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May 7, 2026Journal of Transport & Health1 citationsOpen Access

The uneven geographies of care: Transport disadvantage and healthcare accessibility in Melbourne

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RLRuinan LuoPCPáraic Carroll

Key Points

  • Examine the impact of transport systems on healthcare accessibility in Melbourne's metropolitan areas.
  • Applied a network-based Gaussian Two-Step Floating Catchment Area (G2SFCA) model.
  • Integrated road networks, GTFS public transport data, and population data.
  • Evaluated hospital accessibility across 361 SA2s in Greater Melbourne.
  • Assessed spatial equity using the Gini coefficient.
  • Conducted an intervention simulation for new hospital placements.
  • Car accessibility shows low inequality (Gini = 0.2410) compared to public transport (Gini = 0.6551).
  • Many outer-suburban areas lack hospital access within 60 minutes via public transport.
  • Intervention simulation significantly improved public transport accessibility and equity.
  • Public transport improvements benefited outer suburban areas the most.
  • Findings emphasize the need for better transport and healthcare coordination.

Abstract

Equitable access to healthcare in rapidly growing metropolitan regions depends not only on the location of services, but also on the transport systems through which residents reach them. In Greater Melbourne, continued growth in low-density outer suburbs has occurred alongside the concentration of major hospitals in established middle and inner metropolitan areas, raising concerns about transport disadvantage and spatial inequity in hospital access. However, metropolitan-scale evidence comparing hospital accessibility by private car and public transport remains limited in the Australian context. This study applies a network-based Gaussian Two-Step Floating Catchment Area (G2SFCA) model, integrating road networks, GTFS public transport data, and population data, to evaluate hospital accessibility by private car and public transport across 361 SA2s in Greater Melbourne. Spatial equity is assessed using the Gini coefficient. The analysis also includes an intervention simulation in which hypothetical new hospitals are placed in identified public-transport service blind spots to estimate potential improvements in accessibility and equity. Private-car accessibility is higher and more evenly distributed, following a clear centre–periphery pattern, whereas public transport accessibility is more fragmented and inequitable. Under the baseline scenario, car accessibility shows relatively low inequality (Gini = 0.2410), while public transport accessibility is markedly less equitable (Gini = 0.6551), with many outer-suburban areas unable to reach any hospital within 60 min. The intervention simulation substantially improves public transport accessibility, reducing zero-access areas and lowering inequality across all travel-time thresholds, with the strongest gains observed for public-transport-dependent outer suburban areas. The findings show that hospital accessibility in Greater Melbourne is shaped not only by service location, but also by transport mode, with public transport users facing systematically less equitable access than car users. By combining multimodal G2SFCA modelling, Gini-based equity assessment, and intervention simulation, the study provides a transferable framework for identifying transport-related healthcare inequities and testing targeted policy responses. The results highlight the need for stronger coordination between transport and healthcare planning, more equity-sensitive hospital siting, and targeted improvements in public and flexible transport provision in underserved outer metropolitan areas. • Compares hospital access by car and public transport across Greater Melbourne. • Reveals stark modal inequity: car access is even, public transport highly unequal. • Identifies outer-suburban public transport hospital access deserts within 60 min. • Offers a transferable framework for transport-health planning in underserved areas.

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Cite This Study

Luo et al. (2026) studied this question.

synapsesocial.com/papers/69fbef68164b5133a91a3353https://doi.org/10.1016/j.jth.2026.102334
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