This repository contains all scripts and data relating to my masters of research (MRes) project. The project creates a decision support methodology to prioritise barrier removal on active travel networks. This github repository is still a work in progress, as certain aspects of the decsion support tool are still in progress. Any questions regarding the content, feel free to drop me an email at [email protected]. The full MRes write-up can be found under the "Documents" folder. The full abstract can be found below:
This thesis presents the development of an automated methodology to determine the removal priority of physical obstructions on active travel networks. Obstructions reduce accessibility on active travel networks, particularly for those with additional mobility requirements such as wheelchairs or adapted cycles. To provide decision makers with a tool to identify where the best short-term gains are located, a novel methodology for scoring obstructions is developed.
The developed scoring index uses a combination of five metrics and six sub-metrics to provide an overall score per obstruction. Metrics were process, analysed and visualised using a combination of Python and QGIS. Metrics were weighted through an analytical hierarchy process in consultation with stakeholders from Sustrans. The methodology was applied the National Cycle Network within the United Kingdom. Results are found using two test sites namely York and Pembrokeshire.
Barrier critical width and potential accessibility gain were determined to be the most important metrics, constituting 77.1% of the overall weighting. The resulting scored obstructions displayed the locations of high removal priority barriers across both study areas. Clusters of high removal priority were identified at 95% confidence through use of spatial statistics. These were present in areas connecting suburbs to the city centre in York. In Pembrokeshire clusters were identified predominantly within urban areas.
The open-source and reproducible nature of this project allows decision makers to implement this methodology as a decision support tool on any area of the UK. The multi-factor nature of the index provides a holistic approach to determining the worst obstructions.