Help SimCity's mayor fight pollution and traffic jams by optimizing the city's bike rental system!
SimCity mayor has invested a lot of money to fight against pollution and reduce traffic jams. Her first action was the purchase of a bike rental system. The transition to cities where bikes are a primary mean of transport, and where cars and traffic jams are rarer, will certainly reduce pollution. However, the adoption of alternative means of transport requires infrastructure development to enable their usage (here, bike stations). Unfortunately, the number of bikes, workers and money to promote this project are limited. She wants to optimize this service to avoid empty stations and recruit staff to handle bike re-allocation during traffic spikes. Finally, bikes could be promoted in regions where the rental system has growth potential but has not been adopted yet.
To improve the system, she wishes to predict the number of bikes rented at each station at any moment of the day using weather data. This task seems daunting for Simcity’s employees. Fortunately, a team of data scientists keep on solving environmental problems wants to help the mayor (for free).
The challenge that is posed to them is to use weather data (temperature, humidity, cloud cover) to predict the number of bikes rented at given station for a given day. To make the challenge more interesting, predictions are asked either in the morning or in the afternoon.
References and credits:
This challenge was created by : Aris Tritas, Wang Yuxiang, Mahmut Cavdar, Jonathan Crouzet, Kevin Pasini and Nicolas Bougie.
The competition protocol was designed by Isabelle Guyon.
The starting kit was adapted from an Jupyper notebook designed by Balazs Kegl for the RAMP platform.
This challenge was generated using Chalab, a competition wizard designed by Laurent Senta.
Contact : email@example.com
When predicting the number of bikes rented at a given station, you will use the root mean squared logarithmic error (known as RMSLE) as an evaluation metric. Formally, it is defined as follows:
Indeed, the predicted values follow a logarithmic scaling. Therefore, this metric penalizes linearly errors that are off by an order of magnitude.
This competition is organized solely for test purposes. No prizes will be awarded.
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Start: Nov. 27, 2016, 11:59 p.m.
Description: Development phase: create models and submit them or directly submit results on validation and/or test data; feed-back are provided on the validation set only.
Start: April 30, 2017, 11:59 p.m.
Description: Final phase: submissions from the previous phase are automatically cloned and used to compute the final score. The results on the test set will be revealed when the organizers make them available.
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