Enhanced Predictive Analytics for Driver Shortage Mitigation in Peak-Demand Taxi Services using Multi-Modal Data Fusion and Bayesian Hyperparameter Optimization

**Abstract:** Peak-demand driver shortages significantly impact taxi service efficiency and customer satisfaction. This paper introduces a novel framework for proactively addressing these shortages by leveraging multi-modal data fusion and Bayesian hyperparameter optimization to enhance the accuracy of driver availability forecasts. The system integrates real-time location data, historical demand patterns, weather information, event calendars, and driver behavioral analytics to generate highly granular predictions, enabling dynamic dispatch strategies and incentive programs to mitigate driver shortages before they impact service levels. This approach represents a significant improvement over traditional reactive methods, promoting operational efficiency and enhancing overall service reliability within the taxi industry, estimated to increase fleet utilization by 8-12% within five years and reduce customer wait times by 15-20%.