A public transit network optimization model for equitable access to social services

This repository contains raw data generated for use in Rumpf and Kaul 2021 (referenced below), and includes sets of files for defining transit networks as well as raw data tables generated by the solution algorithm. See the included README for an in-depth explanation of each data set.

The main focus of the study was to develop and test a public transit design model for improving equity of access to social services throughout a city. The main case study was based on the Chicago Transit Authority network, with the goal of making minor alterations to the bus fleet assignments in order to improve equity of access to primary health care facilities. A small-scale artificial network was also generated for use in sensitivity analysis.

The data sets in this repository include network files used by our hybrid tabu search/simulated annealing solution algorithm in order to solve the social access maximization problem (see the GitHub repository referenced below). Also included are the raw data tables from the CTA and artificial network trial sets.

The results of this study indicate that it is indeed possible to significantly increase social service access levels in the least advantaged areas of a community while still guaranteeing that transit service remains near its current level. While improving the access in some areas does require that other areas lose some access, the gains are generally much greater than the losses. Moreover, the losses tend to occur in the areas that already enjoy the greatest levels of access, with the net result being a more even distribution of accessibility levels throughout the city.

本仓库包含了为Rumpf和Kaul（2021年）所著文献生成的基础数据，其中包含用于定义交通网络的一组文件以及由解决方案算法生成的原始数据表。详见附带的README文件，以深入了解每个数据集的详细信息。

本研究的主要焦点在于开发并测试一种旨在提升城市范围内社会服务可及性公平性的公共交通设计模型。主要案例研究基于芝加哥交通管理局的网络，旨在通过微调公交车队分配，以改善对初级卫生保健设施的公平可及性。同时，还生成了一小规模的模拟网络，用于敏感性分析。

本仓库中的数据集包括用于解决社会可及性最大化问题的混合禁忌搜索/模拟退火解决方案算法所使用的网络文件。此外，还包括了CTA和模拟网络的原始数据表。

本研究的成果表明，确实有可能在保证公共交通服务接近当前水平的同时，显著提高社区中弱势地区的公共服务可及性水平。虽然提高某些地区的可及性可能需要其他地区牺牲部分可及性，但总体上，所得利益远大于所失。此外，损失往往发生在原本可及性水平最高的地区，从而使得城市内可及性水平的分布更加均衡。