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

TL;DR

This paper introduces deep hybrid models that integrate urban road network structures with sociodemographic data using graph embedding, significantly improving city-level transportation mode share predictions while maintaining interpretability.

Contribution

The study presents a novel deep hybrid modeling approach that directly incorporates urban road networks into mode share analysis, enhancing prediction accuracy and interpretability.

Findings

DHM improves mode share prediction accuracy by over 20%.

Graph embedding enhances representation of urban structures.

Models provide valuable spatial insights into sociodemographic influences.

Abstract

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.