Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework

Wu, Yao; Tan, Huachun

doi:10.48550/arxiv.1612.01022

Cited by 51 publications

(49 citation statements)

References 4 publications

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“…As the traditional convolutional operation in CNN process the data with a 2D approach, the layout of a city is geographically divided into square blocks in order to extract spatial relationships from all regions [8], nearest regions [9] or in other 2D forms [10]. RNN based methods and their variants [11] are applied to catch temporal correlation, for instance, structuring the historical traffic demand sequence for each region [3] and presented as a 1D feature-level fused architecture [4]. GNN based methods, with natural advantages in utilizing spatial information, model the traffic network by a general graph instead of treating the traffic data arbitrarily (e.g., grids and segments) in CNN and RNN methods.…”

Section: A Existing Methodsmentioning

confidence: 99%

“…Email: mingming.liu@dcu.ie networks whilst the recurrent neural networks (RNN) were used to arrest the temporal information. For short-term traffic prediction, fully connected long short-term memory (LSTM) [3] and CLTFP [4], two architectures mixed the long shortterm memory networks with convolutional operation, were proposed in order to catch both temporal and spatial cues. However, LSTM or other networks with recurrent architecture are computationally intensive and hard to converge the network parameters with global optimisation, since the recursive training accumulates the error for the prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes

Chen¹,

Wu²,

O’Connor³

et al. 2021

Preprint

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Accurately forecasting transportation demand is crucial for efficient urban traffic guidance, control and management. One solution to enhance the level of prediction accuracy is to leverage graph convolutional networks (GCN), a neural network based modelling approach with the ability to process data contained in graph based structures. As a powerful extension of GCN, a spatial-temporal graph convolutional network (ST-GCN) aims to capture the relationship of data contained in the graphical nodes across both spatial and temporal dimensions, which presents a novel deep learning paradigm for the analysis of complex time-series data that also involves spatial information as present in transportation use cases. In this paper, we present an Attention-based ST-GCN (AST-GCN) for predicting the number of available bikes in bike-sharing systems in cities, where the attention-based mechanism is introduced to further improve the performance of a ST-GCN. Furthermore, we also discuss the impacts of different modelling methods of adjacency matrices on the proposed architecture. Our experimental results are presented using two real-world datasets, Dublinbikes and NYC-Citi Bike, to illustrate the efficacy of our proposed model which outperforms the majority of existing approaches.

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Section: A Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes

Chen¹,

Wu²,

O’Connor³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the last 3-4 years, emphasis has been shifted to neuralbased models for traffic forecasting, with the adoption of, e.g., deep belief network (DBN) [12] and stacked autoencoder (SAE) [3], [16]. To extract spatial-temporal features jointly, a number of spatial-temporal deep learning models are proposed: Wu et al [27] combined CNN and LSTM to align spatial and temporal regularities, Zhang et al [33] proposed ST-ResNet that uses residual convolutional units to model the temporal closeness, period, and trend properties of crowd traffic. Wang et al [25] proposed RegionTrans to transfer knowledge from a data-rich source city to a data-scarce target city.…”

Section: Related Workmentioning

confidence: 99%

GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series

Zhang,

Zheng,

et al. 2021

Preprint

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Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and complex spatial-temporal dependency of road networks. For this task, we propose Graph Attention-Convolution-Attention Networks (GACAN). The model uses a novel Att-Conv-Att (ACA) block which contains two graph attention layers and one spectralbased GCN layer sandwiched in between. The graph attention layers are meant to capture temporal features while the spectralbased GCN layer is meant to capture spatial features. The main novelty of the model is the integration of time series of four different time granularities: the original time series, together with hourly, daily, and weekly time series. Unlike previous work that used multi-granularity time series by handling every time series separately, GACAN combines the outcome of processing all time series after each graph attention layer. Thus, the effects of different time granularities are integrated throughout the model. We perform a series of experiments on three real-world datasets. The experimental results verify the advantage of using multigranularity time series and that the proposed GACAN model outperforms the state-of-the-art baselines.

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“…T RAFFIC forecasting is crucial for transportation management, public safety, and route planning in ITS [1]. If accurate traffic forecasting in advance, it will provide some useful assistance to the transportation agencies who implement measures of controlling traffic flow, regulate route planning [2], and give early warnings to prevent the spread of COVID-19 [3]. However, traffic forecasting is very challenging, because of the complex spatio-temporal dependencies.…”

Section: Introductionmentioning

confidence: 99%

DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

Qin¹,

Yu²,

Luo³

et al. 2021

Preprint

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Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies. Therefore, researches pay great attention to deal with the complex spatio-temporal dependencies of traffic system for accurate forecasting. However, there are two challenges: 1) Most traffic forecasting studies mainly focus on modeling correlations of neighboring sensors and ignore correlations of remote sensors, e.g., business districts with similar spatio-temporal patterns; 2) Prior methods which use static adjacency matrix in graph convolutional networks (GCNs) are not enough to reflect the dynamic spatial dependence in traffic system. Moreover, finegrained methods which use self-attention to model dynamic correlations of all sensors ignore hierarchical information in road networks and have quadratic computational complexity. In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously. We not only use the distance-based graph to capture spatial information from nodes are close in distance but also construct a novel latent graph which encoded the structure correlations among roads to capture spatial information from nodes are similar in structure. Furthermore, we divide the neighbors of each sensor into coarse-grained regions, and dynamically assign different weights to each region at different times. Meanwhile, we integrate the dynamic multi-graph convolution network into the gated recurrent unit (GRU) to capture temporal dependence. Extensive experiments on three real-world traffic datasets demonstrate that our proposed algorithm outperforms state-of-the-art baselines.

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Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework

Cited by 51 publications

References 4 publications

A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes

A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes

GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series

DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

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