The Future of UK Logistics Driving

Over the past decade there has been significant pressure to minimise emissions and safety risks related to commercial driving, augmented by growing operational demands. This pressure to meet the triple bottom line of cost, environment, and society has often resulted in the rapid application of vehicle technologies designed to mitigate undesired effects. Often the cognitive and behavioural effects of technologies on the commercial driver have not received in-depth analysis to determine comprehensive viability of proposed technological solutions. As such, this paper aims to identify a timescale for implementation for future technologies of the UK road freight industry, and the likely associated human factors issues, improving upon the currently employed ‘trial-and-error’ approach to implementation which may carry high economic, environmental, safety-related risk. Furthermore, this work aims to examine whether technology alone will be enough to meet future carbon emissions reduction targets, and assess the future role of behavioural and systems interventions.

Results (shown in Figure 7) indicate that technology alone will not be enough to meet carbon reduction targets – even in the most optimistic and unrealistic of cases, projections will still fall approximately 12% short of necessary targets. This highlights the importance of future human factors integration to successfully addressing the triple bottom line. A trajectory for technology implementation is outlined below in Figures 1 – 3, constructed from interviews with subject matter experts. These results may be used for basic guidance on technology implementation and system design for the logistics sector from 2020 - 2050. Perhaps most importantly, this report provides a foundation for future Project 2D research on defining the commercial driving task, and the design and optimisation of commercial driving systems and procedures to effectively meet the triple bottom line. Future work will involve the development of an analytical test-bed for future system design, and human factors guidance on driver training as well as decision support tools, e.g. effective data monitoring.