Integrating Low Traffic Neighbourhoods into UK Cycle Network Planning

Cycling offers a sustainable and viable mode of transport within cities. However, evidence shows that safe and connected infrastructure is required for mass-adoption, requiring large initial investment costs. In the UK, vast kilometres of safe streets for cycling are provided by Low Traffc Neighbourhoods (LTNs), however they are often not integrated properly into cycle network planning. This study introduces a new algorithm for planning cycle networks, prioritising connections between LTNs and existing infrastructure in order to maximise safe cycling with minimal investment cost. Initial results show the potential to rapidly expand safe cycling networks by closing short gaps between LTNs.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-16
DOI
https://doi.org/10.5281/zenodo.15231748
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating Low Traffic Neighbourhoods into UK Cycle Network Planning

Zenodo (CERN European Organization for Nuclear Research)
AI-based Problem Solving and Planning
article

Integrating Low Traffic Neighbourhoods into UK Cycle Network Planning

article en

Abstract

Cycling offers a sustainable and viable mode of transport within cities. However, evidence shows that safe and connected infrastructure is required for mass-adoption, requiring large initial investment costs. In the UK, vast kilometres of safe streets for cycling are provided by Low Traffc Neighbourhoods (LTNs), however they are often not integrated properly into cycle network planning. This study introduces a new algorithm for planning cycle networks, prioritising connections between LTNs and existing infrastructure in order to maximise safe cycling with minimal investment cost. Initial results show the potential to rapidly expand safe cycling networks by closing short gaps between LTNs.

Zenodo (CERN European Organization for Nuclear Research)
Engineering and Physical Sciences Research Council (GB), Complexity Science Hub (AT), Geospatial Research (United Kingdom) (GB), IT University of Copenhagen (DK)
Industry, innovation and infrastructure
Openalex Percentile: Top 75%
AI-based Problem Solving and Planning
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