Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

Zhao, Jiandong; Gao, Yuan; Yang, Zhenzhen; Li, Jiangtao; Feng, Yingzi; Qin, Ziyan; Bai, Zhiming

doi:10.1109/access.2018.2890414

Cited by 51 publications

(27 citation statements)

References 31 publications

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“…, and get the experimental results of the different hyper-parameters shown as Table 2 . Specially, the FCNN model is trained by minimizing the BCEloss with RMSprop optimizer (Zhao et al, 2019) in the light of the AUC of validation and test datasets. As shown in Table 2 , the best hyper-parameters for combination of activation function, the kernel size, stride, the number of neurons in the first layer, learning rate, the dropout probability, and the batch size is Tanh_Tanh, 2, 1, 81, 0.001, 0.1, and 250, respectively.…”

Section: Resultsmentioning

confidence: 99%

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

Wang

Zhang²,

Yang³

et al. 2019

Front. Genet.

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Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional neural network (FCNN), to measure conditional relatedness between genes using both co-expression and prior-knowledge similarities. The results on validation and test datasets show FCNN model yields an average 3.0% and 2.7% higher accuracy values for identifying gene–gene interactions collected from the COXPRESdb, KEGG, and TRRUST databases, and a benchmark dataset of Xiao-Yong et al. research, by grid-search 10-fold cross validation, respectively. In order to estimate the FCNN model, we conduct a further verification on the GeneFriends and DIP datasets, and the FCNN model obtains an average of 1.8% and 7.6% higher accuracy, respectively. Then the FCNN model is applied to construct cancer gene networks, and also calls more practical results than other compared models and methods. A website of the FCNN model and relevant datasets can be accessed from .

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Section: Resultsmentioning

confidence: 99%

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

Wang

Zhang²,

Yang³

et al. 2019

Front. Genet.

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“…The best fitting performance of ZIOP indicates that selecting an appropriate model from probability models and count models is as important as dealing with the excessive zero observations problem in terms of accurately fit driving behaviors. The application of ZIOP model in truck driver' violations will be helpful to build better driving simulation models for ITS [63], [64].…”

Section: Discussionmentioning

confidence: 99%

A Zero-Inflated Ordered Probit Model to Analyze Hazmat Truck Drivers’ Violation Behavior and Associated Risk Factors

Fan

Wang³

2020

IEEE Access

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There are few studies on the violation of truck drivers, especially the hazmat truck driver, although truck driver's violation may cause serious casualties. This paper aims to investigate hazmat truck drivers' violation behavior and identify associated risk factors. Different data sources in intelligent transportation system (ITS) including hazmat transportation management system and traffic safety management system are extracted and emerged together. Three years (2016-2018) of violation data that comprised 11612 trip record in China are employed in this research. Based on Bayesian theory, this study proposes zero-inflated ordered probit (ZIOP) model and three alternative models to exploring the relationship between hazmat truck drivers' violation frequency and the key risk factors. The results show that ZIOP model can handle excessive zero observation problem of violation data properly and differentiate between 'alwayszero group' drivers and drivers who did not violate the traffic rules during research period but would do so in different surroundings. The results also indicate that the violation probability and the violation frequency level of hazmat truck drivers are influenced by driver characteristics, freight order attributes, and drivers' violation records. This research provides guidance for driving training and safety education of hazmat truck drivers, and will be helpful in building better driving simulation models.

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“…In recent years, deep learning methods [29]- [31] derived from neural networks have been applied in many fields. RNN is a kind of ANNs, and is evolved from Hopfield network [21] for modeling serialized data.…”

Section: Proposed Bi-s-sru Based Prediction Model a Principle Ofmentioning

confidence: 99%

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

Liu

et al. 2020

IEEE Access

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In the smart mariculture, the timely and accurate predictions of water quality can help farmers take countermeasures before the ecological environment deteriorates seriously. However, the openness of the mariculture environment makes the variation of water quality nonlinear, dynamic and complex. Traditional methods face challenges in prediction accuracy and generalization performance. To address these problems, an accurate water quality prediction scheme is proposed for pH, water temperature and dissolved oxygen. First, we construct a new huge raw data set collected in time series consisting of 23,204 groups of data. Then, the water quality parameters are preprocessed for data cleaning successively through threshold processing, mean proximity method, wavelet filter, and improved smoothing method. Next, the correlation between the water quality to be predicted and other dynamics parameters is revealed by the Pearson correlation coefficient method. Meanwhile, the data for training is weighted by the discovered correlation coefficients. Finally, by adding a backward SRU node to the training sequence, which can be integrated into the future context information, the deep Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) learning network is proposed. After training, the prediction model can be obtained. The experimental results demonstrate that our proposed prediction method achieve higher prediction accuracy than the method based on RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) with similar or less time computing complexity. In our experiments, the proposed method takes 12.5ms to predict data on average, and the prediction accuracy can reach 94.42% in the next 3∼8 days. INDEX TERMS Smart mariculture, precision agriculture, water quality prediction, SRU, deep learning.

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Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

Cited by 51 publications

References 31 publications

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

A Zero-Inflated Ordered Probit Model to Analyze Hazmat Truck Drivers’ Violation Behavior and Associated Risk Factors

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

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