40 citations · 73 across the 7 of their papers we have counts for
7 papers
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction
Xiaowei Gao, James Haworth, Ilya Ilyankou +4
Predicting traffic accidents is the key to sustainable city management, which requires effective address of the dynamic and complex spatiotemporal characteristics of cities. Curren…
Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis
Jingwei Guo, Yitai Cheng, Meihui Wang +5
Cyclists face a disproportionate risk of injury, yet conventional crash records are too sparse to identify risk factors at fine spatial and temporal scales. Recently, naturalistic…
Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
Ilya Ilyankou, Aldo Lipani, Stefano Cavazzi +2
Sentence transformers are language models designed to perform semantic search. This study investigates the capacity of sentence transformers, fine-tuned on general question-answeri…
Time Series Supplier Allocation via Deep Black-Litterman Model
Jiayuan Luo, Wentao Zhang, Yuchen Fang +4
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficie…
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction
Xiaowei Gao, Xinke Jiang, Dingyi Zhuang +4
Traffic accidents present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic accident prediction mo…
Uncertainty Quantification in the Road-level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN)
Xiaowei Gao, James Haworth, Dingyi Zhuang +2
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 ove…
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Xinke Jiang, Dingyi Zhuang, Xianghui Zhang +3
Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing th…