NEWS

[Job Opportunities] We have an open Project Researcher (Postdoc) position at The University of Tokyo, led by me and Prof. Ryosuke Shibasaki. This research project aims to develop new technologies to process and analyze multimodal big data, mainly social media, human mobility, and disaster information, to better support decision-making processes in disasters such as earthquake, typhoon, and COVID-19. 

[2023/01/31] Promoted to Lecturer at Center for Spatial Information Science, The University of Tokyo, from April 1st, 2023.

[2023/01/30] Our special issue "Modeling and Understanding of Big HumanMobility Data" on GeoInformatica is released.

https://www.springer.com/journal/10707/updates/23959950

[2023/01/26] One paper has been accepted by WWW2023 (TheWebConf2023). 

[2022/12/02] Two talks have been accepted by WSDM 2023 Smart City Day.

https://www.wsdm-conference.org/2023/program/smart-city-day

[2022/11/22] Our special issue "Machine Learning and Location Data" on ACM Transactions on Spatial Algorithms and Systems is released.

Special Issue on Machine Learning and Location Data

[2022/11/19] Two papers have been accepted by AAAI 2023 Main Track.

[2022/11/01] Three papers have been accepted by IEEE BigData 2022 (special session, workshop, poster).

[2022/10/30] One paper has been accepted by IEEE TKDE.

[2022/08/23] One co-authored paper has been accepted by SIGSPATIAL 2022.  

[2022/07/31] One co-authored paper has been accepted by WWW Journal.  

[2022/07/22] Our Special Issue on Remote Sensing has been postponed to 15 January 2023. 

https://www.mdpi.com/journal/remotesensing/special_issues/Transportation_AssetManagement 

[2022/06/16] One paper has been accepted by 25th IEEE Intelligent Transportation Systems Conference (ITSC 2022).

[2022/06/15] One paper has been accepted by ECML PKDD 2022. 

[2022/05/24] One survey paper in collaboration with FIU has been accepted by ACM Computing Surveys CSUR. 

[2022/04/05] One paper has been accepted by GeoInformatica.

[2022/04/01] One co-authored paper has been accepted by IEEE COMPSAC 2022.

[2022/03/22] One paper has been accepted by IEEE Transactions on Visualization and Computer Graphics (TVCG).

[2022/01/26] One paper (extended abstract) has been accepted by ICDE 2022 TKDE Poster Track.

[2021/12/01] One paper has been accepted by AAAI 2022 Main Track, 1,349 papers out of 9,251 submissions, only 15% acceptance rate. 

[2021/11/04] Our paper has been selected as CIKM21 Best Resource Paper Runner Up. 

https://www.cikm2021.org/programme/best-paper-nominations

[2021/10/10] One co-authored paper has been accepted by ACM TSAS SI: Understanding the Spread of COVID-19.

[2021/10/04] The dataset called "Yahoo! Bousai Crowd Data" used in our TKDE 2021 paper "DeepCrowd"  has been published by Yahoo! Japan Research.

English page: https://randd.yahoo.co.jp/en/softwaredata; Japanese page: https://randd.yahoo.co.jp/jp/softwaredata.

Please check our GitHub repository for more details. https://github.com/deepkashiwa20/DeepCrowd

[2021/08/31] One Co-PI research project has been awarded by JST-NSF SICROP Digital Science for Post-COVID-19 Society.  

English page:https://www.jst.go.jp/pr/info/info1518/index_e.html; Japanese page: https://www.jst.go.jp/pr/info/info1518/index.html

[2021/08/09] Two papers have been accepted by  Research Track and Resource Track of CIKM 2021 respectively.

[2021/07/01] Funded Collaborative Research with TOYOTA MOTOR CORPORATION has started.

[2021/06/21] One paper has been accepted by ACM TIST 2021 SI: Deep Learning for Spatio-Temporal Data.

[2021/06/19] One paper has been accepted by ECML PKDD 2021 Applied Data Science Track.

[2021/06/04] Our research has been added as one of Data Platform Initiative Projects by UTokyo Future Society Initiative (UTokyo FSI).

https://www.u-tokyo.ac.jp/adm/fsi/en/projects/dp/project_00031.html 

https://www.u-tokyo.ac.jp/adm/fsi/ja/projects/dp/project_00032.html

[2021/05/16] One co-authored paper has been accepted by KDD 2021 Research Track. 

[2021/04/27] One paper collaborated with Yahoo! Japan Research has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).

https://randd.yahoo.co.jp/jp/papers/641

PUBLICATION (* denotes Equal Contribution and # denotes Corresponding Author) [Google Scholar here]

• [WSDM23 Smart City Day Talk] Z. Wang, R. Jiang, Z. Fan, X. Song, R. Shibasaki, "Towards an Event-Aware Urban Mobility Prediction System", Proc. of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM), 2023. https://doi.org/10.1145/3539597.3575783

• [WSDM23 Smart City Day Talk] Z. Fan, R. Jiang, R. Shibasaki, "Metropolitan-scale Mobility Digital Twin", Proc. of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM), 2023. https://doi.org/10.1145/3539597.3575783

• [WWW23] R. Jiang*, Z. Wang*, Y. Tao*, C. Yang, X. Song#, R. Shibasaki, S. Chen#, M. Shyu, "Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster", Proc. of the ACM Web Conference (WWW), 2023. https://doi.org/10.1145/3543507.3583991

• [AAAI23] R. Jiang*, Z. Wang*, J. Yong, P. Jeph, Q. Chen, Y. Kobayashi, X. Song, S. Fukushima, T. Suzumura, "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting", Proc. of Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023. https://arxiv.org/pdf/2211.14701.pdf

• [AAAI23] H. Wang, J. Chen, T. Pan, Z. Fan, X. Song, R. Jiang, L. Zhang, Y. Xie, Z. Wang, B. Zhang, "Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout", Proc. of Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023. https://arxiv.org/pdf/2211.15182.pdf

• [BigData22] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, R. Shibasaki, "Yahoo! Bousai Crowd Data: A Large-Scale Crowd Density and Flow Dataset in Tokyo and Osaka", Proc. of 2022 IEEE International Conference on Big Data (BigData), 2022. https://doi.org/10.1109/BigData55660.2022.10020886

• [BigData22] X. Hao, R. Jiang#, J. Deng, X. Song, "The Impact of COVID-19 on Human Mobility: A Case Study on New York", Proc. of 2022 IEEE International Conference on Big Data (BigData), 2022. https://doi.org/10.1109/BigData55660.2022.10020695

• [BigData22] Z. Dong, Q. Chen#, R. Jiang#, H. Wang, X. Song, H. Tian, "Learning Latent Road Correlations from Trajectories", Proc. of 2022 IEEE International Conference on Big Data (BigData), 2022. https://doi.org/10.1109/BigData55660.2022.10020759

• [IEEE TKDE22] J. Deng, X. Chen, R. Jiang, X. Song, Ivor W. Tsang, “A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting”, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. https://doi.org/10.1109/TKDE.2022.3218803; https://arxiv.org/pdf/2109.01657.pdf 

• [SIGSPATIAL22] Z. Fan, X. Yang, W. Yuan, R. Jiang, Q. Chen, X. Song, R. Shibasaki, "Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin", Proc. of 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL), 2022. https://doi.org/10.1145/3557915.3561040; https://arxiv.org/abs/2207.03575

• [ITSC22] H. Ma, M. Zhou, X. Ouyang, D. Yin, R. Jiang#, X. Song#, "Forecasting Regional Multimodal Transportation Demand with Graph Neural Networks: An Open Dataset", Proc. of 2022 IEEE International Intelligent Transportation Systems Conference (ITSC), 2022. http://doi.org/10.1109/ITSC55140.2022.9922512

• [WWWJ22] Y. Li, Z. Fan, D. Yin, R. Jiang, J. Deng, X. Song, "HMGCL: Heterogeneous Multigraph Contrastive Learning for LBSN Friend Recommendation", World Wide Web (2022). https://doi.org/10.1007/s11280-022-01092-5

• [ECMLPKDD22] Q. Cao, R. Jiang#, C. Yang, Z. Fan, X. Song, R. Shibasaki, "MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks", Proc. of the 26th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2022. 

https://2022.ecmlpkdd.org/wp-content/uploads/2022/09/sub_954.pdf     https://link.springer.com/chapter/10.1007/978-3-031-26422-1_28

• [ISPRS22] P. Jeph, H. Takayasu, T. Xia, H. Kanasugi, R. Jiang, H. Mizuseki, R. Shibasaki, "RAILDASH: A DASHBOARD SYSTEM TO ANALYZE EFFECTS OF EVENTS ON RAILWAY TRAFFIC USING BIG GPS DATA", ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4/W3-2022, 89–96, 2022. 

https://doi.org/10.5194/isprs-annals-X-4-W3-2022-89-2022

• [COMPSAC22] G. Hangli, L. Lin, R. Jiang, T. Michikata, N. Koshizuka, "Multi-weighted Graphs Learning for Passenger Count Prediction on Railway Network," 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 2022. https://doi.org/10.1109/COMPSAC54236.2022.00059

• [CSUR22] Y. Tao, C. Yang, T. Wang, E. Coltey, Y. Jin, Y. Liu, R. Jiang, Z. Fan, X. Song, R. Shibasaki, S. Chen, M. Shyu, S. Luis, "A Survey on Data-Driven COVID-19 and Future Pandemic Management", ACM Computing Surveys (CSUR), 2022. https://doi.org/10.1145/3542818

• [ICDE22] R. Jiang*, Z. Cai*, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, R. Shibasaki. "DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction (Extended abstract)", Proc. of the 38th IEEE International Conference on Data Engineering (ICDE), 2022. https://doi.org/10.1109/ICDE53745.2022.00136 

• [GeoInformatica22] D. Yin, R. Jiang#, J. Deng, Y. Li, Y. Xie, Z. Wang, Y. Zhou, X. Song#, S. Shang, "MTMGNN: Multi-Time Multi-Graph Neural Network for Metro Passenger Flow Prediction", GeoInformatica, 2022. https://doi.org/10.1007/s10707-022-00466-1

• [mdx official paper] mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations https://arxiv.org/pdf/2203.14188.pdf

• [IEEE TVCG22] C. Yang*, Z. Zhang*, Z. Fan*, R. Jiang#, Q. Chen, X. Song#, R. Shibasaki, “EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control”, IEEE Transactions on Visualization and Computer Graphics (TVCG), 2022. https://doi.org/10.1109/TVCG.2022.3165385 https://arxiv.org/pdf/2007.03180.pdf

• [ACM TSAS22] Z. Fan, C. Yang, Z. Zhang, X. Song, Y. Liu, R. Jiang, Q. Chen, and R. Shibasaki. 2022. Human Mobility-based Individual-level Epidemic Simulation Platform. ACM Trans. Spatial Algorithms Syst (ACM-TSAS). 8, 3, Article 19 (September 2022), 16 pages. https://doi.org/10.1145/3491063

• [ACM TIST22] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, X. Song, and R. Shibasaki. 2022. Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System. ACM Trans. Intell. Syst. Technol (ACM-TIST). 13, 2, Article 21 (April 2022), 24 pages. https://doi.org/10.1145/3472300

• [AAAI22] Z. Wang, R. Jiang#, H. Xue, F. Salim, X. Song, R. Shibasaki, “Event-Aware Multimodal Mobility Nowcasting”, Proc. of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022.  https://doi.org/10.1609/aaai.v36i4.20342

• [CIC21] Y. Tao, R. Jiang, E. Coltey, C. Yang, X. Song, R. Shibasaki, M. Shyu, S. Chen, “Data-Driven In-Crisis Community Identification for Disaster Response and Management”, Proc. of IEEE 7th International Conference on Collaboration and Internet Computing (CIC), 2021. https://doi.org/10.1109/CIC52973.2021.00021

• [CIKM21] [CIKM21 Best Resource Paper Runner-Up] R. Jiang*, D. Yin*, Z. Wang, Y. Wang, J. Deng, H. Liu, Z. Cai, J. Deng, X. Song, R. Shibasaki, "DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction", Proc. of 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021. https://doi.org/10.1145/3459637.3482000 https://arxiv.org/pdf/2108.09091.pdf

• [CIKM21] Z. Wang, R. Jiang#, Z. Cai, Z. Fan, X. Liu, K. Kim, X. Song#, R. Shibasaki, "Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction", Proc. of 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021. https://doi.org/10.1145/3459637.3482482

• [ECMLPKDD21] R. Jiang*, Z. Wang*, Z. Cai, C. Yang, Z. Fan, T. Xia, G. Matsubara, H. Mizuseki, X. Song, R. Shibasaki, "Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19", Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021. https://doi.org/10.1007/978-3-030-86514-6_20; https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_415.pdf

• [KDD21] J. Deng, X. Chen, R. Jiang, X. Song, Ivor W. Tsang, "ST-Norm Spatial and Temporal Normalization for Multi-variate Time Series Forecasting", Proc. of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. https://doi.org/10.1145/3447548.3467330

• [IEEE TKDE21] R. Jiang*, Z. Cai*, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, R. Shibasaki, "DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction." IEEE Transactions on Knowledge and Data Engineering (2021). https://doi.org/10.1109/TKDE.2021.3077056

• [ACM TKDD21] J. Deng, X. Chen, Z. Fan, R. Jiang, X. Song, Ivor W. Tsang. 2021. "The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting." ACM Trans. Knowl. Discov. Data 15, 6, Article 103 (May 2021), 25 pages. https://doi.org/10.1145/3450528

• [ICDE21] Z. Wang, T. Xia, R. Jiang#, X. Liu, K. Kim, X. Song, R. Shibasaki, "Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-graph Convolution Network", Proc. of the 37th IEEE International Conference on Data Engineering (ICDE), 2021. http://doi.org/10.1109/ICDE51399.2021.00154

• [AAAI21] X. Shi, X. Shao, G. Wu, H. Zhang, Z. Guo, R. Jiang, R. Shibasaki, “Social-DPF: Socially acceptable distribution prediction of futures”, Proc. of Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. https://doi.org/10.1609/aaai.v35i3.16357

• [ACM TDS21] R. Jiang, X. Song, Z. Fan, T. Xia, Z. Wang, Q. Chen, Z. Cai, R. Shibasaki, “Transfer Urban Human Mobility via POI Embedding over Multiple Cities”, ACM/IMS Trans. Data Sci. 2, 1, Article 04 (2021).  https://doi.org/10.1145/3416914

• [Neurocomputing20] R. Jiang*, Q. Chen*#, Z. Cai, Z. Fan, X. Song, K. Tsubouchi, and R. Shibasaki, “Will You Go Where You Search? A Deep Learning Framework for Estimating User Search-and-Go Behavior”, Neurocomputing, 2020. https://doi.org/10.1016/j.neucom.2020.10.001

• [SIGSPATIAL20] Q. Chen*, R. Jiang*, Y. Chuang, Z. Cai, Z. Fan, K. Tsubouchi, R. Shibasaki X. Song, “DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction”, Proc. of 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPAITAL), 2020. https://doi.org/10.1145/3397536.3422221

• [ISPRS20] S. Miyazawa, X. Song, R. Jiang, Z. Fan, R. Shibasaki, & T. Sato. (2020). CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 87-94, 2020. https://doi.org/10.5194/isprs-annals-V-4-2020-87-2020 

• [SIGSPATIAL LETTER20] Z. Fan, X. Song, Y. Liu, Z. Zhang, C. Yang, Q. Chen, R. Jiang, R. Shibasaki, "Human Mobility Based Individual-Level Epidemic Simulation Platform", SIGSPATIAL Special 12, 1 (March 2020), 34–40. https://doi.org/10.1145/3404820.3404826

• [AAAI20] X. Shi, X. Shao, Z. Fan, R. Jiang, H. Zhang, Z. Guo, G. Wu, W. Yuan, R. Shibasaki, “Multimodal Interaction-Aware Trajectory Prediction in Crowded Space”, Proc. of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020. https://doi.org/10.1609/aaai.v34i07.6874

• [UbiComp20] Z. Fan, X. Song, R. Jiang, R. Shibasaki, “Decentralized Attention-based Personalized Human Mobility Prediction”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2020. https://doi.org/10.1145/3369830

• [SIGSPATIAL19] Z. Fan, X. Song, Q. Chen, R. Jiang, K. Tsubouchi, R. Shibasaki, “Deep Multiple Instance Learning for Human Trajectory Identification”, Proc. of 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPAITAL), 2019. https://doi.org/10.1145/3347146.3359342

• [KDD19] R. Jiang, X. Song, D. Huang, X. Song, T. Xia, Z. Cai, Z. Wang, K. Kim, R. Shibasaki, “DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events”, Proc. of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019. https://doi.org/10.1145/3292500.3330654

• [MIPR19] D. Huang, X. Song, Z. Fan, R. Jiang, R. Shibasaki, Y. Zhang, H. Wang, Y. Kato, "A Variational Autoencoder Based Generative Model of Urban Human Mobility", Proc. of 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2019. https://doi.org/10.1109/MIPR.2019.00086

• [UbiComp18] R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, Q. Chen, and R. Shibasaki, “Deep ROI-Based Modeling for Urban Human Mobility Prediction”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2018. https://doi.org/10.1145/3191746

• [AAAI18] R. Jiang, X. Song, Z. Fan, T. Xia, Q. Chen, S. Miyazawa, R. Shibasaki, “DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction”, Proc. of Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. https://doi.org/10.1609/aaai.v32i1.11338

• [UbiComp18] Z. Fan, X. Song, T. Xia, R. Jiang, R. Shibasaki, R. Sakuramachi, “Online Deep Ensemble Learning for Predicting Citywide Human Mobility”, Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2018. https://doi.org/10.1145/3264915

• [MIPR18] T. Xia, X. Song, Z. Fan, H. Kanasugi, Q. Chen, R. Jiang, R. Shibasaki, “DeepRailway: A Deep Learning System for Forecasting Railway Traffic”, Proc. of IEEE 2018 International Conference on Multimedia Information Processing and Retrieval (MIPR), 2018. https://doi.org/10.1109/MIPR.2018.00017

• [BigData17] T. Xia, X. Song, D. Huang, S. Miyazawa, Z. Fan, R. Jiang, R. Shibasaki, “Outbound Behavior Analysis Through Social Network Data: a case study of Chinese people in Japan”, Proc. of Big Social Media Data Management and Analysis, IEEE Big Data, 2017. https://doi.org/10.1109/BigData.2017.8258244

• [TENCON15] R. Jiang, J. Zhao, T. Dong, Y. Ishikawa, C. Xiao, Y. Sasaki, “A Density-based Approach for Mining Movement Patterns from Semantic Trajectories”, IEEE TENCON 2015. IEEE Region 10 Conference, Macau, November 2015. https://doi.org/10.1109/TENCON.2015.7373034

• [DEIM15] 姜 仁河, 趙 菁, 董 テイテイ, 佐々木 勇和, 石川 佳治,「密度に基づく意味的な軌跡パターンの発見」，第7回データ工学と情報マネジメントに関するフォーラム (DEIM 2015), E8-3, 2015年3月. https://db-event.jpn.org/deim2015/paper/43.pdf

• [DEIM15] 趙 菁, 姜 仁河, 董 テイテイ, 佐々木 勇和, 肖 川, 石川 佳治,「参加型センシングのためのタスク割当手法」，第7回データ工学と情報マネジメントに関するフォーラム (DEIM 2015), C6-5, 2015年3月. https://db-event.jpn.org/deim2015/paper/46.pdf

• [IPSJ14] 姜 仁河, 杉山 武至, 石川 佳治，「人気経路の推薦のための大規模移動軌跡データ処理」，情報処理学会第76回全国大会, 1N-3, 2014年3月.

https://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=104522&item_no=1&attribute_id=1&file_no=1

FUNDING

• 2021/10 ~ 2025/03       Strategic International Collaborative Research Program (SICORP), Japan Science and Technology Agency (JST) [16,500,000 Yen]

                                          Digital Science for Post-COVID-19 Society

                                     Project Name: Multimodal Data Analytics and Integration for Effective COVID-19, Pandemics and Compound Disaster Response and Management

    https://www.jst.go.jp/pr/info/info1518/index_e.html

                                     Acknowledgment: This work was supported by JST SICORP Grant Number JPMJSC2104.

• 2021/07 ~ 2022/03        Funded Collaborative Research with TOYOTA MOTOR CORPORATION [1,300,000 Yen]

                                          Project Name: Large-Scale Car GPS Trajectory Data Analysis with Deep Learning

• 2020/04 ~ 2022/03       Grant-in-Aid for Early-Career Scientists (20K19859), Japan Society for the Promotion of Science (JSPS) [4,290,000 Yen]

                                     Project Name: A Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction

                                 Fund Link: https://kaken.nii.ac.jp/en/search/?kw=20K19859

                                  Acknowledgment: This work was supported by JSPS KAKENHI Grant Number JP20K19859.

• 2020/04 ~ 2022/03       Strategic International Collaborative Research Program (SICORP), Japan Science and Technology Agency (JST) [825,000 Yen]

    Japan(JST)-US(NSF) Joint Research 2019

                                     Project Name: Multimodal Data Analytics and Integration for Emergency Response and Disaster Management

    Fund Link: https://projectdb.jst.go.jp/grant/JST-PROJECT-19218172/

                                     Acknowledgment: This work was supported by JST SICORP Grant Number JPMJSC2002.