On-Demand Public Transit Systems: Demand Analysis, Network Modelling, and Sustainability Evaluation
Bibliographic record
Abstract
In recent years, with the rapid advancements in information and communication technology, several on-demand public transit (ODT) systems have emerged as innovative solutions for low-density areas. The dissertation explores the network designs, develops demand models, and analyzes the sustainability of ODTs. In particular, the dissertation considers the following key research questions: 1) What are the spatio-temporal patterns of demand for ODT? 2) What are the main factors affecting the demand for ODT? 3) What are the main factors affecting user preference between fixed-route transit (FRT) and ODT? 4) How did the COVID-19 pandemic affect the demand for ODT? Do the impacts vary between small and large urban areas? 5) When and where is each ODT network design most efficient and sustainable? To address these research questions, this dissertation utilizes actual ODT operational data from the City of Belleville and the Town of Innisfil, Ontario. The dissertation is based on five articles introduced in Chapters 3 to 7. Chapter 3 provides an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin-destination patterns of dedicated fleet ODT services, based on data collected from Belleville. Chapter 4 introduces data-driven models for trip production and distribution for dedicated fleet ODTs using data collected from Belleville. In Chapter 5, hybrid choice models are developed to explain the service preference of ODT users among the FRT and ODT services. The models are estimated using a rich dataset that combines the actual level of service attributes obtained from Belleville’s ODT service and self-reported usage behavior obtained from a revealed preference survey of the ODT users. Chapter 6 presents an in-depth analysis of the impact of the COVID-19 pandemic on the demand for crowdsourced ODTs using data from Innisfil, Ontario, and a detailed comparison with the City of Chicago, Illinois. Chapter 7 provides a micro-simulation model calibrated on the crowdsourced ODT data from Innisfil, Ontario, to evaluate the sustainability of several ODT designs. The analyses and the models developed in this dissertation will assist transit agencies in delivering more convenient, attractive, cost-efficient, and sustainable ODT services in low-density settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".