eJMP Interpretation Guide

A Journey Management Plan (JMP) is a documented process for planning and undertaking road transport journeys with the ultimate goal of arriving safely. By planning your journey more carefully, you are more likely to stay fresh and vigilant at the wheel, safeguarding your own safety and the safety of others. Our electronic Journey Management Plan (eJMP) guides the user through a step-by-step process to develop a JMP to minimise the risks associated with commuting to and from work.

The eJMP aims to:

  1. Outline the risk of fatigue when work hours are combined with commuting to and from work.
  2. Identify potential hazards that increase the risk of fatigue whilst travelling.
  3. Establish controls to manage the identified hazards.
  4. Provide a process for applying controls to your travel to and from work to reduce the risk of a fatigue-related accident.

The information provided within this interpretation guides is to assist you in understanding your eJMP and apply the controls you have identified to help safely manage your journey to and from work.

eJMP_report_new

Journey Risk Analysis

ejmp_journey risk analysis

Multiple studies have shown that performance impairment from 17 hours of sustained wakefulness is equivalent to a blood alcohol concentration of 0.05%; a level of alcohol intoxication that is deemed unacceptable when driving, working and/or operating dangerous equipment [1-3]. After 24 hours of sustained wakefulness performance decreased to a level equivalent to a blood alcohol concentration of roughly 0.10%.

DID YOU KNOW - A driver is twice as likely to be involved in an accident with a blood alcohol concentration (BAC) of 0.05 per cent (g/100 mL) than someone with no alcohol; at 0.10 per cent a driver has five times the risk [4]

Like other prediction models, the eJMP uses known biological limits and by association provides a relative fatigue and risk exposure to initiate the implementation of strategies to mitigate fatigue risk associated with an individual’s journey based upon variable assumptions. The eJMP does not propose to provide an absolute prediction of fatigue exposure as each trip has variable factors. The eJMP does not replace an individual’s ability to adjust or stop their journey if their risks change or they experience fatigue symptoms.

Initial Risk (without controls)

The initial eJMP risk score is established by calculating an individual’s hours awake (i.e. combined activities of daily living, work hours and travel time) and factoring in fixed and variable journey hazards to arrive at a score. The table below outlines the association between the eJMP score with known fatigue and risk levels. 

eJMP_score table_2

Residual Risk (after controls implemented)

The Residual Risk Score is calculated after taking into consideration the controls to manage the identified journey hazards from the Initial Risk Score.

Risk Profile - Fixed Factors

ejmp_fixed risk profile

Using Australian safety guidance material [5-8] your eJMP report outlines fixed journey hazards and the risk that you associated with that hazard which forms the basis of your journey risk profile.

DID YOU KNOW - Vehicle collisions account for 35% of all work-related fatalities,  the single biggest cause of death among Australian workers [9].

Definitions
*Travel: the journey for a worker to and from their permanent home at the beginning or end of a series of shifts.
#Commute: the daily journey for a worker to and from their permanent home, or alternative accommodation (e.g. camp or company provided accommodation)

Risk Profile - Variabe Factors

ejmp_hazards_active factors_3png

Variable risk factors are a list of known hazards that have the potential to increase fatigue impairment and risk of a journey accident. The risk of these hazards is ranked according to your responses. As their name suggests, these factors are variable on a trip-by-trip basis and have to be actively managed to mitigate the risk identified.

Multilayer Control Score

ejmp_multilayer_controls_1

 

James Reason’s [10] multilayer control approach (Swiss Cheese Model) forms the basis of determining the effectiveness of an individual’s JMP to manage their journey risk. The Swiss Cheese Model of accident causation illustrates that although many layers of defence lie between hazards and accidents there are flaws in each layer that, if aligned, can allow the accident to occur. In theory, lapses and weaknesses in one control layer (slice of swiss cheese) do not allow a risk to materialise, since other control layers also exist, preventing a single point of failure.

swiss cheese model

Implementation of a variety of controls across multiple layers throughout the Swiss Cheese Model helps reduce the risks a worker may face when undertaking a journey. Controls in the eJMP form the various layers in the Swiss Cheese Model and are assigned a Score determined by their effectiveness based upon where they are placed on the Hierarchy of Controls.

For example, if your working hours (Level 1) has an effectiveness rating of 35%, then a potential exposure of 65% remains. However, if Level 2 (Preparation) is 25% effective this will reduce your existing 65% exposure by 25% to 48.75%....this continues across the other levels of control - until you are left with the residual risk.

DID YOU KNOW - Fatigue crashes are twice as likely to be fatal than any other crash...because you are unable to brake if you are asleep.

Personal Control Plan

ejmp_personal_control_plan

Your personal control plan outlines the hazards you identified in your nominated journey and the actions you propose to take to control that hazard.

DID YOU KNOW - Drivers in remote/regional areas are 5.8 times more likely to have a fatal crash than drivers in city areas [11]

Personal Journey Plan

ejmp_personal_journey_plan_2

Your personal journey plan specifies when, where and how your actions will be implemented to reduce your fatigue risk when undertaking your journey.

DID YOU KNOW - Drivers in remote/regional areas are 5.8 times more likely to have a fatal crash than drivers in city areas [11].

References

  1. Dawson, D. and K. Reid, Fatigue, alcohol and performance impairment. Nature, 1997. 388(6639): p. 235.
  2. Lamond, N. and D. Dawson, Quantifying the performance impairment associated with fatigue. J Sleep Res, 1999. 8(4): p. 255-62.
  3. Fletcher, A., et al., Prediction of performance during sleep deprivation and alcohol intoxication by a quantitative model of work-related fatigue. . Sleep Research Online, 2003. 5(2): p. 67-75.
  4. Borkenstein, R.e.a., The role of the drinking driver in traffic accidents. . Blutalkohol: Alcohol, Drugs and Behavior., 1974. 2 (Supplement 1).
  5. Transport for NSW, C.f.R.S., Road Safety and Your Work. A Guide for Employers. 2019, Transport for NSW, Centre for Road Safety: Chippendale, NSW.
  6. Australia, S., Guide for managing the risk of fatigue at work. 2013.
  7. Mines, D.o.N.R.a., QGN16 Guidance Note for Fatigue Risk Management. 2013.
  8. Regulator, N.R., Guide Fatigue Management. Guidance for the NSW mining and petroleum industry. 2018.
  9. Government, Q., Stopping distances: speed and braking. . 2016.
  10. Reason, J., Human error: models and management. BMJ, 2000. 320(7237): p. 768-70.
  11. Road trauma Australia 2018 statistical summary. 2019, Bureau of Infrastructure, Transport and Regional Economics (BITRE).