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@article{174540,
        author = {Arikirevula Sanjay Praneeth and Jampa Naga Tarun and Keelu Bala krishna and Taralada Geetanjali and Gadde Sridevi},
        title = {Predicting Adaptive Pricing In Ride Hailing Platforms Using Deep  Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {306-311},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174540},
        abstract = {Ride-hailing platforms like Uber and Lyft use dynamic pricing to balance supply and demand, influenced by factors such as traffic, ride demand, weather, and driver availability. This project enhances pricing predictions using Deep Neural Networks (DNNs) by analyzing large-scale datasets to capture complex relationships between time, location, and demand. The model predicts accurate fares, helping users make cost-effective booking decisions. It ensures competitive and responsive pricing, benefiting both companies and customers. By aligning prices with demand, ride-hailing platforms can optimize revenue and improve pricing transparency. This approach enhances fairness, efficiency, and customer satisfaction.},
        keywords = {Surge Pricing, Deep Neural Networks, Ride Hailing Platforms, Machine Learning},
        month = {March},
        }