A Novel Framework for Handling Sparse Data in Traffic Forecast

TL;DR

This paper introduces a deep learning framework that effectively encodes sparse, real-time traffic data to accurately forecast future traffic conditions, leveraging attention mechanisms within a recurrent neural network architecture.

Contribution

It presents a novel deep learning framework combining attention-based encoding and decoding for sparse traffic data forecasting, improving prediction accuracy.

Findings

Effective encoding of sparse traffic reports

Accurate future traffic condition predictions

Utilization of attention mechanisms enhances forecasting

Abstract

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.