Datasets on the freight transportation indices in Bushehr province, Iran

These datasets provide information on the freight transportation indices in Bushehr province, Iran, from 2013 to 2019. The primary datasets are collected monthly, so seasonal statistics were derived from them; upon initial review of the time series data for the considered indices, it was revealed that they exhibit a seasonal pattern. Consequently, the seasons for each year were utilized as a time code, resulting in the definition of 28 time codes in total. File 1 shows the primary dataset on the mass of freight in Bushehr province from 2013-2019, organised by month (Table 1). This file also includes seasonal statistics on this index during those years (Table 2). Furthermore, Figure 1 in this file illustrates the trend of the mass of freight in Bushehr province throughout the 2013-2019 seasons. File 2 shows the primary dataset on the number of truck journeys in Bushehr province from 2013 to 2019, segmented by month (Table 3). Similarly, this file presents seasonal statistics for this index during those years (Table 4). Additionally, Figure 2 in this file indicates the trend of the number of truck journeys in Bushehr province across the 2013-2019 seasons. Files 3 and 4 show the forecasting results of freight transportation indices using the HW model. These files display the predicted values for test dataset seasons and future seasons for two indices of mass of freight and number of truck journeys, respectively. The calculations have been performed using the XLSTAT 2021 add-in in Excel software. Furthermore, these files also contain the optimised parameters obtained using a standard optimisation algorithm in the XLSTAT add-in. Files 5 and 6 show the transition probability matrices of states for the indices of mass of freight and number of truck journeys, respectively. Additionally, File 7 compares the accuracy of four models (HW, HWMC, ARIMA, and ARIMA-MC) in forecasting the considered indices. This comparison has been conducted based on four evaluation measures, as shown in Table 5 and Figures 3 and 4.本数据集涵盖伊朗布什尔省2013年至2019年的货运指数相关信息。核心数据集按月度采集，由此衍生出季节性统计结果；经对所涉指数的时序数据开展初步审查后发现，其呈现出显著的季节性波动特征。据此，我们将每年的四季作为时间编码，最终共计定义28组时间编码。 文件1收录了2013-2019年布什尔省货运总量的原始数据集，按月份整理（表1）。该文件同时包含对应年份该指标的季节性统计数据（表2）。此外，本文件中的图1展示了2013-2019年各季度间布什尔省货运总量的变化趋势。 文件2收录了2013-2019年布什尔省货运卡车通行次数的原始数据集，按月份划分（表3）。同理，该文件呈现了对应年份该指标的季节性统计数据（表4）。此外，本文件中的图2展示了2013-2019年各季度间布什尔省货运卡车通行次数的变化趋势。 文件3与文件4分别展示了采用HW模型对货运指数开展预测的结果。两类文件分别针对货运总量与货运卡车通行次数这两项指标，呈现了测试集季度与未来季度的预测值。本次计算通过Excel软件中的XLSTAT 2021插件完成。此外，上述文件还收录了通过XLSTAT插件内的标准优化算法得到的优化参数。 文件5与文件6分别给出了货运总量与货运卡车通行次数两项指标的状态转移概率矩阵。 此外，文件7对比了HW、HWMC、ARIMA以及ARIMA-MC四种模型对所涉货运指数的预测精度。本次对比基于四项评估指标展开，具体结果详见表5以及图3、图4。