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.
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