Low-Dimensional Models for Traffic Data Processing Using Graph Fourier Transform

Abstract

The reliability of services offered by intelligent transportation systems is attributed to the accuracy and timely availability of road-network traffic information. However, in the present era of big data, compliance with anticipated service quality requirements mandates consistent real-time processing of big spatiotemporal traffic data. Thus, development of low-dimensional models is a crucial challenge in traffic data processing. The authors developed such representations using data graph framework and graph Fourier transform (GFT) approaches. Experimental results on California daily network traffic data showed that, even with a 15:1 compression ratio, GFT-based models offered less than 6 percent reconstruction error (RE), instigating a less than 2 percent increase in mean absolute percentage error of corresponding predictions. The authors also proposed a 3D graph framework, which reduced RE by almost 2 percent compared to its 2D counterpart.