Multi-Agent Systems for Urban Planning

Multi-Agent Systems for Urban Planning

Andrew T. Crooks (George Mason University, USA), Amit Patel (George Mason University, USA) and Sarah Wise (University College London, UK)
DOI: 10.4018/978-1-4666-4349-9.ch003
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Cities provide homes for over half of the world's population, and this proportion is expected to increase throughout the next century. The growth of cities raises many questions and challenges for urban planning including which cities and regions are most likely to grow, what the pattern of urban growth will be, and how the existing infrastructure will cope with such growth. One way to explore these types of questions is through the use of multi-agent systems (MAS) that are capable of modeling how individuals interact and how structures emerge through such interactions, in terms of both the social and physical environment of cities. Within this chapter, the authors focus on how MAS can lead to insights into urban problems and aid urban planning from the bottom up. They review MAS models that explore the growth of cities and regions, models that explore land-use patterns resulting from such growth along with the rise of slums. Furthermore, the authors demonstrate how MAS models can be used to model transportation and the changing demographics of cities. Through these examples the authors also demonstrate how this style of modeling can give insights into such issues that cannot be gleamed from other modeling methodologies. The chapter concludes with challenges and future research directions of MAS models with respect to capturing the dynamics of human behavior in urban planning.
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In the year 2009, for the first time in history, more people lived in urban areas than in rural areas. By 2030, the global urban population is expected to contain 59% of the total world population (UN-Habitat, 2010). This expected growth will present many challenges, especially with respect to land-use planning, housing and transportation. For example, how will land-use change, where will people live, and how will the existing transport infrastructure cope with such increases? These are all important challenges with respect to urban planning. However, as each of these questions has their own associated problems (which we will discuss below), the combination of all of them represents an even greater challenge. This is compounded by the fact that the heterogeneous nature of cities makes it difficult to generalize localized dynamics up to the level of city-wide problems (Crooks, 2012), in the sense that the city is more than the sum of its parts. Our understanding of cities has increased throughout the twentieth century, incorporating ideas and theories from a diverse range of subjects including economics, geography, history, philosophy, mathematics and more recently computer science; however, it is now very clear that there are intrinsic difficulties in applying such understanding to policy analysis and decision-making (Wilson, 2000).

This relates to the notion that human behavior cannot be studied, understood, or predicted in the same way as the subjects of those sciences which explore the physical or chemical world (Wilson, 2000), in the sense that people do not behave as atoms or molecules. The effort to gain a greater understanding of urban problems such as sprawl, congestion, and segregation has recently lead researchers to focus on a bottom-up approach to urban systems, specifically researching the reasoning by which individual decisions are made. One such approach is Agent-Based Modeling (ABM) or Multi-Agent Systems (MAS), which allows one to simulate the individual actions of a diverse group of agents, measuring the resulting system behavior and outcomes over space and time. Some have classed this as a 'new wave' of urban modeling (Torrens, 2002) which can potentially address some of the weaknesses of previous generations of urban models. These improvements include incorporating dynamic feedback mechanisms, greater levels of detail, user interaction, flexibility, behavioral realism, and aggregation of different spaces (Torrens, 2000) when it comes to exploring cities and regions. That is not to say that previous generations of urban models were no good (as put forward by Lee, 1973) but more that these models were being developed when urban planners were just learning how to model cities and regions (Batty, 1994). With advances in computing and data availability, ‘traditional’ urban models have been extensively developed and applied to numerous applications around the world (see Wegener, 1994 for a review). Interested readers might want to read Batty (1976), which provides a detailed summary of the first generation of urban models.

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