AI and Bicycles: How to Make Bike Sharing Smart | Skip to main content
10 Apr 2019 | 17:10 - 17:30 | Big-Data.AI Summit

AI and Bicycles: How to Make Bike Sharing Smart

Grace Hopper Stage
Stage BAS
Using Transport for London’s open data platform, four PhD scientists set out to address a key challenge facing cities today: how to tackle traffic congestion, maximise public transportation efficiency and minimise air pollution. Our team chose to focus on the open data records of approximately 27 million bicycle journeys made over a four-year period to try and solve a part of this dynamic problem. Over a five-week period, our team of data scientists used Python and an SQL database to perform initial queries and explorations of the data. The findings were astounding and led to the development of a predictive flow model using ANNs with Google’s TensorFlow Machine Learning library. By using the data of each bike journey, the team effectively came up with cost reduction strategies for the bike-sharing platform. We will share their process and discuss the practical implications of such an undertaking for bike-sharing platforms across all cities.
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