Container Demand Forecasting at Border Posts of Ports: A Hybrid SARIMASOMSVR Approach An accurate forecast of freight demand at sanitary facilities of ports is one of the key challenges for transport policymakers to better allocate resources and to improve planning operations. This paper proposes a combined hybrid approach to predict the shortterm volume of containers passing through the sanitary facilities of a maritime port. The proposed methodology is based on a threestage process. First, the time series is decomposed into similar smaller regions easier to predict using a selforganizing map (SOM) clustering. Then, a seasonal autoregressive integrated moving averages (SARIMA) model is fitted to each cluster, obtaining predicted values and residuals of each cluster. A support vector regression (SVR) model is finally applied in each cluster using the historical data clustered and the predicted variables from the SARIMA step, testing different hybrid configurations. The experimental results demonstrated that the proposed model outperforms other methodologies based on SVR. The proposed model can be used as an automatic decisionmaking tool by seaport or airport management due to its capacity to plan resources in advance.

Forecast Application of Time Series Model Based on BLS in Port Cargo Throughput During the last decade, there was a dramatic increasing of container throughput in China, especially in server harbor cities such as Shanghai and Shenzhen. It is a necessary and crucial task to enhance the ability of port throughput. In the existing studies, timeseries model is one of the most powerful methods to solve this problem, which can predict the container cargo throughput accurately and effectively. Based on this technical background, this paper employs a novel algorithm to design a new type of timeseries model for predicting port throughput. In the experiments, we firstly use the Matlab to pursue the statistical analyses on the throughput data. Secondly, we apply our method to predict the changing rate of container throughput, and compare the results with several classic timeseries models. Finally, the experimental results show that our method was optimized based on the training data, and outperformed other timeseries models in the prediction of 10 months throughput.