Using structural equations modelling to unravel the influence of land use pattern on travel behaviour of urban adult workers of puget sound region

Since the 1990s a great number of studies attempted to unravel the ways in which land use patterns and urban configuration influence travel behaviour. Since then important methodological advances have been made, namely the wider use of utility based models and the introduction of activities based frameworks, just to name a few. Econometric and statistical advances also enabled the account of more complex correlation structures thus eliminating many biases in the assessment of land use and its reciprocal relationship with travel behaviour. This paper addresses the relations between travel behaviour and land use patterns using a Structural Equations Modelling (SEM) framework. SEM is a multi equation technique which is particularly suited for the study of complex relations, since it allows modelling the effects of land use patterns on travel behaviour while controlling for self selection bias and effects among many endogenous variables. The proposed model structure was developed earlier in a model built for Lisbon (Abreu e Silva, Golob and Goulias, 2006), which concluded for the existence of significant effects of land use patterns in travel behaviour. In that study land use influences travel behaviour in a different way when one considers land use at the home place and the work place separately but in the same model system. This paper continues that research project which aims to compare results from different cities in North America and Europe, using the same modelling framework and comparable databases. The travel behaviour variables included in the model are multidimensional and comprehend both short term and long term mobility decisions. Regarding long term decisions the model includes variables like home location, car ownership levels and transit pass ownership. On the shorter term decisions the variables describe the quantities of mobility by mode (car, transit and soft modes), both in terms of total kilometres travelled and number of trips. The model also includes a trip scheduling variable, which is the total time spent between the first and last trips. The modelled land use variables measure the levels of urban intensity and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants, the transport supply levels, transit and road infrastructures, and accessibility ratios. The land use patterns are described both at the residence and employment zones of each individual included in the model. This plethora of variables is reduced to a more manageable number of variables using a factor analysis technique. In order to explicitly account for self selection bias the land use variables are explicitly modelled as functions of socioeconomic attributes of individuals and their households. The model results are discussed and compared with the results obtained for Lisbon. Methodological findings are also discussed for the continuation of this study as well as guidelines are presented for other comparative studies in Europe. For the covering abstract see ITRD E137145.