Bibliographic record
Abstract
The rapid expansion of low-density suburban areas in North America has led to new travel patterns that require transit services to be more flexible. Flex-Route transit service, which combines fixed-route transit service with elements of demand-responsive transit service, has emerged as a viable transit option to address the travel needs of the residents of these areas. Existing literature in this field, however, is limited and lacks any comprehensive analysis of Flex-Route planning, design and scheduling. This research aims at exploring Flex-Route transit service to provide detailed guidelines for the planning and design of the service, as well as developing a new scheduling system for this type of unique service. Accordingly, the objectives of this research are: assessing the practicality of Flex-Route transit service in serving low-density suburban areas; identifying essential Flex-Route planning steps and design parameters; determining the feasibility and cost of replacing fixed-route transit with Flex-Route service; and developing a Flex-Route-specific dynamic scheduling system that relies on recent developments in computer and communication technologies. In this regard, we develop an analytical model that addresses several design parameters and provide a detailed analysis that includes, among other parameters, finding optimal values for Flex-Route service area and slack time. Furthermore, the analytical model includes a feasibility and cost analysis that estimates the cost incurred by several stakeholders if Flex-Route service is chosen to replace fixed-route service. The core of the scheduling system is a new developed algorithm – the Constrained-Insertion Algorithm- that exploits the powerful search techniques of Constraint Programming. The scheduling system can handle the daily operations of Flex-Route transit services; it accepts daily (or dynamic) inputs and, in minimal time, produces very cost-effective and reliable schedules. Moreover, the scheduling system has the ability to be used as simulation tool to allow transit operators to assess the feasibility and performance of proposed Flex-Route transit services before implementation. The applicability of the analytical model as well as the performance of the scheduling system were subsequently evaluated and validated through process that included testing on a case study in the City of Oakville, Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".