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      Railway freight volume forecast using an ensemble model with optimised deep belief network


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          Forecasting freight traffic contributes to the improvement of traffic facilities and making industrial policy, so it is significant to predict freight volume accurately. Extensive works had proved that ensemble model performed better than single model, so an ensemble model, combining seasonal autoregressive integrated moving average (SARIMA) with deep belief network (DBN), is proposed here. SARIMA, a linear model, is used to find the regularities of railway freight traffic. DBN, a non-linear model, is taken to mine the complex relationships between indexes and railway freight. In order to decide appropriate architecture of DBN, including the number of network layers and neurons in each hidden layer, Gaussian particle swarm optimisation algorithm is designed to decide appropriate architecture of DBN, including the number of network layers and neurons in each hidden layer. Besides, Spearman rank correlation analysis is used for selecting indexes related to freight volume. Experimental results show that, compared with SARIMA, DBN, back propagation neural network, Elman neural network, and radial basis function neural network, the proposed ensemble model obtains best performance, and the mean absolute error is 5.5159 million t and the mean absolute percentage error is 1.9657%.

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          Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

          This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.
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            An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic

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              A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training


                Author and article information

                IET Intelligent Transport Systems
                IET Intell. Transp. Syst.
                The Institution of Engineering and Technology
                27 April 2018
                21 May 2018
                October 2018
                : 12
                : 8
                : 851-859
                School of Traffic and Transportation Engineering, Central South University , Changsha, People's Republic of China
                IET-ITS.2017.0369 ITS.2017.0369.R2

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                Page count
                Pages: 0
                Funded by: China Railway Science and Technology Research Development Program
                Award ID: 2015F024
                Research Article


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