Jinyuan Li1, Hao Li1, Guorong Cui1, Yan Kang1, *, Yang Hu1, Yingnan Zhou2
CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903
Abstract With continuous urbanization, cities are undergoing a sharp expansion within
the regional space. Due to the high cost, the prediction of regional traffic flow is more
difficult to extend to entire urban areas. To address this challenging problem, we present
a new deep learning architecture for regional epitaxial traffic flow prediction called
GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow
information in central area. The method is data-driven, and the spatial relationship of
traffic flow is characterized by dynamically transforming traffic information into images
through a two-dimensional matrix. We introduce adversarial training to improve
performance… More >
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