Traffic speed prediction under weekday using convolutional neural networks concepts

Song, Cheol Ho; Lee, Heeyun; Kang, Changbeom; Lee, Won Young; Kim, Young B.; W., Suk

doi:10.1109/ivs.2017.7995890

Cited by 50 publications

(30 citation statements)

References 13 publications

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“…This information can then be used to improve the prediction accuracy. The study in [34] proposes the use of a deep neural network made of several sub-CNNs. Each CNN processes the temporal evolution of traffic variables measured in a detector in order to predict the traffic speed.…”

Section: State Of the Art On Traffic Predictionmentioning

confidence: 99%

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Mena-Oreja

Gozálvez

2020

IEEE Access

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Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

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Section: State Of the Art On Traffic Predictionmentioning

confidence: 99%

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Mena-Oreja

Gozálvez

2020

IEEE Access

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“…CNN is one of many deep learning methods. Song et al [19] used a CNN model for traffic speed prediction. Experimental results show that the model could capture the local dependencies of traffic data and had an advantage on space and time effects.…”

Section: Related Workmentioning

confidence: 99%

Mode decomposition based deep learning model for multi-section traffic prediction

2020

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Road traffic prediction plays a vital role in real-time traffic management of an intelligent transportation system (ITS). Many prediction models achieve fine results. However, most ignore the intrinsic characteristics of traffic parameter data and do not consider the spatiotemporal effects of road sections, which can reflect the situation of all road traffic. Therefore, multi-section traffic prediction is still an open problem. In this paper, empirical mode decomposition (EMD) is employed to decompose the information of traffic parameters into many intrinsic mode function (IMF) components, which represent the original road traffic information in periodic and random sequences. Then, by considering the superiority of deep learning in multi-dimensional data processing, which can handle the spatiotemporal effects, a prediction model based on a convolutional neural network (CNN) is proposed to achieve the prediction of periodic and random sequences, whose results are combined to obtain the final prediction. The dataset from the Caltrans Performance Measurement System is used to validate the model. The proposed prediction model is compared to several well-known models, such as PCA-BP, Lasso-BP, and standard CNN. Experiments show that the proposed prediction model achieves higher accuracy.

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“…Due to their ability to learn hidden features, CNN's have already been used extensively for wide variety of applications such as image classification, traffic prediction, etc. [12], [13]. The basic architecture of CNN model for healthcare recommender system consists of an input layer, an output layer, and multiple hidden layers in-between.…”

Section: Cnn Model For Healthcarementioning

confidence: 99%

“…6. The complete process is repeated till all the new matrices with lower rank are obtained (line [9][10][11][12][13][14]. Once this process is over,Ů i is stored and n-mode product operation ofŮ i is projected over T un .…”

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confidence: 99%

DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

Aujla

Jindal

Chaudhary

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.

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Traffic speed prediction under weekday using convolutional neural networks concepts

Cited by 50 publications

References 13 publications

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Mode decomposition based deep learning model for multi-section traffic prediction

DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

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