Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Cui, Zhiyong; Ke, Ruimin; Wang, Yinhai

doi:10.48550/arxiv.1801.02143

Cited by 104 publications

(93 citation statements)

References 37 publications

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“…After 2015, capitalising on the success of deep learning [15], several deep learning models are proposed to capture the underlying patterns of traffic data. Recurrent neural networks (RNNs) are adopted to analyse time series patterns [16], [17], while convolutional neural networks (CNNs) are used to capture spatial dependencies by treating a traffic network as a grid [18], [19]. However, these methods omit the road network, which is an essential spatial constraint on how traffic moves in the spatial dimension.…”

Section: Related Workmentioning

confidence: 99%

Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting

Yang¹,

Liu²,

Zhao³

2021

Preprint

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Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and temporal features. Unlike the state-of-the-art methods, which are restricted to a particular road network, we model the universal spatio-temporal correlations that are transferable from cities to cities. To this end, we propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor comprising the data from its neighbors within close time points. Based on this idea, we introduce local spacetime neural network (STNN), which employs novel spacetime convolution and attention mechanism to learn the universal spatio-temporal correlations. The proposed STNN captures local traffic patterns, which does not depend on a specific network structure. As a result, a trained STNN model can be applied on any unseen traffic networks. We evaluate the proposed STNN on two public real-world traffic datasets and a simulated dataset on dynamic networks. The experiment results show that STNN not only improves prediction accuracy by 15% over state-of-the-art methods, but is also effective in handling the case when the traffic network undergoes dynamic changes as well as the superior generalization capability.

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Section: Related Workmentioning

confidence: 99%

Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting

Yang¹,

Liu²,

Zhao³

2021

Preprint

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“…In early works, traffic data was directly used to train a Recurrent Neural Network (RNN) for learning long or short-term dependencies [19]. Cui et al studied the performance of stacked LSTM in traffic prediction and obtained promising results by using pure RNN structure [20]. Nonetheless, RNN still has inherent shortcomings in capturing spatial relationships, which led the researchers to introduce CNN as a spatial module.…”

Section: Realated Workmentioning

confidence: 99%

“…The predictive ability of neural networks can be improved by deepening the model structure. Stacked Unidirectional Bidirectional Recurrent Neural Network (SUBRNN) has been shown to be able to generate higher level feature representations from time series [13]. Therefore, this study constructed SAGRU to learn the temporal dependence of passenger volume sequences.…”

Section: A Stacked Bidirectional Unidirectional Temporal Attention Ga...mentioning

confidence: 99%

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

Gao¹,

Wang²,

Liu³

2021

Preprint

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Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error.

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“…Measurements are recorded every 5-minutes. The adjacency matrix calculated in [20] is used to convert the Fig. 7: tomtom traffic data in Greater Toronto Area data into an undirected graph.…”

Section: A Datasetsmentioning

confidence: 99%

Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)

Najafi,

Parsaeefard,

Leon-Garcia

2021

Preprint

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GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains. Existing approaches mainly focus on static graphs. However, many graphs change over time with their edge may disappear, or node / edge attribute may alter from one time to the other. It's essential to consider such evolution in representation learning of nodes in time varying graphs. In this paper, we propose a Temporal Multi-layered Position-aware Graph Neural Network (TMP-GNN), a node embedding approach for dynamic graph that incorporate the interdependence of temporal relations into embedding computation. We evaluate the performance of TMP-GNN on two different representations of temporal multilayered graphs. The performance is assessed against most popular GNNs on node-level prediction task. Then, we incorporate TMP-GNN into a deep learning framework to estimate missing data and compare the performance with their corresponding competent GNNs from our former experiment, and a baseline method. Experimental results on four real-world datasets yields up to 58% of lower ROC AUC for pairwise node classification task, and 96% of lower MAE in missing feature estimation, particularly for graphs with relatively high number of nodes and lower mean degree of connectivity.

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Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Cited by 104 publications

References 37 publications

Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting

Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)

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