TY  - EJOU
AU  - Wang, Jing-Doo 
AU  - Susanto, Chayadi Oktomy Noto 

TI  - Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 140
IS  - 2
SN  - 1526-1506

AB  - 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 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.
KW  - Traffic flow prediction; sptiotemporal data; heterogeneous data; Conv-BiLSTM; data-centric; intra-data

DO  - 10.32604/cmes.2024.048955