An Intelligent Framework for Dynamic Transportation Optimization Using IoT and Crowd Mobility Patterns

Using Internet of Things, predictive analysis, and a new dynamic scheduling algorithm for smart mobility, this research offers a comprehensive end-to-end solution for dynamic transportation scheduling. The novel approach utilizes real-time data inputs from mobility patterns, weather reports, and traffic sensors to dynamically manage transport operations, effectively addressing the limitations of traditional static methods. The framework includes two major modules: (1) Crowd Mobilization Prediction, which integrates a hybrid model combining CNN for spatial feature extraction, GRU for forecasting temporal prediction, k-NN for classification, and DBSCAN for clustering unsupervised movement patterns; and (2) Dynamic Scheduling, where the proposed adaptive Algorithm dynamically allocates transportation resources in response to predicted demand, traffic levels, and Environmental conditions. Testing the model with actual real-world urban mobile signal datasets highlights the model's ability to reduce waiting times, enhance vehicle dispatch effectiveness, and adapt responsiveness in a range of traffic and demand scenarios. A comparison with conventional scheduling techniques reveals that the suggested approach is more responsive, scalable, and operationally efficient. According to the experimental findings, the proposed framework performs better than traditional methods in terms of prediction accuracy, with an R2 score of 0.98, MAE of 0.120, and MSE of 0.020. The model optimized vehicle usage and reduced passenger waiting times under dynamic situations. The result demonstrates how AI and IoT-based technologies can completely transform urban mobility by improving transportation systems' responsiveness, cost-effectiveness, and resilience to unforeseen shocks. This model provides a foundation for smart infrastructure mobility and has additional resonance with the smart sustainable urban transport vision.