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124 results on '"Jiang, Renhe"'

103. DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction

Author

Jiang, Renhe, Cai, Zekun, Wang, Zhaonan, Yang, Chuang, Fan, Zipei, Chen, Quanjun, Tsubouchi, Kota, Song, Xuan, and Shibasaki, Ryosuke

Abstract

Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to emergency management, traffic regulation, and urban planning. In particular, by meshing a large urban area to a number of fine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented with 4D tensor (Timestep, Height, Width, Channel). Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic. In this study, we revisit the density and in-out flow prediction problem and publish a new aggregated human mobility dataset generated from a real-world smartphone application. Comparing with the existing ones, our dataset holds several advantages including large mesh-grid number, fine-grained mesh size, and high user sample. Towards this large-scale crowd dataset, we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention mechanism based on Convolutional LSTM. Lastly, thorough and comprehensive performance evaluations are conducted to demonstrate the superiority of the proposed DeepCrowd comparing to multiple state-of-the-art methods.

Published
2023
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106. DualSIN

Author

Chen, Quanjun, primary, Jiang, Renhe, additional, Yang, Chuang, additional, Cai, Zekun, additional, Fan, Zipei, additional, Tsubouchi, Kota, additional, Shibasaki, Ryosuke, additional, and Song, Xuan, additional

Published
2020
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112. DeepUrbanEvent

Author

Jiang, Renhe, primary, Song, Xuan, additional, Huang, Dou, additional, Song, Xiaoya, additional, Xia, Tianqi, additional, Cai, Zekun, additional, Wang, Zhaonan, additional, Kim, Kyoung-Sook, additional, and Shibasaki, Ryosuke, additional

Published
2019
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114. A Variational Autoencoder Based Generative Model of Urban Human Mobility

Author

Huang, Dou, Song, Xuan, Fan, Zipei, Jiang, Renhe, Shibasaki, Ryosuke, Zhang, Yu, Wang, Haizhong, Kato, Yugo, Huang, Dou, Song, Xuan, Fan, Zipei, Jiang, Renhe, Shibasaki, Ryosuke, Zhang, Yu, Wang, Haizhong, and Kato, Yugo

Abstract

Recently, big and heterogeneous human mobility data inspires many revolutionary ideas of implementing machine learning algorithms for solving some traditional social issues, such as zone regulation, air pollution, and disaster evacuation el at.. However, incomplete datasets were provided owing to both the concerns of violation of privacy and some technique issues in many practical applications, which leads to some limitations of the utility of collected data. Variational Autoencoder (VAE), which uses a well-constructed latent space to capture salient features of the training data, shows a significant excellent performance in not only image processing, but also Natural Language Processing domain. By combining VAE and sequence-to-sequence (seq2seq) model, a Sequential Variational Autoencoder (SVAE) is built for the task of human mobility reconstruction. It is the first time that this kind of SVAE model is implemented for solving the issues about human mobility reconstruction. We use navigation GPS data of selected greater Tokyo area to evaluate the performance of the SVAE model. Experimental results demonstrate that the SVAE model can efficiently capture the salient features of human mobility data and generate more reasonable trajectories.

Published
2019

124. Resilience Patterns of Multiscale Human Mobility Under Extreme Rainfall Events Using Massive Individual Trajectory Data.

Author

Yao, Yao, Liang, Lin, Zhang, Yatao, Wang, Yujia, Hu, Zhihui, Fan, Yunpeng, Guan, Qingfeng, Jiang, Renhe, and Shibasaki, Ryosuke

Subjects
*PSYCHOLOGICAL resilience, *BUILT environment, *HUMAN mechanics, *EMERGENCY management, *INCOME inequality, *DISASTER resilience
Abstract

Understanding human mobility’s resilience during extreme rainfall is paramount for enhancing disaster response and urban resilience. Most studies, however, have overlooked the complexity of resilience patterns across scales, missing out on the varied spatial anomalies and their underlying causes. To bridge this gap, we propose a framework using massive individual trajectory data to dissect resilience patterns of human mobility across scales. By leveraging a dynamic network model, we quantify human mobility flows and employ resilience curves to determine resilience patterns at urban-agglomeration and regional scales. Our study, centering on the extreme rainfall from Typhoon Mawar, covers Osaka and Nagoya in Japan. The findings reveal a marked reduction in human movement, although the structure of mobility networks remains relatively unchanged. Based on the quadrant distribution of inflows and outflows, we reveal that the ratio of abnormal to normal resilience patterns in human mobility stands at approximately 3:2, a consistency maintained across both scales. Interestingly, abnormal resilience patterns are intricately linked to local geographical settings of the built environment, revealing disparities based on income, gender, and age. These insights are invaluable for policymakers to improve postdisaster recovery efforts and guide future urban infrastructure development toward greater resilience. [ABSTRACT FROM AUTHOR]

Published
2024
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