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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.00465 |
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| _version_ | 1866914174706122752 |
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| author | Bratanova, Alexandra Mason, Claire Evans, David Schleiger, Emma Grimberg, Einat Walker, Gavin Pham, Hien Bulled, Keeley |
| author_facet | Bratanova, Alexandra Mason, Claire Evans, David Schleiger, Emma Grimberg, Einat Walker, Gavin Pham, Hien Bulled, Keeley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00465 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Truck drivers and automation: A methodology for identifying and supporting workforce transition in the Australian road freight sector Bratanova, Alexandra Mason, Claire Evans, David Schleiger, Emma Grimberg, Einat Walker, Gavin Pham, Hien Bulled, Keeley Human-Computer Interaction Computers and Society 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. |
| title | Truck drivers and automation: A methodology for identifying and supporting workforce transition in the Australian road freight sector |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2512.00465 |