# Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network

arXiv: https://arxiv.org/abs/2405.14079 · HTML: https://mlsanity.com/paper/2405.14079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 88 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 26 of 26)

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- [Inductive Graph Neural Networks for Spatiotemporal Kriging](https://mlsanity.com/paper/2006.07527/) (2020)
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- [Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks](https://mlsanity.com/paper/2303.04040/) (2023)
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- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [Joint embedding of structure and features via graph convolutional networks](https://mlsanity.com/paper/1905.08636/) (2019)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood](https://mlsanity.com/paper/1705.02198/) (2017)
- [Latent Space Model for Multi-Modal Social Data](https://mlsanity.com/paper/1510.05318/) (2015)

## Abstract (arXiv)

Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typical example is the prediction of communities' travel mode share by accounting for their sociodemographics like age, income, etc., and travel modes' attributes (e.g. travel cost and time). However, there exist only limited efforts in integrating the structure of the urban built environment, e.g., road networks, into the mode share models to capture the impacts of the built environment. This task usually requires manual feature engineering or prior knowledge of the urban design features. In this study, we propose deep hybrid models (DHM), which directly combine road networks and sociodemographic features as inputs for travel mode share analysis. Using graph embedding (GE) techniques, we enhance travel demand models with a more powerful representation of urban structures. In experiments of mode share prediction in Chicago, results demonstrate that DHM can provide valuable spatial insights into the sociodemographic structure, improving the performance of travel demand models in estimating different mode shares at the city level. Specifically, DHM improves the results by more than 20% while retaining the interpretation power of the choice models, demonstrating its superiority in interpretability, prediction accuracy, and geographical insights.