Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm

Bus passenger flow prediction integrates data analytics and modelling techniques to forecast the number of passengers using bus services, incorporating historical usage patterns, demographics, weather, and events for optimal scheduling and resource allocation. The Bi-LSTM fusion model enhances accuracy by processing past and future features simultaneously, leveraging bidirectional LSTM layers and an attention mechanism to capture temporal dependencies. This approach not only refines insights crucial for urban mobility challenges like traffic management and demand forecasting but also improves route planning and service efficiency. The SMO algorithm initializes with a diverse spider monkey population exploring solution spaces. Through local and global leader phases, it iteratively updates positions based on fitness and probabilistic selections, maintaining a balance between exploration and exploitation. Perturbation-based updates in the local leader phase ensure adaptability, preventing premature convergence, while the global leader phase guides towards better solutions, enhancing efficiency in complex optimization tasks and promoting dynamic adaptation. In Dataset 1, the proposed m