Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the tradition...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 5; no. 4; pp. 5010 - 5017
Main Authors: Li, Ziyue, Yan, Hao, Zhang, Chen, Tsung, Fugee
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoregressive models
Autoregressive moving average
Autoregressive moving-average models
Bayes methods
big data in robotics and automation
Clustering
Correlation
Flow profiles
Intelligent transportation system
Mathematical analysis
Matrix decomposition
Passengers
Predictive models
probability and statistical methods
Short term
Spatiotemporal phenomena
Statistical models
Tensors
Transportation systems
Two dimensional models
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the traditional statistical models fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction method and propose several practical techniques to improve both long-term and short-term prediction accuracy. For long-term prediction, we propose the "tensor decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model, and an effective way to update prediction in real-time; For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplification and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.3004785