TY - JOUR
T1 - Modelling the effect of spatial determinants on freight (trip) attraction
T2 - A spatially autoregressive geographically weighted regression approach
AU - Reda, Abel Kebede
AU - Tavasszy, Lori
AU - Gebresenbet, Girma
AU - Ljungberg, David
PY - 2023
Y1 - 2023
N2 - This paper investigates the effect of spatial and locational characteristics of establishments on freight (trip) attraction (FA/FTA) models. The authors estimated econometric models of FA and FTA as a function of the establishment attributes as well as the spatial and locational determinant variables, using establishment-level data collected from Addis Ababa City, Ethiopia. The interconnected issues of spatial dependency and spatial heterogeneity, together with nonlinear specifications, were incorporated with the application of spatial techniques, including spatial error models (SEM), spatial autoregressive model (SAR), geographically weighted regression (GWR), multiscale-GWR (MGWR), and the combination GWR-SAR/MGWR-SAR. Regarding the explanatory variables, the empirical results revealed that firms in the manufacturing, wholesale and retail sectors located on the wider streets tend to receive more FA and FTA. The closeness to the primary road network and the city entry gate influences the FTA of manufacturing and construction firms. Moreover, retail establishments near the major market tend to receive more tonnage. The models also confirm that FA and FTA are the results of two different processes. Overall, the use of spatial regression techniques improves the accuracy of both FA and FTA models. MGWR-SAR exhibits superior performance by jointly addressing spatial dependency and heterogeneity. The MGWR-SAR model also uncovers the local variability of the variables representing the spatial and locational effects on freight attraction. The methodological analysis and empirical findings of the study could provide useful insights to support urban freight modelling, planning, and decision-making.
AB - This paper investigates the effect of spatial and locational characteristics of establishments on freight (trip) attraction (FA/FTA) models. The authors estimated econometric models of FA and FTA as a function of the establishment attributes as well as the spatial and locational determinant variables, using establishment-level data collected from Addis Ababa City, Ethiopia. The interconnected issues of spatial dependency and spatial heterogeneity, together with nonlinear specifications, were incorporated with the application of spatial techniques, including spatial error models (SEM), spatial autoregressive model (SAR), geographically weighted regression (GWR), multiscale-GWR (MGWR), and the combination GWR-SAR/MGWR-SAR. Regarding the explanatory variables, the empirical results revealed that firms in the manufacturing, wholesale and retail sectors located on the wider streets tend to receive more FA and FTA. The closeness to the primary road network and the city entry gate influences the FTA of manufacturing and construction firms. Moreover, retail establishments near the major market tend to receive more tonnage. The models also confirm that FA and FTA are the results of two different processes. Overall, the use of spatial regression techniques improves the accuracy of both FA and FTA models. MGWR-SAR exhibits superior performance by jointly addressing spatial dependency and heterogeneity. The MGWR-SAR model also uncovers the local variability of the variables representing the spatial and locational effects on freight attraction. The methodological analysis and empirical findings of the study could provide useful insights to support urban freight modelling, planning, and decision-making.
KW - Freight generation
KW - Geographically weighted regression
KW - Multiscale GWR
KW - Spatial autocorrelation
KW - Spatial heterogeneity
KW - Trip attraction
UR - http://www.scopus.com/inward/record.url?scp=85159198010&partnerID=8YFLogxK
U2 - 10.1016/j.retrec.2023.101296
DO - 10.1016/j.retrec.2023.101296
M3 - Article
AN - SCOPUS:85159198010
SN - 0739-8859
VL - 99
JO - Research in Transportation Economics
JF - Research in Transportation Economics
M1 - 101296
ER -