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@article{194576,
        author = {Dr. MK Jayanthi Kannan and Sunil Kumar},
        title = {Intelligent Car Rental @ Dual-Engine Dynamic Pricing and Secure Booking Platform for Intelligent Car Rental Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4195-4203},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194576},
        abstract = {This project proposes an Intelligent Car Rental Platform that combines secure booking management with a dynamic pricing engine. The platform leverages real-time data sources (news events, weather, user search activity) and machine learning to optimize revenue while maintaining competitive pricing. The car rental industry is undergoing a digital transformation, shifting from static, counter-based operations to dynamic, data-driven mobility services. However, existing platforms predominantly rely on fixed-rate pricing models that fail to capture real-time demand fluctuations, leading to revenue leakage during peak periods and suboptimal fleet utilization during low-demand windows. This paper proposes an Intelligent Car Rental Platform that integrates secure booking management with a novel dual-layer dynamic pricing engine. The platform leverages real-time data from multiple sources—including news events, weather conditions, user search activity, and booking pace—to train machine learning models for demand forecasting. We employ Long Short-Term Memory (LSTM) networks to predict short-term demand patterns with 14-day rolling windows, achieving forecast accuracy comparable to industry benchmarks of 88-98%. These forecasts feed into a quadratic programming optimizer that determines optimal price points while respecting business constraints such as price floors, ceilings, and elasticity thresholds. The dual-layer architecture combines rule-based guardrails with ML-based predictions, ensuring both responsiveness and operational stability. Experimental results demonstrate that our approach captures 4-7% uplift in realized daily rates while reducing manual pricing effort by 8-12 hours per analyst weekly. The platform contributes to the growing body of AI-powered mobility solutions by providing a transparent, auditable pricing mechanism that balances revenue optimization with customer satisfaction.},
        keywords = {Dynamic pricing, car rental platforms, LSTM demand forecasting, quadratic programming, machine learning, fleet optimization, real-time pricing engine, Dual-layer pricing strategy integrating rule-based and ML-based models to adjust prices based on demand, seasonality, and external events, comparable to Uber and Airbnb pricing strategies.},
        month = {March},
        }