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Found 8 papers, 5 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.
GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction
To reveal the effectiveness of our model, we evaluate the performance of GRANP on the highD dataset.
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior.
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.
FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling
To address this gap and promote the development of microscopic traffic flow modeling, we establish a public benchmark dataset for car-following behavior modeling.
Semantic-Fused Multi-Granularity Cross-City Traffic Prediction
Accurate traffic prediction is essential for effective urban management and the improvement of transportation efficiency.
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning
The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0. 890 CNY/m3-ww, 0. 530 kWh/m3-ww, 2. 491 kg CO2-eq/m3-ww respectively.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Optimisation of Wastewater Treatment Strategies in Eco-Industrial Parks: Technology, Location and Transport
The expanding population and rapid urbanisation, in particular in the Global South, are leading to global challenges on resource supply stress and rising waste generation.
Optimization and Control
