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09/10/2023
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
Predicting traffic incident risks at granular spatiotemporal levels is c...
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07/25/2023
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 ...
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06/16/2023
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
crucial for transportation management. However, traditional spatial-temp...
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05/10/2023
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Traffic data serves as a fundamental component in both research and appl...
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03/10/2023
Fairness-enhancing deep learning for ride-hailing demand prediction
Short-term demand forecasting for on-demand ride-hailing services is one...
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03/07/2023
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
Recent studies have significantly improved the prediction accuracy of tr...
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08/11/2022
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
Origin-Destination (O-D) travel demand prediction is a fundamental chall...
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03/07/2022
The Braess Paradox in Dynamic Traffic
The Braess's Paradox (BP) is the observation that adding one or more roa...
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09/24/2021
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Spatiotemporal kriging is an important application in spatiotemporal dat...
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05/21/2021
Low-Rank Hankel Tensor Completion for Traffic Speed Estimation
This paper studies the traffic state estimation (TSE) problem using spar...
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06/13/2020
