no code implementations • 16 Apr 2024 • Dayin Chen, Xiaodan Shi, Haoran Zhang, Xuan Song, Dongxiao Zhang, Yuntian Chen, Jinyue Yan
We believe this study has the potential to advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.
no code implementations • 7 Mar 2024 • Zezheng Feng, Yifan Jiang, Hongjun Wang, Zipei Fan, Yuxin Ma, Shuang-Hua Yang, Huamin Qu, Xuan Song
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
no code implementations • 22 Jan 2024 • Jinliang Deng, Xuan Song, Ivor W. Tsang, Hui Xiong
Through this work, we advocate a paradigm shift in LTSF, emphasizing the importance to tailor the model to the inherent dynamics of time series data-a timely reminder that in the realm of LTSF, bigger is not invariably better.
1 code implementation • 1 Dec 2023 • Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song
Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns.
Ranked #1 on Traffic Prediction on PEMS-BAY (using extra training data)
no code implementations • 28 Nov 2023 • Jixiao Zhang, Yongkang Li, Ruotong Zou, Jingyuan Zhang, Zipei Fan, Xuan Song
In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity. To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model.
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
no code implementations • 21 Sep 2023 • Shuang Ao, Tianyi Zhou, Guodong Long, Xuan Song, Jing Jiang
Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes.
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 PeMSD7
no code implementations • 14 Aug 2023 • Yuhe Nie, Shaoming Zheng, Zhan Zhuang, Xuan Song
However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs.
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
The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
Ranked #1 on Time Series Forecasting on Weather (96)
no code implementations • 22 May 2023 • Hongjun Wang, Jiyuan Chen, Lun Du, Qiang Fu, Shi Han, Xuan Song
Recent years have witnessed the great potential of attention mechanism in graph representation learning.
no code implementations • 14 Mar 2023 • Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, Jing Jiang
In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data.
no code implementations • 13 Jan 2023 • Hongjun Wang, Zhiwen Zhang, Zipei Fan, Jiyuan Chen, Lingyu Zhang, Ryosuke Shibasaki, Xuan Song
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
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 • 2 Jul 2022 • Zhiwen Zhang, Hongjun Wang, Jiyuan Chen, Zipei Fan, Xuan Song, Ryosuke Shibasaki
However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.
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 • 21 Jun 2022 • Zhiwen Zhang, Hongjun Wang, Zipei Fan, Jiyuan Chen, Xuan Song, Ryosuke Shibasaki
In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.
no code implementations • 5 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.
no code implementations • 6 Apr 2022 • Mingxin Zhang, Zipei Fan, Ryosuke Shibasaki, Xuan Song
We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies.
no code implementations • 11 Mar 2022 • Yifan Jiang, Zezheng Feng, Hongjun Wang, Zipei Fan, Xuan Song
TrafPS consists of three layers, from data process to results computation and visualization.
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.
no code implementations • 21 Nov 2021 • Dou Huang, Haoran Zhang, Xuan Song, Ryosuke Shibasaki
In this paper, we propose to use a differentiable projection layer in DNN instead of directly solving time-consuming KKT conditions.
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.
no code implementations • 29 Sep 2021 • Han Zheng, Jing Jiang, Pengfei Wei, Guodong Long, Xuan Song, Chengqi Zhang
URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.
no code implementations • 29 Sep 2021 • Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang
They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments.
no code implementations • 29 Sep 2021 • Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, Jing Jiang
Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, i. e., a pessimistic update strategy for the offline dataset and a greedy or no pessimistic update scheme for the online dataset.
no code implementations • 17 Sep 2021 • Jinyu Chen, Haoran Zhang, Xuan Song, Ryosuke Shibasaki
In this study, we propose and open GPS trajectory dataset marked with travel mode and benchmark for the travel mode detection.
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.
no code implementations • 13 Aug 2017 • Quanshi Zhang, Xuan Song, Ryosuke Shibasaki
In this study, we formulate the concept of "mining maximal-size frequent subgraphs" in the challenging domain of visual data (images and videos).
no code implementations • CVPR 2014 • Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki
3D reconstruction from a single image is a classical problem in computer vision.
no code implementations • CVPR 2014 • Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki
Graph matching and graph mining are two typical areas in artificial intelligence.
no code implementations • CVPR 2013 • Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao
We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes.