1 code implementation • 21 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.
Ranked #1 on
Traffic Prediction
on PeMS08
1 code implementation • 1 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).
1 code implementation • 22 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.
1 code implementation • 12 Dec 2022 • Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Toyotaro Suzumura, Shintaro Fukushima
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
2 code implementations • 28 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.
1 code implementation • 27 Nov 2022 • Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Ranked #1 on
Traffic Prediction
on EXPY-TKY
no code implementations • 21 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.
no code implementations • 27 Mar 2022 • Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa, Akira Imakura, Hiroshi Nakamura, Kenjiro Taura, Tomohiro Kudoh, Toshihiro Hanawa, Yuji Sekiya, Hiroki Kobayashi, Shin Matsushima, Yohei Kuga, Ryo Nakamura, Renhe Jiang, Junya Kawase, Masatoshi Hanai, Hiroshi Miyazaki, Tsutomu Ishizaki, Daisuke Shimotoku, Daisuke Miyamoto, Kento Aida, Atsuko Takefusa, Takashi Kurimoto, Koji Sasayama, Naoya Kitagawa, Ikki Fujiwara, Yusuke Tanimura, Takayuki Aoki, Toshio Endo, Satoshi Ohshima, Keiichiro Fukazawa, Susumu Date, Toshihiro Uchibayashi
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks.
1 code implementation • 14 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.
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.
1 code implementation • 2 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.
3 code implementations • 20 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.
1 code implementation • IEEE Transactions on Knowledge and Data Engineering 2021 • Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Kota Tsubouchi, Xuan Song, Ryosuke Shibasaki
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
1 code implementation • 2021 IEEE 37th International Conference on Data Engineering (ICDE) 2021 • Zhaonan Wang, Tianqi Xia, Renhe Jiang, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki
Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment.
no code implementations • 16 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.
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.