Long Term Traffic Forecasting

Long-term traffic forecasting aims to predict traffic conditions hours or even days in advance, enabling proactive traffic management and improved transportation planning. Recent research focuses on developing sophisticated models, such as neural differential equations, mixture-of-experts architectures, and hierarchical transformer networks, to capture complex spatiotemporal dependencies and handle evolving traffic patterns in large-scale networks. These advancements address challenges like capturing long-range correlations, adapting to new sensor data without catastrophic forgetting, and providing more accurate and explainable predictions, ultimately leading to more efficient and resilient transportation systems.