Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas T Kashiyama, Y Pang, Y Sekimoto Transportation research part C: emerging technologies 85, 249-267, 2017 | 50 | 2017 |
Development of people mass movement simulation framework based on reinforcement learning Y Pang, T Kashiyama, T Yabe, K Tsubouchi, Y Sekimoto Transportation research part C: emerging technologies 117, 102706, 2020 | 21 | 2020 |
Replicating urban dynamics by generating human-like agents from smartphone GPS data Y Pang, K Tsubouchi, T Yabe, Y Sekimoto Proceedings of the 26th ACM sigspatial international conference on advances …, 2018 | 14 | 2018 |
Intercity simulation of human mobility at rare events via reinforcement learning Y Pang, K Tsubouchi, T Yabe, Y Sekimoto Proceedings of the 28th International Conference on Advances in Geographic …, 2020 | 13 | 2020 |
Pseudo-pflow: Development of nationwide synthetic open dataset for people movement based on limited travel survey and open statistical data T Kashiyama, Y Pang, Y Sekimoto, T Yabe arXiv preprint arXiv:2205.00657, 2022 | 10 | 2022 |
Modeling and reproducing human daily travel behavior from GPS data: A Markov Decision Process approach Y Pang, K Tsubouchi, T Yabe, Y Sekimoto Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human …, 2017 | 6 | 2017 |
Spatial attention based grid representation learning for predicting origin–destination flow M Cai, Y Pang, Y Sekimoto 2022 IEEE International Conference on Big Data (Big Data), 485-494, 2022 | 4 | 2022 |
Simulating human mobility with agent-based modeling and particle filter following mobile spatial statistics M Cai, Y Pang, T Kashiyama, Y Sekimoto Proceedings of the 29th International Conference on Advances in Geographic …, 2021 | 2 | 2021 |
Development of a reinforcement learning based agent model and people flow data to Mega Metropolitan Area Y Pang, T Kashiyama, Y Sekimoto 2021 IEEE International Conference on Big Data (Big Data), 3755-3759, 2021 | 1 | 2021 |
A cost-and-effect simulation model for compact city approaches: A case study in Japan J Ma, Y Shibuya, Y Pang, H Omata, Y Sekimoto Cities 152, 105212, 2024 | | 2024 |
Nationwide synthetic human mobility dataset construction from limited travel surveys and open data T Kashiyama, Y Pang, Y Shibuya, T Yabe, Y Sekimoto Computer‐Aided Civil and Infrastructure Engineering, 2024 | | 2024 |
Deep Learning Approach to Logistics Trips Generation: Enhancing Pseudo People Flow with Agent-Based Modeling K Zhang, Y Pang, Y Sekimoto 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | | 2023 |
Deep Learning for Destination Choice Modeling: A Fundamental Approach for National Level People Flow Reconstruction Y Pang, Y Sekimoto 2022 IEEE International Conference on Big Data (Big Data), 1900-1905, 2022 | | 2022 |
Uncertainty of Traffic Congestion Estimation Using Nationwide Pseudo Trip Data and Agent-Based Simulation A Tewari, Y Pang, Y Sekimoto 2022 IEEE International Conference on Big Data (Big Data), 3854-3863, 2022 | | 2022 |
Adaptive multilabel image segmentation model with prior constraints based on geometric structure characteristics Y Tian, L Pang, Y Pang, H Zhang Journal of Electronic Imaging 31 (6), 063031-063031, 2022 | | 2022 |
The Provision of Nationwide Pseudo People Flow Data and its Evaluation Y PANG, T KASHIYAMA, Y SEKIMOTO 地理情報システム学会講演論文集 (CD-ROM) 31, 1-1, 2022 | | 2022 |
A Cost-and-Effect Simulation Model for Compact City Approaches Using Pseudo-People-Flow Data J Ma, Y Shibuya, Y Pang, H Omata, S Yoshihide Available at SSRN 4653790, 2019 | | 2019 |
A Cost-and-Effect Simulation Model for Multiple Urban Planning Scenarios Using Pseudo-People-Flow Data J Ma, Y Shibuya, Y Pang, H Omata, S Yoshihide Available at SSRN 4396161, 0 | | |
強化学習を用いた都市圏レベルの人の流れの再現手法の構築 P Yanbo, Y Pang, T Kashiyama, Y Sekimoto | | |