• An evolutionary AI-based decision support system for urban regeneration planning

      Georgakis, Panagiotis; Yusuf, Syed Adnan (University of Wolverhampton, 2010)
      The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.
    • Modeling the population and industry distribution impacts of urban land use policies in Beijing

      Niu, Fangqu; Li, Jun (Elsevier, 2017-11-15)
      Capable tools are desired for urban spatial policies planning in China to safeguard its sustainable development strategy. This study develops an activity-based Land Use/Transport Interaction (LUTI) model to forecast the urban activity impacts of the land-use policies. Essentially, its endogenized and interactive features in residential and employment distribution modeling mark it out from the traditional Lowry models. The LUTI model proposed consists of four models, i.e., a transport sub-model, a residential location model, an employment location model and a real estate rent model. It is then applied to the Beijing metropolitan area to characterize the urban activity evolution trend under the land use policies of recent years. The results show that with the increasing number of floorspace developed on the outskirts, more residents and employers are relocating there and sub-centers are formed to divide the service of central Beijing. This trend is consistent with the objective of government planning to develop more sub-centers around central Beijing by decentralizing industries to guide residential population growth patterns. The model provides a capable planning tool for urban spatial policy makers and demonstrates its first success in Beijing scenario.