Towards Effective Integration of On-Demand Transit into Transit Systems: Developing Guiding Principles for Planning and Exploring the Determinants of Ridership and Trip Cancellations
Bibliographic record
Abstract
In response to evolving travel behaviour, increasing interest in mobility-on-demand, and rising appreciation of flexibility, on-demand transit (ODT) has emerged as an alternative or supplement to conventional transit service in various areas. ODT is a bus service that runs based on traveller’s requests without adhering to a schedule, unlike fixed bus routes. In recent years, transit agencies have shown an increased interest in running ODT and integrating it with existing transit services. With its increased popularity after the pandemic, along with a lack of planning principles and ridership prediction models of ODT, this thesis develops guiding principles based on best practices in literature and industry practice. Next, the thesis enhances our understanding of ODT trip making and associated travel patterns by developing novel ridership prediction models considering socioeconomic characteristics, travel behaviour, supply conditions, and external factors. Finally, the dissertation addresses the challenges of trip cancellations, focusing on risk-level analysis and prediction for improved operational efficiency. All the analysis and models are based on discrete trip data records, obtained from the Regional Municipality of Durham, in Southern Ontario. Key findings of the dissertation show that ODT service can operate in various forms with varying productivity and objectives, including replacing underperforming bus routes, attracting new ridership in low-density neighbourhoods, and providing minimum service when FRT is not financially efficient to operate. Furthermore, the data analysis of ODT trips showed that trip patterns and the level of service of ODT vary between urban and rural areas where the detour time, trip distance, and deadhead time are substantially longer in rural ODT zones. The ridership models showed that areas with lower income and a higher proportion of visible minorities are associated with higher ODT ridership, showing that ODT fills an essential equity gap in the transit network. Lastly, the cancellation models show that service zones in urban areas have a lower tendency for trip cancellations compared to rural areas. Moreover, increasing schedule deviation, walking time at pickup and drop-off, and early bookings increase the probability of trip cancellation, with schedule deviation having the highest effect among them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".