Over the last few decades there has been a gradual transformation in both the spatial and temporal patterns of urban activities. The percentage share of non-discretionary travel such as morning rush-hour commuting has been declining with the increased income level. Discretionary activities appear to rise prominently in urban business and leisure hubs, attracting large volumes of crowds which in turn imply new and changed demand for building floorspace and urban infrastructure.
Despite impressive advances in the theories and models of infrastructure demand forecasting, there appear to be an apparent research gap in addressing the practical needs of infrastructure planning in and around those growing urban activity hubs. First, land use and transport interaction models which have to date been the mainstay of practical policy analytics tend to focus on non-discretionary activities such as rush-hour commuting. Secondly, the emerging activity based models, while providing significant new insights into personal, familial activities, especially the discretionary travel, are so data hungry and computing intensive that they have not yet found their roles in practical policy applications.
This dissertation builds on the insights from above schools of modelling to develop a new approach that addresses the infrastructure planning needs of the growing urban hubs while keeping the data and computing realistic in medium to high income cities. The new model is designed based on an overarching hypothesis that considerable efficiency and welfare gains can be achieved in the planning and development of urban business and leisure hubs if the infrastructure provisions for discretionary and non-discretionary activities can be coordinated. This is a research theme that has been little explored in current literature.
The new infrastructure demand forecasting model has been designed with regard to the above hypothesis and realistic data availability, including those emerging online. The model extends the framework of land use transport interaction models and aim to provide a practical modelling tool. Land use changes are accounted for when testing new infrastructure investment initiatives and especially the road and public transport loads are assessed throughout all time periods of a working day. The new contribution to the modelling methodology includes the extension to the land use transport interaction framework, the use of social media data for estimating night market activity distribution and a rapid estimation of road traffic speeds from Google directions API, and model validation. Another new contribution is the understanding of the nature and magnitude of future infrastructure demand through assessing three alternative land use scenarios: (1) business as usual, (2) inner city regeneration for a major business hub around the night market, and (3) dispersed suburban growth with distant subcentres. The model is able to assess the implications for future infrastructure demand and user welfare through discerning the distinct discretionary and non-discretionary activity patterns.