Planning and transportation professionals are promoting a variety of sustainable travel alternatives, such as public transit usage, walking, and cycling, as affordable transportation options to counter the negative effects of widespread car use. In their traditional form, these alternative transport modes do not always offer the flexibility or convenience of the car; therefore, innovative solutions have been developed to allow active and public transport to compete better with the car. Shared bicycle systems have been adopted by a growing number of cities and regions throughout the world, yet little is known about the users of the systems and their motivations. A survey was conducted in Montreal, Quebec, Canada, in the summer of 2010 to determine the factors that encouraged individuals to use the system and the elements that influenced frequency of use. The factor found to have the greatest effect on the likelihood for use of a shared bicycle system was the proximity of home to docking stations. Ownership of a yearly shared bicycle membership was associated with cyclists riding shared bicycles 15 additional times per year. Respondents indicated that they valued the shared bicycle's trendy status and the role that it could play in bicycle theft prevention. The potential of shared bicycle systems can be maximized by increasing the number of docking stations in residential neighborhoods and by emphasizing the popularity of shared bicycles and theft prevention in advertising campaigns.

Residential mobility and relocation choice are essential parts of integrated transportation and land use models. These decision processes have been examined and modeled individually to a great extent but there remain gaps in the literature on the underlying behaviors that connect them. Households may partly base their decision to move from or stay at a current location on the price and quality of the available alternatives. Conversely, households that are on the market for a new location may evaluate housing choices relative to their previous residence. How and the degree to which these decisions relate to each other are, however, not completely understood. This research is intended to contribute to the body of empirical evidence that will help answer these questions. It is hypothesized that residential mobility and location choice are related household decisions that can be modeled together using a two-tier hierarchical structure. This paper presents a novel nested logit (NL) model with sampling of alternatives and a proposed procedure for sampling bias correction. The model was estimated using fullinformation maximum likelihood estimation methods. The results confirm the applicability of this NL model and support similar findings from other empirical studies in the residential mobility and location choice literature.

IntroductionGenerally defined as the ease of access between entities in different locations (Harris, 2001), the concept of accessibility reflects the possibilities for activities, such as employment and shopping, and has long been theorized as a principal determinant of residential choice behavior. The classic monocentric city model in urban economics held central the trade-off relationship between commuting and land costs in explaining household locations (Alonso, 1964;Muth, 1969).Research on the influence of accessibility on residential choice is extensive but the empirical results have been mixed. Recent reviews of this literature have found, in general, accessibility to have modestly positive explanatory variables (Blijie, 2005; Zondag and Pieters, 2005), but their impacts on residential choice behavior are secondary to and confounded by other household, dwelling, and neighborhood characteristics

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Ridesharing serves to mitigate pollution and congestion with minimal investment of public capital while also increasing the efficiency of the transportation system. This research addresses the gaps in the literature on the structure and formation of ridesharing by identifying individual, household, and physical-environment characteristics that correspond with an individual's choice to rideshare instead of drive alone. For a fuller understanding of ridesharing behavior, there first must be a better understanding of who is in the vehicle, not just how many. A distinction is made between intrahousehold (internal) and interhousehold (external) ridesharing. With the Vermont add-on sample of the 2009 National Household Travel Survey, a multinomial logit model and nested logit model were developed to examine the determinants of ridesharing. The analysis in this research stresses the importance of the way ridesharing behavior is extracted from survey data. Further, a new method for calculating household vehicle availability is presented; the method places less importance on drivers who are not full-time workers. Results indicate that employment density, longer distance traveled to work, and working in a small urban area have positive influences on the likelihood of ridesharing. Higher vehicle availability in the household, age, sex (male), and a longer time spent per trip on the journey to work were all found to influence the propensity to rideshare negatively. Cost of travel does not significantly affect ridesharing.