Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections
Forecasting vehicles' future motion is crucial for real-world applications such as the
navigation of autonomous vehicles and feasibility of safety systems based on the Internet of …
navigation of autonomous vehicles and feasibility of safety systems based on the Internet of …
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
Deep learning methods are emerging as popular computational tools for solving forward
and inverse problems in traffic flow. In this paper, we study a neural operator framework for …
and inverse problems in traffic flow. In this paper, we study a neural operator framework for …
Urban network-wide traffic volume estimation under sparse deployment of detectors
Sensing network-wide traffic information is fundamental for the sustainable development of
urban planning and traffic management. However, owing to the limited budgets or device …
urban planning and traffic management. However, owing to the limited budgets or device …
Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets
Unimodal, concave relationships between average network productivity and accumulation
or density aggregated across spatially compact regions of urban networks—so called …
or density aggregated across spatially compact regions of urban networks—so called …
3D tensor-based point cloud and image fusion for robust detection and measurement of rail surface defects
Q Wang, X Wang, Q He, J Huang, H Huang… - Automation in …, 2024 - Elsevier
Railway transportation safety relies on the accurate location, detection, and measurement of
rail surface defects. However, the absence of image and point cloud fusion methods to …
rail surface defects. However, the absence of image and point cloud fusion methods to …
A Two-stage Learning-based method for Large-scale On-demand pickup and delivery services with soft time windows
With the rapid growth of the on-demand logistics industry, large-scale pickup and delivery
with soft time windows has become widespread in various time-critical scenarios. This …
with soft time windows has become widespread in various time-critical scenarios. This …
Adaptive and simultaneous trajectory prediction for heterogeneous agents via transferable hierarchical transformer network
Simultaneously and accurately predicting trajectories of multiple heterogeneous agents is
crucial for intelligent transportation systems (ITS) applications, eg, connected and …
crucial for intelligent transportation systems (ITS) applications, eg, connected and …
MDTGAN: Multi domain generative adversarial transfer learning network for traffic data imputation
J Fang, H He, M Xu, H Chen - Expert Systems with Applications, 2024 - Elsevier
Accurate, real-time, and efficient traffic data, crucial for intelligent transportation systems,
which is often disrupted in the real world due to the influence of weather, interference, and …
which is often disrupted in the real world due to the influence of weather, interference, and …
Identifying Critical Links in Urban Transportation Networks Based on Spatio-Temporal Dependency Learning
The urban transportation network is crucial for societal development, but it is prone to
failures like congestion caused by accidents or disasters. In particular, often network-wide …
failures like congestion caused by accidents or disasters. In particular, often network-wide …
Routing hypergraph convolutional recurrent network for network traffic prediction
W Yu, K Ruan, H Tang, J Huang - Applied Intelligence, 2023 - Springer
Effectively predicting network traffic is a fundamental but intractable task in IP network
management and operations. Many methods that can capture complex spatiotemporal …
management and operations. Many methods that can capture complex spatiotemporal …