Deep learning-based container throughput forecasting: a triple bottom line approach

Shankar, Sonali; Punia, Sushil; Ilavarasan, P. Vigneswara

doi:10.1108/imds-12-2020-0704

Cited by 16 publications

(4 citation statements)

References 59 publications

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“…In order to reduce the complexity of training, corresponding sampling methods are proposed [21]. The first step is to select the important neighbor throughput for the throughput to be encoded, and the neighbor throughput is calculated by the structure compactness function.…”

Section: B Evaluation Process Of Forecasting Resultsmentioning

confidence: 99%

A Data-Driven Collaborative Forecasting Method for Logistics Network Throughput Based on Graph Learning

Hou

Jia

2023

IEEE Access

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In order to achieve more optimal resource scheduling effect for logistics networks, it is essential to collaboratively predict throughput amount of different network nodes in future timestamps. However, the logistics networks are actually a kind of connected complex networks, in which a node denotes a single logistics station and all nodes are associated by implicit relationships. When it comes to collaborative forecasting towards logistics network throughput, all the nodes are required to be integrated together as a whole research object. Therefore, this work introduces graph learning to extract graph-level features of logistics networks, and proposes a data-driven collaborative forecasting method for logistics network throughput based on graph learning. Firstly, information characteristics of graph-level logistics networks is defined as vectorized format. Then, the graph learning framework is formulated, so as to fit the nonlinear relationship between logistics networks and dynamic throughput amount. At last, some simulations are also taken to testify performance of the proposal. The research results show that the graph neural network can find the temporal correlation between data and combine preprocessed multi-layer feature vector with temporal attention weight vectors. And the proposal is able to well implement collaborative forecasting towards logistics networks, with the assistance of graph learning.

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Section: B Evaluation Process Of Forecasting Resultsmentioning

confidence: 99%

A Data-Driven Collaborative Forecasting Method for Logistics Network Throughput Based on Graph Learning

Hou

Jia

2023

IEEE Access

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“…Preprocessing of raw data is one of the difference between the three types of models above and the last type of model. The decomposition and ensemble models have been widely applied in many different fields as seen from previous literature, like gasoline consumption forecasting ( 34 ), marine cargo volume ( 35 ), foreign exchange rates forecasting ( 36 ), and container throughput forecasting ( 37 – 40 ). For example, to better predict container throughput, Du et al ( 37 ) introduced a decomposition and ensemble model named variational mode decomposition–butterfly extreme learning machine–error correction strategy (VMD-BELM-ECS); its mean absolute error (MAE) is 2.8465, whereas ARIMA’s MAE is 13.4 and least-square support vector machine’s MAE is 12.64.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Xiao

Xue

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

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“…Ref. [12] employed a deep learning method to forecast the container throughput of Los Angeles Port. Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Nonparametric Functional Data Analysis for Forecasting Container Throughput: The Case of Shanghai Port

Zou

Chen

2022

JMSE

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Transportation is one of the major carbon sources in China. Container throughput is one of the main influencing factors of ports’ carbon emission budget, and accurate prediction of container throughput is of great significance to the study of carbon emissions. Time series methods are key techniques and frequently used for container throughput. However, the existing time series methods treat container throughput data as discrete points and ignore the functional characteristics of the data. There has recently been interest in developing new statistical methods to predict time series by taking into account a continuous set of past values as predictors. In addition, to eliminate the linear constraint in the functional time series prediction approach, we propose a functional version of a nonparametric model that allows using a continuous path in the past to predict future values of the process, including functional nonparametric regression and functional conditional quantile and functional conditional mode models, to forecast the container throughput of Shanghai Port. For the purpose better forecasting, an experiment was conducted to compare our functional data analysis approaches with other forecasting methods. The results indicated that nonparametric functional forecasting methods exhibit more significant performance than other classical models, including the functional linear regression model, nonparametric regression model, and autoregressive integrated moving-average model. At the same time, we also compared the prediction accuracy of the three nonparametric functional methods.

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Deep learning-based container throughput forecasting: a triple bottom line approach

Cited by 16 publications

References 59 publications

A Data-Driven Collaborative Forecasting Method for Logistics Network Throughput Based on Graph Learning

A Data-Driven Collaborative Forecasting Method for Logistics Network Throughput Based on Graph Learning

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Nonparametric Functional Data Analysis for Forecasting Container Throughput: The Case of Shanghai Port

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