23-Ind-B5 Ergonomics · December 2017
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams — Dec. 2017 — 98-Ind-B5 Ergonomics. Three-hour, open-book exam (all notes, books and any non-communicating calculator permitted); the paper's own instructions state "a total of five (5) questions" while the printed marking scheme and all eight pages contain exactly four (Part A, Questions 1–2, mandatory; Part B, Questions 3–4, choose one) — a minor editorial inconsistency in the source, not a dropped question. The paper requires 4 of its own marked total; all four are solved below for completeness.
Reference texts: Sanders & McCormick, Human Factors in Engineering and Design (7th ed.) — error taxonomy, task analysis, mental workload measurement, macroergonomic assessment process; Waters, Putz-Anderson & Garg, NIOSH Applications Manual for the Revised NIOSH Lifting Equation (1994) — the RWL/LI formula and multiplier tables reproduced on the exam's own pages 7–8; NIOSH, Elements of Ergonomics Programs (1997) and CSA Z1004 (Canada) — workplace musculoskeletal-disorder (MSD) prevention programs; CSA Z1002 — hazard identification, elimination and risk assessment; ISO 11228 series — manual handling limits.
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
Mental workload is the proportion of an operator's limited information-processing capacity that a task, or a set of concurrent tasks, demands relative to the capacity available – it is not the same as physical effort, and it rises sharply whenever tasks compete for the same cognitive/perceptual resource (e.g. two visual-attention tasks) even if each task alone is undemanding. A taxi driver's task set – monitoring traffic, reading the GPS display, listening to dispatch radio, operating a cell phone and conversing with passengers – is exactly this kind of resource-competing bundle, so workload assessment for this industry has to capture concurrent demand, not just single-task difficulty.
Two established, complementary measurement approaches: (1) Subjective rating – the NASA Task Load Index (NASA-TLX). Administered at the end of a shift (or after defined trip segments), it yields a multidimensional score across mental demand, physical demand, temporal demand, effort, performance and frustration, and is inexpensive to deploy across a large taxi fleet. (2) Physiological/behavioural measurement – eye-tracking (fixation duration, gaze dispersion, blink rate) or heart-rate variability (HRV) recorded continuously via an in-cab sensor or dash-mounted camera while driving, giving an objective, moment-to-moment workload signal that can be time-stamped against specific events (e.g. a dispatch call arriving while merging in traffic).
The two methods trade off in almost every dimension that matters for this application. NASA-TLX is low-cost and easy to deploy fleet-wide (a short questionnaire, no hardware), but it is retrospective and subjective: it cannot be completed while the driver is actually driving (that would itself be an unsafe secondary task), so it necessarily averages workload over an entire shift or trip and cannot pinpoint the specific moment of peak demand – e.g. it cannot distinguish "workload was high because of one bad five-minute dispatch-call-while-merging event" from "workload was evenly moderate throughout." Eye-tracking/HRV is the opposite: it is continuous and objective, giving exact temporal resolution to identify precisely which concurrent-task combination (GPS glance + radio call, say) produces the highest measured demand, but it requires in-cab equipment installation, calibration per driver, ongoing data-quality management, and meaningfully higher cost to deploy across a large fleet; some drivers may also find a continuously recording sensor intrusive.
Recommendation. For this safety-critical, real-time driving context the objective physiological/eye-tracking measure is recommended as the primary instrument, precisely because the highest-risk moments are the brief high-workload spikes (a dispatch call during a complex traffic merge) that a shift-average subjective score cannot resolve and that self-report is structurally unable to capture while driving is ongoing. NASA-TLX is retained as a secondary, low-cost cross-check – administered post-shift to validate that the objective measure's high-workload episodes correspond to what drivers themselves perceived as the most demanding parts of the shift, and to capture workload sources (e.g. passenger conflict) that a sensor cannot see directly.
Policy implications. Quantified, time-stamped workload data gives a municipal regulator an evidence base it does not currently have: it can identify which specific concurrent-task combinations (e.g. manual cell-phone handling while driving, versus a hands-free/integrated system) drive the largest workload spikes and target restrictions at those combinations rather than banning devices uniformly; it can inform hours-of-service or break-scheduling rules if workload is shown to climb across a long shift; it can support a business case for mandating integrated, driver-attention-aware dispatch/GPS displays (reducing the number of separate devices competing for the same visual channel); and it can be used to set a measurable, auditable safety standard for taxi fleets, rather than relying on incident counts alone, which under-count near-misses that never become a reportable collision.
