Abstract
New research on Machine Learning - Intelligent Systems is the subject of a report. According to news reporting from Jiangxi, People’s Republic of China, by NewsRx journalists, research stated, “Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Jiangxi Province, Jiangxi Province Graduate Innovation Special Fund. The news correspondents obtained a quote from the research from East China Jiaotong University, “Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial-temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder-decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial-temporal dependencies and accurately predict traffic flow.”
NETL
NSTL
Springer Nature