ORSUM: a Machine Learning Approach for Intelligent Transportation
Keywords:Ride-Sharing, Carpooling, K-Means Clustering, Machine Learning
Ride-hailing applications such as Uber and Lyft responds to requests similar to taxis calls, whereby a driver drives from any location nearby to fetch passengers to make proï¬t. This paper presents ORSUM, a ride-sharing application which allows drivers and commuters to share the cost of a journey should be able to provide the desired convenience and costs for moving about in a city. In this study, the proposed application capitalizes on machine learning approach to learn usersâ€™ daily travel patterns and recommend â€œride buddiesâ€ for which the ride is to be shared with. ORSUM has four distinct modules; Orsum Machine Learning module (ORSUMML), Azure Database, Orsum Web Application, and Orsum Application. The machine learning module runs as a standalone Django web applica-tion that is separate from the Orsum Web Application and only interacts with the Orsum Web Application. ORSUMML is developed using the Python based Sci-kit learn or C# based Accord.Net. Evaluation of ORSUM showed high user acceptance rate to the application, comparable to existing applications such as Uber and Lyft.
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