Jing-Doo Wang1, Chayadi Oktomy Noto Susanto1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955
- 20 May 2024
Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >
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