A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models

Is the Spatial-Temporal Dependence Model Reliable for the Short-Term Freight Volume Forecast of Inland Ports? A Case Study of the Yangtze River, China

“…On the other hand, considering that current research emphasizes only time series and regression analysis, Moscoso-López et al [46] used a two-stage approach involving an ensemble of the best SVR models to forecast ro-ro (roll-on-roll-off) freight flow, which was verified as a promising tool in freight forecasting. For further analysis of the influence of the size of the autoregressive window and machine learning models, Ruiz-Aguilar et al [11] used a variety of well-known machine learning models (individual learners) and ensemble models to predict the number of inspections at the Border Inspection Posts (BIPs) on a specific day. According to previous studies, neural networks and support vector regression generally perform better than other machine learners in the forecast of freight volume.…”

“…The freight volumes displayed periodicity with day [12] and season [15] as the grain size, respectively. In addition, Ruiz-Aguilar et al [11] point out that although the prediction result will improve with the increasing size of the autoregressive window, the improvement will reduce with each increment of the size of the autoregressive window, up to a point of 21 days in the past, which implies that δ = 3 (weeks) may be a good choice. We verified this value through further experiments.…”

“…On other hand, the best time series model was used for Step 1 in Figure 4. Formula (4) depicts the model of time series forecast for freight volumes [11]; the parameters are introduced above.…”

“…The forecasting of freight volumes could be very helpful for the planning and operation of port entities [11,12]. From a long-term perspective, these predictions are an important basis for the evaluation of port investment, whether or not projects should be unertaken, and when they will be implemented [13,14].…”

“…Ruiz et al [17] compared the effectiveness of SARIMA and various machine learning methods in freight volume prediction, and their results showed that machine learning methods performed better. There are also differences between long-term and short-term forecasts, since the autoregression's size affects the forecast's performance [11]. However, it can be seen that most of the freight flow forecasting of ports is based on their past freight flow data without considering the effect of their neighboring ports [13].…”