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Using Genetic Algorithm and Geograph...

Del Rio Reyes, Emiliano.

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Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis./
Author:	Del Rio Reyes, Emiliano.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	70 p.
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Geographic information science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31298259
ISBN:	9798382747293

Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis.
Del Rio Reyes, Emiliano.

Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 70 p.

Source: Masters Abstracts International, Volume: 85-11.

Thesis (M.S.)--The University of Texas at El Paso, 2024.

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.

ISBN: 9798382747293Subjects--Topical Terms:

3432445
Geographic information science.
Subjects--Index Terms:

Metropolitan Planning Organizations

Using Genetic Algorithm and Geographic Information System for Equity-Driven Transportation Planning and Analysis.
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500 $a Advisor: Raheem, Adeeba A.
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520 $a 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.
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