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.
@article{arxiv.2301.05292,
title = {A Novel Framework for Handling Sparse Data in Traffic Forecast},
author = {Nikolaos Zygouras and Dimitrios Gunopulos},
journal= {arXiv preprint arXiv:2301.05292},
year = {2023}
}