Dynamic Ride Pricing Model Using Machine Learning
Authors:-Assistant Professor Ms. Preeti Kalra, Mr. Jitesh Pahwa, Mr. Anirudh Sharma, Mr. Dev Malhotra, Mr. Kunal Pandey
Abstract-Dynamic Ride Pricing is a vital feature in the ridesharing industry that allows companies to adjust ride fares based on shifts in supply, demand, weather conditions, and other relevant factors. This study details the development of a machine learning-driven dynamic pricing model designed to optimize fare adjustments in real time. By analyzing key variables such as trip distance, weather, and historical patterns of supply and demand, the algorithm can deliver pricing that is both contextually relevant and responsive. The model aims to achieve a balance between profitability and customer satisfaction by swiftly adapting to fluctuating market conditions. Leveraging advanced machine learning techniques, it ensures pricing that is not only accurate but also fair and responsive. By integrating these factors into a unified pricing strategy, the model provides an optimized solution that enhances operational efficiency and meets consumer needs, ultimately contributing to a more equitable and efficient pricing system in the ridesharing sector.
