indicating the imaged position, and the solar shading amount data acquisition step includes a step of acquiring a solar orbit based on the position data, the imaging data and the solar orbit. And calculating the solar shading amount data .

In addition, the program according to the present invention is based on imaging data captured by a sky camera that is installed on a moving body moving on a road and shoots a sky image, and stores solar radiation shielding amount data indicating the direct reach of sunlight. A computer that functions as a solar radiation shielding amount data acquisition means for acquiring each road location, and a road surface temperature prediction means for predicting a road surface temperature for each road location based on the solar radiation shielding amount data. Is associated with position data indicating an imaged position, and the solar shading amount data acquisition means acquires a solar orbit based on the position data, and based on the imaging data and the solar orbit, Sunlight shielding amount data is calculated .

  Embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a configuration diagram of a road surface temperature prediction system 1 according to the present embodiment. For the road surface temperature prediction system 1, for example, a computer similar to a known personal computer or server computer can be used. CPU 10, RAM 12, input / output unit 14, communication unit 16, database 18, external storage medium 20, hard disk 22, a display unit 24, an input unit 26, and a bus 28. The CPU 10, RAM 12, and input / output unit 14 are connected to each other via a bus 28. The input / output unit 14 is connected to the communication unit 16, the database 18, the external storage medium 20, the hard disk 22, the display unit 24, and the input unit 26. ing.

  The CPU 10 controls each part of the road surface temperature prediction system 1 and performs various calculations. For example, the CPU 10 performs calculation in a heat balance model described later and pattern recognition processing in a neural network. The RAM 12 operates as a work memory for the CPU 10. The RAM 12 holds programs and parameters related to various processes performed by the CPU 10. The input / output unit 14 relays data transmission / reception between the CPU 10, the communication unit 16, the database 18, the external storage medium 20, and the hard disk 22. Further, the communication unit 16, the database 18, the external storage medium 20, and the hard disk 22 are controlled according to instructions from the CPU 10. A conventionally known hard disk can be used as the hard disk 22 and stores computer programs and data. A program according to the present invention is also stored. As the external storage medium 20, any computer-readable information storage medium such as a flexible disk, a CD-ROM, a CD-RW, a DVD-RAM, a USB flash memory, a ROM card, and a removable hard disk can be used. And memorize data. A program according to the present invention is also stored. The database 18 stores data for pattern recognition in a neural network, which will be described later, and stores data necessary for calculation in a heat balance model, which will be described later. The display unit 24 is a display unit such as a display, and performs display according to an instruction from the CPU 10 to the user of the road surface temperature prediction system 1. The input unit 26 is an input unit such as a keyboard or a mouse, and receives an input by a user operation of the road surface temperature prediction system 1 and outputs it to the CPU 10.

  FIG. 2 is a relationship diagram between the road surface temperature prediction system 1 according to the present embodiment and a system existing outside thereof. The road surface temperature prediction system 1 is connected to a weather prediction system 2, a solar radiation shielding amount acquisition system 3, and a traffic volume acquisition system 4 via a communication network 5, and can acquire data from each system.

  The communication unit 16 of the road surface temperature prediction system 1 is connected to the communication network 5, and accesses the weather prediction system 2, the solar radiation shielding amount acquisition system 3, and the traffic volume acquisition system 4 in accordance with instructions from the CPU 10. The solar radiation shielding amount acquisition system 3 and the traffic volume acquisition system 4 receive weather data, solar radiation shielding amount data, and traffic volume data, which will be described later, and output them to the CPU 10 via the input / output unit 14.

  FIG. 3 is a functional block diagram of the road surface temperature prediction system 1 according to the present embodiment. The road surface temperature prediction system 1 functionally includes a weather data acquisition unit 30, a solar radiation shielding amount acquisition unit 32, a traffic volume acquisition unit 34, a heat balance calculation unit 36, a neural network 38, and a road surface temperature output unit 40. Has been.

  The meteorological data acquisition unit 30 acquires meteorological data by receiving meteorological forecast data and meteorological observation data that are meteorological data from the meteorological forecasting system 2. Specifically, the weather prediction system 2 includes, for example, a local weather prediction model (ANEMOS) based on a GPV (Grid Point Value, a value on a grid point of a weather element or a physical quantity) of the Japan Weather Association. In the weather forecast system 2, the national land numerical data such as 1 km altitude data, land use data, GPV data announced by the Japan Meteorological Agency such as the Meteorological Agency area model (RSM) and forecast guidance, seawater temperature data, snow line elevation data, etc. Weather forecast data is calculated based on the value data. The weather data acquisition unit 30 acquires GPV weather prediction data of weather, temperature, cloudiness, snowfall, and wind as weather data.

  Next, the solar shading amount data acquisition unit 32 acquires solar shading amount data from the solar shading amount acquisition system 3. The solar radiation shielding amount data refers to the direct rate, which is the rate at which solar radiation is not obstructed by buildings, trees, etc. that exist on the side of the road for each point of the road by performing observation as described below. It can be assumed that the data is obtained every time and the ratio is digitized.

  As the solar shading amount acquisition system 3 for acquiring the solar shading amount, it is preferable to use an observation vehicle 50 as shown in FIG. In the observation vehicle 50, a GPS antenna 52 for grasping the position of the own vehicle, a video camera 54 with a fisheye lens for photographing a whole sky image around the vehicle, and a temperature for measuring the temperature are shown in FIG. Temperature sensor 55 and a road surface temperature sensor 56 for measuring the road surface temperature are installed.

  FIG. 5 is a hardware configuration diagram of the observation vehicle 50. In the observation vehicle 50, a computer 60, a GPS logger 70, a back lamp 82, a vehicle speed detection circuit 84, a thermometer 86, a radiation thermometer 88, an all-sky camera 90, and a digital video camera 92 are installed. The thermometer 86 constitutes the temperature sensor 55, the radiation thermometer 88 constitutes the road surface temperature sensor 56, and the all-sky camera 90 and the digital video camera 92 constitute the fish camera-equipped video camera 54.

  A computer similar to a conventionally known personal computer can be used as the computer 60, and the computer 60 includes a control unit 62, a communication unit 64, and a storage unit 66. The control unit 62 controls each unit of the computer 60, and also stores information input from each device in the storage unit 66 and calculates a solar radiation shielding amount from the stored information as described below. The communication unit 64 is connected to the GPS logger 70, the all-sky camera 90, and the digital video camera 92 so that data can be transmitted and received. Further, the control unit 62 may be configured to control the GPS logger 70, the all-sky camera 90, and the digital video camera 92.

  The GPS logger 70 can be the same as a conventionally known GPS logger. The GPS logger 70 includes a control unit 72, a storage unit 74, a GPS antenna 76, a gyro sensor 78, and a communication unit 80. Is done. The communication unit 80 is connected to the back lamp 82, the vehicle speed detection circuit 84, the thermometer 86, the radiation thermometer 88, and the computer 60 so that data can be transmitted and received. In addition, the control unit 72 controls each unit of the GPS logger 70 and also performs processing of storing signals input from the GPS antenna 76, the gyro sensor 78, and the communication unit 80 in association with each other in the storage unit 74. Further, in accordance with an instruction from the computer 60, processing for reading data stored in the storage unit 74 and transmitting it to the computer 60 is also performed. Further, the controller 72 can be configured to control the thermometer 86 and the radiation thermometer 88.

  The GPS antenna 76 can be the same as a conventionally known GPS antenna that constitutes the GPS antenna 52. When the GPS antenna 76 receives a signal from a GPS satellite, the GPS logger 70 is connected to the observation vehicle 50. Position, orientation and speed of That is, latitude / longitude / altitude data (position data) indicating the latitude / longitude / altitude of the position where the observation vehicle 50 exists, azimuth data indicating the azimuth (direction in which the observation vehicle 50 is directed), and the earth of the observation vehicle 50 on the earth Get speed data indicating the speed at.

  The back lamp 82 is a back lamp provided in a normal vehicle and lights up when the observation vehicle 50 moves backward. Then, the GPS logger 70 receives information about whether or not the back lamp 82 is lit, and determines that the observation vehicle 50 is moving backward when it is lit. The vehicle speed detection circuit 84 is a circuit that detects the vehicle speed of the observation vehicle 50 provided in the normal vehicle, and outputs the vehicle speed of the observation vehicle 50 as numerical data. The GPS logger 70 receives the numerical data, and acquires movement speed data indicating the movement speed of the observation vehicle 50. The gyro sensor 78 may be the same as a conventionally known gyro sensor, and the gyro sensor 78 outputs the angular velocity of the observation vehicle 50 with respect to three axes orthogonal to each other. The GPS logger 70 acquires angular velocity data indicating the angular velocity of the three axes from the gyro sensor 78. If the GPS antenna 76 cannot receive a signal from a GPS satellite, the mobility from the final position / orientation / velocity data obtained from the signal from the GPS antenna 76 is determined as backward information, moving speed data, angular velocity. Based on the data, position data indicating the position where the observation vehicle 50 exists, direction data indicating the direction in which the observation vehicle 50 faces, and speed data indicating the speed of the observation vehicle 50 on the earth are acquired.

  A thermometer 86 similar to a conventionally known thermometer can be used, and the thermometer 86 measures the outside temperature of the observation vehicle 50 and outputs temperature data indicating the outside temperature to the GPS logger. The radiation thermometer 88 can be the same as a conventionally known radiation thermometer, and the radiation thermometer 88 measures the surface temperature of the road surface by measuring infrared radiation from the road surface. Road surface temperature data indicating the surface temperature of the road surface is output to the GPS logger 70.

  The control unit 72 acquires the position data, azimuth data, speed data, temperature data, road surface temperature data, the date and time when each of these data was acquired, or the observation date and time when each data was observed, acquired as described above. Data is stored in association with the storage unit 74. That is, each data is always observed during observation, and the simultaneously acquired data can be stored in association with each other. The observation date / time data and each of the data may be stored in association with each other.

  Next, as shown in FIG. 4, the all-sky camera 90 is installed on the roof portion of the observation vehicle 50 with the imaging unit facing upward in the observation vehicle 50. That is, the all-sky camera 90 performs all-sky shooting in the upward direction of the observation vehicle 50. The omnidirectional camera 90 can be the same as a conventionally known omnidirectional camera, but the circumference designed to capture a semicircular field of view particularly with an angle of view of 180 degrees or more. It is desirable to use a fisheye lens. Then, the all-sky camera 90 outputs imaging data as an imaging result to the computer 60 or the digital video camera 92. The imaging results of the all-sky camera 90 will be described below.

  First, the case where the observation vehicle 50 exists in a position as shown in FIG. 6 will be described. That is, the observation vehicle 50 exists at the point 104 on the road 100, and the building 102 exists on the side of the road. While the observation vehicle 50 moves on the road 100, it acquires solar radiation shielding amount data at several points.

  In this way, when the building 102 exists on the side of the road and the image is picked up by the omnidirectional camera 90, a circular image pickup result (sky map) as shown in FIG. 7 is obtained. That is, in the imaging result, the circumference of the circle is a photograph of the lens surface of the all-sky camera 90, and the center of the circle is a photograph of the direction in which the lens is facing. By photographing in this way, the result is that the building 102 is reflected in a part of the circle. In other words, a building or a solar shading object such as a tree around the point 104 appears in the imaging result.

  The imaging data is also constantly observed during observation, and is recorded in the digital video camera 92 as a moving image by recording with the digital video camera 92. Alternatively, it may be stored in the storage unit 66 of the computer 60. The imaging data is also stored in association with imaging date / time data indicating the date / time when the imaging data was stored or the date / time when the imaging data was stored, and the imaging data and the GPS logger are determined by the imaging date / time data and the observation date / time data. The position data, azimuth data, speed data, air temperature data, and road surface temperature data stored in 70 are associated with each other.

  And the control part 62 calculates the amount of solar radiation shielding based on the imaging data acquired as mentioned above, and the solar orbit determined by the revolution degree and rotation motion of the earth. This calculation process will be described in detail below.

First, the time angle t, altitude angle h, and azimuth angle A of the solar orbit are based on latitude φ, longitude λ, time H, solar declination δ, and time difference ET, using the following formulas (1) and (2). And Equation (3). The solar declination δ and the time difference ET vary depending on the date.
t = λ−9 + ET + (H−12) (1)
sinh = sinφsinδ + cosφcosδcost (2)
sinA = −cosδsint / cosh (3)

  As described above, the altitude angle h and the azimuth angle A of the sun are different values based on the latitude / longitude / date and time of the measurement point. The control unit 62 observes the date and time in a range that can be regarded as the same date and time as the date and time indicated by the imaging date and time data in the imaging data stored in the storage unit 66 or the digital video camera 92 in association with the imaging time data. The position data and direction data stored in the storage unit 74 in association with the date / time data are read out. In this way, the latitude / longitude / altitude of the place where the image data was imaged and the direction of the observation vehicle 50 when the image was captured can be acquired.

  When the latitude / longitude / elevation of the point where the imaged data is imaged can be acquired in this way, the position of the sun for each date and time at the point is acquired by Equation (1), Equation (2), and Equation (3). be able to. An example in which the positions are plotted on the imaging data is shown in FIG. The solar orbit becomes a line when connecting the points plotted every day, and in FIG. 8, the solar orbit for each month is shown as a line. The solar orbit 110 represents the January 1, the solar orbit 112 represents the April 1, and the solar orbit 114 represents the July 1 solar orbit. The imaging data does not normally change from day to day, but the sun orbit draws a different line every day in this way on the imaging data. In this process, since the solar orbit varies depending on the altitude, the altitude correction at the measurement point and the altitude correction of the solar radiation shielding object are performed according to the altitude of the measurement point indicated by the position data. Furthermore, the direction correction which rotates imaging data is also performed according to the direction of the observation vehicle 50 shown by direction data.

  And the control part 62 calculates the ratio (sunlight direct rate) in which there is no solar radiation obstruction | occlusion like the building 102 on a solar trajectory in imaging data. Specifically, for example, by acquiring solar orbit shielding data as shown in FIG. 9, the direct delivery rate can be calculated. FIG. 9 shows whether or not there is a shield in the solar orbit for each hour and minute, with the vertical axis as the distance from the reference point and the horizontal axis as the time on a certain road and month and day. It is a graph. The graph displays black when there is an obstruction on the solar orbit and white when there is no obstruction. Each becomes solar orbit shielding data representing the presence or absence of shielding of the sun orbit for each point and hour. And the ratio of the black and white for every point turns into the direct rate of the sunlight in the said point.

  In this way, it is possible to calculate the direct rate of solar rays for each latitude / longitude / altitude / month / day at the point where the observation vehicle 50 performs the observation travel, and the solar radiation shielding amount data acquisition unit 32 can calculate the direct rate. Is acquired as solar shading amount data. In addition, the sunlight shielding amount data can use, for example, the daylight exposure time of the day. The calculation of the direct sunlight rate of the sunlight may be performed by a computer provided separately based on each data acquired by the observation vehicle 50, for example.

  Next, the traffic volume acquisition unit 34 acquires traffic volume data from the traffic volume acquisition system 4 for each point of the road. As the traffic volume acquisition system 4, for example, there is a public institution responsible for road administration, and the traffic volume acquisition unit 34 can acquire traffic volume data indicating the traffic volume of vehicles provided by the public agency for each point. it can. The obtained traffic volume data may be actual traffic volume data or traffic volume prediction data. For example, when the road surface temperature is calculated by a heat balance model described later for a future date and time at a certain point, The amount acquisition unit 34 acquires the traffic volume prediction data at the location and the date and time as traffic volume data. That is, the traffic volume acquisition unit 34 may perform traffic volume prediction based on actual traffic volume data and acquire traffic volume prediction data.

  And based on the weather data acquired by the weather data acquisition unit 30, the solar radiation shielding amount data acquired by the solar radiation shielding amount acquisition unit 32, and the traffic volume data acquired by the traffic volume acquisition unit 34, heat The balance calculator 36 performs a heat balance calculation by a heat balance model for calculating the road surface temperature at a specific point and a specific date and time. In the heat balance calculation, the road surface temperature can be calculated by calculating the heat balance of the road surface. The calculation of the heat balance as a prior art is described in detail in “Kenji Jun,“ Meteorology of water environment-Surface water balance and heat balance ”, Asakura Shoten, October 10, 2000, p.128-159”. Has been.

  FIG. 10 is a conceptual diagram of heat balance calculation in the present embodiment. Heat entering the road surface (atmospheric radiation, solar radiation, cloud radiation, underground heat conduction flux, heat generated by traffic) and heat exiting the road surface (radiation from the ground, sensible heat flux, latent heat flux) The temperature of the road surface can be calculated by calculating the balance of the underground heat conduction flux. And by obtaining these heat balance calculation elements based on solar radiation shielding data, traffic data, and weather data, it is possible to calculate the road surface temperature in consideration of the surrounding conditions of the road I have to. The specific calculation method will be described below.

In the heat balance calculation, first road to calculate the net radiation amount R _n is the amount of radiation net absorption. Net radiation amount R _n is, atmospheric radiation, solar radiation, radiation from the cloud, a radiation amount that is absorbed into the final road surface by balance of the radiation from the ground, the albedo alpha, horizontal surface solar radiation (the solar radiation If the amount is S ^↓ , the injection rate (black body degree) is ε, the Stefan-Boltzmann constant is σ, the road surface temperature is T _S , and the long-wave radiation from the atmosphere is F ^↓ , the following equation (4) It is known that
R _n = (1−α) × S ^↓ −ε × (σ × T _S ⁴ −F ^↓ ) (4)

And the horizontal solar radiation amount S ^↓ which is one of the parameters of the heat balance model changes according to the solar radiation shielding amount data. That is, when solar radiation is blocked, the horizontal solar radiation amount S ^↓ is zero. For example, when calculating the heat balance of one day, the numerical value obtained by multiplying the direct solar radiation rate indicated by the solar radiation shielding amount data by the horizontal solar radiation amount when there is no shielding object is the horizontal solar radiation amount S ^↓ . Further, the albedo α, the emission rate ε, and the long wave radiation amount from the atmosphere F ^↓ can be calculated from the weather data. Therefore, based on the solar radiation-shielding amount data and the meteorological data by equation (4), the net amount of radiation R _n can be calculated as a function of surface temperature T _S, based on the solar radiation-shielding amount data, it is possible to heat balance calculations become able to.

Next, as shown in FIG. 10, the sensible heat flux H, latent heat flux IotaE, underground heat conduction flux G, transportation by the relationship of the heat W and net radiation amount R _n generated (heat balance) of the following formula ( 5).
R _n + W = H + ιE + G (5)

The heat W generated by traffic changes according to traffic data. That is, the exhaust heat transfer coefficient w _C per one _vehicle, TR (h) the traffic time zone average in the time h, Ttr virtual temperature of the vehicle, when the road surface temperature is T _S, W is the following formula It is expressed as (6).
W = w _C × TR (h) × (Ttr−T _S ) (6)

Thus, heat W generated by the traffic can be calculated as a function of surface temperature T _S from the traffic, by obtaining the traffic data, based on the traffic amount indicated by the traffic data, the heat balance Calculations can be performed.

Further, the sensible heat flux H and the latent heat flux ιE are represented by the following formulas (7) and (8), respectively. Where c _P is the constant pressure specific heat of air, ρ is the density of air, C _H is the bulk transport coefficient of sensible heat flux, U ′ is the sum of ground wind and wind speed due to vehicle traffic, T is the ground temperature, and ι is water or The latent heat of vaporization of ice, β is the evaporation efficiency of the road surface, q _S is the saturation specific humidity with respect to the ground temperature T, and q is the saturation specific humidity with respect to the ground surface temperature T _S. The wind speed and surface temperature of the ground wind can be acquired from weather data.
_{H = c P × ρ × C} H × U '× (T S -T) ··· (7)
ιE = ι × ρ × β × C _H × U ′ × (q _S −q) (8)

From these equations, it can be seen that the sensible heat flux H and the latent heat flux ιE are functions of the wind speed due to vehicle traffic. Since the wind speed due to vehicle traffic is determined based on the traffic volume indicated by the traffic volume data, the sensible heat flux H and the latent heat flux ιE can be calculated based on the traffic volume data and weather data. Then, sensible heat flux H can be calculated as a function of surface temperature T _S.

Further, since the underground heat transfer flux G is represented by the following formula (9), by performing the heat balance calculation in road shown in Equation (10) can calculate the surface temperature T _S. However, λ _G is the thermal conductivity in the ground, _TG is the underground temperature, and z is the depth from the road surface (z = 0 is the road surface).
G (z) = − λ _G (dT _G / dz) (9)
−λ _G (dT _G / dz) | _{z = 0} = R _n + W−H−ιE (10)

  In this way, based on the solar shading data, traffic data, and weather data, the road surface temperature corresponding to the point (specific point on the road) corresponding to the solar shading data, traffic data, and weather data. Can be calculated. And if prediction data is used for each data, the predicted value of road surface temperature is acquirable. The road surface temperature output unit 40 outputs the acquired road surface temperature or the predicted value of the road surface temperature, for example, by displaying it on the display unit 24. At the time of this output, it is desirable to output the road surface temperature or the predicted value of the road surface temperature in association with the date / time data and data indicating the corresponding point (for example, latitude / longitude / altitude).

  Although the road surface temperature is calculated by an analytical method in the above embodiment, the road surface temperature is often obtained using the neural network 38 without measuring some data. For example, if a neural network that performs supervised learning is used as the neural network 38, the correspondence relationship between the solar shading amount data, the traffic volume data, the weather data, and the road surface temperature data is represented by, for example, position data. Each time the data is input to the neural network, the corresponding road surface temperature data can be obtained. In this case, when acquiring the solar shading amount data, the road surface temperature data stored in association with the imaging data for calculating the solar shading amount data is used as the road surface temperature data. By using temperature data stored in association with imaging data for calculating the solar shading amount data as temperature, road surface temperature data with higher accuracy can be obtained.

  Specific examples are shown in FIGS. FIG. 11 is a graph plotting road surface temperature data for each point. FIG. 12 is a graph plotting the irradiation time as the solar shading amount data for each point. As can be seen from these figures, there is a correlation between the solar radiation shielding amount and the road surface temperature, and the correlation can be stored in the neural network 38.

  When acquiring the road surface temperature data, the solar radiation shielding amount data, the traffic volume data, and the weather data are input to the neural network 38, so that the road surface temperature data corresponding to the solar radiation shielding amount data, the traffic volume data, and the weather data is obtained. Is output. Thus, the heat balance calculation part 36 can acquire road surface temperature by using a neural network.

  As described above, the road surface temperature prediction system 1 can calculate the road surface temperature in consideration of the surrounding conditions of the road such as the shielding of solar radiation by the shielding object and the traffic volume. Furthermore, once the imaging data is acquired, it is possible to obtain solar shading amount data for each month and day, which makes it easy to calculate the road surface temperature for each point.

It is a hardware block diagram of the road surface temperature prediction system concerning an embodiment of the invention. It is a related figure of the road surface temperature prediction system concerning embodiment of this invention, and the system which exists in the exterior. It is a functional block diagram of the road surface temperature prediction system concerning an embodiment of the invention. 1 is an external view of an observation vehicle according to an embodiment of the present invention. It is a functional block diagram of the observation vehicle concerning an embodiment of the invention. It is a bird's-eye view of the road concerning an embodiment of the invention. It is imaging data concerning an embodiment of the invention. It is the imaging data which described the solar orbit concerning embodiment of this invention. It is a graph of the solar orbit shielding data for every point and hour concerning the embodiment of the present invention. It is a conceptual diagram of the heat balance concerning embodiment of this invention. It is a graph which shows the road surface temperature data for every point concerning embodiment of this invention. It is a graph which shows the solar radiation shielding amount data for every point concerning embodiment of this invention.

Explanation of symbols

  1 road surface temperature prediction system, 2 weather prediction system, 3 solar radiation shielding amount acquisition system, 4 traffic volume acquisition system, 5 communication network, 10 CPU, 12 RAM, 14 input / output unit, 16, 64, 80 communication unit, 18 database, 20 external storage medium, 22 hard disk, 24 display unit, 26 input unit, 28 bus, 30 weather data acquisition unit, 32 solar radiation shielding amount acquisition unit, 34 traffic volume acquisition unit, 36 heat balance calculation unit, 38 neural network, 40 road surface Temperature output unit, 50 observation vehicle, 52 GPS antenna, 54 video camera with fisheye lens, 55 temperature sensor, 56 road surface temperature sensor, 60 computer, 62, 72 control unit, 66, 74 storage unit, 70 GPS logger, 76 GPS antenna, 78 Gyro sensor, 82 Back lamp, 8 Vehicle speed detecting circuit, 86 a thermometer, 88 a radiation thermometer, 90 all sky cameras, 92 digital video camera.

Claims (5)

Solar radiation shielding amount data indicating a direct reach rate of sunlight based on imaging data captured by the all-sky camera, having an all-sky camera installed on a moving body moving on a road and capturing a whole-sky image, Solar shading data acquisition means to acquire for each point of the road,
See containing and a road temperature prediction means for predicting surface temperature for each point of the road based on the direct ratio of the sunlight indicated by the solar radiation-shielding amount data,
The image data is associated with position data indicating the imaged position,
The solar shading amount data acquisition means acquires a solar orbit based on the position data, and calculates the solar shading amount data based on the imaging data and the solar orbit,
A road surface temperature prediction system characterized by that. In the road surface temperature prediction system according to claim 1 ,
Traffic volume data acquisition means for acquiring traffic volume data indicating the traffic volume of the road;
Further including
The road surface temperature predicting means predicts the road surface temperature further based on the traffic volume data;
A road surface temperature prediction system characterized by that. In the road surface temperature prediction system according to claim 1 or 2 ,
The road surface temperature predicting means predicts the road surface temperature for each point of the road by changing the solar radiation amount, which is a parameter of the heat balance model, according to the direct reach rate of solar rays indicated by the solar radiation shielding amount data.
A road surface temperature prediction system characterized by that. Based on image data captured by an all-sky camera that is installed on a moving body that moves on the road and captures an all-sky image, solar radiation shielding amount data indicating the direct rate of sunlight is acquired for each point on the road Solar radiation shielding data acquisition step,
See containing and a road surface temperature prediction step of predicting a surface temperature for each point of the road on the basis of the solar radiation-shielding amount data,
The image data is associated with position data indicating the imaged position,
The solar shading amount data acquisition step includes a step of acquiring a solar orbit based on the position data, and a step of calculating the solar shading amount data based on the imaging data and the solar orbit.
The road surface temperature prediction method characterized by the above-mentioned. Based on image data captured by an all-sky camera that is installed on a moving body that moves on the road and captures an all-sky image, solar radiation shielding amount data indicating the direct rate of sunlight is acquired for each point on the road Solar shading amount data acquisition means, and road surface temperature prediction means for predicting the road surface temperature for each point of the road based on the solar shading amount data,
Cause the computer to function as,
The image data is associated with position data indicating the imaged position,
The solar shading amount data acquisition means acquires a solar orbit based on the position data, and calculates the solar shading amount data based on the imaging data and the solar orbit,
A road surface temperature prediction program characterized by that.

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