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- Title
Traffic Flow Prediction with Heterogeneous Spatio temporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism.
- Authors
Jing-DooWang; Chayadi Oktomy Noto Susanto
- 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 ofmodels 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 vehicle types, which are interconnected and significantly impact the accuracy of forecast outcomes. In addition, this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes. The experimental findings demonstrate a performance improvement of 21.68% when incorporating the vehicle type feature.
- Subjects
TEMPORAL databases; TRAFFIC flow; ARTIFICIAL neural networks; DEEP learning; INTELLIGENT transportation systems; FORECASTING
- Publication
CMES-Computer Modeling in Engineering & Sciences, 2024, Vol 140, Issue 2, p1711
- ISSN
1526-1492
- Publication type
Article
- DOI
10.32604/cmes.2024.048955