A two-stage procedure for forecasting freight inspections at Border Inspection Posts using SOMs and support vector regression

Abstract

The number of goods which passes through a border inspection post (BIP) may cause important congestion problems and delays in the port system, having an effect in the level of service of the port. Therefore, a prediction of the daily number of goods subject to inspection in BIPs seems to be a potential solution. This study proposes a two-stage procedure to better predict freight inspections. In the first stage, a Kohonen self-organising map (SOM) is employed to decompose the whole data into smaller regions which display similar statistical characteristics. In the second stage, support vector regression (SVR) is used to forecast the different homogeneous regions individually. The results obtained are compared with the single SVR technique. The experiment shows that SOM–SVR models outperform the single SVR models in the inspection forecasting. The application of the proposed technique may become a supporting tool for the prediction of the number of goods subject to inspection in BIPs of other international seaports or airports, and provides relevant information for decision-making and resource planning.