A survey of machine learning applications in advanced transportation systems: Trends, techniques, and future directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, artificial intelligence (AI) has revolutionized numerous sectors, including advanced transportation systems (ATS). This paper presents a comprehensive review of the latest machine learning (ML) applications within ATS, encompassing air, marine, and land transport modes. The review systematically categorizes and evaluates ML applications in four key subdomains : more-electric aircraft (MEA), all-electric ships (AES), high-speed rail (HSR), and electric vehicles (EV). A total of 124 articles were analyzed, spanning January 2014 to December 2023, to identify the global focus and results of ML in ATS. Our findings reveal that ML methods significantly improve predictive maintenance , energy management , fault diagnosis, and system optimization in ATS. However, the adoption and integration of ML face challenges related to data quality , model complexity, and real-time implementation. This review serves as a multidisciplinary research roadmap, considering ATS as a whole and taking a broad perspective of ML applications in ATS; highlighting open challenges and future directions, including dealing with data limitations, computational demands, applying transformers for time series forecasting, applying other emerging ML methods in ATS, and combining different ML approaches. The insights provided aim to facilitate further adoption of ML by both academia and industry, ultimately contributing to the evolution of intelligent and efficient transportation systems.
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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 | 0.000 | 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 it