Agent-Based Modeling and the City: A Gallery of Applications

Abstract

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

Smart Cities: GIS Data for Realistic Simulations

“…Agent-based simulations are composed of agents and environment. Agent-based models of cities were summarized by (Crooks et al, 2021). Important trends are the development of large-scale simulations (see e.g., Santana et al (2018), Manzoor et al (2021), Huang et al (2022)) and real-time GIS applications (Li et al, 2020).…”

Understanding students’ residential dynamics around university campuses: A computational agent-based modelling approach

“…First, ABM starts with defining the type and behavior of agents in the system. By defining agents' behavior, agents start to have a DNA embedded decision-making framework that guides them through the process to achieve certain objectives or make specific decisions [21], [22]. In other words, ABM can be simply defined as a simulation process that consists of agents with different behaviors and decision-making frameworks in a controlled environment to examine how agents would interact.…”

An Overview of Fractal Geometry Applied to Urban Planning

“…These models focus on replication of urban form, but the explicit chain of processes that links the fractal urban signatures to the underlying socio-economic processes remains largely unarticulated. The next generation of dynamic urban models extends to Agent-Based Models (ABM) which focus on human and institutional actors in the urban system and model the individual-level decision-making processes that produce large-scale patterns [74]. These models focus on social science questions and include examples such as the small-world network model [75] and the emergence of firms [76] that produce power-law size distribution based on preferential attachment, or "the rich get richer", as the recurring generator.…”