Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

Chen, Xinyu; Lei, Mengying; Saunier, Nicolas; Sun, Lijun

doi:10.48550/arxiv.2104.14936

Cited by 2 publications

(2 citation statements)

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“…There are several factors that could affect the model's robustness, such as sensor failures and demand surges. In transportation research, a very straightforward way to solve the exogenous uncertainty problem from sensor failure is to use imputation methods (Tang et al, 2015;Chen et al, , 2021. For example, recent work uses a variational Bayes approach to predict missing values accurately .…”

Section: Robustness In Traffic Signal Controlmentioning

confidence: 99%

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Shi,

Devailly,

Larocque

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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A number of deep reinforcement-learning (RL) approaches propose to control traffic signals.Compared to traditional approaches, RL approaches can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simulation). However, these RL methods require training from massive traffic sensor data. To offset this relative inefficiency, some recent RL methods have the ability to first learn from small-scale networks and then generalize to unseen city-scale networks without additional retraining (zero-shot transfer). In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows.

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Section: Robustness In Traffic Signal Controlmentioning

confidence: 99%

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Shi,

Devailly,

Larocque

et al. 2024

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

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“…For example, Wang et al [4] and Wei et al [6] included Toeplitz temporal regularizer to ensure the observations from adjacent timestamps to be similar. In Chen et al [13], temporal variation is introduced as a generative approach to ensure each time series follows parametric autoregressive (AR) models. These regularization-based methods have shown superior performance in modeling corrupted spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection

Wang,

Miranda-Moreno,

Sun

2021

Preprint

Self Cite

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Spatiotemporal traffic data (e.g., link speed/flow) collected from sensor networks can be organized as multivariate time series with additional spatial attributes. A crucial task in analyzing such data is to identify and detect anomalous observations and events from the data with complex spatial and temporal dependencies. Robust Principal Component Analysis (RPCA) is a widely used tool for anomaly detection. However, the traditional RPCA purely relies on the global low-rank assumption while ignoring the local temporal correlations. In light of this, this study proposes a Hankel-structured tensor version of RPCA for anomaly detection in spatiotemporal data. We treat the raw data with anomalies as a multivariate time series matrix (location × time) and assume the denoised matrix has a lowrank structure. Then we transform the low-rank matrix to a third-order tensor by applying temporal Hankelization. In the end, we decompose the corrupted matrix into a low-rank Hankel tensor and a sparse matrix. With the Hankelization operation, the model can simultaneously capture the global and local spatiotemporal correlations and exhibit more robust performance. We formulate the problem as an optimization problem and use tensor nuclear norm (TNN) to approximate the tensor rank and l1 norm to approximate the sparsity. We develop an efficient solution algorithm based on the Alternating Direction Method of Multipliers (ADMM). Despite having three hyper-parameters, the model is easy to set in practice. We evaluate the proposed method by synthetic data and metro passenger flow time series and the results demonstrate the accuracy of anomaly detection.

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Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

Cited by 2 publications

References 12 publications

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection

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