Journal of the Eastern Asia Society for Transportation Studies Vol.15, 2024

Investigation of Road Visibility Conditions using Precipitation Intensity
measured by Weather Radar during Winter Seasons
Yuki NAKAMURA a, Toru HAGIWARA b, Sho TAKAHASHI c,
Yasuhiro NAGATA d, Ryoichi TSURUMAKI e, Naoki MATSUOKA fa

Graduate School of Engineering, Hokkaido University, Hokkaido, 060-8628,
Japan; E-mail: ynakamura.hokudai@gmail.combGraduate School of Engineering, Hokkaido University, Hokkaido, 060-8628,
Japan; E-mail: hagiwara@eng.hokudai.ac.jpcGraduate School of Engineering, Hokkaido University, Hokkaido, 060-8628,
Japan; E-mail: stakahashi@eng.hokudai.ac.jpdHokkaido Development Engineering Center, Hokkaido, 001-0011,
Japan; E-mail: nagata@decnet.or.jpeHokkaido Weather Technology Center, Hokkaido, 064-8555,
Japan; Email: ryoichi.tsurumaki@howtecc.jp
fHokkaido Weather Technology Center, Hokkaido, 064-8555,
Japan; Email: naoki.matsuoka@howtecc.jp

Abstract: In Hokkaido, Japan, in winter, poor road visibility occurs due to snowstorm andsnowfall, the risk of traffic restriction and crash increases. Snowfall information along the roadis necessary to contribute to road safety. Aim of the present study is to explore applicability of
precipitation intensity measured by XRAIN as snowfall information on the road. We
investigated relationship between precipitation intensity and value of the WIPS measured by
the CCTV Camera which shows road visibility conditions using cross-correlation analysis. As
a result, under snowfall and snowstorm conditions, it was revealed that road visibility could be
estimated by precipitation intensity. Whereas, time series of values of the WIPS andprecipitation intensity was different under strong wind and slightly snowfall condition. In thiscase, it was also revealed that we should consider advection of snow particles in the air.
Keywords: Poor road visibility, Precipitation intensity, XRAIN, Value of the WIPS, Crosscorrelation

1. INTRODUCTION
1.1 Background
In Hokkaido, Japan, poor road visibility frequently occurs due to snowstorm during
winter season, and the risk of traffic restriction and crash increases (2001, 2006). Poor roadvisibility suddenly occurs and changes rapidly, quantitative evaluation is difficult. In the actual
situation of winter road maintenance is supported by road administrator’s experience and hunch,
who is familiar with regional weather condition. Evaluating poor road visibility in real-time
would be minimize the risk and aid to judgement of driver’s behavior (2005, 2018, and 2022).
Prior researches about weather and traffic, in the United States of America, Maze, T. H.
et al. introduced research results and literatures about the effects of traffic demand and safety
due to weather conditions (2006). Ting et al. developed framework to analyze effects of driver’s
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behavior on highway under severe winter weather condition, and indicated usefulness
evaluation using framework (2015). In Canada, K. C. Dey et al. considered possibility of ITS
innovation takes in various data sources to minimize effects of road weather condition (2015).
In Hokkaido, Japan, Nagata and Hagiwara et al. (2006) evaluated winter road visibility
conditions used by value of the Weighted Intensity of Power Spectra (hereinafter WIPS)
measured by the Closed-circuit Television (hereinafter CCTV) Camera, and developed
visibility inspection system for road administrator. Nagata and Hagiwara et al. (2006) revealed
a high correlation between 15-level visibility evaluation indexes obtained from value of the
WIPS and visibility evaluation by human eyes. Nakamura et al. (2018) detected poor roadvisibility due to snowstorm using value of the WIPS measured by the CCTV camera installed
along National Route (hereinafter NR) 244 and NR 272 in Hokkaido. When snowfall wasn’t
observed, value of the WIPS and weather data could evaluate poor road visibility occurred bystrong wind. Next, Nakamura et al. evaluated road visibility condition using values of the WIPS
measured by the CCTV camera and on-board camera installed on the road patrol vehicle togenerate road visibility information as a route (2020, 2022). The result showed positive
correlation between value of the WIPS measured by on-board camera and driver’s subjective
visibility. In addition, value of the WIPS by on-board camera indicated the section where poorroad visibility condition may occur due to snowstorm. However, driving frequency of road
patrol vehicle is limited. Evaluating and estimating poor road visibility condition in real-time
are necessary. At this time, observation points by Automated Meteorological Data Acquisition
System (hereinafter AMeDAS) are limited, using snowfall information with high spatiotemporal resolution is desirable.
Precipitation intensity observed by weather radar might be available as snowfall
information with high spatio-temporal resolution. Weather radar could observe precipitation
intensity with high spatio-temporal resolution. In Finland, precision for the amount value ofsnowfall estimated by weather radar was verified by comparing observed snowfall value from
Europe gauge (2015). In Japan, since 2016, the X-band multi-parameter radar network(hereinafter XRAIN) for river maintenance and prevention activities such as landslide hasdeveloped by the Ministry of Land, Infrastructure, Transport and Tourism (hereinafter MLIT)(2017). XRAIN could observe precipitation per 250-meter square grid mesh every 1-minutes,
road visibility evaluation in detail might be possible. In Hokkaido, Japan, Omiya et al. (2022)
considered the possibility to evaluate snowstorm in real-time on the ground using XRAIN. It isnoted that real-time precipitation intensity by XRAIN is based on observation of particles in
the air, this research didn't deal in drifting snow on the ground.
1.2 Objective
Aim of the present study is to explore applicability of precipitation intensity by XRAIN
as snowfall information along the road to evaluate poor road visibility condition due to snowfall
and snowstorm. Using cross-correlation analysis, we investigated relationship between
precipitation intensity measured by XRAIN and value of the WIPS measured by the CCTV
Camera which shows road visibility conditions. In winter, road visibility can be changed byblowing snow due to strong wind even when snowfall is light. Therefore, wind velocity andwind direction data from AMeDAS are also used to consider the road visibility change effectedby wind.

2. A METHOD FOR IVVESTIGATION OF THE TIME VARIATION BETWEENWEATHER CONDITIONS AND VISIBILITY CONDITIONS ON THE ROAD
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2.1 Investigate Road Section and Time Period
In Haboro area, a 22-km/section of NR 232 from 56.0 kilometer post (hereinafter K.P.)
to K.P. 78.0 was selected for investigation. Fig. 1 shows a map of the 22-km/section on NR 232,
the locations of K.P., CCTV cameras, and AMeDAS. In Haboro area, when the winter pressure
pattern weakens, a small low-pressure system or convergence zone develops around the west
coast of Hokkaido. When winter atmospheric pressure pattern, poor road visibility due to thewinds from various directions and developed snow clouds may be occurred. The west side of
the road is the ocean. When the wind from west is strong, snowstorm might be occurred. Table
1. shows average air temperature, average wind velocity, and major wind direction of each of
11 time periods during two winter seasons. These 11 time periods have a greater chance ofoccurring poor visibility caused by snowstorm (wind velocity was over 6.0 (m/s), and major
wind direction was west). There was a high possibility that wind velocity would increase andthat snowstorms would cause poor visibility.

Fig. 1 Map of the 22-km/section in Haboro area included the 22 km/section of NR 232
Table 1. Time Periods under Poor visibility was Likely to Occur due to Snowstorm
MM/DD/YYNo.

Time Period

Average
Temperature(℃)

Averagewind velocity
(m/s)

Major
wind direction

Dec. 26, 2020.

1

12:00-16:00

-5.7

10.5

West-Northwest

Dec. 27, 2020.

2

11:00-15:00

-4.5

8.0

West or West-Northwest

Dec. 29, 2020.

3

10:00-14:00

-5.4

9.7

West

4

12:00-13:30

-7.2

3.3

South-Southeast

5

13:40-16:00

-6.6

11.9

West-Northwest

6

10:00-11:00

-9.4

1.8

East-Southeast

7

11:10-14:00

-4.7

7.6

West-Northwest

Dec. 30, 2021.

88:00-12:00

-2.5

6.0

West-Northwest

Jan. 29, 2022.

9

11:00-15:00

-4.8

7.6

West or West-Southwest

10

12:00-13:20

-4.1

3.0

Nort or North-Northeast

11

13:30-16:00

-3.6

6.8

Northwest

Dec. 31, 2020.

Dec. 28, 2021.

Feb. 3, 2022.

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2.2 Collecting Weather and Visibility Data for Investigation
2.2.1 Precipitation intensity of XRAIN (mm/h)
The present study, we applied precipitation intensity measured by XRAIN as snowfall
information on the road. XRAIN was developed by MLIT as precipitation observation system.
We downloaded precipitation intensity data from “MLIT XRAIN CXMP composite dataset” inData Integration and Analysis System (hereinafter DIAS) (2017). This dataset consists of“MLIT X-band multi-parameter radar” and “C-band multi-parameter radar” and provides
precipitation intensity per 250-meter square grid mesh every 1-minutes. The present study, weused precipitation intensity per 1-kilometer square grid mesh instead of 250-meter one. Fig. 2
shows the location of the Hakodake radar station close to the 22-km/section on NR 232 and 10kilometer square grid mesh in Haboro area included the 22-km/section. The 10-kilimeter squaregrid meshes (Fig. 3) consist of 100 1-kilometer square ones to provide precipitation intensity.
The observation of precipitation intensity by XRAIN is based on rain particle in the air.
In winter, most particles in the air are snow. The size of snow particle is smaller than that ofrain one, reflection intensity by weather radar under snowfall might be different from underrainfall. In addition, considering advection of snow particles and falling velocity from the air isnecessary. Table 2. shows elevation angles in winter season at Hakodake radar station. Altitudeof winter snow clouds are lower than summer clouds, elevation angles in winter season has
been changed from November, 2021. The brackets degree in Table 2 represents winter elevation
angles. The relationship between size and shape of snow particles is unclear, and falling velocity
of snow particles ranges from 1.0 to 4.0 (m/s). When snowfall was observed by the mesh justabove the road, considering advection and falling velocity, time lag from 5 to 15 minutes might
be occur until snowfall reflected on the ground (2019). Non-vertical fall of snow particle inwinter tends to make time lag large (2022). When a time lag increases, observed data by XRAIN
might be different from actual snowstorm condition on the ground. The present study, tominimize effects of the difference due to time lag as much as possible, average value frombefore 10-minutes to the current time is used as represented precipitation intensity at the current
time. When using precipitation intensity by XRAIN as snowfall information for winter snow
evaluation, reflection intensity of radar and advection of snow particles are issues.

Fig. 2 Map of the radar station and 10-km/square grid mesh in Haboro area
Table 2. Angle of Elevation in Winter Season
Angle of Elevation

Altitude

Height from Groundθ0

θ1

Hakodake：1,129(m)1,251(m)：0.1°

0.1°

0.4°

Weather Radar：1,149(m)1,569(m)：0.4°（-0.4°） （0.1°）729(m)：-0.4°
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2.2.2 Still images captured by the CCTV camera
MLIT installs CCTV cameras about 8 to 10 meters above the road for monitoring roadand weather conditions. In the 22-km/section of NR 232, there are seven CCTV cameras. Table
3. shows the main specifications of CCTV cameras. Currently, HD quality (1280×720) is
standard for CCTV cameras in Japan. CCTV cameras in Haboro area are SD quality (720×480).
CCTV camera records the video images continuously and transmitted via the fiber-optic
network independently maintained by MLIT to the road maintenance office. Road
administrators could confirm video images directly (2006). Still images by the CCTV camera
used in the present study were captured at 5-minute intervals from video images. Therefore, ifroad visibility condition changed in 5-minute rapidly, it might be difficult to evaluate visibility
condition in a few minutes from still image. Poor road visibility may occur suddenly andrapidly, we recognize as an issue that time resolution of still image is 5-minute. The glass sidein front of CCTV camera case is equipped with wipers and defrosters to prevent from adhesionof snow/ice. The present study, still images captured by CCTV cameras at K.P. 67.8 and K.P.
72.0 were used for road visibility evaluation. These two cameras hardly occurred adhesion ofsnow/ice and almost hasn't been changed the shooting direction in winter season. Other fivecameras sometimes occurred heavy adhesion of snow/ice and changed the shooting direction
in winter season.
Table 3. Main Specifications of CCTV Cameras
Image Sensor
Veneer CCD (Color)
Valid Pixels
About 380,000 pixels or more
Definition
Horizontal: 480 or more
S/N
47dB or more
Minimum Illuminance Level
Electronic Sensitivity OFF: 0.4 lux or lessElectronic Sensitivity ON: 0.007 lux or less
Camera Case
Installed Wiper and Defroster
2.2.3 Temperature, wind velocity, and wind direction measured by Haboro AMeDAS
Haboro AMeDAS locates near Haboro town. It has been observed air temperature, windvelocity and wind direction every 10-minutes. Observation points of AMeDAS are limited,evaluating weather condition along the road accurately is difficult. However, there is noobserved weather data about temperature, wind velocity and direction with high spatio-temporal
resolution rather than AMeDAS in the target area. The present study, weather data observed by
Haboro AMeDAS was used. Table 1. shows average value of air temperature, wind velocity,and major wind direction during each of 11 time period.
2.2.4 Value of the Weighted Intensity of the Power Spectrum (WIPS)
The present study, we adopted value of the WIPS as road visibility assessment index
developed by Hagiwara et al. (2006). Value of the WIPS represents the difference in spatial
frequencies within the still image based on the human contrast sensitivity function. Positive
correlation between driver’s subjective visibility and value of the WIPS was revealed (2020).
We considered value of the WIPS could evaluate road visibility condition. Snowstorm and fog
are major factors of poor road visibility. Value of the WIPS focused on subjective visibility
evaluation by human eyes, so it doesn’t classify the factors of poor road visibility.
The followings are image processing method for calculation value of the WIPS (2006).
First, still image was captured from CCTV camera every 5-minutes. Second, a two-dimensional
image of 256×256 pixels was cropped from still image. Third, the grayscale intensity of eachpixel was calculated from the intensities of the red-green-blue (RGB) components recorded in
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the two-dimensional image. The grayscale intensity ranges from 0 to 255. Fourth, the image
was broken into sinusoidal gratings of different spatial frequencies using the two-dimensional
fast Fourier transform (hereinafter FFT). The power spectrum value computed by FFT
corresponds to the amplitude of the spatial frequency for each cycle per degree. At the final
step, the power spectrum intensities in the range of 1.5 to 18 cycles per degree were summarized.
In Fig. 3, under clear road visibility conditions, the power spectra for each spatial frequency
component of the still image are great and value of the WIPS is large. Under poor road visibility
conditions, such as those in fog or snow, the power spectra are small. As a result, value of the
WIPS is small.

WIPS: 11.3

WIPS: 6.9

Fig. 3 Values of the WIPS and visibility conditions
2.3 Cross-Correlation between Precipitation Intensity and Value of the WIPS
Cross-correlation is used to know the closeness of the relationship between two
variables in the form of time series data (2021). In the relationship between two time series (𝑦𝑖and 𝑥𝑖 ), the series 𝑥𝑖 may be related to past time lag (𝜏) of the x-series. We calculated thecross-correlation between two time series variables, precipitation intensity and value of the
WIPS, where 𝜏 = −𝜏𝑚𝑎𝑥 , … , 𝜏𝑚𝑎𝑥 represents the time lag. Cross-correlation is ranged from 1.0 to +1.0, where zero indicates no relationship between the two variables. In the present study,
we adopted value of the WIPS as 𝑦𝑖 and precipitation intensity as 𝑥𝑖 . Cross-correlation was
calculated by equation (1). When one or more 𝑥𝑖+𝜏 , with 𝜏 negative, are predictors of 𝑦𝑖 , itis that 𝑥 leads 𝑦. When one or more 𝑥𝑖+𝜏 , with 𝜏 positive, are predictors of 𝑦𝑖 , it is that 𝑥
lags 𝑦.
𝑁−𝜏

𝑥𝑖 − 𝑥 𝑦𝑖+𝜏 − 𝑦
𝑖=1

𝐶 𝜏 =

𝑁

𝑥𝑖 − 𝑥 2
𝑖=1

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎(1)𝑁𝑦𝑖 − 𝑦 2
𝑖=1

𝐶 𝜏 ：Cross-correlation
𝜏 ：Time lag (min.)𝑁：i=1 to N, Number of data
𝑥𝑖 ：Precipitation intensity (mm/h）
𝑦𝑖 ：Value of the WIPS
𝑥：Average value of the precipitation intensity
𝑦：Average value of the WIPS
The present study, we supposed a linear relationship between two variables, and adopted
cross-correlation for analysis. The time period for calculating the cross-correlation was set to 1
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hour. Number of data: N in Equation 1 was 12. We explored time zone of each of 11 time periods
when the precipitation intensity became strong, and determined time when the precipitation
intensity became peak within the time zone. One hour period for calculating cross-correlation
was set from 30 minutes before the peak time to 30 minutes after the peak time. Then, −𝜏𝑚𝑎𝑥is -60 (min.) and 𝜏𝑚𝑎𝑥 is 60 (min.). If there were multiple time zones of the precipitation
intensity, the cross-correlation was calculated per each of multiple time zones.
In calculating cross-correlation, unit of the time lag between two variables was set to 5minutes due to data limitation. Precipitation intensity by XRAIN is observed every 1-minute.
While, still image by the CCTV camera captured every 5-minute from video image. Therefore,
still image couldn't be captured from CCTV camera continuously. Time interval alignment in
two variables is necessary to calculate cross-correlation. To align observation condition
between two variables as much as possible, time interval was 5-minute. For example, value ofthe WIPS calculated from still image captured by the CCTV camera from 8:25 to 8:30 as
representative value at 8:30. Likewise, average precipitation intensity from 8:21 to 8:30 as
representative value at 8:30. Next, value of the WIPS calculated from still image captured by
the CCTV camera from 8:30 to 8:35 as representative value at 8:35. Likewise, average
precipitation intensity from 8:26 to 8:35 as representative value at 8:35. In this way, timeinterval was 5-minute, the present study calculated cross-correlation.
In addition, we conducted visual confirmation for still images by the CCTV camera toprevent from difference between value of the WIPS and actual road visibility condition. By
conducting visual confirmation, we considered that still image and value of the WIPS show
actual road visibility condition. When we evaluate actual road visibility condition, how to deal
with both observation data should be considered as an issue. To further improve the observation
accuracy and fully understand the relationship between precipitation intensity and value of the
WIPS, we also recognize that additional analysis is needed.

3. RELATIONSHIP BETWEEN WEATHER CONDITIONS AND VISIBILITY
CONDITIONS
We tried to investigate applicability of precipitation intensity by XRAIN as snowfall
information on the road. Cross-correlation was applied to investigate relationship between
precipitation intensity by XRAIN and value of the WIPS by the CCTV camera. This section
shows detailed results for the four time periods which are as follows:
・No. 2 time period on December 27, 2020・No. 4 and No. 5 time periods on December 31, 2020・No. 9 time period on January 29, 2022
From Fig. 4 to Fig. 9, for each of the four time periods contain the following three graphs
and weather maps.
1) Two Line charts: Above line chart in Fig. 4 (a) to Fig. 9 (a) shows the time series of windvelocity, and major wind direction. Below line chart in Fig. 4 (a) to Fig. 9 (a) shows the timeseries of precipitation intensity and value of the WIPS. The large value of the vertical axis shows
strong wind velocity and large precipitation intensity. It should be noted that large value of the
WIPS means poor road visibility condition. Red points on line of value of the WIPS show still
images in Fig. 4 (b) to Fig. 9 (b).
2) Still images: To understand actual road visibility condition, Fig. 4 (b) to Fig. 9 (b) show still
images captured by the CCTV camera corresponded to red points on line chart of value of the2401

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WIPS. Red boxed frame on the still image shows a two-dimensional image of 256×256 pixels
cropped from still image.
3) Bar charts: Bar charts shown in Fig. 5 (c) to Fig. 9 (c) are results of cross-correlation between
precipitation intensity and value of the WIPS. The vertical axis shows the cross-correlation, thehorizontal axis shows time lag. The white bar shown in Fig. 5 (c) to Fig. 9 (c) was the highest
cross-correlation.
4) Weather maps around Hokkaido: Fig. 4 (d) to Fig. 9 (d) show weather maps in each day
around Hokkaido, Japan. Left weather map shows at 9:00 (JST). Right one does at 21:00 (JST).
3.1 No. 2 Time period on December 27, 2020.
In this time period, precipitation intensity was slight although strong wind. At K.P. 67.8,
precipitation intensity continued almost 0.0 (mm/h). We could confirm clear road visibility
conditions at four still images in Fig. 4 (b). Time series of value of the WIPS was flat andcontinuously showed clear visibility conditions.
At K.P. 72.0, from 11:00 to 12:30, precipitation intensity was under 0.5 (mm/h). We
could confirm slightly poor road visibility condition from No. 2 still image in Fig. 5 (b). In Fig.
5 (c), from 11:35 to 12:35 (CC-1), the sign of the highest cross-correlation was negative andtime lag was 5.0 (min). Value of the WIPS moved in synchronization with precipitation intensity.
According to weather map shown in Fig. 4 (d) and Fig. 5 (d), a low pressure moved
away from Hokkaido through the night. In the day time, there was a low pressure around
Hokkaido, poor road visibility might be occurred due to snowstorm. However, from theseresults, snowstorm didn’t occur and road visibility condition was clear on December 27, 2020.

Fig. 4 Time series of weather and visibility conditions at K.P. 67.8 on December 27, 2020.

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Fig. 5 Time series of weather and visibility conditions at K.P. 72.0 on December 27, 2020.
3.2 No.4 and No. 5 time periods on December 31, 2020 at K.P.67.8.
In No. 4 time period, wind velocity wasn’t strong and wind direction was South. At K.P.
67.8, from 12:00 to 13:15, precipitation intensity was almost 0.0 (mm/h). The time series ofvalue of the WIPS was flat and road visibility conditions were clear.
After, in No. 5 time period, wind velocity increased in two times. Wind direction changed
from West. From 13:20 to 14:00, precipitation intensity was about 1.5 (mm/h). We could
confirm very poor road visibility condition from No. 2 still image in Fig. 6 (b). From 14:00 to
15:00, time series of precipitation intensity and value of the WIPS didn’t move insynchronization. At 14:30, precipitation intensity was almost 0.0 (mm/h), value of the WIPS
was 6.0. We could confirm very poor road visibility condition from No. 3 still image in Fig. 6(b). At this time, wind velocity was over 12.0 (m/s). There were much fresh snow particles on
the ground due to snowfall observed by XRAIN. After the snowfall, by the change in windvelocity and direction, poor road visibility occurred due to snowstorm. In Fig. 6 (c), from 13:10
to 14:10 (CC-1), the sign of the highest cross-correlation was negative, and time lag was 10.0
(min.). Value of the WIPS moved in synchronization with precipitation intensity.
From 14:40 to 15:30, precipitation intensity increased again (1.5 (mm/h)). We could
confirm poor road visibility condition from No. 4 still image in Fig. 6 (b). Poor road visibility
occurred due to snowstorm. In Fig. 6 (c), from 14:40 to 15:40 (CC-2), the sign of crosscorrelation value was positive. Value of the WIPS didn't move in synchronization with2403

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precipitation intensity. At this time, advection of snow particles may be occurred in the air.
According to Fig. 6 (d), a low pressure was approaching the northeastern Hokkaido.
When interval of isobars was narrow, wind velocity was likely to strong and snowstorm mayoccur. However, in No. 4 time period, wind wasn’t strong and wind direction was South, roadvisibility didn’t change. After the change in wind velocity and direction, poor road visibility
occurred due to snowstorm.

Fig. 6 Time series of weather and visibility conditions at K.P. 67.8 on December 31, 2020.
3.3 No.4 and No. 5 time periods on December 31, 2020 at K.P.72.0.
In No. 4 time period, wind velocity wasn’t strong and wind direction was South. At K.P.
72.0, from 13:10 to 13:30, precipitation intensity increased (about 1.5 (mm/h)). However, thetime series of value of the WIPS was flat and showed clear road visibility conditions. We could
confirm clear road visibility condition from No.1 still image in Fig. 7 (b). In Fig. 7 (c), from
12:45 to 13:45 (CC-1), the sign of the highest cross-correlation was negative, and time lag was
15.0 (min.). Time series of precipitation intensity and value of the WIPS was different.
After, in No. 5 time period, wind velocity increased around 14.0 (m/s) and wind directionchanged from West. While, from 13:30 to 14:30, precipitation intensity decreased under 0.5(mm/h). At 13:45, precipitation intensity was almost 0.8 (mm/h), value of the WIPS was 6.6.
At 14:30, although precipitation intensity was almost 0.2 (mm/h), value of the WIPS was 4.3.
We could confirm very poor road visibility conditions from No. 2 and No. 3 still images in Fig.
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7 (b). At this time, wind velocity was over 12.0 (m/s). There were much fresh snow particles on
the ground due to snowfall in No. 4 time period. After the snowfall, by the change in windvelocity and direction, poor road visibility occurred due to snowstorm. From 14:30 to 15:30,
precipitation intensity increased again (1.0 (mm/h)). We could confirm poor road visibility
condition from No. 4 still image in Fig. 7 (b). Poor road visibility occurred due to snowstorm.
After 15:30, precipitation intensity decreased (almost 0.0 (mm/h)). In Fig. 7 (c), from 14:20 to
15:20 (CC-2), the sign of the highest cross-correlation was positive. Value of the WIPS didn’t
move in synchronization with precipitation intensity. At this time, advection of snow particles
may be occurred in the air.
According to Fig. 7 (d), a low pressure was approaching the northeastern Hokkaido.
Interval of isobars was narrow and wind velocity was likely to strong, snowstorm might beoccur. However, in No. 4 time period, wind velocity wasn’t strong and wind direction was South,road visibility didn’t change. After the change in wind velocity and direction, poor roadvisibility occurred due to snowstorm.

Fig. 7 Time series of weather and visibility conditions at K.P. 72.0 on December 31, 2020.

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3.4 No.9 time period on January 29, 2022.
In No. 9 time period, wind velocity was slightly strong and wind direction was West. At
K.P. 67.8, from 12:30 to 14:30, precipitation intensity increased (2.0 (mm/h)). We could confirm
poor road visibility conditions from No. 2 and No. 4 still images in Fig. 8 (b). In Fig. 8 (c), from12:25 to 13:25 (CC-1), and from 13:35 to 14:35 (CC-2), the sign of the highest cross-correlation
was negative. Time lag was 0.0 and 5.0 (min.). Value of the WIPS moved in synchronization
with precipitation intensity. Poor road visibility occurred due to snowfall.
At K.P. 72.0, from 12:30 to 13:00, precipitation intensity increased (about 1.0 (mm/h)).
We could confirm poor road visibility condition from No.2 still image in Fig. 9 (b). In Fig. 9(c), from 12:10 to 13:10 (CC-1), the highest cross-correlation was 0.8, time lag was 10.0 (min.).
After, precipitation intensity decreased and wind velocity increased. In this case, poor roadvisibility occurred due to snowstorm. From 13:00 to 14:00, precipitation intensity increased
again (about 1.0 (mm/h)). We could confirm very poor road visibility conditions from No. 3
and No. 4 still images in Fig. 9 (b). In Fig. 9 (c), from 13:00 to 14:00 (CC-2), the sign of thehighest cross-correlation was negative. Time lag was 0.0 (min). From 13:30 to 14:00,
precipitation intensity was around 0.5 (mm/h), wind velocity was slightly strong. In this case,poor road visibility occurred due to snowfall.
According to Fig. 8 (d) and Fig. 9 (d), there was a low pressure around Hokkaido.
However, interval of isobars was wide, wind velocity was not so strong. Under these weather
condition, poor road visibility mainly occurred due to snowfall.

Fig. 8 Time series of weather and visibility conditions at K.P. 67.8 on January 29, 2022.
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Fig. 9 Time series of weather and visibility conditions at K.P. 72.0 on January 29, 2022.

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3.5 Results of Cross-Correlation
We calculated the cross-correlation and time lag between two time series variables,
precipitation intensity and value of the WIPS. Results of 11 time periods could be classified
into three categories depending on the cross-correlation value and time lag. Table 4. and Table
5. show the number of time period in Table 1., time period of calculating cross-correlation, thehighest cross-correlation, time lag, and Category in K.P. 67.8 and K.P. 72.0 respectively.
In Category-1, precipitation intensity was almost 0.0 (mm/h) and road visibility condition
was clear. In Category-2, value of the WIPS moved in synchronization with precipitation
intensity. Cross-correlation was larger than 0.4, and time lag between two time series variables
was relatively small. In Category-3, the relationship between precipitation intensity and valueof the WIPS were complex. The sign of cross-correlation was positive, cross-correlation was
weak less than 0.4, and time lag between two time series variables was large (over 15.0 (min.)).
Category-1 shows a time period that value of the WIPS shows clear visibility condition
under none precipitation intensity. It should be noted that snowstorm on the road might occur
under none precipitation if there are fine and fresh snow particles on the ground around the
CCTV location. No. 2 time period is classified into Category-1. Wind velocity was over 8.0(m/s) and there was little precipitation intensity during No. 2 time period. The time series ofvalue of the WIPS shown clear visibility conditions during No. 2 time period. In this case, snow
particles didn't pile on the ground due to strong wind from the previous day. As a result, on
December 27, 2020, snowstorm didn't occur despite strong wind.
Category-2 shows a time period that value of the WIPS shows poor road visibility underprecipitation intensity. Major wind direction was West. Time lag between precipitation intensityand value of the WIPS was relatively small. The sign of the highest cross-correlation was
negative. Value of the WIPS moved in synchronization with precipitation intensity. In Category2, there are two causes of poor road visibility under the similar cross-correlation. One is poorroad visibility due to snowstorm. Under poor road visibility due to snowstorm, time lags wereranged from 0.0 to 10.0 (min.). Poor road visibility affected by not only precipitation but strong
wind from West. Falling velocity of snow particles ranges from 1.0 to 4.0 (m/s). It wasconsidered there will be some time lag until snowfall observed by XRAIN is reflected on the
ground. The other is one due to snowfall. Under poor road visibility due to snowfall, time lags
were ranged from 0.0 to 5.0 (min.). Wind velocity was weak, and major wind direction wasn't
West. It was considered snowfall made road visibility worse directly.
Category-3 shows a time period that value of the WIPS didn't move in synchronization
with precipitation intensity. One of the reasons is that snowstorm due to strong wind didn’t
occur when wind direction wasn’t West. Geographical features of the road affected occurrenceof poor road visibility (2022). East side of NR 232 is high cliff. Then, wind from east side
becomes weak. Another reason is that wind velocity and direction were not stable, roadvisibility has been affected by them and become unstable. In these cases, as a result, crosscorrelation was positive and weak, time lags between two time series variables tend to large.
Road visibility condition changes complicatedly due to various factors, we need to
conduct inspection the applicability of precipitation intensity by XRAIN as snowfall
information on the road in other area. Matsushita et al. reported that there isn’t the highoccurrence area of freezing rain or sleet in Hokkaido, Japan (2004). Therefore, the present study,
we dealt with poor road visibility due to snowfall and snowstorm as evaluation targets.

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Table 4. Results of Cross-correlation and Time Lag [K.P. 67.8]
K.P. 67.8
Date
Dec. 26, 2020.

Time Period
in TABLE 1
No.1

Time
Period
12:00-13:00

The Highest
Cross-correlation-0.79

Time lag(min.)
5

Category

12:15-13:15

-0.74

5

2

12:55-13:55

-0.64

0

2

14:55-15:55

-0.74

0

2

Dec. 27, 2020.

No.2

Dec. 29, 2020.

No.3

10:00-11:00

-0.57

5

2

Dec. 31, 2020.

No.4 and
No.5
No.5

13:10-14:10

-0.52

10

2

14:40-15:40

0.50

25

3

Dec. 28, 2021.

No.6

10:25-11:25

0.47

10

3

10:45-11:45

-0.60

0

2

Dec. 30, 2021.

No.6 and
No.7
No.8

8:00-9:00

-0.35

35

3

8:25-9:25

0.34

35

3

11:00-12:00

-0.68

10

2

12:25-13:25

-0.75

0

2

13:35-14:35

-0.78

5

2

No.10

12:20-13:20

-0.70

5

2

No.11

14:35-15:35

0.53

0

3

Jan. 29, 2022.

Feb. 3, 2022.

No.9

No precipitation

2

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Table 5. Results of Cross-correlation and Time Lag [K.P. 72.0]
K.P. 72.0
Date
Dec. 26, 2020.

Time PeriodTime
in Table 1.
PeriodNo.1
12:15-13:15

The Highest
Cross-correlation-0.80

Time lag(min.)
5

Category

12:55-13:55

-0.59

0

2

14:55-15:55

-0.72

5

2

2

Dec. 27, 2020.

No.2

11:35-12:35

-0.84

5

2

Dec. 29, 2020.

No.3

10:00-11:00

-0.71

0

2

11:05-12:05

-0.72

0

2

No.4

12:55-13:55

-0.81

15

3

No.5

14:35-15:35

0.59

25

3

Dec. 28, 2021.

No.6

10:15-11:15

0.60

10

3

Dec. 30, 2021.

No.8

8:50-9:50

-0.70

0

2

11:00-12:00

-0.84

10

2

12:10-13:10

-0.79

10

2

13:30-14:30

-0.59

0

2

No.10

12:05-13:05

-0.71

0

2

No.11

14:50-15:50

0.47

15

3

Dec. 31, 2020.

Jan. 29, 2022.

Feb. 3, 2022.

No.9

4. SUMMARY
The present study, we investigated applicability of precipitation intensity by XRAIN as
snowfall information to estimate road visibility in winter season in Haboro area of Hokkaido.
Using cross-correlation, we explored the degree of influence between precipitation intensityand value of the WIPS. As a result of investigation, in Category-1 and Category-2, time seriesof precipitation intensity and value of the WIPS were related. In Category-3, time series of themwere different. Therefore, in Category-1 and Category-2, applicability of road visibility
estimation using precipitation intensity was indicated. Whereas, in Category-3, when windvelocity was so strong, it was also revealed that we should consider the advection of snow
particles in the air. Using precipitation intensity by XRAIN as snowfall information on the roadto evaluate snowfall and snowstorm in winter season, accuracy of observation, falling velocity
and advection of snow particles should be considered.
ACKNOWLEDGMENTS
The present study, we used "the XRAIN CXMP composite dataset" provided by the
Ministry of Land, Infrastructure, Transport and Tourism of Japan (MLIT). This dataset was2410

Journal of the Eastern Asia Society for Transportation Studies Vol.15, 2024

provided by Data Integration and Analysis System (DIAS). DIAS was developed and operated
under the auspices of the Ministry of Education, Culture, Sports, Science and Technology ofJapan (MEXT).
This work was partly supported by the Committee on Advanced Road Technology under
the authority of the Ministry of Land, Infrastructure, Transport, and Tourism in Japan (Project
name “Technological development of digital twin-oriented transportation management system
in winter road”, Principal Investigator: Assoc. Prof. Sho Takahashi, Hokkaido University).
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