A Novel Framework for Handling Sparse Data in Traffic Forecast
This addresses traffic forecasting for urban planning and management, but appears incremental as it builds on existing deep learning and attention mechanisms without introducing a new paradigm.
The paper tackles the problem of forecasting future traffic conditions using sparse, time-evolving GPS data from vehicles, and presents a deep learning framework that encodes this sparse information to predict traffic, though no concrete numerical results are provided.
The ever increasing amount of GPS-equipped vehicles provides in real-time valuable traffic information for the roads traversed by the moving vehicles. In this way, a set of sparse and time evolving traffic reports is generated for each road. These time series are a valuable asset in order to forecast the future traffic condition. In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition. Our framework consists of a recurrent part and a decoder. The recurrent part employs an attention mechanism that encodes the traffic reports that are available at a particular time window. The decoder is responsible to forecast the future traffic condition.