Assessing impacts of the built environment on mobility: A joint choice model of travel mode and duration

Abstract

This paper introduces a joint choice model for travel mode and duration to quantify the mobility impacts of urban design changes on the built environment. The model is formulated as a Random Forest classifier that predicts the mode-duration probabilities of a given trip. A novel series of predictor features are proposed which measure the urban form, demographics, and service densities on different scales of the transportation network. Through a sensitivity analysis and a proof-of-concept case study, we find that a dense, mixed-use environment with good coverage of a multi-modal mobility network can significantly promote active transportation and public transit use. However, we also find that ultra-dense, centralized developments can lead to increased travel time and increased vehicle use in the urban periphery. Our modeling and analysis method provides a simplified and effective way to assess urban design and planning scenarios from different mobility perspectives and facilitates data-driven, mobility-aware urban design and planning that can help identify better solutions more quickly.

A clustering-based approach to quantifying socio-demographic impacts on urban mobility patterns

“…If a population needs to be synthesized using all socio-demographic features of interest (e.g., 14 features in this study), a high-dimensional contingency table (i.e., the complete distribution across all selected features) needs to be estimated using conventional methods like iterative proportional fitting procedure (Guo and Bhat, 2007). With the clustering presented in this study, each travel agent can be described by one single integer indicating the cluster membership, and a population can be described by a simple vector of cluster percentages (Yang et al, 2023). This can be a significant improvement from the perspective of model simplicity and efficiency.…”