Traffic flow prediction with vehicle trajectories

M Li, P Tong, M Li, Z Jin, J Huang… - Proceedings of the AAAI …, 2021 - ojs.aaai.org
This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph
Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle …

Temporal multi-graph convolutional network for traffic flow prediction

M Lv, Z Hong, L Chen, T Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …

Traffic flow prediction via spatial temporal graph neural network

X Wang, Y Ma, Y Wang, W Jin, X Wang, J Tang… - Proceedings of the web …, 2020 - dl.acm.org
Traffic flow analysis, prediction and management are keystones for building smart cities in
the new era. With the help of deep neural networks and big traffic data, we can better …

Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting

S Lan, Y Ma, W Huang, W Wang… - … on machine learning, 2022 - proceedings.mlr.press
As a typical problem in time series analysis, traffic flow prediction is one of the most
important application fields of machine learning. However, achieving highly accurate traffic …

Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction

J Jiang, C Han, WX Zhao, J Wang - … of the AAAI conference on artificial …, 2023 - ojs.aaai.org
As a core technology of Intelligent Transportation System, traffic flow prediction has a wide
range of applications. The fundamental challenge in traffic flow prediction is to effectively …

[HTML][HTML] Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations

G Li, VL Knoop, H Van Lint - Transportation Research Part C: Emerging …, 2021 - Elsevier
Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy
decisions in advanced traffic control and guidance systems. Recently, deep learning …

A general traffic flow prediction approach based on spatial-temporal graph attention

C Tang, J Sun, Y Sun, M Peng, N Gan - IEEE Access, 2020 - ieeexplore.ieee.org
Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of
intelligent transportation systems. However, it is very challenging since the complex spatial …

ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction

Y Liu, T Feng, S Rasouli, M Wong - Neurocomputing, 2024 - Elsevier
Accurately predicting traffic flow characteristics is crucial for effective urban transportation
management. Emergence of artificial intelligence has led to the surge of deep learning …

Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

B Yu, Y Lee, K Sohn - Transportation research part C: emerging …, 2020 - Elsevier
The traffic state in an urban transportation network is determined via spatio-temporal traffic
propagation. In early traffic forecasting studies, time-series models were adopted to …

Multi-view dynamic graph convolution neural network for traffic flow prediction

X Huang, Y Ye, X Yang, L Xiong - Expert Systems with Applications, 2023 - Elsevier
The rapid urbanization and continuous improvement of road traffic equipment result in
massive daily production of traffic data. These data contain the long-term evolution of traffic …