Generative Adversarial Networks for High Fidelity Traffic Simulation and Prediction in Intelligent Transportation Systems
DOI:
https://doi.org/10.53469/wjimt.2024.07(06).20Keywords:
Generative Adversarial Networks (GANs), Intelligent Transportation Systems (ITS), Cross-Modal Data Generation, Traffic Scenario Simulation, Urban Traffic ManagementAbstract
Intelligent transportation systems (ITS) face increasing challenges in coping with complex urban traffic scenarios, including congestion propagation, vehicle rerouting, and the combined impact of environmental factors. The study proposes a framework based on generative adversarial networks (GANs) combined with advanced cross-modal data generation techniques to reconstruct, simulate, and predict traffic scenarios with high fidelity. The framework improves traffic perception and prediction capabilities by generating synthetic traffic images, videos, and text-based event alerts, effectively filling the gaps caused by data scarcity or sensor failure. The framework is validated in a real traffic disturbance scenario - the sudden closure of a main road during peak traffic hours. The results show that the framework performs very well: the traffic anomaly detection rate is improved by 12%, the structural similarity index (SSIM) of spatial reconstruction reaches 0.95, and the congestion prediction accuracy (CPA) reaches 91%. In addition, the framework can accurately model complex spatiotemporal patterns, enabling practical applications in path optimization, signal control, and connected vehicle coordination, reducing traffic delays by 15% and improving intersection efficiency by 10%. This study demonstrates the effectiveness and versatility of generative AI in intelligent transportation systems, providing practical insights into solving modern urban transportation challenges. The proposed framework pushes the state-of-the-art in traffic modeling and lays a solid foundation for the development and innovation of future smart cities.
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