Spatiotemporal graph neural networks with uncertainty quantification for traffic incident risk prediction
Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets
predominantly feature zero values, indicating no incidents, with sporadic high-risk values for …
predominantly feature zero values, indicating no incidents, with sporadic high-risk values for …
Uncertainty quantification via spatial-temporal tweedie model for zero-inflated and long-tail travel demand prediction
Understanding Origin-Destination (OD) travel demand is crucial for transportation
management. However, traditional spatial-temporal deep learning models grapple with …
management. However, traditional spatial-temporal deep learning models grapple with …
Uncertainty in Graph Neural Networks: A Survey
Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …
Cross-mode knowledge adaptation for bike sharing demand prediction using domain-adversarial graph neural networks
For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of
available bikes according to predicted demand. Existing methods for bike sharing demand …
available bikes according to predicted demand. Existing methods for bike sharing demand …
Uncertainty-aware Traffic Prediction under Missing Data
Traffic prediction is a crucial topic because of its broad scope of applications in the
transportation domain. Though recent studies have achieved promising results, most of them …
transportation domain. Though recent studies have achieved promising results, most of them …
Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder
Origin-destination (OD) crowd flow, if more accurately inferred at a fine-grained level, has
the potential to enhance the efficacy of various urban applications. While in practice for …
the potential to enhance the efficacy of various urban applications. While in practice for …
Uncertainty Quantification in the Road-level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN)
Urban road-based risk prediction is a crucial yet challenging aspect of research in
transportation safety. While most existing studies emphasize accurate prediction, they often …
transportation safety. While most existing studies emphasize accurate prediction, they often …
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network
Transportation mode share analysis is important to various real-world transportation tasks as
it helps researchers understand the travel behaviors and choices of passengers. A typical …
it helps researchers understand the travel behaviors and choices of passengers. A typical …
Design of Transit-Centric Multimodal Urban Mobility System with Autonomous Mobility-on-Demand
This paper addresses the pressing challenge of urban mobility in the context of growing
urban populations, changing demand patterns for urban mobility, and emerging …
urban populations, changing demand patterns for urban mobility, and emerging …
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Quantifying uncertainty is essential for achieving robust and reliable predictions. However,
existing spatiotemporal models predominantly predict deterministic values, often …
existing spatiotemporal models predominantly predict deterministic values, often …