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@article{188085,
        author = {Mohini Bharat Todkari},
        title = {Designing Location-Based Services for ETA Prediction and Navigation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1314-1323},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188085},
        abstract = {Estimated Time of Arrival (ETA) prediction and navigation are fundamental components of modern location-based services (LBS), with wide-ranging applications in ride-sharing, public transport, logistics, and smart cities. Over the past decade, artificial intelligence has transformed how ETA is predicted, shifting from rule-based models to advanced deep learning architectures such as recurrent neural networks (RNNs), graph neural networks (GNNs), and transformers. This review provides a comprehensive synthesis of the literature, datasets, models, and evaluation metrics used in the domain. Experimental results from key benchmark datasets (e.g., NYC Taxi, Porto, Didi) confirm the superior accuracy and adaptability of attention-based models, particularly under dynamic traffic conditions. The review also proposes a theoretical model for ETA systems, presents comparative tables and diagrams, and identifies future research directions, including explainable AI, federated learning, edge deployment, and sustainability integration. By addressing current limitations and embracing future technologies, ETA prediction systems can evolve into intelligent, ethical, and high-performance tools for urban mobility.},
        keywords = {Estimated Time of Arrival (ETA); Location-Based Services (LBS); Artificial Intelligence (AI); Deep Learning; Navigation Systems; Graph Neural Networks; Transformer Models; Federated Learning; Edge Computing; Smart Transportation.},
        month = {December},
        }