Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
RNN with particle flow for probabilistic spatio-temporal forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and
financial networks. Many classical statistical models often fall short in handling the …
financial networks. Many classical statistical models often fall short in handling the …
Adaptive spatial-temporal fusion graph convolutional networks for traffic flow forecasting
S Li, L Ge, Y Lin, B Zeng - 2022 International Joint Conference …, 2022 - ieeexplore.ieee.org
Traffic flow forecasting is a significant issue in the field of transportation. Early works model
temporal dependencies and spatial correlations, respectively. Recently, some models are …
temporal dependencies and spatial correlations, respectively. Recently, some models are …
Dmgf-net: an efficient dynamic multi-graph fusion network for traffic prediction
Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic
prediction can greatly improve the utilization of public resources. Dynamic interaction of …
prediction can greatly improve the utilization of public resources. Dynamic interaction of …
Supply-demand-aware deep reinforcement learning for dynamic fleet management
Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
Deep spatial–temporal sequence modeling for multi-step passenger demand prediction
Supply–demand imbalance poses significant challenges to transportation systems such as
taxis and shared vehicles (cars and bikes) and leads to excessive delays, income loss, and …
taxis and shared vehicles (cars and bikes) and leads to excessive delays, income loss, and …
Multi-level Graph Memory Network Cluster Convolutional Recurrent Network for traffic forecasting
L Sun, W Dai, G Muhammad - Information Fusion, 2024 - Elsevier
Traffic forecasting plays a vital role in the management of urban road networks and the
development of intelligent transportation systems. To effectively capture spatial and temporal …
development of intelligent transportation systems. To effectively capture spatial and temporal …
Knowledge adaption for demand prediction based on multi-task memory neural network
Accurate demand forecasting of different public transport modes (eg, buses and light rails) is
essential for public service operation. However, the development level of various modes …
essential for public service operation. However, the development level of various modes …
Learning heterogeneous spatial–temporal context for skeleton-based action recognition
Graph convolution networks (GCNs) have been widely used and achieved fruitful progress
in the skeleton-based action recognition task. In GCNs, node interaction modeling …
in the skeleton-based action recognition task. In GCNs, node interaction modeling …
Coupledmuts: Coupled multivariate utility time-series representation and prediction
Ubiquitous Internet of Things (IoT) sensors in the smart city generate various urban utility
sequential data, such as electricity and water usage records, which are defined as …
sequential data, such as electricity and water usage records, which are defined as …