Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

Li, Ziyue; Yan, Hao; Zhang, Chen; Tsung, Fugee

doi:10.1109/lra.2020.3004785

Cited by 26 publications

(7 citation statements)

References 20 publications

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“…Smart transport has been an essential chapter, yet with many works focusing on demand prediction [20,22], trajectory [23,26,50], or etc. Congestion root analysis instead should gain more attention since it is safety-related.…”

Section: Congestion Causes Analysismentioning

confidence: 99%

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

Lan

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. This paper takes the traffic congestion analysis as a concrete case, where a traffic intersection is usually regarded as a DAG. In a road network of multiple intersections, different intersections can only have some overlapping and distinct variables observed. For example, a signalized intersection has traffic light-related variables, whereas unsignalized ones do not. This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized. To this end, we innovatively propose a multi-modal regression for linear causal relationship description of different variables. Then we develop a novel Causality Difference (𝐶𝐷) measure and its differentiable approximator. Compared with existing SOTA measures, 𝐶𝐷 can penalize the causal structural difference among DAGs with distinct nodes and can better consider the uncertainty of causal orders. We rigidly prove our design's topological interpretation and consistency properties. We conduct thorough simulations and one case study to show the effectiveness of our MM-DAG. The code is available under https://github.com/Lantian72/MM-DAG.

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Section: Congestion Causes Analysismentioning

confidence: 99%

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

Lan

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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“…Thus, water use data for the study sites followed non-stationary stochastic patterns with non-uniform variance. Accordingly, for ARMA model development [21,27] for water consumption, non-stationarity was removed via second order differencing [28].…”

Section: Study Area and Water Use Data Compilation And Preprocessingmentioning

confidence: 99%

“…The auto-regressive (AR) polynomial constitutes the autoregressive model at a predefined order p describing the dependence of the variable (e.g., water consumption over a specified time period) on its values in a previous time. The moving average (MA) polynomial describes the linear dependence of the forecast errors resulting from the autoregressive model on the second predefined order q (Section S1, Supplementary Materials) [21,27]. The AR and MA polynomials were combined considering both the variable linear relationship and linear dependence of the forecast errors [29].…”

Section: Study Area and Water Use Data Compilation And Preprocessingmentioning

confidence: 99%

Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities

Zhou

Khan

Choi

et al. 2021

Water

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Water use patterns were explored for three small communities that are located in proximity to agricultural fields and rely on their local wells for potable water supply. High-resolution water use data, collected over a four-year period, revealed significant temporal variability. Monthly, daily, and hourly water use patterns were well described by autoregressive moving average (ARMA) models. Model development was supported by unsupervised clustering analysis via self-organizing maps (SOMs) that revealed similarities of water use patterns and confirmed the time-series water use model attributes. The inclusion of ambient temperature and rainfall as model attributes improved ARMA model performance for daily and hourly water use from R2 ~0.86–0.87 to 0.94–0.97 and from R2 ~0.85–0.89 to 0.92–0.98, respectively. Water use predictions for an entire year forward in time was feasible demonstrating ARMA models’ performance of (i) R2 ~0.90–0.94 and average absolute relative error (AARE) of ~2.9–4.9% for daily water use, and (ii) R2~0.81–0.95 and AARE ~1.9–3.8% for hourly water use. The study suggests that ARMA modeling should be useful for analysis of temporally variable water use in support of water source management, as well as assessing capacity building for small water systems including water treatment needs and wastewater handling.

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“…Learning an efficient representation of this large-scale data for further downstream tasks is necessary but challenging. In recent years, inspired by the great success of self-supervised representation learning in computer vision 1 Luxuan Wang is with Interdisciplinary Programs Office, The Hong Kong University of Science and Technology, Hong Kong SAR (Email: lwangda@connect.ust.hk) 2 Lei Bai is with The Shanghai AI Laboratory, Shanghai, China (Email: baisanshi@gmail.com) 3 Ziyue Li is with Information System, University of Cologne, 50923 Cologne, NRW, Germany (Email: zlibn@wiso.uni-koeln.de) 4 Rui Zhao is with SenseTime Reasearch and Qing Yuan Research Institute of Shanghai Jiao Tong University, Shanghai, China (Email: zhaorui@sensetime.com) 5 Fugee Tsung is with The Hong Kong University of Science and Technology and The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China (Email:season@ust.hk) and natural language process, many studies [13]- [15] have proposed various methods to learn latent representations of time series, and contrastive methods are currently the state-of-art approaches among discriminative self-supervised learning methods. However, the current approaches still have several significant shortcomings: Firstly, most recent studies [13], [14] only learn instancelevel representations, which is unsuitable for point-wise tasks, e.g., forecasting and anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised Classification of Weather Systems based on Spatiotemporal Contrastive Learning

Wang

2022

Preprint

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Correlated time series analysis plays an important role in many real-world industries. Learning an efficient representation of this large-scale data for further downstream tasks is necessary but challenging. In this paper, we propose a time-step-level representation learning framework for individual instances via bootstrapped spatiotemporal representation prediction. We evaluated the effectiveness and flexibility of our representation learning framework on correlated time series forecasting and cold-start transferring the forecasting model to new instances with limited data. A linear regression model trained on top of the learned representations demonstrates our model performs best in most cases. Especially compared to representation learning models, we reduce the RMSE, MAE, and MAPE by 37%, 49%, and 48% on the PeMS-BAY dataset, respectively. Furthermore, in real-world metro passenger flow data, our framework demonstrates the ability to transfer to infer future information of new cold-start instances, with gains of 15%, 19%, and 18%. The source code will be released under the GitHub https://github.com/bonaldli/ Spatiotemporal-TS-Representation-Learning.

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Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

Cited by 26 publications

References 20 publications

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities

Self-supervised Classification of Weather Systems based on Spatiotemporal Contrastive Learning

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