Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Models of Transportation and Land Use Change: A Guide to the Territory

Modern urban regions are highly complex entities. Despite the difficulty of modeling every relevant aspect of an urban region, researchers have produced a rich variety models dealing with inter-related processes of urban change. The most popular types of models have been those dealing with the relationship between transportation network growth and changes in land use and the location of economic activity, embodied in the concept of accessibility. This paper reviews some of the more common frameworks for modeling transportation and land use change, illustrating each with some examples of operational models that have been applied to real-world settings.Transport, land use, models, review network growth, induced demand, induced supply

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach

Integrated Urban Metabolism Analysis Tool (IUMAT)

A number of tools are available today for simulating different aspects of urban activity, but these efforts are fragmented and do not effectively reflect the interrelationships between very diverse groups of urban sectors and resource flows. There is a critical need for robust and reliable urban metabolism analysis tools that integrate socio-economic elements of urbanization and physicality of the built environment into evaluating sustainability in cities. This dissertation outlines the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) that dynamically measures the environmental impacts of land cover, transportation, and consumption of energy, water and materials employing a holistic framework. It includes examination of the existing scholarship on urban metabolism as well as description of the calculative framework for IUMAT. The scope of work is establishment of the Residential Energy Model that would serve as a template for the larger Energy, Water and Materials (EWM) Model. The EWM model takes a bottom-up approach to generate spatial resource demand profiles based on building and neighborhood characteristics. Finally, Residential Energy Consumption Survey (RECS) 2009 data is used to explain how the proposed framework makes use of actual data to find determinants of resources’ demand and unravel correlations between environmental consequences and myriad of urban variables. Quantile regression is explored as a robust method for large-scale energy modeling that is a prototype for resource use projection within other urban sectors

Urban metabolism and land use modeling for urban designers and planners: A land use model for the Integrated Urban Metabolism Analysis Tool

Predicting the resource consumption in the built environment and its associated environmental consequences (urban metabolism analysis) is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. There is a critical need for a single integrated framework to analyze the consequences of urban growth and eventually predict the impacts of sustainable policies on the urbanscape. This dissertation presents the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) – an analytical framework that simulates urban metabolism by integrating urban subsystems in a single comprehensive computational environment. It reviews the existing literature on urban sustainability, urban metabolism, as well as introducing the general framework for IUMAT. IUMAT uses three separate models for quantifying environmental impacts of land-use transition, consumption of resources, and transportation. This work outlines the development of IUMAT Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict land use change patterns. By using Density-Based Spatial Clustering and normal equations, this dissertation introduces a method for generating building-form variables from Light Detection and Ranging (LIDAR) data, which can be used as a new determinant factor in land-use change modeling. The proposed Land-use Model, within IUMAT or other analytical models, can be useful to local planning officials in understanding the complexity of land-use change and developing enhanced land-use policies

Urban Network Gridlock: Theory, Characteristics, and Dynamics

AbstractThis study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one- dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and re-distributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc