Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks

Ahmad Ali; Yanmin Zhu; Muhammad Zakarya

doi:10.1016/j.ins.2021.08.042

ScienceGate Book Chapters

JOURNAL ARTICLE

Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks

Ahmad Ali Yanmin Zhu Muhammad Zakarya

Year: 2021 Journal: Information Sciences Vol: 577 Pages: 852-870 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2021.08.042

Get Full-Text PDF Get Analytical Report

Keywords:

Closeness Computer science Component (thermodynamics) Generalization Traffic flow (computer networking) Intelligent transportation system Artificial neural network Data mining Baseline (sea) Artificial intelligence Flow (mathematics) Machine learning Mathematics Transport engineering

Metrics

212

Cited By

20.82

FWCI (Field Weighted Citation Impact)

Refs

1.00

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Traffic control and management

Physical Sciences → Engineering → Control and Systems Engineering

Transportation Planning and Optimization

Social Sciences → Social Sciences → Transportation

Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks

Metrics

Citation History

Topics

Related Documents

Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction

Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks

Dynamic Spatio-Temporal Adaptive Convolutional Neural Networks for Traffic Flow Prediction

Transformer-Based Traffic Flow Prediction Considering Spatio-Temporal Correlations of Bridge Networks