City Logistics : Chapter 8. In : The Sage Handbook of Transport Studies
Bibliographic record
Abstract
This chapter sheds light on urban freight transportation and city logistics. It identifies the key issues and challenges in the supply and shipment of goods to and from large urban areas from Europe, Japan, North America, as well as emerging and developing countries. A comprehensive review of data is presented, showing that urban freight statistics do exist in many cities but data collections are made in a piecemeal approach and with very different methods, making comparisons difficult. Overall, urban freight has become crucial to modern urban economies, as customized and more frequent deliveries are required by stores, businesses and households. The chapter presents issues such as trucks’ environmental impacts, goods movement’s inefficiencies, and 'logistics sprawl', i.e. the location of warehouses in suburban areas. Strategies and policies are presented, showing the emerging field of 'city logistics', i.e. innovative projects for clean and energy efficient urban deliveries. Local policies from cities around the world are presented and compared, showing the difficulties for many local governments to manage and regulate urban goods movements, whose drivers are global supply chains and changing consumer demands. Some success stories are presented such as London’s Low Emission Zone, Motomachi Urban Consolidation Center, the Clean Truck Program in Los Angeles ports.
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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 | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.027 |
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".