Malcolm Morgan

Carbon & Place ( https://www.carbon.place ) is an ongoing research project to produce a free family of web tools intended to explain the spatial variation in per-capita carbon footprints across Great Britain and how they can be reduced. The tools present results via interactive maps using GIS data, small area statistics, surveys, and models to aid planners, policymakers, and communities in understanding their climate impact. Local people can benefit from disaggregated analysis as it can be more personally relevant and account for local circumstances and needs. This paper provides an overview of the project, its open-source website, and analysis pipeline, as well as reporting on its progress to date and future work.

A lack of cycle parking is a known barrier to promoting the uptake of cycling in urban areas. Unlike cars that can be parked on the roadside with little additional infrastructure, bikes usually require dedicated parking facilities. The existing research and guidance on where cycle parking should be provided primarily focuses on key destinations such as train stations or schools. Thus, there is a gap in knowledge about the amount of general-purpose cycle parking required and how it should be distributed across a city. This paper presents a novel method for analysing and prioritising the spatial distribution of cycle parking. The method draws on established portfolio management techniques but applies them in a spatial context. Using the case study of London, we demonstrate that it is possible to identify areas that have a deficit of cycle parking as well as locations that have the most significant potential for increasing cycling uptake by providing additional cycle parking.

Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression; the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050.

Origin–destination datasets representing millions of travel desire lines and routes are common in transport planning, but visualising such datasets is challenging. Existing methods often produce illegible results, low spatial resolution, or only a relative indication of the variation of flow on each road. This paper presents a new open-source algorithm called overline along with an accompanying method, to efficiently convert disparate geographical transport data into a policy-relevant summary form. Specifically, overline aggregates many individual routes into a route network map. These vector and raster maps provide total flow counts for each road and junction and are scalable to regional or national datasets. The method is demonstrated by visualising four million routes for a publicly accessible web mapping application, the Propensity to Cycle Tool, across the whole of England and Wales.