A survey of machine learning applications in advanced transportation systems: Trends, techniques, and future directions
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".