An Adaptive Scaled Network for Public Transport Route Optimisation

We have a new article published in the journal Public Transport entitled An Adaptive Scaled Network for Public Transport Route Optimisation. LUCAS researchers teamed up with colleagues from Cardiff University to tackle the challenge of automatic bus route optimisation. The first step was the creation of several bus route optimisation instances based on the city of Nottingham. We are making these datasets publicly available to enable other researchers to apply their own optimisation algorithm. Published alongside the instance data are our results as presented in the publication and a python program used to evaluate route sets.

The data (under a CC BY 4.0 licence) is available from: https://data.mendeley.com/datasets/kbr5g3xmvk

Journal article link: TBA

Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE

We have a new article on urban energy simulation accepted for publication in the ISPRS International Journal of Geo-Information (IJGI). Using the Open Geospatial Consortium CityGML EnergyADE schema as the central data model, the paper describes the creation of urban scenes from standard UK map and energy survey datasets. Simulation of these scenes to estimate residential building energy demand is undertaken using CitySim+ - an improved, CityGML EnergyADE compliant, version of the spatially-explicit urban simulation software, CitySim. Further details are available in paper. The full abstract is below.

Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

Journal article link: https://www.mdpi.com/2220-9964/8/4/163

Review of the European reference framework for sustainable cities

Amanda Winter has written a policy review for the International Journal of Community Well-Being.

Abstract:

*This review examines the European Reference Framework for Sustainable Cities, an online framework for use by urban practitioners to evaluate and visualize the sustainability profile and priorities of an urban sustainability plan, policy, or initiative. This review presents recognized benefits and challenges from a testing phase of the framework, how it fits into the European Urban Agenda, and more broadly how indicator frameworks connect to the global urban sustainability context.

Journal link: https://doi.org/10.1007/s42413-018-0007-z

Lei Xie at Urban Transitions 2018

Sustainable city is a multi-faceted and contested concept which is recognized differently by different actors. Public participation is considered an essential part of sustainable urban governance, where citizens are being included in specific ways in shaping understandings of the priorities and pressures of urban development, service delivery, and the future of the urban area. Public participation has been incorporated into sustainable development policy processes in cities around the world, although the rationale, motivations and methods of such practices vary in different socio-political and economic conditions.

Lei Xie is going to attend Urban Transitions conference in Barcelona this November. Its main theme is Integrating Urban and Transport Planning, Environment and Health for Healthier Urban Living.

Adopting three cases spanning two continents, Shanghai, Nottingham and Stuttgart (Germany) offer a context of sharp contrast regarding urban scale, administrative and political culture, understandings of urban sustainability, economic development and public resources. This article aims to understand the rationales and outcomes of public participation in these highly varied cities, focusing on a public citizens’ survey utilised by all three city authorities. Triangulation methods are used and include document analysis, semi-structured interviews with government officials and civil society organizations. This paper provides an early discussion on the emerging findings of this ongoing research project.

The early results indicate that contextual factors play an important role in the design and use of public surveys. In places where the local economy is strong, manifold issues around the quality of life often beyond the fulfilment of basic needs are being surveyed, while a poorer context is linked with more questions around social problems and individual behaviours. Under different political systems, the city authorities have shown different intentions when engaging the public. In China where the political system remains restrictive and top-down, government’s incorporation of the public has been symbolic with an intention to mobilize public’s consent on closed policy debates, where local development priorities are often set by central government. In comparison, in Stuttgart where democracy is boosted by a large middle class and strong local autonomy, the city has shown firm commitment to engage the public via citizen surveys, but key tensions arise between economic development and the area’s reliance on car manufacturing on the one hand, the environmental damage this causes on the other. In Nottingham, a city that suffers from low wages and high unemployment, like many former manufacturing cities in Northern England and the Midlands, the Westminster-centric system of UK governance and neoliberal political culture leaves the city with little flexibility in how resources are used and priorities set. In the latter case, citizens are understood as customers whose satisfaction is central to local service delivery and their individual agency is considered important in resolving social, economic and environmental problems through behaviour change. In sum, different cities have not only shown varying perception of sustainability but also how public engagement plays a role to realizing sustainable urban governance.

Urban Transitions 2018. Integrating Urban and Transport Planning, Environment and Health for Healthier Urban Living. 25 - 27 November, 2018. Sitges, Barcelona, Spain.

Predicting ages of residential buildings from map data

Dr Julian Rosser and co-authors have a new article accepted in Computers, Environment and Urban Systems (CEUS). The paper describes a machine learning approach to inferring the age of residential buildings based on features extracted from map databases. Building age is not commonly available in the UK at the individual property level, however, such data is vital in estimating energy usage. The problem is treated as a supervised classification task and where a random forest is trained to estimate an age category / band according to the building’s shape and neighbourhood characteristics. Approaches for improving the predictive model performance by exploiting the predicted class probabilities, and spatial / topological relations between buildings are then tested. Taking inspiration from graph-based techniques used in image segmentation methods, we can introduce some spatial reasoning to post-process and improve class predictions.

Further details are available in paper. The full abstract is below.

The age of a building influences its form and fabric composition, and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points.

Article link (open access copy, before typesetting): https://www.researchgate.net/publication/326920098_Predicting_residential_building_age_from_map_data

Journal link (subscription required): https://doi.org/10.1016/j.compenvurbsys.2018.08.004