Measurement of Train Driver Mental Workload Using the RNASA-TLX Method
DOI: 10.28989/angkasa.v16i2.2172
archive: archived pipeline: cataloged
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the high mental workload experienced by train drivers (machinists) and assistant drivers in Indonesia’s national railway system, motivated by the fact that human factors contributed to 20% of train accidents in 2015. The primary objective was to quantify the mental workload of these operators using the Revised NASA Task Load Index (RNASA-TLX), a subjective measurement tool adapted from the standard NASA-TLX to specifically assess visual and auditory demands relevant to navigation-based driving tasks. The research was conducted at the Yogyakarta Train Crew Unit (UPT Crew KA Yogyakarta). The study utilized a sample of 26 respondents selected from a population of 130 personnel, comprising 6 first-grade, 14 junior-grade, and 6 senior-grade machinists. Each participant completed the RNASA-TLX questionnaire twice: once reflecting their perception of workload when acting as a machinist and once when acting as an assistant machinist. The RNASA-TLX method involved three stages: pairwise weighting of six indicators (mental, visual, auditory, time, driving difficulty, and information comprehension difficulty), rating each indicator on a 0–100 scale, and calculating the Weighted Workload (WWL) score. Statistical analysis included Pearson correlation tests to examine the relationship between years of service and workload, and independent t-tests to compare workload across different job roles and rank levels. The results indicated that both machinists and assistant machinists experienced "High" mental workload, with WWL scores of 75.54 and 75.85, respectively, falling within the 50–79 range. For machinists, mental demands were the dominant factor, driven by safety responsibilities and the need to memorize signals and operational procedures. For assistant machinists, visual demands were the primary driver, necessitating constant monitoring of signals and signals under varying weather conditions. Statistical analysis revealed no significant correlation between years of service and workload scores for either role. Furthermore, independent t-tests showed no significant differences in workload between different machinist ranks (First, Junior, Senior) or between the machinist and assistant roles. The authors attribute the uniformly high workload to external factors such as 24-hour operational schedules, physical environmental stressors (noise, heat), and internal factors like fatigue and health conditions. The significance of this study lies in its confirmation that mental workload is consistently high across all operational roles and seniority levels in the Indonesian railway context, suggesting that experience alone does not mitigate cognitive load. The findings imply that current operational policies may not adequately address the cognitive strain on drivers, highlighting the need for leadership interventions to manage workload. Recommendations include enhancing communication and coordination systems, improving time management, and upgrading technical knowledge to reduce confusion and increase efficiency. The study suggests future research should combine RNASA-TLX with other diagnostic tools, such as fishbone diagrams, and explore the impact of route distance on workload.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 4 | 2026-08-23 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 1 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: self report data
- Theoretical Contribution: theory or model