An integrated agent-based model of travel demand and package deliveries
Bibliographic record
Abstract
With the increased shift from in-store shopping to e-commerce, we can expect the share of delivery vehicles in cities to rise as well. This puts great pressure on cities and surrounding areas as emissions rise and space becomes scarce. Because the last leg of the delivery chain is so costly, there have been many studies on how the last-mile issue can be diminished. However, most studies only cover deliveries independently from the orders. This paper presents a modelling approach that integrates travel demand, package orders and subsequent deliveries within the same framework. One of the benefits of the integrated modelling approach is that the model can evaluate the agents’ locations at all times during the simulation. This allows us to model different states of delivery on a microscopic level. We have applied the model to Karlsruhe, Germany with a synthetic population of just over 300.000 agents. The results show that the model simulates both package orders and the subsequent delivery tours realistically. The model framework allows for detailed analysis of delivery states and success rates and can be used for scenario analysis of different delivery methods and changes in online shopping behaviour.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".