no code implementations • 11 Oct 2023 • Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems.
1 code implementation • 25 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.
Ranked #1 on Traffic Prediction on Beijing Traffic
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.
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
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.
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.