Truck drivers and automation: A methodology for identifying and supporting workforce transition in the Australian road freight sector

TL;DR

This paper introduces a comprehensive methodology to identify viable workforce transition pathways for truck drivers in Australia amid automation, combining task analysis, skill assessment, market conditions, and empirical validation.

Contribution

It presents a novel, integrated approach for workforce transition analysis that extends beyond traditional skill and wage comparisons, validated with real-world historical data.

Findings

Identifies 17 high-transferability occupations for truck drivers.

Highlights key transition pathways like bus driving and earthmoving.

Shows wage and employment trade-offs across different transition options.

Abstract

Transition to autonomous trucks (ATs) is coming, and is expected to create both challenges and opportunities for the driver workforce. This paper presents a novel methodology for identifying viable occupational transitions for truck drivers as transport automation advances. Unlike traditional workforce transition analyses that focus primarily on skill similarity, wages, and employment demand, this methodology incorporates four integrated components: task-level automation analysis, skill similarity assessment, labour market conditions analysis, and empirical validation using historical transition patterns. Applying this methodology to Australian truck drivers shows that while ATs will automate core driving tasks, many non-driving responsibilities will continue requiring a human, suggesting occupational evolution rather than wholesale displacement. A skill similarity analysis identifies 17 occupations with high transferability, while labour market analysis reveals significant trade-offs between wage levels and job availability across potential transition pathways. Key findings indicate that bus and coach driving, along with earthmoving plant operation, emerge as high-priority transition options, offering comparable wages and positive employment growth. Delivery and forklift driving present medium-priority pathways with abundant opportunities but lower wages. A regression analysis of historical transitions confirms that skill similarity, wage differentials, geographic accessibility, and qualification requirements all significantly influence actual transition patterns, with some viable pathways currently underutilised. The research provides policymakers, industry stakeholders, and educational institutions with evidence-based guidance for supporting workforce adaptation to technological change. The proposed methodology is generalisable beyond trucking to other sectors facing automation.