COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting

arXiv:2403.01091v1 Announce Type: new
Abstract: This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their tendency to model temporal and spatial relationships independently, thereby inadequately accounting for complex high-order interactions of both worlds. Moreover, the diversity of …

abstract applications arxiv attention cs.ai cs.ir cs.lg cs.si development forecast forecasting future graph graph neural network management network neural network paper perspective planning state temporal traffic transportation type urban urban planning