EXPLANATORY ANALYSES OF WORK TRIP GENERATION USING MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR)

Abstract

In transportation planning, forecasts have commonly followed the sequential four-step model in which, trip generation (production and attraction) plays a critical role. Among the methods applied to model trip generation, regression with Gaussian distribution of errors are recognized as the most prevailing techniques to describe the relationships between production/attraction and explanatory variables by estimating the global, fixed coefficients. Considering that, trip generation is almost impressed by spatial factors which vary over the study area; the main objective of this research is to apply Mixed Geographically Weighted Regression (MGWR) on 253 traffic analysis zones (TAZs) in Mashhad, Iran, by applying travel demand data and relating factors in 2018 to investigate the spatial non-stationarity which are not revealed when global specifications are applied. The influence of certain explanatory variables on response variables may be global, whereas others are local, accordingly, MGWR performs better compared with geographically weighted regression. The results of Moran’s I as spatial autocorrelation index performing on residuals of global, mixed models proved the reliability of the proposed model over the traditional one. The spatial model indicated an improvement in model performance using goodness-of-fit criteria with the coefficient of determination varying from 0.84–0.95 compared with 0.76 and 0.6 in the conventional model. The results of such analysis can provide descriptive and predictive tools at the planning-level for decision-makers.