Geosimulation, Automata, and Traffic Modeling

Torrens, Paul M.

doi:10.1108/9781615832538-031

Cited by 18 publications

(18 citation statements)

References 18 publications

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“…A similar mechanism is employed in graph-based CA. Agent automata do support movement, and there are many examples in the pedestrian and vehicle traffic literature (Torrens, 2005). Batty (2001) and Schweitzer (2003) have built agent-based models of city-scale residential mobility, with a focus on the movement of agents as particles under the influence of attraction surfaces, with that attraction determined by agglomeration factors.…”

Section: The Methodological Importance Of Geographymentioning

confidence: 99%

A Geographic Automata Model of Residential Mobility

Torrens

2007

Environ Plann B Plann Des

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In this paper is described a model of residential mobility, built to simulate individual households, their perception of and reaction to varying conditions across different scales of interaction, and their movements to occupy housing in a physical, social, and economic environment. The methodology underpinning the model is based on an automata core, which leverages the advantages it offers in terms of representing individual entities and their rule-based interactions. This methodology is extended, however, to incorporate geography-specific functionality, with advantages for the modeling of human systems. The applicability of the methodology is demonstrated through the development of a rich model of residential mobility, in which individual households interact with other households and real-estate infrastructure, dynamically in space and time, to form synthetic communities and artificial property submarkets. Use of the model for what-if experimentation is demonstrated with synthetic economic and sociodemographic simulation scenarios.

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Section: The Methodological Importance Of Geographymentioning

confidence: 99%

A Geographic Automata Model of Residential Mobility

Torrens

2007

Environ Plann B Plann Des

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“…It enables researchers to study emergent system-level patterns and outcomes from generalized interactions at the individual agent level [4][5][6]. ABMs have been widely applied in many fields, including ecology, biology, social science, economic science, computer science, and the geospatial sciences [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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“…This has some serious limitations and agent-cells in such simulations can often behave unrealistically, with the ability to see behind them as they walk, for example [254]. The information that is communicated via the neighborhood search in pedestrian-based automata models tends also to be relatively straightforward, including details such as the presence of agents in the neighborhood filter [378]. The approach is, however, reasonably efficient in computation as it is well-handled natively in the cellular automata scheme [379].…”

Section: Visionmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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Geosimulation, Automata, and Traffic Modeling

Cited by 18 publications

References 18 publications

A Geographic Automata Model of Residential Mobility

A Geographic Automata Model of Residential Mobility

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Computational Streetscapes

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