PREDICTING DRIVER DEMAND IN RIDE-SHARING AND FOOD DELIVERY SERVICES USING TIME SERIES ANALYSIS AND ENSEMBLE MODELS

Abstract

The growing demand for efficient ride-sharing and food delivery services has necessitated innovative solutions to predict and manage driver demand accurately. These services rely heavily on timely driver availability to meet customer expectations and maintain operational efficiency. Historically, driver demand in such industries has been addressed through manual forecasting methods and basic statistical models. These methods often fell short in handling the dynamic and unpredictable nature of demand, especially during peak hours, special events, or under sudden environmental changes. Traditional systems were limited by their inability to incorporate real-time data, leading to delays, customer dissatisfaction, and inefficient resource allocation. Before the advent of AI, challenges were addressed using rudimentary scheduling systems, fixed staffing policies, and reactive approaches, which often failed to adapt to rapidly changing conditions. These shortcomings, coupled with the rapid growth of on-demand services, highlighted the need for more robust, data-driven solutions. Motivated by the limitations of traditional methods, the development of this research aims to leverage advanced machine learning techniques for accurate demand forecasting. This innovation is inspired by the success of AI-driven systems in solving complex, non-linear problems across various industries. The core issue with traditional systems lies in their inability to process and analyse large datasets in real time, resulting in poor predictions and operational inefficiencies. To address this, the proposed system employs machine learning models, particularly ensemble approaches and time-series analysis, to predict driver demand effectively. By utilizing historical data, external factors like weather and events, and real-time inputs, the system generates precise forecasts, enabling proactive resource management. This research not only enhances operational efficiency but also improves customer satisfaction, reduces wait times, and optimizes resource allocation in ride-sharing and food delivery services.