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Found 14 papers, 3 papers with code
Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation.
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for EEG Super-resolution
The ESTformer, with the fixed masking strategy, adopts a mask token to up-sample the low-resolution (LR) EEG data in case of disturbance from mathematical interpolation methods.
EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction
The growing popularity of deep learning has led to the development of numerous methods for trajectory prediction.
A multi-functional simulation platform for on-demand ride service operations
In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement.
Semantic-Fused Multi-Granularity Cross-City Traffic Prediction
Accurate traffic prediction is essential for effective urban management and the improvement of transportation efficiency.
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data
In addition, Subtype-Former also achieved outstanding results in pan-cancer subtyping, which can help analyze the commonalities and differences across various cancer types at the molecular level.
Improved Multi-step FCS-MPCC with Disturbance Compensation for PMSM Drives -- Methods and Experimental Validation
In this paper, an improved multi-step finite control set model predictive current control (FCS-MPCC) strategy with speed loop disturbance compensation is proposed for permanent magnet synchronous machine (PMSM) drives system.
CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban Computing
Therefore, a comprehensive and multifaceted dataset is required to enable more extensive studies in urban computing.
Numerical simulation of hot accretion flows (IV): effects of black hole spin and magnetic field strength on the wind and the comparison between wind and jet properties
One interesting finding, among others, is that even in the case of very rapidly spinning black hole where the jet is supposed to be the strongest, the momentum flux of jet is smaller than that of wind, while the total energy flux of jet is larger than that of wind by at most a factor of 10.
High Energy Astrophysical Phenomena
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach
To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks.
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs.
PCA-Based Missing Information Imputation for Real-Time Crash Likelihood Prediction Under Imbalanced Data
However, few research focuses on the missing data imputation in real-time crash likelihood prediction, although missing values are commonly observed due to breakdown of sensors or external interference.
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables.
A Physarum-Inspired Approach to Optimal Supply Chain Network Design at Minimum Total Cost with Demand Satisfaction
A supply chain is a system which moves products from a supplier to customers.
