Application of network generalized extreme value model to estimate residential location and commute mode choice

Relationship between jobs-housing spatial distribution and travel to work were studied from a microscopic perspective. Based on random utility maximization theory, factors depicting the individual and socio-economic characteristics and attribute of travel and land use are defined as exogenous variables, meanwhile, the model choice sets are the combination of residential location choice and commute mode choice subsets. Discrete choice model specified as Network Generalised Extreme Value(NetworkGEV) is employed to investigate the joint decisions of where to live and how to get to workplace which attempts to describe the change of aggravated traffic congestion and discover the potential change caused by residential relocation and travel mode shift under different employment location patterns. This model is estimated in Biogeme, and the direct and cross elasticities are calculated to analyze the change of alternatives probability brought by factors variation. The results reveal that the model yields plausible estimation of exogenous variables in the joint residential location and travel mode choice context. Compared with suburban commuters, commuters in CBD are more sensitive to the increase of travel time and intend to change travel mode and residential location thus trading off the disutility of traffic congestion.