Estimating freight production/attraction using nighttime satellite imagery

Abstract

An appropriate understanding and accurate estimation of zonal freight production (FP) and attraction (FA) volume is one of the most important topics and the initial step in developing the origin and destination (OD) matrix. In general, zonal FP and FA are difficult to be obtained as observed data, particularly in developing countries, owing to the lack of large-scale freight surveys. Thus, several proxy indicators are often used for estimating FP and FA, such as the gross regional product (GRP), population, and the number of establishments. However, these indicators do not often estimate the zonal FP and FA accurately and are even not available readily. The nighttime light intensity (NLI) data that is human-induced light emission data and one of the open alternative data, possibly improves the accuracy of FP and FA estimation. However, few studies have examined the relationship between NLI and FP/FA. Therefore, the objectives of this study are to examine whether NLI can be a significant indicator for estimating zonal FP and FA relative to other socio-economic indicators and to examine the types of NLIs that can accurately estimate the FP and FA through a case study in Japan. As a result of the analysis, NLI data are identified as a significant variable for the estimation of FP and FA among various socio-economic variables. Although some of the indicators related to secondary industries provide higher accuracy, NLI exhibits better estimates than the popular indicators such as GRP and population. In addition, we propose three types of total NLI that multiply the unit NLI by the zonal area and this generates higher accuracy of unit NLI, the average light intensity of all pixels in a zone. We have also found that the use of habitable area for the estimation of total NLI provides the highest accuracy among the four types of NLIs including unit NLI. Through the analysis, NLI data is sufficiently applicable for estimating zonal FP and FA, relative to other important socio-economic indicators, although space transferability should be examined in other study areas. These findings could help estimating FP/FA particularly in countries and regions with limited statistical data available.