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Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts.

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Summary
This summary is machine-generated.

This study uses machine learning to identify transit deserts, finding that density and design are key factors. Solutions involve reducing density and increasing green spaces, with a focus on gender-specific transit needs.

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Area of Science:

  • Urban Planning
  • Transportation Science
  • Data Science

Background:

  • Transit deserts, areas with insufficient public transport, disproportionately affect certain demographics.
  • Understanding the factors contributing to transit deserts is crucial for equitable urban development.

Purpose of the Study:

  • To identify factors contributing to transit deserts using a machine learning framework.
  • To propose actionable solutions for transforming transit deserts into transit oases.
  • To analyze transit needs, considering gender disparities during peak hours.

Main Methods:

  • A multi-class supervised machine learning framework was employed, evaluating Support Vector Machine, Decision Tree, Random Forest, and K-nearest Neighbor algorithms.
  • The Random Forest model was selected, enhanced by Diverse Counterfactual Explanation and SHapley Additive Explanation for in-depth analysis.
  • Feature importance was ranked, revealing density, design, transit distance, built environment diversity, and sociodemographics as significant factors.

Main Results:

  • Key factors contributing to transit deserts include population density, transit accessibility, and urban design.
  • Diverse Counterfactual Explanation suggests reducing population density and increasing green spaces can alleviate transit deserts.
  • SHapley Additive Explanation revealed differential impacts of features across various transit deserts, highlighting gender-specific transit needs, particularly for women.

Conclusions:

  • Transit desert identification and solutions are influenced by data aggregation and demographic separation, especially by gender.
  • Addressing transit deserts requires prioritizing disadvantaged groups and improving transit design and accessibility.
  • Machine learning models, interactive dashboards, and participatory planning can advance equitable transit solutions.