A model for exploring the relationship between payment structures, fatigue, crash risk, and regulatory response in a heavy-vehicle transport system

Investigations of heavy vehicle crashes have predominantly taken a reductionist view of accident causation. However, there is growing recognition that broader economic factors play a significant role in producing conditions that exacerbate crash risk, especially in the area of fatigue. The aim of this study was to determine whether agent-based modelling (ABM) may be usefully applied to explore the effect of driver payment methods on driver fatigue, crash-risk, and the response of enforcement agencies to major heavy-vehicle crashes. Simulation results showed that manipulation of payment methods within agent-based models can produce similar patterns of behaviour among simulated drivers as that observed in real world studies. Simulated drivers operating under 'per-km' and 'per-trip' piece rate incentive systems were significantly more likely to drive while fatigued and subsequently incur all associated issues (loss of license, increased crash risk, increased fines) than those paid under 'flat-rate' wage conditions. Further, the pattern of enforcement response required under 'per-km' and 'per-trip' systems was significantly higher in response to greater numbers of major crashes than in flat-rate regimes. With further refinement and collaborative design, ABMs may prove useful in studying the potential effects of economic policy settings within freight or other transport systems ahead of time.