Blockchain‐Enabled Car Sharing: Enhancing Reliability and Vehicle History Management
Bibliographic record
Abstract
ABSTRACT The rising expenses associated with car ownership have driven individuals to seek more affordable alternatives, such as car rentals. However, conventional car rental services often come with high costs due to leasing companies' overhead expenses. Consequently, car sharing has emerged as a popular and cost‐effective solution that reduces expenses and promotes eco‐friendliness by reducing the number of vehicles on the roads. Nonetheless, centralization and reliability remain persistent challenges in car‐sharing implementation. To address these issues, we propose a decentralized crowd car sharing and renting platform called CROWDCARLINK, leveraging blockchain technology's power. This innovative platform enables individuals and leasing companies to rent vehicles while securely recording each car's maintenance and lease history on the blockchain. Within CROWDCARLINK, garages are pivotal contributors, adding vehicle information in a reliable and immutable manner. By utilizing blockchain technology, our platform ensures transparency and fosters trust, effectively overcoming the limitations imposed by centralization. Our architectural design incorporates smart contracts, which help streamline processes and facilitate seamless transactions within the platform. To demonstrate the feasibility of our approach, we have developed a prototype utilizing a private Ethereum blockchain with Proof of Authority (PoA) consensus. We believe that the architectural design and the practical solution presented here will play an integral role in shaping the future of smart transportation. Our platform aims to benefit individuals and the environment by offering a cost‐effective and efficient solution, paving the way for a more sustainable and advanced transportation ecosystem.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".