MACHINE LEARNING–ENABLED RIDE-SHARING OPTIMIZATION: A CASE STUDY OF THE SLYFT APPLICATION AT UNILAG

Authors

  • O. A. George

    Department of Systems Engineering, Faculty of Engineering, University of Lagos, Lagos State, Nigeria

  • T. Osibemekun

    Department of Systems Engineering, Faculty of Engineering, University of Lagos, Lagos State, Nigeria

  • O. J. Sopeyin

    Department of Systems Engineering, Faculty of Engineering, University of Lagos, Lagos State, Nigeria

DOI:
https://doi.org/10.51459/futajeet.2026.20.1.582
Keywords:
pathfinding algorithms, ride sharing, machine learning, slyft application, commuting routes
Abstract

The rise in booming demand for efficient transport solutions has led to innovations among ride-sharing apps. A ride-sharing app optimized for university workers and students commuting at the University of Lagos (UNILAG) was developed in this research. For enhancing commuting conditions through route optimization, ride sharing marketplace historical information such as the pickup and drop locations, timestamps, ride time duration, distances, user ratings and demographic information was acquired and organized. Using the TensorFlow platform, a deep neural network model was developed and trained using this data for estimation of optimal routes. The performance of the model was tested and re-developed to be reliable and accurate. Additionally, the vehicle sharing platform was integrated with the UNILAG car-sharing system to ensure efficient data transfer for a perfect user experience. Findings report that the efficiency and satisfaction of users greatly improved, with the total travel time reduced by 20% and a drastic rise in the adoption rate of the platform by both staff and students. Further, the app was extensively tested on usability, compatibility, network performance, as well as functionality testing, to confirm its trustworthiness and durability. The study fostered travel efficiency enhancement as well as brought in an element of community through encouraging carpooling by students and staff. Integration of advanced predictive analytics has the potential to boost efficiency of future use of Slyft app and extensive adoption outside the field of just the university campus.

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Published
2026-03-31
Issue
Vol. 20 No. 1 (2026): FUTA Journal of Engineering and Engineering Technology
Section
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How to Cite

MACHINE LEARNING–ENABLED RIDE-SHARING OPTIMIZATION: A CASE STUDY OF THE SLYFT APPLICATION AT UNILAG. (2026). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 20(1), 54-64. https://doi.org/10.51459/futajeet.2026.20.1.582

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