Article
DRIVER DEMAND PREDICTION USING MACHINE LEARNING AND TIME SERIES MODELS
The growing demand for efficient ride-sharing and food delivery services has necessitated innovative solutions to accurately predict and manage driver demand. These services rely heavily on timely driver availability to meet customer expectations and ensure operational efficiency. Historically, driver demand in such industries has been addressed using manual forecasting methods and basic statistical models. However, these approaches often fall short in capturing the dynamic and unpredictable nature of demand—especially during peak hours, special events, or sudden environmental changes. Traditional systems, limited by their inability to incorporate real-time data, have led to delays, customer dissatisfaction, and inefficient resource allocation. Before the advent of AI, these challenges were tackled using rudimentary scheduling systems, fixed staffing policies, and reactive strategies that failed to adapt to rapidly changing conditions. These shortcomings, combined with the rapid growth of on-demand services, highlighted the urgent need for robust, data-driven solutions. This research is motivated by the limitations of traditional forecasting methods and aims to leverage advanced machine learning techniques to improve demand prediction. Inspired by the success of AI-driven systems in addressing complex, non-linear problems across various industries, the proposed system utilizes ensemble learning techniques and time-series analysis to forecast driver demand accurately. By incorporating historical trends, external variables such as weather and events, and real-time data, the system delivers precise forecasts that enable proactive resource management. This research not only enhances operational efficiency but also improves customer satisfaction by reducing wait times and optimizing driver allocation in ride-sharing and food delivery platforms.
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