A Comparative Analysis between Efficient Attention Mechanisms for Traffic Forecasting without Structural Priors

A Comparative Analysis between Efficient Attention Mechanisms for Traffic Forecasting without Structural Priors

Dot-product attention is a powerful mechanism for capturing contextual information. Models that build on top of it have acclaimed state-of-the-art performance in various domains, ranging from sequence modelling to visual tasks. However, the main bottleneck is the construction of the attention map, w...

Full description

Bibliographic Details
Main Authors:	Andrei-Cristian Rad, Camelia Lemnaru, Adrian Munteanu
Format:	Article
Language:	English
Published:	MDPI AG 2022-10-01
Series:	Sensors
Subjects:	artificial neural networks deep learning intelligent transportation systems
Online Access:	https://www.mdpi.com/1424-8220/22/19/7457

Similar Items

STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting
by: Xiangyuan Kong, et al.
Published: (2020-01-01)

TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction
by: He Yang, et al.
Published: (2024-08-01)

Attention based graph Bi-LSTM networks for traffic forecasting
by: Zhao, Han, et al.
Published: (2021)

Short‐term traffic travel time forecasting using ensemble approach based on long short‐term memory networks
by: Xingli Jia, et al.
Published: (2023-06-01)

Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix
by: Dancho Panovski, et al.
Published: (2020-01-01)

Extracting long‐term spatiotemporal characteristics of traffic flow using attention‐based convolutional transformer
by: Ali Reza Sattarzadeh, et al.
Published: (2024-10-01)

Spatio-temporal causal graph attention network for traffic flow prediction in intelligent transportation systems
by: Wei Zhao, et al.
Published: (2023-07-01)

A comprehensive review on deep learning approaches in wind forecasting applications
by: Zhou Wu, et al.
Published: (2022-06-01)

Short-Term Traffic Prediction With Deep Neural Networks: A Survey
by: Kyungeun Lee, et al.
Published: (2021-01-01)

Intelligent Traffic Management Using Machine Vision Techniques in Smart Cities
by: Peyman Babaei
Published: (2024-12-01)

Q-LAtte: An Efficient and Versatile LSTM Model for Quantized Attention-Based Time Series Forecasting in Building Energy Applications
by: Jieui Kang, et al.
Published: (2024-01-01)

Graph Neural Networks and Open-Government Data to Forecast Traffic Flow
by: Petros Brimos, et al.
Published: (2023-04-01)

Dynamic spatial‐temporal network for traffic forecasting based on joint latent space representation
by: Qian Yu, et al.
Published: (2024-08-01)

Short‐term origin‐destination demand forecasting in bus rapid transit based on dual attentive multi‐scale convolutional network
by: Xinyi Li, et al.
Published: (2024-01-01)

Short term traffic flow prediction in heterogeneous condition using artificial neural network
by: Kranti Kumar, et al.
Published: (2015-12-01)

Scale-attentional U-Net for the segmentation of the median nerve in ultrasound images
by: Beom Suk Kim, et al.
Published: (2022-10-01)

Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation
by: Ande Chang, et al.
Published: (2025-01-01)

ROBUST FORECASTING OF TRAFFIC FLOW INTENSITY
by: Alexey A. Vlasov
Published: (2022-06-01)

Attention-Driven Recurrent Imputation for Traffic Speed
by: Shuyu Zhang, et al.
Published: (2022-01-01)

AI-based neural network models for bus passenger demand forecasting using smart card data
by: Sohani Liyanage, et al.
Published: (2022-09-01)

Traffic Prediction in Smart Cities Based on Hybrid Feature Space
by: Noureen Zafar, et al.
Published: (2022-01-01)

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor
by: Ashkan Safari, et al.
Published: (2023-09-01)

Smart Organization of Imbalanced Traffic Datasets for Long-Term Traffic Forecasting
by: Mustafa M. Kara, et al.
Published: (2025-02-01)

A systematic framework for urban smart transportation towards traffic management and parking
by: Kai Huang, et al.
Published: (2022-09-01)

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective
by: Juan S. Angarita-Zapata, et al.
Published: (2019-01-01)

A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting
by: Shiyan Yi, et al.
Published: (2023-04-01)

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting
by: Ping-Huan Kuo, et al.
Published: (2018-01-01)

Research and forecasting of educational process parameters by using artificial intelligence
by: Anis F. Galimyanov, et al.
Published: (2022-02-01)

AHRNN: Attention‐Based Hybrid Robust Neural Network for emotion recognition
by: Ke Xu, et al.
Published: (2022-03-01)

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods
by: Muhammad Usman Tariq, et al.
Published: (2024-04-01)

Traffic Density Classification Using Sound Datasets: An Empirical Study on Traffic Flow at Asymmetric Roads
by: Khac-Hoai Nam Bui, et al.
Published: (2020-01-01)

Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
by: Seungyo Ryu, et al.
Published: (2020-03-01)

Traffic flow prediction models – A review of deep learning techniques
by: Anirudh Ameya Kashyap, et al.
Published: (2022-12-01)

Forest Height Inversion by Convolutional Neural Networks Based on L-Band PolInSAR Data Without Prior Knowledge Dependency
by: Dandan Li, et al.
Published: (2023-01-01)

Transformer model-based multi-scale fine-grained identification and classification of regional traffic states
by: Jun Zhang, et al.
Published: (2024-12-01)

An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks
by: Maurizio Capra, et al.
Published: (2020-07-01)

A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism
by: Wei Ye, et al.
Published: (2024-01-01)

Improving traffic time‐series predictability by imputing continuous non‐random missing data
by: Meng Miao, et al.
Published: (2023-10-01)

SSA-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting
by: Fei Wang, et al.
Published: (2024-06-01)

Hourly electricity price forecast for short-and long-term, using deep neural networks
by: Dombi Gergely, et al.
Published: (2022-12-01)