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No file available [This article belongs to Volume - 55, Issue - 1]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-19-01-2023-498
Title : TrapNet: Traffic Prediction using Convolution and Self-Attention Networks
Arun Kumar, , R. Sunitha,

Abstract : Traffic forecasting has emerged as a core component of intelligent transportation systems. Traffic forecasting is crucial for public safety and resource optimization that can be modelled as saptio-temporal data. The uncertainty hinders spatio-temporal data prediction in time-series data, the existence of diverse data patterns and incompetence in accessing and accommodating spatial dynamics, causing inconsistent performance. Most recent traffic prediction works are based on deep learning models,

Keywords : Traffic Prediction, Attention Networks Traffic, trapnet, traffic, prediction