Purpose

Arcology design combines urban planning and architecture with the mechanics of ecology, essentially forming a dense but ideally independently sustainable human habitation system. The complexity posed by these systems is well-suited for systems engineering analysis.

From a historical perspective, arcologies are mostly the subject of science fiction. In a typical scenario, the principles of arcology development are applied to the design of self-contained habitats, where the major challenge lies in finding ways for large groups of people to tolerate living in close proximity to one another. In addition to maximizing quality of life for its residents, the arcology needs to limit consumption of land, energy, time, and human resources. Achieving an appropriate balance in these (often competing) criteria requires that designers look beyond maximization of personal productivity and/or economic performance, and explicitly consider relationships among all factors affecting system functionality, performance, and resource consumption. The common characteristics of ``good design solutions'' include:

(1)

Retrofit of present-day urban sprawls with large three-dimensional integrated urban forms (i.e., municipal hyperstructures),

(2)

Extensive use of multi-functional spaces, and

(3)

Use of sophisticated transportation networks and systems to transfer of people and resources (e.g., power and water) between spaces.

Present-day trends in population growth and urbanization suggest that as time marches forward, spearheading ideas associated with arcology development will only become more important. To put this observation on a quantitative footing, we first note that as of July 2006, the World's population is 6.5 billion (and growing annually at 1.14%). Since 1900 there has been a significant movement of the World's population to urban areas, growing from 14% in 1900 to approximately 50% in 2000. The United Nations reports that by 2030, not only will 60% of the worlds population be urban, but nearly all of the anticipated growth will be urban growth[].

Rural-to-urban area migration is driven by a number of factors including: (1) the declining importance of agriculturally-based economies, (2) improved opportunity for access to services, and (3) improved opportunities that urban areas provide for specialization (when people congregate in urban areas they can specialize to a much greater extent than in rural areas[46]). Rural-to-urban area migration is enabled by access to housing and the ability of communication and transportation networks to form in response to supply and demand mechanisms. The latter is especially important because in order for the operation of high-population urban areas to be sustainable, efficient modes of transportation are needed to transport goods and people throughout the arcology.

From the earliest days of transportation infrastructure development, new transportation networks - initially railway, then road - were ultimately built to increase the value of the land surrounding the network[]. Road and rail access stimulates urban growth, which in turn drives the need for more network development. Clearly, this cycle cannot continue forever. It is reasonable to expect that since land is a limited resource, its value will only increase at faster and faster rates as the worlds population increases. Since fewer and fewer people will have the economic means to migrate to sprawling (low-density) urban areas, high-density urban area will become more common. Thus, it may only be a matter of decades until modeling techniques tailored toward the needs of arcologies and/or other forms of well-integrated compact cities become common place. Early indicators of this outcome can be found in experimental prototypes for sustainable living (e.g., BioSphere 2 [3]) and long-term plans for dense sustainable communities and major city structures in East Asia [12,23,24,44,45].

Project Objectives

This thesis defines and describes a simulation framework for the execution of optimized demand-responsive multimodal mass transit schemes, such as those found in complex urban environments. Attention is focused on resident and employer needs (i.e., shuttling passengers from their source stations to their destination stations.) By measuring the performance of various solutions, we aim to determine effective strategies for efficiently transferring people to their destinations in relation to input parameters such as demand, transit network topology, and the relative size(s) of the vehicles used in the fleet. This framework also allows us to experiment with different urban planning layouts and to investigate relationships between services provided by data networks and transportation networks. For example, if a city decides to spend money upgrading their data infrastructure so more people might be able to telecommute to work, then this may have a critical impact on the load on their mass transit system.

While the simulation and optimization models are certainly generic enough to apply to most ordinary forms of mass transit, for two reasons this project chooses to frame models in the in the context of an arcology. First, the word ``arcology'' still remains rather unique in the global namespace of the engineering field, and connotes a flair for futurism. More importantly, the design focus of arcologies as an autonomous structure encourages us to analyze it in terms of control volumes, defining the flows of input and output products in ways much more conducive to identifying resource consumption and environmental impact. While the concept of analysis via the definition of control volumes may come naturally to engineers trained in thermodynamics, it is refreshing to see efforts emerging to track our ``carbon footprint'' as part of a global carbon dioxide emissions budget. Hopefully this step will preclude more complete tracking and accounting (and eventually optimization) of human environmental resource use and waste reclamation.