Trends and Advances in Urban Logistics Research: A Systematic Literature Review
Bibliographic record
Abstract
It is important to establish appropriate performance indicators so that decision‐makers can better determine the best alternatives for sustainable urban freight distribution systems. This literature about urban logistics and routing problems is structured and analyzed through a systematic literature review of a total of 201 papers from 2002 to 2023. Three main axes were considered: problem modeling and solution approaches, multimodal transportation, and indicators to assess the performance and sustainability of the distribution networks. There is a growing trend of research on this topic. Indeed, the paper highlighted the academic interest in the analysis of case studies to test the scenarios and network configurations and proposed solution approaches, as well as the adoption of greener transportation modes. To the best of our knowledge, no previous studies have analyzed the literature from the thematic lines proposed in this review, especially those that refer to performance indicators to assess both the freight distribution networks and the transportation modes considered. Advancing stochastic modeling, expanding case studies to underrepresented regions, integrating AI‐driven multimodal logistics, and developing social impact indicators are key research directions to enhance the sustainability and efficiency of urban logistics. This review provides a structured foundation for future research by identifying gaps in the literature and offering a thematic roadmap to advance the study and implement sustainable urban logistics solutions. In addition, its findings can assist decision‐makers and logistics planners in evaluating current practices, identifying opportunities for improvement, and supporting the development of more sustainable and efficient distribution strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".