The Delivery Time Performance Study of a Novel Collaborative Delivery System Integrating Drones and Ground Public Transit for Urban Last‐Mile Delivery
Bibliographic record
Abstract
With the rapid expansion of e‐commerce, effectively managing last‐mile delivery while satisfying consumer demands for timeliness and convenience has become increasingly imperative. Traditional delivery systems, which predominantly depend on human labor and freight vehicles, encounter significant challenges, including traffic congestion, escalating labor expenses, and environmental degradation. The advancements in drone technology present a promising solution for the logistics domain. This study proposes a novel collaborative delivery system that integrates drones with ground public transit, and aims to assess the delivery time performance of this system. Within this system, drones are responsible for direct parcel delivery to customers, while ground public transportation vehicles handle the transportation of drones and parcels between designated stops. The study develops a delivery strategy and establishes an operational framework to attain the delivery time for each customer. Subsequently, this study assesses the delivery time performance of this delivery system by introducing relevant metrics. Numerical experiments conducted across three distinct scenarios validate the proposed methodology, demonstrating the system’s potential in urban last‐mile delivery. The findings indicate that the collaborative delivery system exhibits good performance, especially in multi‐parcel delivery mode compared to single‐parcel delivery mode. Moreover, sensitivity analyses are performed to explore the effects of departure intervals and dwell time and obtain valuable insights on system operations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".