ORSUM: a Machine Learning Approach for Intelligent Transportation

  • Authors

    • Imran Medi
    • Aida Mustapha
    • Vinothini Kasinathan
  • 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 users’ daily travel patterns and recommend “ride buddies†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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  • How to Cite

    Medi, I., Mustapha, A., & Kasinathan, V. (2019). ORSUM: a Machine Learning Approach for Intelligent Transportation. International Journal of Engineering & Technology, 8(1.7), 162-170. https://doi.org/10.14419/ijet.v8i1.7.25972