visualize data including a menu, two tables and a calendar, and the column of the right hand side has a map and
and area to visualize charts. We describe now these components. The controls on the left column include the following:
<ul>
<li><b>Models Table:</b> The models table show information about the characteristics and the status of the models that are
or have been trainned. In the down side of the table there are two button that allow create new models. In addition on
the bottom-right side there is a button to reload (update) the data of the table.
This last button is important because inmediately after you submit the creation of the model the new model does NOT
<li><b>Models Table:</b> The models table, located on the top of the left column, show information about the characteristics and
the status of the models that are or have been trainned. In the down side of the table there is one button that allows to create new models.
In addition on the bottom-right side there is a button to reload (update) the data of the table.
This last button is important because inmediately after you submit the creation of a new model this one does NOT
show up in the table, the user should press the reload button to be able to see it.</li>
<li><b>Calendar:</b> The calendar is used to choose the day to be predicted, or the starting day of the period to be predicted.
This is used once the models have been trainned. By default the selected day corresponds to the current day. In addition
in the calendarthe school, local and bank hollidays are shown if the data is avaible. Notice that is a prediction that involves
weather is required only few days in the future are susceptible to be predicted.</li>
This is used once the models have been trainned. By default the selected day corresponds to the current day. Notice that is a prediction
that involves weather is required only few days (4 days) in the future are susceptible to be predicted.</li>
<li><b>Matrix Output:</b> Shows the predicted matrix at a future instance of time, actual hour can be chosen by a slider that can be found
inmediately above this control.</li>
</ul>
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<ul>
<li><b>Load Points:</b>Allows to visualize the bike stations on the map.</li>
<li><b>Create Voronoi:</b> Generate Voronoi areas from the set of points previously loaded, by default these are the locations of the
bike stations. This voronoi areas can be used as the zonning for the OD Matrix computation.</li>
bike stations.</li>
<li><b>Load Areas:</b>This allows the user to specify the zonning for the OD Matrix computation by providing a geojson. Notice that the geojson
Features should have an integer "zone_id" property that is used for identify each of the zones. These "zone_id" properties should
start in 1 and should be in consective order (i.e. no gaps are allowed).</li>
start in 1 and should be in consective order (i.e. no gaps are allowed). Right now this only allows to visualize, no areas can be uploaded dinamically</li>
</ul>
<h5>Create/Train Model</h5>
The trainning of a model can be initiated by pressing the <b>New Model</b>
button located inside the model table. This opens up a new window.
In the case no sensor is selected the process will involve all the sensors and will take a long time.
Once the loop sensor is selected the avaiable data is shown in the graph area below the map.
The historic traffic stored within the platform is shown in blue, the temperature in green and the
precipitation in cyan (for the time being the temperature and the precipitation are not used for the trainning of models).
In addition, other information related the flow measurements are shown in the table on the left hand side, the aforementioned Loop Table.
In the window the following information can be seen/specified:
button located inside the model table. This opens up a new window with two menus one for the areas available in the platform another one for specifiying the number
of features to be consider for the model:
<ul>
<li><b>Areas:</b> The identification of the zonning to be used for the computation. The zonning should be created in advance, by defult the "districts" are generated. </li>
<li><b>Num Features:</b> number of features considered to create the model:
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@@ -277,8 +271,7 @@
<li><b>0:</b> completed</li>
<li><b>-1:</b> trainning still in process</li>
</ul></li>
<li><b>Score</b>: Score, quality, of model, using the test data set. Only avaiable if the Type is equal to 2 (Random Forest).
This value is better the closer to 1.</li>
<li><b>Score</b>: Score, quality, of model, using the test data set. This value is better the closer to 1.</li>
