Electric-Car-Sharing in Urban Logistics – The Analysis of Implementation and Maintenance
Bibliographic record
Abstract
The implementation of the sustainable transport policy in cities aims at finding various solutions that support the use of ‘clean energy’ in urban logistics. One of current global sustainable transport trends involves various measures related to electromobility. An example of electromobility in urban logistics is the e-car-sharing, or a short-term electric car rental. Since electric car-sharing is rather new to the transport market, operators and cities and metropolises may face various difficulties while implementing such services. Based on still operating and discontinued e-car-sharing systems, the authors analysed strengths and weaknesses of those systems with the focus on implementation and maintenance issues. The goal of the article is to determine strengths and challenges for e-car-sharing in urban logistics. The article is designed to assist stakeholders interested in the implementation of e-car-sharing. The analysis was provided under the international research project of ‘Electric travelling platform to support the implementation of electromobility in Smart Cities based on ICT applications’ funded from the National Research and Development Centre as a part of the ERA-NET CoFund Electric Mobility Europe Programme.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".