  Classical demand modeling analyzes travel behavior using only low-dimensional
numeric data (i.e. sociodemographics and travel attributes) but not
high-dimensional urban imagery. However, travel behavior depends on the factors
represented by both numeric data and urban imagery, thus necessitating a
synergetic framework to combine them. This study creates a theoretical
framework of deep hybrid models with a crossing structure consisting of a
mixing operator and a behavioral predictor, thus integrating the numeric and
imagery data into a latent space. Empirically, this framework is applied to
analyze travel mode choice using the MyDailyTravel Survey from Chicago as the
numeric inputs and the satellite images as the imagery inputs. We found that
deep hybrid models outperform both the traditional demand models and the recent
deep learning in predicting the aggregate and disaggregate travel behavior with
our supervision-as-mixing design. The latent space in deep hybrid models can be
interpreted, because it reveals meaningful spatial and social patterns. The
deep hybrid models can also generate new urban images that do not exist in
reality and interpret them with economic theory, such as computing substitution
patterns and social welfare changes. Overall, the deep hybrid models
demonstrate the complementarity between the low-dimensional numeric and
high-dimensional imagery data and between the traditional demand modeling and
recent deep learning. It generalizes the latent classes and variables in
classical hybrid demand models to a latent space, and leverages the
computational power of deep learning for imagery while retaining the economic
interpretability on the microeconomics foundation.
