A survey on federated learning in intelligent transportation systems

R Zhang, J Mao, H Wang, B Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The development of Intelligent Transportation System (ITS) has brought about
comprehensive urban traffic information that not only provides convenience to urban …

Deep learning for trajectory data management and mining: A survey and beyond

W Chen, Y Liang, Y Zhu, Y Chang, K Luo… - arxiv preprint arxiv …, 2024 - arxiv.org
Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …

Difftraj: Generating gps trajectory with diffusion probabilistic model

Y Zhu, Y Ye, S Zhang, X Zhao… - Advances in Neural …, 2023 - proceedings.neurips.cc
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …

FedPT-V2G: Security enhanced federated transformer learning for real-time V2G dispatch with non-IID data

Y Shang, S Li - Applied Energy, 2024 - Elsevier
The rising popularity of electric vehicles (EVs) underscores the potential of vehicle-to-grid
(V2G) technology to contribute to load peak-shaving, valley-filling, and photovoltaic (PV) self …

Synmob: Creating high-fidelity synthetic gps trajectory dataset for urban mobility analysis

Y Zhu, Y Ye, Y Wu, X Zhao, J Yu - Advances in Neural …, 2023 - proceedings.neurips.cc
Urban mobility analysis has been extensively studied in the past decade using a vast
amount of GPS trajectory data, which reveals hidden patterns in movement and human …

[PDF][PDF] A survey on uncertainty quantification methods for deep neural networks: An uncertainty source perspective

W He, Z Jiang - perspective, 2023 - jiangteam.org
A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty
Source's Perspective Page 1 A Survey on Uncertainty Quantification Methods for Deep Neural …

Anomalous sub-trajectory detection with graph contrastive self-supervised learning

X Kong, H Lin, R Jiang, G Shen - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Some anomalous vehicle trajectories may contain fraudulent behavior or traffic accident
information. Existing research mostly starts from a global view, treating the entire trajectory …

[HTML][HTML] Towards trustworthy cybersecurity operations using Bayesian Deep Learning to improve uncertainty quantification of anomaly detection

T Yang, Y Qiao, B Lee - Computers & Security, 2024 - Elsevier
Uncertainty quantification of cybersecurity anomaly detection results provides critical
guidance for decision makers on whether or not to accept the results. Improving the …

Filtering limited automatic vehicle identification data for real-time path travel time estimation without ground truth

A Li, WHK Lam, W Ma, AHF Chow… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Automatic Vehicle Identification (AVI) technology has been widely used for real-time path
travel time estimation. For a study path equipped with AVI sensors at both ends, the …

[HTML][HTML] Uncertainty-aware probabilistic graph neural networks for road-level traffic crash prediction

X Gao, X Jiang, J Haworth, D Zhuang, S Wang… - Accident Analysis & …, 2024 - Elsevier
Traffic crashes present substantial challenges to human safety and socio-economic
development in urban areas. Develo** a reliable and responsible traffic crash prediction …