A Systematic Review of The Implementation of Pointing and Calling and Risk-Triggered Commentary in The Railway Industry
Bibliographic record
Abstract
Safety is fundamental in the railway industry. Human factors play an important role in train operations. Human error among railway operators has the potential to result in fatal accidents. This study aims to synthesize and critically review the available literature on human error prevention through a human-centered approach in the railway industry. Common error prevention methods applied by railway operators are pointing-and-calling (PAC) and risk-triggered commentary (RTC). This research adopted the PRISMA 2000 guideline. The literature search in Google Scholar and the Scopus database yielded 470 records; however, only 10 met the inclusion and exclusion criteria. Findings from this systematic review indicate that there are two main differences between PAC and RTC, namely the sensory component involved in error prevention and the concept of implementation. In PAC, there is a combination of the operator's brain, eyes, hands, mouth, and ears, which enhances situational awareness, strengthens memory, improves concentration, speeds up attention focus, and improves operator response. The implementation of PAC has been shown to reduce subway door opening errors in Toronto by 53% and signal violations in San Diego by 38%. In contrast, the application of RTC combines visual and auditory sensory elements to enhance the operator’s awareness of safety-related hazards.Telaah Sistematis Penerapan Tunjuk Sebut dan Risk-Triggered Commentary pada Industri PerkeretaapianKeselamatan adalah hal yang fundamental pada industri perkeretaapian. Faktor manusia berperan penting pada operasi kereta api. Human error pada operator perkeretaapian berpotensi menimbulkan kecelakaan fatal. Penelitian ini bertujuan untuk mensintesis dan mengulas secara kritis literatur yang tersedia terkait pencegahan human error dengan pendekatan manusia pada industri perkeretaapian. Metode pencegahan error yang umum diterapkan oleh operator perkeretaapian adalah tunjuk sebut dan risk-triggered commentary (RTC). Penelitian ini mengacu pedoman PRISMA 2000. Pencarian literatur pada database Google Scholar dan Scopus menghasilkan 470 catatan; namun, hanya 10 yang memenuhi kriteria inklusi dan eksklusi. Temuan dari telaah sistematis adalah terdapat dua perbedaan utama antara tunjuk sebut dan RTC, yaitu komponen sensorik yang terlibat dalam pencegahan error dan konsep pelaksanaannya. Pada tunjuk sebut, terdapat kombinasi antara otak, mata, tangan, mulut dan telinga operator sehingga dapat meningkatkan kesadaran situasional, memperkuat memori, meningkatkan konsentrasi, mempercepat fokus perhatian dan meningkatkan respon operator. Praktik tunjuk sebut terbukti menurunkan kesalahan buka pintu kereta bawah tanah di Toronto sebesar 53% dan pelanggaran sinyal di San Diago sebesar 38%. Berbeda dengan penerapan RTC mengkombinasikan elemen sensorik visual dan pendengaran sehingga meningkatkan kesadaran operator terhadap bahaya terkait keselamatan
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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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".