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      Understanding Private Car Aggregation Effect via Spatio-Temporal Analysis of Trajectory Data

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          Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

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            Limits of predictability in human mobility.

            A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
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              Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

              Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existing traffic flow prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of traffic data, thus cannot yield satisfactory prediction results. In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic and weekly-periodic dependencies. More specifically, each component contains two major parts: 1) the spatial-temporal attention mechanism to effectively capture the dynamic spatialtemporal correlations in traffic data; 2) the spatial-temporal convolution which simultaneously employs graph convolutions to capture the spatial patterns and common standard convolutions to describe the temporal features. The output of the three components are weighted fused to generate the final prediction results. Experiments on two real-world datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.
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                Author and article information

                Contributors
                Journal
                IEEE Transactions on Cybernetics
                IEEE Trans. Cybern.
                Institute of Electrical and Electronics Engineers (IEEE)
                2168-2267
                2168-2275
                April 2023
                April 2023
                : 53
                : 4
                : 2346-2357
                Affiliations
                [1 ]College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
                [2 ]School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China
                [3 ]Department of Applied Sciences, Ecole Normale Superieure, Bujumbura, Burundi
                [4 ]Intelligent System Group, Zhejiang Lab, Hangzhou, China
                Article
                10.1109/TCYB.2021.3117705
                34653012
                db7acbe7-be87-4182-9f6e-e74e0288f731
                © 2023

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

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