Comparative urban growth modeling based on multi-modal accessibility

Authors: Cong Cong*, University of Illinois at Urbana-Champaign, Yoonshin Kwak, University of Illinois at Urbana-Champaign, Brian Deal, University of Illinois at Urbana-Champaign
Topics: Urban and Regional Planning, Spatial Analysis & Modeling, Transportation Geography
Keywords: multi-modal transportation, urban growth, equity
Session Type: Virtual Paper
Day: 4/7/2021
Start / End Time: 4:40 PM / 5:55 PM
Room: Virtual 26
Presentation File: No File Uploaded


The role of land use management in supporting sustainable and equitable development policies has been widely acknowledged. A common planning challenge towards sustainability is to reduce growth-induced environmental degradation, but equitable access to environmental and social resource (e.g., employment, healthcare, food and green space) is also important in building socially and economically sustainable cities. One way to better understand the complex relationship between urban growth factors and growth-induced inequity at different geographic scales is to include different travel modes in evaluating human mobility. This paper attempt to use multiple travel modes and open-sourced geospatial data to explore the oftentimes nonlinear relationship between development patterns and accessibility attractors across geographic scales. The research questions addressed in this paper are: (1) what are the relationships between development patterns and accessibility attractors such as employment, transportation, and recreational opportunities when taking into consideration different travel modes and spatial scales, and (2) under what conditions urban growth promote both environmental and social sustainability when adding non-auto travel modes into the urban growth dynamic? We construct urban development models under two counterfactual scenarios – car-based (only driving), and car-free (consider four different travel modes – walking, biking, driving, and public transit, respectively). By comparing a series of relationships by modes, we estimate the probability of future developments under each individual scenarios and discusses implications for sustainable development policies.

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