Evolving Technologies Shaping Public Transit
Why this work is in the frame
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Bibliographic record
Abstract
The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. \n\nAlthough studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. \n\nA mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. \n\nThe results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it