Search Results for author: Zekun Cai

Found 10 papers, 6 papers with code

Continuous Temporal Domain Generalization

no code implementations25 May 2024 Zekun Cai, Guangji Bai, Renhe Jiang, Xuan Song, Liang Zhao

Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions.

Domain Generalization

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

1 code implementation25 Sep 2023 Zekun Cai, Renhe Jiang, Xinyu Yang, Zhaonan Wang, Diansheng Guo, Hiroki Kobayashi, Xuan Song, Ryosuke Shibasaki

Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.

Multivariate Time Series Forecasting Time Series +2

Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling

no code implementations24 Jun 2023 Zhiling Guo, Yinqiang Zheng, Haoran Zhang, Xiaodan Shi, Zekun Cai, Ryosuke Shibasaki, Jinyue Yan

In recent years, single-frame image super-resolution (SR) has become more realistic by considering the zooming effect and using real-world short- and long-focus image pairs.

Image Super-Resolution

ST-ExpertNet: A Deep Expert Framework for Traffic Prediction

no code implementations5 May 2022 Hongjun Wang, Jiyuan Chen, Zipei Fan, Zhiwen Zhang, Zekun Cai, Xuan Song

Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances.

Traffic Prediction

Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction

1 code implementation CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki

Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.

Decision Making Management

DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

3 code implementations20 Aug 2021 Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.

Time Series Time Series Analysis +1

VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction

no code implementations16 Nov 2019 Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Xuan Song, Kota Tsubouchi, Ryosuke Shibasaki

In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.

Management Traffic Prediction

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

1 code implementation 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki

Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.

Management Video Prediction

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