Zobrazeno 1 - 10
of 558
pro vyhledávání: '"Chen, Xiaoxu"'
The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the Transformer paradigm for model
Externí odkaz:
http://arxiv.org/abs/2405.18036
Origin-destination (OD) demand matrices are crucial for transit agencies to design and operate transit systems. This paper presents a novel temporal Bayesian model designed to estimate transit OD matrices at the individual bus-journey level from boar
Externí odkaz:
http://arxiv.org/abs/2403.04742
Accurately forecasting bus travel time and passenger occupancy with uncertainty is essential for both travelers and transit agencies/operators. However, existing approaches to forecasting bus travel time and passenger occupancy mainly rely on determi
Externí odkaz:
http://arxiv.org/abs/2401.17387
Autor:
Xu, Pengbo, Gao, Tianyan, Wang, Yu, Yin, Junping, Zhang, Juan, Zheng, Xiaogu, Zhang, Zhimin, Hu, Xiaoguang, Chen, Xiaoxu
In the realm of numerical weather forecasting, achieving higher resolution demands increased computational resources and time investment, and leveraging deep learning networks trained solely on data significantly reduces the time expenditure during f
Externí odkaz:
http://arxiv.org/abs/2401.16254
Blind face restoration is an important task in computer vision and has gained significant attention due to its wide-range applications. Previous works mainly exploit facial priors to restore face images and have demonstrated high-quality results. How
Externí odkaz:
http://arxiv.org/abs/2312.15736
By hiding the front-facing camera below the display panel, Under-Display Camera (UDC) provides users with a full-screen experience. However, due to the characteristics of the display, images taken by UDC suffer from significant quality degradation. M
Externí odkaz:
http://arxiv.org/abs/2308.10196
Time series analysis is a fundamental task in various application domains, and deep learning approaches have demonstrated remarkable performance in this area. However, many real-world time series data exhibit significant periodic or quasi-periodic dy
Externí odkaz:
http://arxiv.org/abs/2308.01011
The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Towards this end, we develop a time-varying reduced-rank vector autoregressi
Externí odkaz:
http://arxiv.org/abs/2211.15482
Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems; for example, it can help passengers make better decisions on departure time, route choice, and even transport mode choice and
Externí odkaz:
http://arxiv.org/abs/2206.06915
Publikováno v:
In Emotion, Space and Society November 2024 53
