Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Qin, Guoyang"'
The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning tech
Externí odkaz:
http://arxiv.org/abs/2408.17258
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including chan
Externí odkaz:
http://arxiv.org/abs/2407.17246
$\textbf{This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner}$. Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation
Externí odkaz:
http://arxiv.org/abs/2406.08743
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or sourc
Externí odkaz:
http://arxiv.org/abs/2405.03185
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. This problem attracts many studies to contribute to data-driven solutions. Existing imputation solutions mainly include lo
Externí odkaz:
http://arxiv.org/abs/2312.01728
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting. However, because STTD is often massive in scale, practitioners need
Externí odkaz:
http://arxiv.org/abs/2307.01482
Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from s
Externí odkaz:
http://arxiv.org/abs/2306.05627
Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on t
Externí odkaz:
http://arxiv.org/abs/2303.05660
Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication tec
Externí odkaz:
http://arxiv.org/abs/2210.11780
Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in making better decisions. However, traffic data in reality often has cor
Externí odkaz:
http://arxiv.org/abs/2205.09390