<abstract xmlns="http://www.w3.org/1999/xhtml"><p>To predict the logistics needs of the port, an evaluation algorithm for the port logistics park based on the PCASVM model was proposed. First, a quantitative indicator set for port logistics demand analysis was established. Then, based on the grey correlation analysis method, the specific indicator set of port logistics demand analysis was selected. The advantages of both principal component analysis and support vector machine algorithms were combined. The PCA-SVM model was constructed as a predictive model of the port logistics demand scale. The empirical analysis was conducted. Finally, from the perspective of the structure, demand, flow pattern and scale of port logistics demand, the future logistics demand of Shenzhen port was analysed. Through sensitivity analysis, the main influencing factors were found out, and the future development proposals of Shenzhen port were put forward. The results showed that the port throughput of Shenzhen City in 2016 was 21,328,200 tons. Compared with the previous year, it decreased by about 1.74 %. In summary, the PCA-SVM model accurately predicts the logistics needs of the port.</p></abstract>

To predict the logistics needs of the port, an evaluation algorithm for the port logistics park based on the PCASVM model was proposed. First, a quantitative indicator set for port logistics demand analysis was established. Then, based on the grey correlation analysis method, the specific indicator set of port logistics demand analysis was selected. The advantages of both principal component analysis and support vector machine algorithms were combined. The PCA-SVM model was constructed as a predictive model of the port logistics demand scale. The empirical analysis was conducted. Finally, from the perspective of the structure, demand, flow pattern and scale of port logistics demand, the future logistics demand of Shenzhen port was analysed. Through sensitivity analysis, the main influencing factors were found out, and the future development proposals of Shenzhen port were put forward. The results showed that the port throughput of Shenzhen City in 2016 was 21,328,200 tons. Compared with the previous year, it decreased by about 1.74 %. In summary, the PCA-SVM model accurately predicts the logistics needs of the port.

Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model

Polish Maritime Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

To predict the logistics needs of the port, an evaluation algorithm for the port logistics park based on the PCASVM model was proposed. First, a quantitative...

Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model

Publié en ligne: 11 janv. 2019

Pages: 29 - 35

DOI: https://doi.org/10.2478/pomr-2018-0109

Mots clés
port logistics demand, support vector machine, principal component analysis, economic hinterland

© 2019 Bianjiang Hu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model

Publié en ligne: 11 janv. 2019

Pages: 29 - 35

DOI: https://doi.org/10.2478/pomr-2018-0109

Mots clésport logistics demand, support vector machine, principal component analysis, economic hinterland

© 2019 Bianjiang Hu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
port logistics demand, support vector machine, principal component analysis, economic hinterland