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Yanbo Pang
Yanbo Pang
在 iis.u-tokyo.ac.jp 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
502017
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
212020
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
142018
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
132020
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
102022
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
62017
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
42022
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
22021
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
12021
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
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