Bibliographic record
Abstract
With advances in mobile technologies, social networks and global positioning (GPS) in the digital world, alternative mobility systems (taxis, carpool, demand-responsive services, peer-to-peer ridesharing, carsharing) have garnered interest from both public and private sectors as potential solutions to address last mile problem in public transit. Although there are number of models to optimize flexible or dynamic transit operations there has not been any methodology to evaluate equilibrium demand and effect on social welfare for these systems in an integrated supply-demand context. This study lays the groundwork for studying the equilibrium of these systems, and proposes an agent-based adjustment process to evaluate the properties of a stable sate as an agent-based stochastic user equilibrium (SUE). Four sets of experiments are conducted: (1) illustration with a simple 2-link network, (2) evaluation of a dynamic dial-a-ride policy, and (3 &4) illustration using real data from Oakville, Ontario & Manhattan, NY. The experiments demonstrate that the proposed model with multiple sample populations can generate an invariant distribution of demand and welfare effects and it can effectively be used to measure the effect of changes in flexible transport services operation policies on ridership. Moreover, this study also explores flexible transport services as two-sided markets, and extends the proposed agent-based day-to-day adjustment process to include day-to-day adjustment of the service operator(s) as a two-sided market. Additional computational experiments and a case study are conducted. Findings confirm the existence of thresholds from which network externalities cause two-sided and one-sided market equilibria to diverge. The Ramsey pricing criterion is used for social optimum to show that perfectly matched states from the proposed day-to-day process are equivalent to a social optimum. A case study using real data from Oakville, Ontario, as a first/last mile problem example demonstrates the sensitivity of the two-sided day-to-day model to operating policies.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".