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A micro-scale simulation model is developed for studying how the design and layout of neighbourhoods (see Local Design) can improve the sustainability of suburban developments. This micro-scale simulation model also aims to test how the design of suburban areas can impact on the functioning of the city at the metropolitan scale (see Strategic Design) because of traffic implications and, in the longer term, the relative attractiveness of suburbs.

The use of simulation in this context is motivated by the complexity of the interdependencies between urban form, travel pattern and welfare. These different aspects need to be integrated in order to provide policy guidance.

In fact, dispersed urbanisation or ‘cul-de-sac’ street layouts suggest further reliance on automobile travel and difficulties in providing accessible public infrastructure and transport. Low density design is therefore often seen as unsustainable. On the other hand, open space within suburbs and landscape amenities related to low density urban development have significant influence on residential location choice. Therefore, dense and compact suburban forms seem to constrain strongly the living quality of households, while they have not proven ability to reduce car-dependence or, more generally, to be less-demanding in energy use.

Theoretical examples of street layouts for Northstowe, Cambridge

Further to this trade-off between welfare and sustainability, that is linked to the design of the built environment, equity issues then arise from the social segregation within suburban areas. The location choice of a household depends on its ability to pay for neighbourhood qualities (local amenities and accessibility) and whether the household is dependent on public transport for city-orientated travel.

Using simulation at the neighbourhood scale, we seek to offer an integrated treatment of these questions. The method consists in simulating residential choice and travel patterns for different local plan scenarios in the case of suburban areas where important migration is likely to occur in the future (as given by the macro-scale model; see Strategic Design). A systematic variation of the main characteristics of the local design is undertaken within these scenarios, including density thresholds, the spatial arrangement of facilities and residential areas, the connectivity of local networks (eg, ‘cul-de-sac’ or ‘grid’), or the accessibility to public transport. Each scenario can be related to Local Design archetypes. Welfare levels for each household groups and the share of non-motorised trips are used as criteria for evaluating the different local plans (see Assessment).

Various tools and models are used as part of this microsimulation framework: (i) Vector GIS (Geographical Information System) is used to design local plan scenarios at the scale of building blocks with different street patterns (ii) Residential urban economics is then used to represent the trade-off between transport costs and the consumption of housing and local amenities, and allocate households within the development site (using bid-rent theory) (iii) The local amenities (eg, open-space) and parts of the transport costs are computed from GIS analyses (iv) Logit model assumptions are used to provide multi-modal accessibility measures and, once households have chosen where to live, to represent travel mode choices (v) Adequate estimation of residential preferences for local attributes is also researched, using hedonic analysis techniques, as well as the calibration of modal split for short-distance trips (intra-neighbourhood trips) (vi) Finally, the microsimulation model is made theoretically consistent and combined with the gravity-based model at the macro-scale so that feedbacks between the strategic and local scales can be considered, and reveal long-run effects of neighbourhood design on the attractiveness of suburban areas.

The Martin Centre, University of Cambridge Faculty of the Built Environment, University of the West of England Institute for Transport Studies, University of Leeds The Bartlett School of Planning, University College London Transport Operations Research Group, Newcastle University