Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond
H Cui, Q Meng, TH Teng, X Yang - Transport reviews, 2023 - Taylor & Francis
Predicting traffic states has gained more attention because of its practical significance.
However, the existing literature lacks a critical review regarding how to address the …
However, the existing literature lacks a critical review regarding how to address the …
[HTML][HTML] Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application
values in the maritime industry. However, using such big data from the Automatic …
values in the maritime industry. However, using such big data from the Automatic …
A novel hybrid deep learning model with ARIMA Conv-LSTM networks and shuffle attention layer for short-term traffic flow prediction
AR Sattarzadeh, RJ Kutadinata… - … A: Transport Science, 2023 - Taylor & Francis
Traffic flow prediction requires learning of nonlinear spatio-temporal dynamics which
becomes challenging due to its inherent nonlinearity and stochasticity. Addressing this …
becomes challenging due to its inherent nonlinearity and stochasticity. Addressing this …
Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion
Although generative AI has been successful in many areas, its ability to model geospatial
data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide …
data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide …
Neural network applications in hybrid data-model driven dynamic frequency trajectory prediction for weak-damping power systems
G Wang, C Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This article proposes a hybrid data-model driven dynamic frequency trajectory (DFT)
prediction method for achieving an accurate perception of low-inertia and weak-damping …
prediction method for achieving an accurate perception of low-inertia and weak-damping …
Periodic Attention-based Stacked Sequence to Sequence framework for long-term travel time prediction
Travel time analysis and prediction are keystones for building intelligent transportation
systems in the new era, which has gained wide attention from the research community. Over …
systems in the new era, which has gained wide attention from the research community. Over …
PFNet: Large-Scale Traffic Forecasting With Progressive Spatio-Temporal Fusion
C Wang, K Zuo, S Zhang, H Lei, P Hu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Traffic flow forecasting on a large-scale sensor network is of great practical significance for
policy decision-making, urban management, and transport planning. Recently, several …
policy decision-making, urban management, and transport planning. Recently, several …
Network-level short-term traffic state prediction incorporating critical nodes: A knowledge-based deep fusion approach
The critical nodes (CNs) in urban transportation networks, defined as road entities (such as
road segments or detectors in a road network) that present highly volatile traffic states, can …
road segments or detectors in a road network) that present highly volatile traffic states, can …
ProSTformer: Progressive Space-Time Self-Attention Model for Short-Term Traffic Flow Forecasting
Traffic flow forecasting is essential and challenging to intelligent city management and
public safety. In this paper, we attempt to use a pure self-attention method in traffic flow …
public safety. In this paper, we attempt to use a pure self-attention method in traffic flow …
An improved convolutional network capturing spatial heterogeneity and correlation for crowd flow prediction
H Zhang, Y Liu, Y Xu, M Liu, P An - Expert Systems with Applications, 2023 - Elsevier
Crowd flow prediction plays an important role in urban management and public safety.
However, the existing prediction models still have some shortcomings in capturing spatial …
However, the existing prediction models still have some shortcomings in capturing spatial …