Renhe Jiang 研究室

主宰者：Renhe Jiang

東京大学

AI 要約（直近 5 年の研究成果）

本研究室は、都市における人間の移動パターンと流動性を、データ駆動型アプローチで分析・予測することを中心に研究を展開しています。大規模な移動データや交通データから空間的・時間的な依存関係を抽出し、将来の人口分布や利用者の目的地選択を予測するモデルを開発しています。機械学習やニューラルネットワークの手法を用いて、複雑な都市現象をとらえる研究が特徴です。特に、災害時における人間の避難行動や都市の回復力の評価が重要なテーマとなっています。豪雨やその他の極端気象現象下での人口流動の変化を捉え、防災対応や避難計画に役立つ分析を行っています。また、電気自動車の充電インフラの配置評価や、鉄道駅の利用者吸引力の定量化など、都市計画・インフラ整備に向けた応用研究も進められています。加えて、大規模言語モデルなどの最新AI技術を活用し、ニュースから災害情報を抽出して移動予測に組み込む、あるいは複数の交通モード間の需要変動を統合的にモデル化するなど、従来型の手法を拡張する取り組みも行われています。これらを通じ、人工知能を用いたスマートシティの実現に向けた基礎研究が展開されています。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

外部リンク

研究成果（82 件）

[2026] Mobility vulnerability index for community vulnerability assessment in disaster response
DOI: https://doi.org/10.1038/s44304-026-00212-9
[2025] LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
DOI: https://doi.org/10.36227/techrxiv.174612962.26131807/v4
[2025] Video-based Transparent Object Segmentation via Temporal Feature Aggregation
DOI: https://doi.org/10.1145/3746027.3755084
[2025] From human mobility to building functions: A deep learning approach for urban building classification in Megacity Tokyo
DOI: https://doi.org/10.1016/j.cities.2025.106557
[2025] Leveraging Spatial–Temporal Heterogeneity and Cross-Mode Interactions: A Meta-Learning Approach for Multimodal Transportation Demand Prediction
DOI: https://doi.org/10.1109/tits.2025.3614270
[2025] Tiered Spatio-Temporal Difficulty: Curriculum Scheduler for Multi-Sensor Traffic Flow Prediction
DOI: https://doi.org/10.1109/tmc.2025.3608620
[2025] Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation
DOI: https://doi.org/10.24963/ijcai.2025/856
[2025] Trajectory generative models: a survey from unconditional and conditional perspectives
DOI: https://doi.org/10.1007/s10707-025-00558-8
[2025] Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
DOI: https://doi.org/10.1145/3711896.3737146
[2025] Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions
DOI: https://doi.org/10.18653/v1/2025.findings-emnlp.683

続きを表示（残り 72 件）

[2025] Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
DOI: https://doi.org/10.1109/tmc.2025.3590606
[2025] A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
DOI: https://doi.org/10.1145/3726302.3729884
[2025] Editorial High-Performance Recommender Systems Based on Spatiotemporal Data
DOI: https://doi.org/10.1109/tbdata.2024.3451088
[2025] Uncertainty-aware traffic accident risk prediction via multi-view hypergraph contrastive learning
DOI: https://doi.org/10.1016/j.inffus.2025.103331
[2025] Road‐Level Charging Gaps and Infrastructure Enhancements in Japan's Electric Vehicle Transition
DOI: https://doi.org/10.1029/2024ef005559
[2025] Seed: Bridging Sequence and Diffusion Models for Road Trajectory Generation
DOI: https://doi.org/10.1145/3696410.3714951
[2025] Traffic forecasting with patch-based graph convolutional recurrent network
DOI: https://doi.org/10.1007/s10707-025-00544-0
[2025] A Survey of RAG-Reasoning Systems in Large Language Models
DOI: https://doi.org/10.18653/v1/2025.findings-emnlp.648
[2025] CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events
DOI: https://doi.org/10.1145/3690624.3709231
[2025] Non-Homophilic Graph Pre-Training and Prompt Learning
DOI: https://doi.org/10.1145/3690624.3709219
[2025] LiDAR-assisted image restoration for extreme low-light conditions
DOI: https://doi.org/10.1016/j.knosys.2025.113382
[2025] Clustered factor analysis for multivariate spatial data
DOI: https://doi.org/10.1016/j.spasta.2025.100889
[2025] Continuous Sign Language Recognition With Multi-Scale Spatial-Temporal Feature Enhancement
DOI: https://doi.org/10.1109/access.2025.3526330
[2025] Resilience Patterns of Multiscale Human Mobility Under Extreme Rainfall Events Using Massive Individual Trajectory Data
DOI: https://doi.org/10.1080/24694452.2024.2435927
[2024] Multimodal Low-light Image Enhancement with Depth Information
DOI: https://doi.org/10.1145/3664647.3680741
[2024] Exploring urban railway station-based attractiveness considering demographic-specific demands: Case study of Odakyu line, Japan
DOI: https://doi.org/10.1016/j.cities.2024.105620
[2024] Foundation Models for Information Retrieval and Knowledge Processing
DOI: https://doi.org/10.3724/2096-7004.di.2024.0067
[2024] Next Point-of-Interest Recommendation With Adaptive Graph Contrastive Learning
DOI: https://doi.org/10.1109/tkde.2024.3509480
[2024] Population Distribution Forecasting Based on the Fusion of Spatiotemporal Basic and External Features: A Case Study of Lujiazui Financial District
DOI: https://doi.org/10.3390/ijgi13110395
[2024] Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction
DOI: https://doi.org/10.1145/3681771.3699908
[2024] Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
DOI: https://doi.org/10.1145/3678717.3691216
[2024] Introduction to the Special Issue on Machine Learning and Location Data: Part II
DOI: https://doi.org/10.1145/3701218
[2024] SIMformer: Single-Layer Vanilla Transformer Can Learn Free-Space Trajectory Similarity
DOI: https://doi.org/10.14778/3705829.3705853
[2024] Foundation Models for Information Retrieval and Knowledge Processing
DOI: https://doi.org/10.3724/2096-7004.di.2024.0057
[2024] Introduction to the Special Issue on Machine Learning and Location Data
DOI: https://doi.org/10.1145/3698154
[2024] Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
DOI: https://doi.org/10.1145/3637528.3671961
[2024] Hyper-relational knowledge graph neural network for next POI recommendation
DOI: https://doi.org/10.1007/s11280-024-01279-y
[2024] A Survey on Deep Active Learning: Recent Advances and New Frontiers
DOI: https://doi.org/10.1109/tnnls.2024.3396463
[2024] Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
DOI: https://doi.org/10.1109/icde60146.2024.00047
[2024] Learning spatio-temporal dynamics on mobility networks for adaptation to open-world events
DOI: https://doi.org/10.1016/j.artint.2024.104120
[2024] Modeling interpretable social interactions for pedestrian trajectory
DOI: https://doi.org/10.1016/j.trc.2024.104617
[2024] Anomalous Sub-Trajectory Detection With Graph Contrastive Self-Supervised Learning
DOI: https://doi.org/10.1109/tvt.2024.3382685
[2024] Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
DOI: https://doi.org/10.1109/tkde.2024.3371931
[2024] Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
DOI: https://doi.org/10.52202/079017-3957
[2024] Continuous Temporal Domain Generalization
DOI: https://doi.org/10.52202/079017-4064
[2024] Taming the Long Tail in Human Mobility Prediction
DOI: https://doi.org/10.52202/079017-1736
[2023] Towards an Event-Aware Urban Mobility Prediction System
DOI: https://doi.org/10.1145/3539597.3575783
[2023] TTS-Norm: Forecasting Tensor Time Series via Multi-Way Normalization
DOI: https://doi.org/10.1145/3605894
[2023] Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
DOI: https://doi.org/10.1145/3583780.3615160
[2023] Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks
DOI: https://doi.org/10.1109/tmc.2023.3310789
[2023] Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
DOI: https://doi.org/10.24963/ijcai.2023/231
[2023] Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
DOI: https://doi.org/10.1609/aaai.v37i7.25976
[2023] Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
DOI: https://doi.org/10.1609/aaai.v37i4.25590
[2023] Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster
DOI: https://doi.org/10.1145/3543507.3583991
[2023] Metropolitan-scale Mobility Digital Twin
DOI: https://doi.org/10.1145/3539597.3575782
[2022] Multi-weighted Graphs Learning for Passenger Count Prediction on Railway Network
DOI: https://doi.org/10.1109/compsac54236.2022.00059
[2022] DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction (Extended abstract)
DOI: https://doi.org/10.1109/icde53745.2022.00136
[2022] MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction
DOI: https://doi.org/10.1007/s10707-022-00466-1
[2022] Learning Latent Road Correlations from Trajectories
DOI: https://doi.org/10.1109/bigdata55660.2022.10020759
[2022] EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control
DOI: https://doi.org/10.1109/tvcg.2022.3165385
[2022] Documentation-based functional constraint generation for library methods
DOI: https://doi.org/10.1109/icst53961.2022.00056
[2022] Human Mobility-based Individual-level Epidemic Simulation Platform
DOI: https://doi.org/10.1145/3491063
[2022] Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System
DOI: https://doi.org/10.1145/3472300
[2022] Modeling Interpretable Social Interactions for Pedestrian Trajectory
DOI: https://doi.org/10.2139/ssrn.4147620
[2022] A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting
DOI: https://doi.org/10.1109/tkde.2022.3218803
[2022] The Impact of COVID-19 on Human Mobility: A Case Study on New York
DOI: https://doi.org/10.1109/bigdata55660.2022.10020695
[2022] Yahoo! Bousai Crowd Data: A Large-Scale Crowd Density and Flow Dataset in Tokyo and Osaka
DOI: https://doi.org/10.1109/bigdata55660.2022.10020886
[2022] Online trajectory prediction for metropolitan scale mobility digital twin
DOI: https://doi.org/10.1145/3557915.3561040
[2022] RAILDASH: A DASHBOARD SYSTEM TO ANALYZE EFFECTS OF EVENTS ON RAILWAY TRAFFIC USING BIG GPS DATA
DOI: https://doi.org/10.5194/isprs-annals-x-4-w3-2022-89-2022
[2022] Forecasting Regional Multimodal Transportation Demand with Graph Neural Networks: An Open Dataset
DOI: https://doi.org/10.1109/itsc55140.2022.9922512
[2022] HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation
DOI: https://doi.org/10.1007/s11280-022-01092-5
[2022] mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
DOI: https://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927975
[2022] Event-Aware Multimodal Mobility Nowcasting
DOI: https://doi.org/10.1609/aaai.v36i4.20342
[2021] Social-DPF: Socially Acceptable Distribution Prediction of Futures
DOI: https://doi.org/10.1609/aaai.v35i3.16357
[2021] Transfer Urban Human Mobility via POI Embedding over Multiple Cities
DOI: https://doi.org/10.1145/3416914
[2021] Data-Driven In-Crisis Community Identification for Disaster Response and Management
DOI: https://doi.org/10.1109/cic52973.2021.00021
[2021] Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
DOI: https://doi.org/10.1145/3459637.3482482
[2021] ST-Norm
DOI: https://doi.org/10.1145/3447548.3467330
[2021] Documentation‐based functional constraint generation for library methods
DOI: https://doi.org/10.1002/stvr.1785
[2021] DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
DOI: https://doi.org/10.1109/tkde.2021.3077056
[2021] The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting
DOI: https://doi.org/10.1145/3450528
[2021] Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
DOI: https://doi.org/10.1109/icde51399.2021.00154

科研費（0 件）

まだデータがありません（KAKEN 取り込み後に表示）。

所属学会・役職（0 件）

まだデータがありません（学会データ連携後に表示）。

AI 要約（直近 5 年の研究成果）

外部リンク

関連研究室(8 件)

研究成果（82 件）

科研費（0 件）

所属学会・役職（0 件）