Analyzing Fleet Efficiency and Passenger Delay in Demand‐Responsive Transit: A Dual‐Model Approach With CVRPTW and TAMOS
Bibliographic record
Abstract
This study proposes an integrated framework that combines real‐time simulation with offline optimization to evaluate and enhance the operational performance of demand‐responsive transit (DRT) systems. Using the Kalamazoo Metro DRT as a case study, the Transportation Analysis and Mobility Optimization System (TAMOS) is employed to replicate dynamic booking behavior and vehicle dispatch logic. These real‐time operations are benchmarked against a static capacitated vehicle routing problem with time windows (CVRPTW), solved using Google OR‐Tools (v9.6) with the PARALLEL_CHEAPEST_INSERTION strategy to minimize fleet mileage while respecting vehicle capacity and time window constraints. Results show that the current fleet of 41 vehicles achieves a 74% service rate with an average pickup delay of 19.6 min. In contrast, the optimized CVRPTW solution fulfills 100% of trip requests with only 22 vehicles, assuming a relaxed pickup delay of 10 min. However, reducing the allowable delay to 5 min lowers trip feasibility to 65%, underscoring the operational sensitivity to temporal thresholds. The dual‐model approach illustrates how integrating real‐time simulation with optimization can quantify trade‐offs between service quality and operational efficiency. Additionally, the study introduces several enhancements to the OR‐Tools solver, including dynamic time windows, passenger‐level detour constraints, and integration with the Google Maps API for real‐world travel time matrices, improving model realism and decision relevance. The proposed framework is adaptable to various urban contexts and scalable across international settings, offering practical guidance for transit agencies in fleet sizing, delay tolerance, and service design under dynamic demand conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".