Demand Response Transit (DRT) services (e.g., dial-a-ride) play a crucial part in supporting transportation systems in small urban and more rural areas. However, the exploration of DRT trip patterns and accessibility, particularly the differences tied to demographics such as gender and age, remains an underdeveloped field. This study begins to fill this research gap by statistically and spatially analyzing real-world DRT trip data from Tennessee. DRT trip purposes were identified based on origin and destination land uses. Statistical analysis was conducted to evaluate DRT travel patterns for passengers of different demographic groups, particularly women and the elderly. Spatial analysis identified areas with higher DRT trip demand and limited DRT accessibility as potential essential destination deserts. The results show that women took more DRT trips across all purposes (e.g., Home-Healthcare, Home-Home, and Home-Leisure) except for Home-Work. About 36.8% of the DRT trips were made by the elderly, primarily for Home-Healthcare trips (67%), and they preferred shorter DRT trips. Spatial analysis revealed disparities in potential essential destination deserts for the elderly and females, as well as differences in possible deserts by trip purpose (e.g., healthcare-related trips). This study contributes to the literature by proposing a methodological framework for assessing DRT travel patterns and accessibility, which has been excluded from most of the prior literature on accessibility.

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. In this study, a new tool named GTFS2STN for converting GTFS data into the spatiotemporal networks is introduced. To analyze the travel time variability, it is important to transform a transit network to a spatiotemporal network to enable a comprehensive analysis of transit system accessibility. GTFS2STN is a new tool that converts General Transit Feed Specification (GTFS) data into spatiotemporal networks, addressing the lack of open-source solutions for transit analysis. The tool includes a web application that generates isochrone maps and calculates travel time variability between locations. Validation against Google Maps APIs shows that journey time (i.e., the summation of the transit time, walking time, and waiting time) differences in the Mean Absolute Percentage Error are typically within 12%. A before–after analysis shows that for the transit journey time in 2024 in Nashville, Tennessee, 8 out of 10 pivotal bus stops showed a significantly decreased journey time compared with the case of 2019. A further set of before–after analyses shows that although journey time between transit sites significantly dropped on May 2020 during COVID-19 emergencies, the journey time almost totally recovered to the before-COVID-19 level by November 2020. By supporting any valid GTFS schedule, GTFS2STN enables the analysis of historical and planned transit systems, making it valuable for long-term accessibility assessment and travel time variability studies.

Improving transit access for people in low-income communities is an important consideration for transit providers. However, there has only been a limited amount of research on the transit accessibility of affordable housing units. This paper aims to develop a method for evaluating the transit equity of existing affordable housing units and propose easy-to-implement modifications to local bus services to increase transit accessibility levels (assuming housing locations do not change in the short term). The proposed method has three steps, and it is applied to three cities in Tennessee with primarily bus-based transit systems. The first step measures the transit accessibility of specific affordable housing locations and citywide transit accessibility levels using a web-based platform built using open-source software. The second step evaluates the transit equity of affordable housing programs at the city level using Lorenz curves and Gini coefficients, and the transit equity of specific affordable housing locations by proposing a simple inequity index. The results reveal that the level of transit equity for affordable housing units differs across housing programs and cities. In the third step, an example of a modification to a local bus route is evaluated for one affordable housing location with a high inequity index to demonstrate the applicability of the method. The substantial increase in accessible jobs after modification, from 135 to 6,400, highlights the potential effectiveness of implementing short-term transit service changes to improve the accessibility of existing affordable housing locations. This three-step method primarily relies on open datasets that are also available for other regions in the U.S.A.