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@article{194955,
        author = {B Manohar Prasad and T Subhashini and N Prasanna and O A N Dev Varma and V L Priyanka},
        title = {Uber Trips Analysis: Machine Learning Clustering for Analysing Travel Patterns},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8147-8152},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194955},
        abstract = {The rapid growth of ride-hailing services has generated massive amounts of trip data, providing valuable insights into urban mobility and travel behaviour. Analysing such large-scale datasets manually is inefficient and prone to error. This research presents a machine learning–based approach to analyse Uber trip data and identify significant travel patterns using clustering techniques. The study utilizes a publicly available Uber trips dataset containing spatial and temporal attributes such as pickup and drop-off locations, trip time, distance, and fare amount. Data preprocessing techniques including handling missing values, normalization, and feature selection were applied to enhance data quality. The K-Means clustering algorithm was implemented to group trips based on similarity in spatial and temporal features. The performance of the clustering model was evaluated using silhouette score and visual analysis. Results indicate that distinct clusters representing high-demand travel zones and peak travel periods can be effectively identified. The findings demonstrate that machine learning clustering can provide meaningful insights into passenger movement patterns and support better transportation planning, demand forecasting, and resource allocation.},
        keywords = {Travel Pattern Analysis, Machine Learning, Uber Trips, K-Means, Clustering, Transportation Analytics.},
        month = {March},
        }