Predicting Bike Sharing Patterns

Project Background

Bike-sharing systems are the new generation of traditional bike rentals where the whole process from membership, rental and return back has become automatic. Through these systems, the user is able to easily rent a bike from a particular position and return back to another position. Currently, there are about over 500 bike-sharing programs around the world which are composed of over 500 thousand bicycles. Today, there exists a great interest in these systems due to their important role in traffic, environmental and health issues.

Apart from interesting real-world applications of bike-sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure, and arrival position is explicitly recorded in these systems. This feature turns the bike-sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of the important events in the city could be detected via monitoring these data.

Data Set

The bike-sharing rental process is highly correlated to environmental and seasonal settings. For instance, weather conditions, precipitation, day of the week, season, the hour of the day, etc. can affect the rental behaviors. The core data set is related to the two-year historical log corresponding to the years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then extracted and added the corresponding weather and seasonal information. Weather information is extracted from http://www.freemeteo.com.

Associated tasks

Regression: Prediction of bike rental count hourly or daily based on the environmental and seasonal settings.
Event and Anomaly Detection:
Count of rented bikes is also correlated to some events in the town which easily are traceable via search engines. For instance, a query like "2012-10-30 Washington d.c." in Google returns related results to Hurricane Sandy. Some of the important events are identified in [1]. Therefore the data can be used for validation of anomaly or event detection algorithms as well.

Files

hour.csv- bike-sharing counts aggregated on an hourly basis. Records: 17379 hours
day.csv - bike-sharing counts aggregated on a daily basis. Records: 731 days
Your_first_neural_network.ipynb: Project notebook with main function calls
my_answers.py: This contains the training, forward pass and neural net functions

Dataset characteristics

Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv