Abstract
Agent based modelling has been widely accepted as a promising tool for urban planning purposes thanks to its capability to provide sophisticated insights into the social behaviours and the interdependencies that characterise urban systems. In this paper, we report on an agent based model, called TransMob, which explicitly simulates the mutual dynamics between demographic evolution, transport demands, housing needs and the eventual change in the average satisfaction of the residents of an urban area. The ability to reproduce such dynamics is a unique feature that has not been found in many of the like agent based models in the literature. TransMob, is constituted by six major modules: synthetic population, perceived liveability, travel diary assignment, traffic micro-simulator, residential location choice, and travel mode choice. TransMob is used to simulate the dynamics of a metropolitan area in South East of Sydney, Australia, in 2006 and 2011, with demographic evolution. The results are favourably compared against survey data for the area in 2011, therefore validating the capability of TransMob to reproduce the observed complexity of an urban area. We also report on the application of TransMob to simulate various hypothetical scenarios of urban planning policies. We conclude with discussions on current limitations of TransMob, which serve as suggestions for future developments.
Highlights
The growing population in many parts of the world is highly urbanised and the complexity of large cities make urban planning increasingly challenging
The previous section has presented the comparisons of TransMob results against various survey data in a bid to validate its capability in satisfactorily reproducing the observed complexity of the dynamics of the selected urban area
This paper has presented an agent based model, TransMob, for the simulation of the dynamics between demographic evolution, transport demand, housing needs and the eventual change in the liveability perception of the population
Summary
The growing population in many parts of the world is highly urbanised and the complexity of large cities make urban planning increasingly challenging. Traditional and widely applied Land Use Transport (LUT) models are relatively computationally inexpensive and comprehensive in simulating the spatial dependency between land use and transport planning They are nondynamic, failing to capture the processes (i.e., learning and decision making) that are instrumental to social behaviours and unable to provide a microscopic view of urban systems [1]. Many other agent based models for transport and urban planning can be found in the literature, with different geographical scales and at various levels of complexity of agent behaviours and autonomy [5,6,7,8,9,10,11,12,13,14] They proved that with a large real world scenario, agent based modelling, while being able to reproduce the complexity of an urban area and predict emergent behaviours in the area, can have no significant performance issues [12]
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