When high speed rail rolling stock, Shinkansen, travels during snowfall, snow adheres on its bogie part. An increase in the amount of adhering snow may cause its falling into the track. This falling has a risk of destruction of track infrastructure and so on. Therefore, prediction of the largest amount of adhering snow on the bogie with the risk of falling on the day is required in order to make a decision on the necessity of the next day's removing adhering snow on the previous day. For this reason, improving the prediction accuracy of the amount of adhering snow is expected super effective to support deciding on the necessity of removing snow on the next day.
In the competition, participants will create a model that predicts the amount of adhering snow quantitatively by analyzing historical data of weather conditions, traveling conditions and amount of adhering snow. Though it may be a very complicated problem as it requires viewpoints of both meteorology and railway operation, we would like to make use of your great ideas, technical skills, and creativity to find an innovative solution and keep on improving the quality of railway operations.

Data

The following data is given for creating algorithms (see Download Data page for more details).


Adhering snow data

Amount of snow accumulated on the railway bogie part measured by laser scanner when the railway stops at Toyama station and Itoigawa station.


Geographic data

These data include kilometrage and longitude / latitude information of civil engineering structures (railway station / tunnel) and JR-owned measuring instruments (anemometer / snow depth meter).


Train traveling data

Diagram: Indicates common timetable corresponding to each train (local and express) ID.

Train without adhering snow at the arrival of Kanazawa station: It indicates the ID number of train whose amount of adhering snow is zero at Kanazawa station as it leaves from Rolling stock maintenance depot to Kanazawa station. As for the other train ID numbers, the amount of adhering snow is unknown as they turn back at Kanazawa station.


Meteorological Agency data

Of the historical meteorological data acquired from Meteorological Agency, we have extracted data with following condition.

Peroid: Dec 1, 2015 ~ Mar 31, 2016 and Dec 1, 2016 ~ Mar 31, 2017

Locations: Kanazawa・Toyama・Itoigawa

※Other than the above condition, participants can use open data as long as they are free.


Anemometer data

Data measured in seconds by anemometers uniquely set up (at 20 points in total) by JR West.

Wind speed (Instantaneous): Measured in units of 0.1m/s.

Wind direction (Instantaneous): A direction given by degrees. North corresponds to 0°, and the angles increase clockwise.


Snow depth data

Depth of snow measured on track surface and on trench located at the outside of the railway track. Depending on the set up and measurement point of snow depth gauge, the data to be utilized may vary. For more details, please refer to [doc_1.pptx].

Track: Measured with the height of rail surface as the reference (0cm)

Trench:  Measured with the bottom of gutter as the reference (0cm)


Glossary of technical terms

Adhering snow: Amount of adhering snow on the rolling stock bogie.

Kilometrage: Accumulated distance from Kanazawa station, which indicates a location of station, tunnel, and each measuring instrument.

Train ID: An identifier given to each train in a diagram.


Basic information

Operating section: Kanazawa station -> Itoigawa station

Target variable: Amount of snow accretion on the railway bogie part at the arrival of Toyama station for all railways during test set period

Train set period: Jan 19 to Dec 31, 2016

Test set period: Jan 1 to Mar 31, 2017


Submissions are scored on the WMAE (Weighted Mean Absolute Error).
The ratio of weights is the sum of 1 and the absolute value of the true value multiplied by 10 ^ 4.


Determination of the final rankings
The evaluation during the challenge is evaluated on part of the test dataset (public score) , and the evaluation after the challenge is evaluated on the remaining part of the test dataset (private score).
The leaderboard automatically switches to the final evaluation at the end of the challenge, and the final ranking is determined based on that. For this reason, the ranking may change significantly during and after the challenge.

Use of the system
・You may have one account per participant. Creating a team is not permitted in this competition.
・Submissions can be made up to 3 times per day.

Handling of information
・ Public disclosure of algorithms, ideas or other materials created to participate in this competition shall not be restricted, only if it is clearly stated that those are created to participate in this competition at the time of publication.
・ However, at the public disclosure, please specify it (including links) in the forum of this competition so that all participants can view it.

Data limitations
・You may use external data as long as it can be obtained for free and does not infringe any third party rights.
・In predicting y at time t, you may use the information available until time t.

①From Meteorological Agency database, participants can use hourly information of temperature, precipitation, wind speed, relative humidity, and weather during the same day as the target date of prediction.
These information are provided by Japan Weather Association and are forecasted the day before.
Let's say we are making a prediction on Jan 11, 2017:
　　data with index before 2017-01-12 00:00:00 can be used.
Please note: 24:00 of one day is the same time as 00:00 of the following day.
② From Meteorological Agency database, data of snow depth, anemometer and the other items not listed in the above ① can be used as long as they are disclosed on or before 4 AM on the target date of prediction.
Participants can also use external data as input data as long as they can be acquired on or before 4 AM on the target date of prediction.
Let's say we are making a prediction on Jan 11, 2017:
　　data with index before 2017-01-11 04:00:00 can be used.
③ Please not that the weather information (other than ①) considered necessary for improving prediction accuracy of the amount of snow accretion can be used as secondary input data by estimating the relationship with (primary) input data of ① (See figure below).


Tips for modeling
Though participants can only use temperature, precipitation, wind speed, relative humidity, and weather as input data for prediction dates in this competition, we would like to introduce the ideas to estimate other weather conditions from the meteorological point of view based on these meteorological conditions (*For more details, please refer to [doc_2.pptx] on the data download page).

A method to estimate amount of snowfall from precipitation
Precipitation during snowfall indicates the depth of water when the drifted snow melts into water.
Amount of snowfall indicates the depth of snow (snow depth) that has been precipitated. Even with the same precipitation, the value of snowfall will change depending on the density ratio of water and snow.
Since it is shown that the density of snow changes depending on the temperature from past experimental law, it is possible to estimate the amount of snowfall from the temperature and the precipitation during snowfall.

A method to estimate solar radiation from sunshine duration
　The amount of solar radiation is an indicator of the strength of energy due to the sun's sunshine, and an increase in the amount of solar radiation during snowfall or snowfall acts in the direction of melting snow (the snow density increases). It is possible to estimate based on the relational expression between sunshine hours and solar radiation at a point where solar radiation data is not observed. As will be described later, it is possible to estimate the sunshine hours from the weather.

A method to estimate sunshine duration from the weather
　It is possible to find a correlation between weather and sunshine duration by analyzing statistically based on the historical weather data. For reference, we show the relationship between weather and sunshine duration in case of using the Meteorological Agency data of Kanazawa city which is prepared in this competition. By using the above relation, it can be used to estimate the sunshine duration condition at the point where the sunshine duration is not observed and the target date of prediction.

References
Historical weather information: Meteorological Agency
Weather forecast: The meteorological society of japan (tenki.jp)
Method of predicting the amount of adhering snow on rolling stock bogie
Snow control measures on the Tohoku Shinkansen line between Hachinohe and Shin-Aomori: Part 6
Science of the atmosphere close to the ground surface

Disclosure policy

As a general rule, in accordance with Article 4, Paragraph 1 of the terms of participation, diclosing any contents such as insights and deliverables transmitted through the information or data provided by our company in relation to this competition is not permitted, however, only after the completion of this competition and for non-commercial purposes, it will be possible to disclose the contents within the score of the table below
Model *1
Public
Analysis results *2
Public
Public : Posting to social media sites, blogs and source repositories, and citing to papers
Restricted : Using in a limited range from research, education to seminars, where many unspecified people cannot access
*1 Execution unit source code and learned models
*2 The insights obtained using the information and data provided, or the solutions including scripts and processed data such as summary statistics

※Notes

・SOTA Challenge has a different end date for each participant. The above contents can be disclosed only after you agree to the Terms of Participation.
・If you publish information using this data on social media sites, blogs, thesis, seminars, or any other places, please specify that it was created in connection with this competition.
・When posting or publishing outside of the competition’s forum, please clearly state the publication information (including the link URL to the pages) on the contest forum in order to let all the participants know it.

▼ Special Contract on this Contest
- The data distributed in this competition does not need to be deleted after the competition. However, it can be used only for the purpose of participating in this competition.
- In regard to posting and/or other publication on any social media site, blog or similar by the participant of information relating to any algorithm developed by said participant or any other materials produced for or related to this contest (including materials used for final submissions and/or final evaluations/inspections), no restrictions apply to posting and/or publication as l ong as the participant clearly explains that the posted/published items relate to this contest, and absolutely posts the positing and/or publication information (including the link URL to the pages) on the contest forum in order to let all the participants know it.


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Enforced on April 1, 2018
Last updated on January 18, 2019