Unified spatial-temporal neighbor attention network for dynamic traffic prediction
Traffic prediction plays an essential role in many real-world applications ranging from route
planning to vehicular communications. The goal of making accurate prediction is …
planning to vehicular communications. The goal of making accurate prediction is …
[HTML][HTML] Spatial-temporal load prediction of electric bus charging station based on S2TAT
G **ao, H Tong, Y Shu, A Ni - International Journal of Electrical Power & …, 2025 - Elsevier
In recent years, electric buses have advanced rapidly due to their green and low-carbon
attributes. To address range anxiety and optimize charging strategies, accurately predicting …
attributes. To address range anxiety and optimize charging strategies, accurately predicting …
Self-supervised spatiotemporal clustering of vehicle emissions with graph convolutional network
Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air
pollution from road traffic, is a challenging representation learning task due to the lack of …
pollution from road traffic, is a challenging representation learning task due to the lack of …
One size fits all: A unified traffic predictor for capturing the essential spatial–temporal dependency
Traffic prediction is a keystone for building smart cities in the new era and has found wide
applications in traffic scheduling and management, environment policy making, public …
applications in traffic scheduling and management, environment policy making, public …
Dynamic spatiotemporal interactive graph neural network for multivariate time series forecasting
Z Gao, Z Li, H Zhang, J Yu, L Xu - Knowledge-Based Systems, 2023 - Elsevier
Multivariate time series (MTS) forecasting holds significant importance in decision-making
for complex real-world phenomena. However, the presence of nonlinear temporal …
for complex real-world phenomena. However, the presence of nonlinear temporal …
DenseLight: efficient control for large-scale traffic signals with dense feedback
Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road
network, which in turn enhances fuel utilization efficiency, air quality, and road safety …
network, which in turn enhances fuel utilization efficiency, air quality, and road safety …
Graph neural rough differential equations for traffic forecasting
Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine
learning. A prevalent approach in the field is to combine graph convolutional networks and …
learning. A prevalent approach in the field is to combine graph convolutional networks and …
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 …
DSTGCS: an intelligent dynamic spatial–temporal graph convolutional system for traffic flow prediction in ITS
Accurate traffic prediction is indispensable for relieving traffic congestion and people's daily
trips. Nevertheless, accurate traffic flow prediction is still challenging due to the traffic …
trips. Nevertheless, accurate traffic flow prediction is still challenging due to the traffic …
Anchor-Enhanced Geographical Entity Representation Learning
R Chen, J Lei, H Yao, T Li, S Li - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Geographical entity representation learning (GERL) aims to embed geographical entities
into a low-dimensional vector space, which provides a generalized approach for utilizing …
into a low-dimensional vector space, which provides a generalized approach for utilizing …