Search Results for author: Renhe Jiang

Found 16 papers, 13 papers with code

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

1 code implementation21 Aug 2023 Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.

Time Series Traffic Prediction

Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

1 code implementation1 Jun 2023 Jiewen Deng, Jinliang Deng, Renhe Jiang, Xuan Song

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).

Representation Learning Time Series +1

Learning Structured Components: Towards Modular and Interpretable Multivariate Time Series Forecasting

1 code implementation22 May 2023 Jinliang Deng, Xiusi Chen, Renhe Jiang, Du Yin, Yi Yang, Xuan Song, Ivor W. Tsang

SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns.

Multivariate Time Series Forecasting Time Series

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

2 code implementations28 Nov 2022 Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Boyuan Zhang, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Xuan Song

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.

Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin

no code implementations21 Jun 2022 Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, Ryosuke Shibasaki

In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.

Retrieval Trajectory Prediction

Event-Aware Multimodal Mobility Nowcasting

1 code implementation14 Dec 2021 Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.

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

A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting

1 code implementation2 Sep 2021 Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang

Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view.

Multi-Task Learning Time Series +1

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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