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The objective of this proposal is to explore the role of visual information in determining the users’ subjective valuation of multidimensional trip attributes that are relevant in decision-making, but are neglected in standard travel demand models. The subjective valuation estimates that will be produced in this project –and the methodology proposed to produce those estimates– are relevant in assessing welfare improvements that come from a more efficient use of public space devoted to transportation. In particular, a case study in New York City will be conducted to analyze the effect of using images to describe train and platform passenger congestion scenarios in discrete choice experiments to value subway crowding perceptions. Although the concept of congestion is usually associated with cars, transit congestion is a growing problem. In fact, the motivation for this project comes from the rising subway demand in NYC (1.8 billion trips in 2014), with overcrowding delays that have extended to non-peak hours and weekends (weekend overcrowding delays grew 141.2% in 2014-2015). In addition, quality of service of public transportation systems is difficult to measure and model, and hard to illustrate in standard preference surveys.