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Zipei Fan
Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Chiba, Japan, 113-0033

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Journal article
Published: 03 May 2021 in IEEE Transactions on Knowledge and Data Engineering
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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.

ACS Style

Renhe Jiang; Zekun Cai; Zhaonan Wang; Chuang Yang; Zipei Fan; Quanjun Chen; Kota Tsubouchi; Xuan Song; Ryosuke Shibasaki. DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction. IEEE Transactions on Knowledge and Data Engineering 2021, PP, 1 -1.

AMA Style

Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Kota Tsubouchi, Xuan Song, Ryosuke Shibasaki. DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction. IEEE Transactions on Knowledge and Data Engineering. 2021; PP (99):1-1.

Chicago/Turabian Style

Renhe Jiang; Zekun Cai; Zhaonan Wang; Chuang Yang; Zipei Fan; Quanjun Chen; Kota Tsubouchi; Xuan Song; Ryosuke Shibasaki. 2021. "DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction." IEEE Transactions on Knowledge and Data Engineering PP, no. 99: 1-1.

Regular paper
Published: 01 June 2020 in CCF Transactions on Pervasive Computing and Interaction
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Extracting identifiable information from human trajectories is a fundamental task in many location-based services (LBS), such as personalized POI recommendation system, irregular human movement detection and privacy protection. Existing studies assume that we have collected sufficient trajectory data for each user, and therefore a classifier could be trained to distinguish the users. However, in many real-world scenarios, due to unregistered users or less active users, human trajectory data is very fragmentary and we can hardly collect sufficient training samples for each user to train the classifier. Moreover, we could hardly define a clear user set for user identification because the set of users are dynamic and changing everyday (there are always new users and inactive users everyday in the real-world human trajectory dataset). Bearing these in mind, we propose an one-shot learning framework for human trajectory identification to handle the insufficient samples and dynamic user set problems. Sliced encoder neural network is designed to encoder the spatiotemporal characteristics and Siamese network is applied to extract the discriminative features for identification. Experiments are conducted on real-world human trajectory dataset to show the advantageous performance of our algorithm.

ACS Style

Zipei Fan; Xuan Song; Quanjun Chen; Renhe Jiang; Ryosuke Shibasaki; Kota Tsubouchi. Trajectory fingerprint: one-shot human trajectory identification using Siamese network. CCF Transactions on Pervasive Computing and Interaction 2020, 2, 113 -125.

AMA Style

Zipei Fan, Xuan Song, Quanjun Chen, Renhe Jiang, Ryosuke Shibasaki, Kota Tsubouchi. Trajectory fingerprint: one-shot human trajectory identification using Siamese network. CCF Transactions on Pervasive Computing and Interaction. 2020; 2 (2):113-125.

Chicago/Turabian Style

Zipei Fan; Xuan Song; Quanjun Chen; Renhe Jiang; Ryosuke Shibasaki; Kota Tsubouchi. 2020. "Trajectory fingerprint: one-shot human trajectory identification using Siamese network." CCF Transactions on Pervasive Computing and Interaction 2, no. 2: 113-125.

Journal article
Published: 06 June 2016 in ISPRS International Journal of Geo-Information
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With the rapid spread of mobile devices, call detail records (CDRs) from mobile phones provide more opportunities to incorporate dynamic aspects of human mobility in addressing societal issues. However, it has been increasingly observed that CDR data are not always representative of the population under study because it only includes device users alone. To understand the discrepancy between the population captured by CDRs and the general population, we profile principal populations of CDRs by analyzing routines based on time spent at key locations and compare these data with those of the general population. We employ a topic model to estimate typical routines of mobile phone users using CDRs as topics. The routines are extracted from field survey data and compared between those of the general population and mobile phone users. We found that there are two main population groups of mobile phone users in Dhaka: males engaged in an income-generating activity at a specific location other than home and females performing household tasks and spending most of their time at home. We determine that CDRs tend to omit students, who form a significant component of the Dhaka population.

ACS Style

Ayumi Arai; Zipei Fan; Dunstan Matekenya; Ryosuke Shibasaki. Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users. ISPRS International Journal of Geo-Information 2016, 5, 85 .

AMA Style

Ayumi Arai, Zipei Fan, Dunstan Matekenya, Ryosuke Shibasaki. Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users. ISPRS International Journal of Geo-Information. 2016; 5 (6):85.

Chicago/Turabian Style

Ayumi Arai; Zipei Fan; Dunstan Matekenya; Ryosuke Shibasaki. 2016. "Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users." ISPRS International Journal of Geo-Information 5, no. 6: 85.