Search Results for author:
Found 20 papers, 9 papers with code
Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities
Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities.
ADL4D: Towards A Contextually Rich Dataset for 4D Activities of Daily Living
However, existing datasets for 4D HOI (3D HOI over time) are limited to one subject inter- acting with one object only.
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution.
Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series.
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time.
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach
To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain.
Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed.
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility
Although DRL has achieved important advancements in this field, the existing works pay little attention to the explainability and safety issues related to DRL controllers, particularly the safety under adversarial attacks.
Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction
By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation.
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Specifically, we propose a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors.
Individual Mobility Prediction via Attentive Marked Temporal Point Processes
Individual mobility prediction is an essential task for transportation demand management and traffic system operation.
Low-Rank Hankel Tensor Completion for Traffic Speed Estimation
This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors.
BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks
The distilled models exhibit their strength against all white box attacks with an exception of C&W.
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learning
Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with each other and regulate human-driven traffic flow.
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.
Non-recurrent Traffic Congestion Detection with a Coupled Scalable Bayesian Robust Tensor Factorization Model
However, it remains a challenging task to construct an analytical framework through which the natural spatial-temporal structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC.
Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization
Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability.
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning
In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed.
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow.
