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Found 7 papers, 4 papers with code
Spatial-temporal Forecasting for Regions without Observations
Our key insight is to learn from the locations that resemble those in the region of interest, and we propose a selective masking strategy to enable the learning.
Urban Region Representation Learning with Attentive Fusion
Our model is powered by a dual-feature attentive fusion module named DAFusion, which fuses embeddings from different region features to learn higher-order correlations between the regions as well as between the different types of region features.
Contrastive Trajectory Similarity Learning with Dual-Feature Attention
Trajectory similarity measures act as query predicates in trajectory databases, making them the key player in determining the query results.
A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction
To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions.
Intelligent Autonomous Intersection Management
Connected Autonomous Vehicles will make autonomous intersection management a reality replacing traditional traffic signal control.
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning
We then extend our architecture to learn heuristics for the real-time version of combinatorial optimization problems where all input features of a problem are not known a prior, but rather learned in real-time.
Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories
In this paper, we propose an efficient traffic optimization solution, called Coordinated Learning-based Lane Allocation (CLLA), which is suitable for dynamic configuration of lane-directions.
