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6. Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?

Author

Wang, Qingyi, Wang, Shenhao, Zheng, Yunhan, Lin, Hongzhou, Zhang, Xiaohu, Zhao, Jinhua, and Walker, Joan

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Economics - General Economics
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.

Published
2023
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20. Dissolving the Segmentation of a Shared Mobility Market: A Framework and Four Market Structure Designs

Author

Guo, Xiaotong, Qu, Ao, Zhang, Hongmou, Noursalehi, Peyman, and Zhao, Jinhua

Subjects
Mathematics - Optimization and Control
Abstract

In the governance of the shared mobility market of a city or of a metropolitan area, there are two conflicting principles: 1) the healthy competition between multiple platforms, such as between Uber and Lyft in the United States, and 2) economies of network scale, which leads to higher chances for trips to be matched, and thus higher operation efficiency, but which also implies monopoly. The current shared mobility markets, as observed in different cities in the world, are either monopolistic, or largely segmented by multiple platforms, the latter with significant efficiency loss. How to keep the competition between platforms, but to reduce the efficiency loss due to segmentation with new market designs is the focus of this paper. We first propose a theoretical framework of shared mobility market segmentation and then propose four market structure designs thereupon. The framework and four designs are first discussed as an abstract model, without losing generality, thus not constrained to any specific city. High-level perspectives and detailed mechanisms for each proposed market structure are both examined. Then, to assess the real-world performance of these market structure designs, we used a ride-sharing simulator with real-world ride-hailing trip data from New York City to simulate. The proposed market designs can reduce the total vehicle-miles traveled (VMT) by 6\% while serving more customers with 8.4\% fewer total number of trips. In the meantime, customers receive better services with on-average 5.4\% shorter waiting time. At the end of the paper, the feasibility of implementation for each proposed market structure is discussed., Comment: 31 pages, 13 figures, 2 table

Published
2022
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