Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis
Abstract
Efforts to address equitable access in public transit planning have gained momentum, spurred by incentives for Metropolitan Planning Organizations (MPOs) and State Departments of Transportation (DOTs). However, traditional strategies often fall short of meeting the needs of disadvantaged communities, particularly in underserved areas. This study presents a pioneering methodology leveraging Genetic Algorithms (GA) and Geographic Information Systems (GIS) to optimize bus stop placement, aiming to enhance equitable access in public transit systems. Focusing on Route 16 of Sun Metro in El Paso, Texas—a critical feeder route linking the Westside Transit Center to Upper Valley neighborhoods—the research commences with a comprehensive analysis of demographic data and existing transit conditions to pinpoint disparities and accessibility challenges. By harnessing GA and GIS, the study proposes solutions tailored to equity factors, resulting in notable improvements in accessibility metrics. The research underscores the imperative of modernizing evaluation methodologies and integrating emerging technologies. Despite encountering challenges such as data availability constraints, computational demands, and the dynamic nature of urban environments, the study advocates for developing adaptive models. This research contributes significantly to advancing equitable transit systems and practices, offering valuable insights and a replicable methodology to enhance accessibility and equity in public transportation networks.
Subject Area
Civil engineering|Geographic information science|Transportation
Recommended Citation
Del Rio Reyes, Emiliano, "Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis" (2024). ETD Collection for University of Texas, El Paso. AAI31298259.
https://scholarworks.utep.edu/dissertations/AAI31298259