A multi-directional recurrent graph convolutional network model for reconstructing traffic spatiotemporal diagram
ABSTRACT The Time Space Diagram (TSD) can abstractly represent multiple data sources
and the macroscopic state of road traffic. However, the TSDs may be incomplete due to …
and the macroscopic state of road traffic. However, the TSDs may be incomplete due to …
On prediction of traffic flows in smart cities: a multitask deep learning based approach
With the rapid development of transportation systems, traffic data have been largely
produced in daily lives. Finding the insights of all these complex data is of great significance …
produced in daily lives. Finding the insights of all these complex data is of great significance …
Countrywide origin-destination matrix prediction and its application for covid-19
Modeling and predicting human mobility are of great significance to various application
scenarios such as intelligent transportation system, crowd management, and disaster …
scenarios such as intelligent transportation system, crowd management, and disaster …
Metro Station functional clustering and dual-view recurrent graph convolutional network for metro passenger flow prediction
The metro system is indispensable for alleviating traffic congestion in the urban
transportation system. Precise metro passenger flow (MPF) prediction is crucial in ensuring …
transportation system. Precise metro passenger flow (MPF) prediction is crucial in ensuring …
Coupledgt: Coupled geospatial-temporal data modeling for air quality prediction
Air pollution seriously affects public health, while effective air quality prediction remains a
challenging problem since the complex spatial-temporal couplings exist in multi-area …
challenging problem since the complex spatial-temporal couplings exist in multi-area …
SPRNN: A spatial–temporal recurrent neural network for crowd flow prediction
The capability of predicting the future trends of crowds has rendered crowd flow prediction
more critical in building intelligent transportation systems, and attracted substantial research …
more critical in building intelligent transportation systems, and attracted substantial research …
Advances in spatiotemporal graph neural network prediction research
Y Wang - International Journal of Digital Earth, 2023 - Taylor & Francis
Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
Spatial–temporal attention fusion for traffic speed prediction
A Zhang, Q Liu, T Zhang - Soft Computing, 2022 - Springer
Accurate vehicle speed prediction is of great significance to the urban traffic intelligent
control system. However, in terms of traffic speed prediction, the modules that integrate …
control system. However, in terms of traffic speed prediction, the modules that integrate …
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 …
Multi-view fusion neural network for traffic demand prediction
D Zhang, J Li - Information Sciences, 2023 - Elsevier
The extraction of spatial-temporal features is a crucial research in transportation studies, and
current studies typically use a unified temporal modeling mechanism and fixed spatial graph …
current studies typically use a unified temporal modeling mechanism and fixed spatial graph …