Abstract
Abstract: In recent years, Ride-on-Demand (RoD) services such as Uber, Ola, and Rapido have emerged as popular alternatives to traditional taxi/cab services. These services operate 24/7 and cater to tens of thousands of customers. Unlike traditional cabs, RoD services do not offer a fixed price. Instead, they utilize Dynamic Pricing to balance supply and demand, taking into account factors such as location, time of booking, ride demand, and driver availability to improve their service. However, the unpredictable and fluctuating nature of dynamic pricing has posed a significant challenge for customers, leading them to pay higher fares without their knowledge. To address this issue, it is crucial to estimate dynamic prices accurately and provide the lowest possible fares to customers. We leverage datasets of Uber and Lyft, to compare and forecast the services that offer the most competitive pricing. We also evaluate the contribution of different features to dynamic pricing, determining which factors play the most significant role in determining fare prices.
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