Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities
Bibliographic record
Abstract
RPCM forms a crucial element of preventive maintenance strategies, particularly in light of escalating vehicular pressures and the advent of extreme weather patterns. Consequently, there is a growing demand for cost-effective solutions that leverage emergent technologies such as the IoT, AI, and cloud computing. This research intends to articulate the evolutionary trajectory of road solutions while delineating the prevalent challenges and offering viable trajectories for future enhancements. To achieve this objective, a systematic literature review was executed using the Scopus and Web of Science databases, the aim of which was to discern the inherent challenges of existing solutions. Following a stringent elimination process of duplicates and irrelevant studies, a corpus of 74 research papers was assembled for review. Assessment criteria encompassed the sensing platforms and algorithms deployed, the variety of road deformities detected, and the overall accuracy of the proposed solutions. The analysis revealed a variety of methodologies applied to RPCM, each bearing distinct advantages and limitations. Notably, SP-based monitoring solutions utilizing ML techniques and improved data gathering methodologies exhibited superior outcomes relative to alternative approaches. To conclude, this research elucidates the wide-ranging methodologies in RPCM, critically examining their respective advantages and drawbacks. Among the methodologies surveyed, SP-based monitoring deploying ML techniques emerges as a compelling approach, demonstrating the potential for enhancing accuracy and data gathering techniques. These insights form a valuable foundation for the conception and development of future cost-effective and efficacious RPCM solutions.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".