AI-Intelligent Demand-Sensitive Transportation Scheduling with IoT-Crowd Prediction an Reinforcement Learning Optimization

Efficient scheduling of transportation in smart cities is made difficult by the dynamic nature of passenger demand and crowd movements. Previous methods for scheduling, which use established timetables and explicit guidelines, struggle to adapt to real-time changes in demand, often resulting in long wait times for passengers, inefficient usage of transportation resources, and higher overall operating costs. This research presents a cluster-aware intelligent transportation framework that combines an approach to forecasting transportation demand with an adaptive scheduling process using data from IoT-based mobile signals. The use of DBSCAN to determine areal mobility clusters from the mobile signal data allowed a Cluster-Aware CNN-GRU model to learn complex patterns of spatial-temporal movement, resulting in the accurate prediction of the future demand for transportation. The proposed forecasting methodology achieved high levels of accuracy in its prediction of demand, with a MAE of 0.1033, and an RMSE of 0.1290. The forecasted demand was then utilized as input to a Deep Q-Network (DQN) scheduling module, which allocated transportation resources dynamically based on the forecasted mobility needs of the population. Analysis of the experimental data demonstrates that using the hybrid forecasting/scheduling framework led to a reduction in both passengers wait times and operational costs while also increasing vehicle utilization as compared to traditional scheduling methods. The combination of the Cluster-Aware CNN-GRU demand forecasting and DQN-based scheduling provides a scalable, efficient, and demand-responsive means for managing urban transportation systems.