Ecocity
Secret url:
https://competitions.codalab.org/competitions/16058?secret_key=a67fc121-7c8d-4da7-84f4-e4972d6748f6
Previous
DevelopmentCurrent
FinalEnd
Competition EndsDO NOT USE THIS VERSION: use [LRI GREEN]
Ecocity
Brought to you by Green
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 : green@chalearn.org
Ecocity: Evaluation
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.
Ecocity: Rules
This competition is organized solely for test purposes. No prizes will be awarded.
The authors decline responsibility for mistakes, incompleteness or lack of quality of the information provided in the challenge website. The authors are not responsible for any contents linked or referred to from the pages of this site, which are external to this site. The authors intended not to use any copyrighted material or, if not possible, to indicate the copyright of the respective object. The authors intended not to violate any patent rights or, if not possible, to indicate the patents of the respective objects. The payment of royalties or other fees for use of methods, which may be protected by patents, remains the responsibility of the users.
ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS" THE ORGANIZERS DISCLAIM ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS, PUBLICATIONS, OR INFORMATION MADE AVAILABLE THROUGH THIS WEBSITE.
Participation in the organized challenge is not-binding and without obligation. Parts of the pages or the complete publication and information might be extended, changed or partly or completely deleted by the authors without notice.
Development
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.
Final
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.
Competition Ends
Never
You must be logged in to participate in competitions.
Sign In