Joint analysis of urban shopping destination and travel mode choice accounting for potential spatial correlation between alternatives

In recent years, there have been important developments in the joint analysis of the travel behavior based on discrete choice models as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class. The objective of this work is to describe the simultaneous choice of shopping destination and travel-to-shop mode in downtown area by making use of the cross-nested logit (CNL) structure that allows for potential spatial correlation. The analysis uses data collected in the downtown areas of Maryland-Washington, D.C. region for shopping trips, considering household, individual, land use, and travel-related characteristics. The estimation results show that the dissimilarity parameter in the CNL model is 0.37 and significant at the 95% level, indicating that the alternatives have high spatial correlation for the short shopping distance. The results of analysis reveal detailed significant influences on travel behavior of joint choice shopping destination and travel mode. Moreover, a Monte Carlo simulation for a group of scenarios arising from transportation policies and parking fees in downtown area, was undertaken to examine the impact of a change in car travel cost on the shopping destination and travel mode switching. These findings have important implications for transportation demand management and urban planning.