Date of Award
8-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Industrial Engineering
Committee Chair/Advisor
Emily Tucker
Committee Member
Thomas Sharkey
Committee Member
Qi Luo
Abstract
Urban parks and green-spaces significantly enhance community well-being by improving physical health, mental wellness, and environmental quality. Given these extensive benefits, ensuring fair and widespread access to urban parks represents a critical priority in urban planning. Despite the advantages of parks, optimizing their location poses unique and complex challenges distinct from traditional facility location problems, such as those involving emergency services or schools. The core distinction arises from the decentralized nature of residents’ park selection behaviors. Unlike centralized allocations typically managed by public administrators, park usage decisions are driven by individual preferences and behaviors. This decentralized decision-making introduces two additional challenges: ensuring fairness and addressing the uncertainty derived from varied and unpredictable individual preferences. Consequently, accurately capturing and modeling decentralized resident behaviors is central to effectively addressing these distinctive challenges. This thesis mainly contributes to existing literature by developing a mathematical model that integrates decentralized resident behavior into park location optimization decisions. To represent this behavior, the model employs an equilibrium allocation framework to capture endogenous factors, particularly the impact of park crowding level. Crowding level significantly influences park attractiveness, as increasing usage decreases comfort and convenience, thus reducing residents’ valuations. Incorporating crowding as an endogenous factor improves the realism between residents’ choices and park usage levels. The developed mathematical model seeks to maximize fairness in residents’ park access, targeting the improvement of conditions for the most underserved residents. Specifically, the objective function is structured to maximize the minimal perceived park valuation among all residents, ensuring that resource allocation addresses areas of greatest unfairness. Through this, the model actively reduces disparities in park access, enhancing overall community satisfaction.
Computational experiments were conducted using real-world data from the city of Asheville, North Carolina. Sensitivity analyses systematically evaluated how critical input parameters could impact model decisions and resident benefits outcomes. In summary, this thesis advances the field of park location optimization by addressing decentralization, fairness, and uncertainty through a equilibrium-based mathematical modeling frame- work. By incorporating user-choice behaviors and conducting analyses, the proposed approach provide decision-makers and urban planners with practical tools and critical insights to enhance fairly park access, improve community well-being, and optimize resource allocation strategies.
Recommended Citation
Liu, Lu, "Optimizing Park Locations While Considering Resident Behavior" (2025). All Theses. 4612.
https://open.clemson.edu/all_theses/4612