SUMMARY In this paper, the dependence of correlations between spectral accelerations at multiple periods on magnitude (M) and distance (R) has been investigated. For this purpose, a relatively large dataset of ground motion records (GMRs), containing 1551 records with a wide range of seismic characteristics, was selected. It is shown that the difference in the correlation coefficient is statistically meaningful when the general GMR dataset is divided into two subsets based on an arbitrary M or R. The observed difference is more meaningful in the case of magnitude when compared with distance. The general dataset of GMRs was then divided into four separate subsets based on optimum values of M and R, so that the four obtained subsets were given the greatest dissimilarity in terms of the correlation coefficients. The correlation coefficients between spectral accelerations at multiple periods were calculated in the case of the four subsets and compared with the available correlations in the literature. The conditional mean spectrum was also calculated by means of the conventional correlation coefficients, as well as by using the proposed M and R dependent correlation coefficients. The results show that, despite the commonly available findings in the literature, this dependence is significant and should not be neglected in the conditional spectra calculation process. Copyright © 2013 John Wiley & Sons, Ltd.

No abstract

No abstract

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.