Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

Z Ul Abideen, X Sun, C Sun - Journal of Forecasting - Wiley Online Library
The challenges in citywide traffic flow are intricate, encompassing various factors like
temporal and spatial dependencies, holidays, and weather. Despite the complexity, there …

Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks

C Chen, K Li, SG Teo, X Zou, K Li, Z Zeng - ACM Transactions on …, 2020 - dl.acm.org
Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …

Transformer Based Period Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

J Yin, B Li, Z Zhou - Available at SSRN 4572179 - papers.ssrn.com
Accurately forecasting traffic flow is crucial for the effective operation of intelligent
transportation systems (ITS), as it enables optimal resource allocation and efficient traffic …

Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

W Ding, T Zhang, J Wang, Z Zhao - arXiv preprint arXiv:2308.05601, 2023 - arxiv.org
Inter-city highway transportation is significant for urban life. As one of the key functions in
intelligent transportation system (ITS), traffic evaluation always plays significant role …

Multi-scale Fusion Dynamic Graph Neural Network For Traffic Flow Prediction

W Weng, Q Chen, Y Dai, J Chen, D Chen - Proceedings of the 2023 2nd …, 2023 - dl.acm.org
As a cornerstone of intelligent transportation systems, traffic flow prediction has garnered
extensive research attention. However, traffic flow data exhibits pronounced spatio-temporal …

MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion

D Yang, S Li, Z Peng, P Wang, J Wang… - … on Information and …, 2019 - search.ieice.org
Accurate traffic flow prediction is the precondition for many applications in Intelligent
Transportation Systems, such as traffic control and route guidance. Traditional data driven …

Staf: Convolutional Spatio-Temporal Transformer Architecture Based on Augmented Feature Learning for Traffic Flow Forecasting

AJ Fofanah, D Chen, L Wen, S Zhang - Available at SSRN 4826658 - papers.ssrn.com
Traffic forecasting is pivotal in multivariate time series forecasting, playing a crucial role in
the research of intelligent transportation systems. Despite considerable advancements …

Forecasting traffic flow with spatial–temporal convolutional graph attention networks

X Zhang, Y Xu, Y Shao - Neural Computing and Applications, 2022 - Springer
Traffic flow prediction is crucial for intelligent transportation system, such as traffic
management, congestion alleviation and public risk assessment. Recently, attention …

Multi-Range Spatial-Temporal Attention Network for Traffic Flow Forecasting

J Chen, J Li - 2024 IEEE 7th Advanced Information Technology …, 2024 - ieeexplore.ieee.org
With the increasing complexity of urban traffic systems and the growing significance of traffic
flow management, accurate traffic flow prediction has emerged as a key challenge in urban …

Multicomponent Spatial‐Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data

S Liu, S Dai, J Sun, T Mao, J Zhao… - Computational …, 2021 - Wiley Online Library
Predicting traffic data on traffic networks is essential to transportation management. It is a
challenging task due to the complicated spatial‐temporal dependency. The latest studies …