Comparison of Methods for Defining Geographical Connectivity for Variables of Trip Generation Models

Abstract

The four-step procedure for travel demand modeling is ubiquitously used in the United States and elsewhere due to institutional and financial requirements. Trip generation analysis, which is the first step, is based on traffic analysis zones which are geographical units. Therefore, the zonal trip generation totals are observations measured at different geographical locations. Spatial distribution of the observations limits the methods that can be applied in analyzing the data and influences the final conclusions that can be reached. Most past efforts of incorporating spatial effects in trip generation models have been using contiguity of the zones as the major criterion for defining spatial relationship of the observations. In this study, we tested for the presence of spatial autocorrelation in both trip attraction and trip production variables. We found significant spatial autocorrelation in trip attraction variables but insignificant spatial autocorrelation in trip production variables for the data collected from the Las Vegas valley. We evaluated four alternative methods for defining geographical connectivity: (1) contiguity, (2) separation, (3) combined contiguity and separation, and (4) economic linkage (accessibility measure). Comparison of the trip attraction model indicated that the model estimated using geographical connectivity matrix with elements defined by separation (distance between centroids of the zones) was the best fitted. A further comparison indicated that this model outperformed the one without spatial variables by minimizing deviation of the modeled trips from observed trips. We recommend using separation between the zones to define spatial connectivity as opposed to contiguity only. Contiguity only excludes zones that may have an impact on the observation under consideration.