Machine Learning for Demand Forecasting of Bike Rental

Bike sharing programs are popular around the world that competes with other forms of public transportation in urban environments. Currently, there are over 500 bike-sharing programs around the world including over 50 IT-based bikesharing in four major cities: Los Angeles, Chicago, San Francisco and New York. These systems use a network of kiosks for users to rent a bike from a one location and return it to a different place on an as-needed basis. The automated kiosks gather all sorts of bike usage data, including duration of rent, departure and arrival locations among others.

While Bikesharing is a sustainable and environmentally friendly transportation, making predictions about future station states is challenging. In addition, Bikesharing systems are highly dynamic, and riders’ behavior is difficult to predict. Therefore their operation suffers from the effects of the fluctuating demand in space and time that leads to severe system inefficiencies and degrading the level of service, system performance and causing disappointment that may result in loss of users. The knowledge of future demand patterns can aid in reducing relocation costs and increasing system performance.

In May 2014, the machine learning competition website kaggle.com opened the competition “Forecast use of a city bikeshare system”. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.

Using our advanced artificial intelligence technique and given previous rental history, hourly measured weather, indication if today is a holiday/weekday/weekend., we have been able to precisely make predictions on future station states such as current demand for the bikes, demand for bikes at particular hour, and demand for bikes at particular hour and particular day of the week. Tour system will help to solve the dynamic bikesharing rebalancing and inventory balancing, bikesharing rental network scheduling, and optimal routes and that will keep the system balanced.

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