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Found 10 papers, 5 papers with code
Large-Scale Demand Prediction in Urban Rail using Multi-Graph Inductive Representation Learning
With the expansion of cities over time, URT (Urban Rail Transit) networks have also grown significantly.
Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals
Emotions play a significant role in the cognitive processes of the human brain, such as decision making, learning and perception.
Applied metamodelling for ATM performance simulations
Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.
Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons
Furthermore, this approach facilitates the continuous assessment of changing cycling environments, allows for a short-term evaluation of measures, and is efficiently deployed in different locations or contexts.
Attitudes and Latent Class Choice Models using Machine learning
Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities.
Bayesian Active Learning with Fully Bayesian Gaussian Processes
In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient and non-optimal querying, leading to unnecessary data labeling.
Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance
The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative.
Market Design for Tradable Mobility Credits
Further, it is more robust in the presence of forecasting errors and non-recurrent events due to the adaptiveness of the market.
QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly.
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion.
