Random utility modelling has been established as one of the main paradigms for the implementation of land use and transport interaction (LUTI) models. Despite widespread application of such models, the respective literature provides relatively little detail on the theoretical consistency of the overall formal framework of the random utility based LUTI models. To address this gap, we present a detailed formal description of a generic land use and spatial interaction model that adheres to the random utility paradigm through the explicit distinction between utility and cost across all processes that imply behaviour of agents. The model is rooted in an extended input-output table, with the workforce and households accounts being disaggregated by socio-economic type. Similarly, the land account is broken down by domestic and non-domestic land use types. The model is developed around two processes. Firstly, the generation of demand for inputs required by established production; the estimation of the level of demand between sectors, households and land use types is supported by social accounting techniques. When appropriate the implicit production functions are assumed depended on costs of inputs, which gives rise to price-elastic demands. Secondly, the spatial assignment of demanded inputs (industrial activity, workforce, land) to locations of production; here sequences of decisions are used to distribute demand (both spatially and, when necessary, a-spatially) and to propagate costs and utilities of production and consumption that emerge from imbalances between supply and demand. The implementation of this generic model is discussed in relation to the case of the Greater South East region of the UK, including London, the South East and the East of England. We present the calibration process, data requirements, necessary assumptions and resulting implications. We discuss outputs under various land use strategies and economic scenarios, such as regulated versus competing land uses, constrained versus unconstrained densities, and high versus low economic and population growth rates. By adjusting the design constraints of the spatial planning and infrastructure supply strategies we aim to improve their sustainability.
