Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
Source abstract
Abstract
Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Traffic control and management · Time Series Analysis and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad 44000, Pakistan
School of Software, Dalian University of Technology, Dalian 116024, China
Department of Computer Games Development, Faculty of Computing and Artificial Intelligence, Air University, Islamabad 44000, Pakistan
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
School of Software, South China University of Technology, Guangzhou 510006, China
School of Software, Tsinghua University, Beijing 100084, China
Deanship of Elearning and Distance Education, King Faisal University, P.O. Box 400, Al Ahsa 31982, Saudi Arabia
Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt
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