This paper presents a reference-dependent model for residential location choice. The key contribution of the model is its incorporation of reference dependence that explicitly recognizes the role of the status quo and captures asymmetric responses toward gains and losses in making location choice decisions. The study uses a retrospective residential search survey and a dwelling supply data set from the Toronto Real Estate Board in Ontario, Canada, to estimate the model at the elemental level of individual dwelling units. The study applies a mixed logit formulation that captures unobserved heterogeneity and avoids imposing independence of irrelevant alternatives restrictions on the choice probabilities. Several types of variables, including dwelling characteristics, land uses and other zonal attributes, accessibility measures, and household socio-demographics, are tested in the model. Although the current dwelling is assumed to be the reference point in framing evaluation of alternative dwellings, all gains and losses are measured by a comparison of current and prospective dwellings in the modeling framework. The results reveal that households prefer gains in the number of bedrooms, but they are more sensitive to the equal amounts of losses. A similar loss aversion attitude is observed for the percentage of open areas and unemployment rate. It is also found that decision makers are sensitive only to the losses for the level of service attributes. The reference-dependent model performs better than a conventional location choice model in terms of model fit and provides important behavioral insights.

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This study proposes a framework to analyze public discourse in Twitter to understand the impacts of COVID-19 on transport modes and mobility behavior. It also identifies reopening challenges and potential reopening strategies that are discussed by the public. First, the study collects 15,776 tweets that relate to personal opinions on transportation services posted between May 15 and June 15, 2020. Next, it applies text mining and topic modeling techniques to the tweets to determine the prominent themes, terms, and topics in those discussions to understand public feelings, behavior, and broader sentiments about the changes brought about by COVID-19 on transportation systems. Results reveal that people are avoiding public transport and shifting to using private car, bicycle, or walking. Bicycle sales have increased remarkably but car sales have declined. Cycling and walking, telecommuting, and online schools are identified as possible solutions to COVID-19 mobility problems and to reduce car usage with an aim to tackle traffic congestion in the post-pandemic world. People appreciated government decisions for funding allocation to public transport, and asked for the reshaping, restoring, and safe reopening of transit systems. Protecting transit workers, riders, shop customers and staff, and office employees is identified as a crucial reopening challenge, whereas mask wearing, phased reopening, and social distancing are proposed as effective reopening strategies. This framework can be used as a tool by decision makers to enable a holistic understanding of public opinions on transportation services during COVID-19 and formulate policies for a safe reopening.

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