DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

Zheng, Chuanpan; Fan, Xiaoliang; Wen, Chenglu; Chen, Longbiao; Wang, Cheng; Li, Jonathan

doi:10.1109/tits.2019.2932785

Cited by 85 publications

(18 citation statements)

References 47 publications

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“…From the results, it appears that the complex structure of the proposed model requires a higher computational cost than the other approaches. However, we deem that for traffic prediction applications the computational overload of the AST-MTL model is acceptable since the training is generally carried out in an offline manner where considerable computational resources are available [39]. Moreover, when it comes to real-time prediction, AST-MTL can predict the traffic condition in a matter of seconds.…”

Section: ) Run-time Analysismentioning

confidence: 99%

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Buroni¹,

Lebichot

Bontempi³

2021

IEEE Access

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Road traffic forecasting is crucial in Intelligent Transportation Systems (ITS). To achieve accurate results, it is necessary to model the dynamic nature and the complex non-linear dependencies governing traffic. The goal is particularly challenging when the prediction involves more than just one traffic variable. This paper proposes a novel multi-task learning model, called AST-MTL, to perform multi-horizon predictions of the traffic flow and speed at the road network scale. The strategy combines a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to learn related tasks while improving generalization performance. The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. Our experiments employ new sets of GPS data, called OBU data, to perform traffic prediction in the freeway and urban contexts. The experimental results prove our model can effectively perform multi-horizon traffic forecasting for different types of roads and outperform state-of-the-art models.INDEX TERMS Deep learning, multi-task learning, graph mining, traffic prediction.

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Section: ) Run-time Analysismentioning

confidence: 99%

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Buroni¹,

Lebichot

Bontempi³

2021

IEEE Access

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“…It can learn hierarchical feature representations from incomplete traffic data for prediction. Furthermore, Zheng et al (2019) proposed DeepSTD, a two-phase end-to-end deep learning framework to leverage spatio-temporal disturbances to predict citywide traffic flow.…”

Section: Deep Learning For Traffic Volume Predictionmentioning

confidence: 99%

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Fan

Xiang

Gong

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

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“…As one of the essential components in intelligent transportation systems (ITSs), traffic forecasting can provide a scientific basis for the management and planning of urban transportation systems [1]- [3]. According to predicted traffic states, transportation departments can deploy and guide traffic flows in advance, thereby improving the operating efficiency of road networks and alleviating traffic jams [4], [5].…”

Section: Introductionmentioning

confidence: 99%

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

et al. 2021

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Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic speed forecasting tasks when compared with traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines.

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DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

Cited by 85 publications

References 47 publications

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

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