TRAFFIC PREDICTION FOR IOV USING SPATIOTEMPORAL RESTRICTIONS, BIDIRECTIONAL MEMORY, LOCAL SEARCH, AND NONLINEAR ANALYSIS

Abstract

In this research, we introduce a novel framework that makes use of spatiotemporal limitations, bidirectional memory networks, local search optimization, and nonlinear analysis approaches to anticipate traffic within the Internet of Vehicles (IoV) ecosystem. The suggested model uses sophisticated memory networks to capture temporal and spatial dependencies, therefore integrating the dynamic and intricate interactions between cars and their surroundings. While nonlinear analysis guarantees robustness in handling a variety of traffic circumstances, the addition of local search techniques enables the model to modify predictions depending on specific traffic patterns. Our methodology is a feasible solution for smart transportation systems since extensive trials on real-world datasets show that it performs much better than existing methods in terms of accuracy and efficiency.