TRUE Lab: Yuyol Shin and prof. Yoon's paper on comparative study on traffic forecasting models is accepted in AAAI 2022 AI for Transportation workshop

December, 2021

Traffic forecasting plays a crucial role in intelligent transportation systems. The spatial-temporal complexities in transportation networks make the problem especially challenging. The recently suggested deep learning models share basic elements such as graph convolution, graph attention, recurrent units, and/or attention mechanism. In this study, we designed an in-depth comparative study for four deep neural network models utilizing different basic elements. For base models, one RNN-based model and one attention-based model were chosen from previous literature. Then, the spatial feature extraction layers in the models were substituted with graph convolution and graph attention. To analyze the performance of each element in various environments, we conducted experiments on four real-world datasets. The results demonstrates that each basic elements have notable characteristics in forecasting outcomes.