A hybrid deep learning based traffic flow prediction method and its understanding

Wu, Yao; Tan, Huachun; Qin, Lingqiao; Ran, Bin; Jiang, Zhuxi

doi:10.1016/j.trc.2018.03.001

Cited by 557 publications

(235 citation statements)

References 46 publications

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“…Convolutional neural networks (CNNs) have been successful in dealing with spatial features of road networks [3], [4]. Besides, recurrent neural networks (RNNs) with long short-term memories (LSTM) [4], [5] and gated recurrent unit (GRU) [6] have been incorporated, considering the traffic flow prediction as a time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Capsule Network for Traffic Speed Prediction in Complex Road Networks

Kim

Wang

Zhu

et al. 2018

2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments. The spatio-temporal traffic data can be converted into an image where the traffic data are expressed in a 3D space with respect to space and time axes. Although convolutional neural networks (CNNs) have been showing surprising performance in understanding images, they have a major drawback. In the max pooling operation, CNNs are losing important information by locally taking the highest activation values. The inter-relationship in traffic data measured by sparsely located sensors in different time intervals should not be neglected in order to obtain accurate predictions. Thus, we propose a neural network with capsules that replaces max pooling by dynamic routing. This is the first approach that employs the capsule network on a time series forecasting problem, to our best knowledge. Moreover, an experiment on real traffic speed data measured in the Santander city of Spain demonstrates the proposed method outperforms the state-of-the-art method based on a CNN by 13.1% in terms of root mean squared error.Index Terms-traffic speed prediction, capsule network (Cap-sNet), convolutional neural network (CNN)

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Section: Introductionmentioning

confidence: 99%

A Capsule Network for Traffic Speed Prediction in Complex Road Networks

Kim

Wang

Zhu

et al. 2018

2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

View full text Add to dashboard Cite

show abstract

“…Inspired by the visual neuroscience, CNNs are designed to fully exploit the three main ideas, namely sparse connectivity, weight sharing, and equivariant representations [15]. This kind of neural network is suited for processing data in the form of multiple arrays, Although CNNs are mainly used for image classification, they have been used to learn spatial features of traffic flow data at nearby locations which exhibit strong spatial correlations [201].…”

Section: • Convolutional Neural Networkmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

238

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This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learningbased data-driven multistage optimization with a learning-while-optimizing scheme is presented.

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“…The proposed DSTR-RNet model constructed locally-connected neural network layers (LCNR) to model road network topology and integrated residual learning to model the spatio-temporal dependency, which maintained the spatial precision and topology of the road network, as well as improved the prediction accuracy. Wu et al proposed a DNN-Based Traffic Flow prediction model (DNN-BTF) to improve the prediction accuracy, which made full use of the weekly/daily periodicity and spatial-temporal characteristics of traffic flow [18]. An attention-based model was introduced into their work that automatically learned to determine the importance of past traffic flow.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

et al. 2020

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Centralized publishing of big location data can provide accurate and timely information to assist in traffic management and for facilitating people to decide travel time and route, mitigate traffic congestion, and reduce unnecessary waste. However, the spatio-temporal correlation, non-linearity, randomness, and uncertainty of big location data make it impossible to decide an optimal data publishing instance through traditional methods. This paper, accordingly, proposes a publishing interval predicting method for centralized publication of big location data based on the promising paradigm of deep learning. First, the adaptive adjusted sampling method is designed to address the challenge of finding a reasonable release time via a prediction mechanism. Second, the Maximal Overlap Discrete Wavelet Transform (MODWT) is introduced for the decomposition of time series in order to separate different features of big location data. Finally, different deep learning models are selected to construct the entire framework according to various time-domain features. Experimental analysis suggests that the proposed prediction scheme is not only feasible, but also improves the prediction accuracy in contrast to the traditional deep learning mechanisms.

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A hybrid deep learning based traffic flow prediction method and its understanding

Cited by 557 publications

References 46 publications

A Capsule Network for Traffic Speed Prediction in Complex Road Networks

A Capsule Network for Traffic Speed Prediction in Complex Road Networks

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

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