Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Yu, James J. Q."'
The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport Systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized ser
Externí odkaz:
http://arxiv.org/abs/2407.09239
Autor:
Yang, Yixuan, Lu, Junru, Zhao, Zixiang, Luo, Zhen, Yu, James J. Q., Sanchez, Victor, Zheng, Feng
Designing 3D indoor layouts is a crucial task with significant applications in virtual reality, interior design, and automated space planning. Existing methods for 3D layout design either rely on diffusion models, which utilize spatial relationship p
Externí odkaz:
http://arxiv.org/abs/2406.03866
Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-ter
Externí odkaz:
http://arxiv.org/abs/2310.16063
Traffic forecasting is a fundamental problem in intelligent transportation systems. Existing traffic predictors are limited by their expressive power to model the complex spatial-temporal dependencies in traffic data, mainly due to the following limi
Externí odkaz:
http://arxiv.org/abs/2308.14377
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 data mining research. Nonetheless, GPS trajectories contain personal
Externí odkaz:
http://arxiv.org/abs/2304.11582
Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cases and produce a single-point prediction, such as a scalar value for t
Externí odkaz:
http://arxiv.org/abs/2303.09273
In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic. It has been well investigated and examined that deep learning-based Spatio-temporal models have an edge when exploiting Spatio-
Externí odkaz:
http://arxiv.org/abs/2303.07184
Uncertainty is an essential consideration for time series forecasting tasks. In this work, we specifically focus on quantifying the uncertainty of traffic forecasting. To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepS
Externí odkaz:
http://arxiv.org/abs/2208.05875
Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecasting is multi-modal, influenced by physical interaction with scene cont
Externí odkaz:
http://arxiv.org/abs/2202.03954
Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generation intelligent applications. However, due to the openness of network
Externí odkaz:
http://arxiv.org/abs/2110.07567