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<p>Transportation is crucial for accessing essential destinations such as jobs. Lack of transportation access can increase economic polarization, health disparities, and food insecurity. This research develops an optimization model that maximizes a novel accessibility index by optimizing existing transit route headways. The proposed accessibility index highlights areas of low access to key destinations and high poverty density. Application of the model and scenario discovery are demonstrated through a case study of Amherst, Massachusetts, for access to healthcare and social assistance jobs. Since there is no explainable mapping between headway and access in the literature, an XGBoost regression model is used to estimate the impact of transit headway on access. Results are presented through scenario discovery and show that multiple combinations of optimal headways can substantially increase access to healthcare and social assistance jobs in low-income areas. The proposed optimization model accounts for social equity and is flexible in that it can be expanded to incorporate multiple destination types (e.g., education and food) and weights for the various destination types to address the specific needs of an area of interest.</p>

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A Scenario Discovery Approach to Transit Network Optimization for Improved Access in Areas of Persistent Poverty

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Efthymia Kostopoulou,
  • Tolu Oke,
  • Jimi Oke,
  • Eleni Christofa

摘要

Transportation is crucial for accessing essential destinations such as jobs. Lack of transportation access can increase economic polarization, health disparities, and food insecurity. This research develops an optimization model that maximizes a novel accessibility index by optimizing existing transit route headways. The proposed accessibility index highlights areas of low access to key destinations and high poverty density. Application of the model and scenario discovery are demonstrated through a case study of Amherst, Massachusetts, for access to healthcare and social assistance jobs. Since there is no explainable mapping between headway and access in the literature, an XGBoost regression model is used to estimate the impact of transit headway on access. Results are presented through scenario discovery and show that multiple combinations of optimal headways can substantially increase access to healthcare and social assistance jobs in low-income areas. The proposed optimization model accounts for social equity and is flexible in that it can be expanded to incorporate multiple destination types (e.g., education and food) and weights for the various destination types to address the specific needs of an area of interest.