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Lookup NU author(s): Chris Larkin, Dr Craig RobsonORCiD, Dr Alistair FordORCiD
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Low Traffic Neighbourhoods (LTNs) form an important component of urban active travel infrastructure and policy in the United Kingdom. These zones aim to prioritise street space for cycling and walking by reducing motorised traffic within the neighbourhood. While the impacts of these zones have been studied at a local scale, research often relies on bespoke datasets provided by local councils, which are frequently unavailable or do not exist for many urban areas. To examine LTNs at a larger scale, there is a need for tools and methods to identify their locations. This study develops an open, data-driven approach to identify and evaluate plausible LTNs for any Local Authority District in the UK, creating the foundations for a national LTN dataset. The methods are applied to Newcastle Upon Tyne to demonstrate the tool. First, we separate the city into neighbourhoods based on areas where people can cycle or walk comfortably before encountering a severance, such as major roads. For each neighbourhood, three metrics are produced using OpenStreetMap data to measure the transport characteristics of the zone: the density of modal filtering, the presence of traffic through-routes, and permeability difference between active modes and vehicle modes passing through a neighbourhood. Across Newcastle, we identify 215 unique neighbourhoods with 339 modal filters. Notably, 55% of neighbourhoods contain no modal filtering, while 66% have traffic through-routes. Permeability differences with neighbourhoods range from 0 m to 4046 m. Metrics are combined to provide an overall LTN plausibility score, which is visualised through automatically generated web map outputs.
Author(s): Larkin C, Robson C, Ford A
Publication type: Article
Publication status: Published
Journal: Journal of Cycling and Micromobility Research
Year: 2025
Volume: 6
Print publication date: 01/12/2025
Online publication date: 23/10/2025
Acceptance date: 17/10/2025
ISSN (electronic): 2950-1059
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.jcmr.2025.100097
DOI: 10.1016/j.jcmr.2025.100097
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