Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?
Name
2303.04204v2.pdf
Description
Accepted version
Size
51.19 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Wang, Qingyi
Wang, Shenhao
Zheng, Yunhan
Lin, Hongzhou
Zhang, Xiaohu
Zhao, Jinhua
Walker, Joan
Date Issued
January 2024
Journal
Transportation Research Part B: Methodological
Publisher
Elsevier BV
Citation
Wang, Qingyi, Wang, Shenhao, Zheng, Yunhan, Lin, Hongzhou, Zhang, Xiaohu et al. 2024. "Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?." Transportation Research Part B: Methodological, 179.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.trb.2023.102869
