A new international study, led by the SURF Lab of the University of Cyprus in collaboration with Chalmers University of Technology and University College London, finds that automated digital tools can reliably analyze city street networks – helping governments and planners make faster, more cost-effective decisions.
Researchers tested automated and manually edited (hybrid) modelling methods across four cities – Nicosia, London, Gothenburg, and Madrid – to understand how well automation can support urban planning.
Key Insights
Lower costs, faster results: Fully automated models remove the need for time-intensive manual editing, significantly reducing the cost and time required for urban analysis.Reliable for strategic planning: Automated tools accurately capture large-scale patterns of movement and accessibility, making them suitable for city-wide and regional policy decisions.Scalable for multiple cities: Because the process is automated and reproducible, it can be applied consistently across many cities – supporting national and international comparisons.Targeted use of expert input: Manual refinement is only needed in specific cases, such as detailed neighbourhood planning or complex urban areas.
What This Means for Decision Makers
Urban analysis is a critical step before making investments in transport, housing, and public space. Traditionally, preparing accurate street network models has required specialist input and significant time.
This study shows that automated workflows can deliver comparable results for most planning needs at a fraction of the effort, allowing policymakers to:
Test more scenarios before committing to projects Make evidence-based decisions more quickly Allocate resources more efficiently
While automated tools perform well for large-scale decisions, the study highlights that local, fine-grained analyses – such as pedestrian movement in specific neighbourhoods – may still benefit from targeted expert review.
Automated street network modelling offers a practical and cost-efficient solution for modern urban planning. It enables governments, local authorities, planners and designers to scale up analysis, improve transparency, and support better-informed decisions – while reserving expert input for where it matters most.