Towards More Sustainable Cities: Tools and Policies for Urban Goods Movements
Bibliographic record
Abstract
Although urban freight transportation is crucial to address societal demands, it also has a major negative impact on the environment, the economy, and society. Then, the growing interest in promoting more sustainable and liveable cities is pushing to point out more in depth the role of urban goods movements within the planning process, as well as city administrators to implement new sustainable city logistics actions/policies/measures. Therefore, after a brief overview, the study presents more advanced techniques (models and methodologies) to help in the assessment of planning scenario. This paper concentrates on the importance of urban freight transport and logistics. Then, technical and logistics actions are outlined, and future last‐mile delivery challenges are discussed. The main objective is to support urban planners, on a strategic and tactical scale, in obtaining an overview of urban freight transport systems that point out the challenges for implementing sustainable city logistics scenarios. It is also of interest to technicians because they can identify the most suitable methodologies, as well as the features that they need for selecting and assessing ex ante the effects of city logistics measures. This work is useful for researchers in various sectors because it allows them to formalise and then to solve the problems for simulating the complex system of urban goods movements where there are different actors with own interests that are conflicting.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".