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Maputo Mobility Prediction Hackathon Powered by Yango

Helping Mozambique
$1 000 USD
Completed (over 2 years ago)
Forecast
36 joined
13 active
Starti
Nov 25, 23
Closei
Nov 25, 23
Reveali
Nov 25, 23
About

The data was collected from the track logs of transportation service providers (Yango partners) in September 2023, for Maputo.

The data contains aggregated statistics on the numbers of cars and speed of travel along Maputo's main highways.

The set of data includes 2 tables, the first containing statistical data with an interval of time and persistent_id (the id of a road section), the second containing a road graph, with persistent_id and other attributes such as the geometry of a section, its name.

Additional information on the days of the week.

For example, first_holiday_05_1_ is:

  • the first day off (usually Saturday, but there may be a holiday in the middle of the week)
  • the 6th hour (from 5 to 6)
  • the 2nd quarter (from 15 minutes to 30 minutes)

And first_holiday_05_ is for the entire 6th hour of the first weekend

  • first_weekday = Monday
  • second_weekday = Tuesday
  • other_weekday = Wednesday
  • lbo_weekday = Thursday
  • last_weekday = Friday
  • first_holiday = Saturday
  • other_holiday = Sunday

Files
Description
Files
This is additional information regarding each road segement.
Test resembles Train.csv but without the target-related columns. This is the dataset on which you will apply your model to.
Train contains the target. This is the dataset that you will use to train your model.
Is an example of what your submission file should look like. The order of the rows does not matter, but the names of the "ID" must be correct.
This file describes the variables found in train and test.
Variable definitions for the Graph table.
This is a starter notebook to help you make your first submission.