PFFN: Periodic Feature-Folding Deep Neural Network for Traffic Condition Forecasting

Tiange Wang, Zijun Zhang, Kwok-Leung Tsui

Abstract: Accurate forecasting of traffic conditions is critical for improving urban transportation safety, stability, and efficiency. It is challenging to produce explicit traffic forecasts due to complex and dynamic spatiotemporal contexts. Most existing works only capture partial characteristics and features of traffic data, and there still exists a great potential of uplifting the forecasting performances. In this article, we propose a periodic feature-folding network (PFFN) that globally folds the spatial-temporal-geo-character features of collected data into pivotal levels to enhance the forecasting performance of traffic conditions. Meanwhile, the historical and recent traffic information within the subgraph is locally folded to capture similar traffic patterns and determine meaningful high-level features in latent space via a novel gated attention plug (GAP). During forecasting, the auxiliary road attributes are jointwise folded with locally engineered features to realize the multistep forecasting. Experimental results on two publicly accessible real-world urban traffic data sets show that the proposed PFFN can outperform the state-of-the-art benchmarks, significantly improving the performance of forecasting short-term traffic conditions.