Scope and Contributions of this Work

This research and development project serves to realize an urban multi-modal transit simulation designed during the course of the systems engineering master's program. The work takes a systems approach to modeling human habitats and the transportation networks that keep them running. The hope is that such a simulation framework will create a baseline model of current day capacity, against which future models may be evaluated with respect to performance and investment decisions. The hypothesis of this work is that these tools will be instrumental in making a case for the development and construction of highly efficient arcologies or other forms of well-integrated compact cities. But nominally, and for the meantime, urban multi-modal transportation frameworks can be applied to the evaluation and tracking of present-day transit oriented growth philosophies.

Chapter 2 describes the formulation of a generic arcology system model - the result is a series of conceptual templates represented as classes and relationships among classes. Chapter 3 covers many of the practical details one needs to consider in creating discrete event simulation environments. Chapter 4 is all about the specific commuting transit system model analyzed in this work. Chapter 5 contains simulation scenarios, factorial design of experiments, and parametric analyses for various mass-transit topologies. The project conclusions and opportunities for future work are covered in Chapter 6.

Contributions. The contributions of this work are as follows:

1. A hierarchical level-of-detail organization that allows data from both top-down parametric models to interact with data generated from clusters of detailed simulation objects. This allows us to seed detailed objects in a subsystem using available aggregate data from the live system, and compare the live results to data generated by tallying up the individual contributions from individual simulation objects. The hierarchical organization also makes the simulation easier to partition across distributed compute nodes.

2. Definition of a data interchange schema between elements of a multi-modal transit infrastructure. The communication provides just enough information about each piece of passenger, cargo, vehicle , and connectivity graphs and defines minimal interfaces to allow them to report to and receive suggestions from a global transit optimization engine.

3. An inherent focus on meeting the needs and goals of the inhabitants. Many transportation simulations focus on maximizing throughput or minimizing delays or fuel expenditure. However, these metrics may not serve to help evaluate the layout of the urban area itself. This simulation infrastructure would ideally be used to measure the effectiveness of optimizing the layout of an urban area to reduce the need to load the transit infrastructure with commuters, people running petty errands, and other frequent but necessary tasks.

An ideal city would have a higher ``efficiency'' ratio, tracked by an admittedly somewhat elusive ``productivity'' metric divided by the amount of energy directly needed to produce it and energy overhead required to nominally sustain production.

$\eta=\frac{GDP}{E{\scriptstyle _{direct}}+E_{sustinence}}$

A simplified multimodal mass transit optimization solver coupled to the simulation attempts to create a demand-responsive fleet schedule for several types of defined vehicle types that service transit networks within the simulation. This tool aims to provide a quasi-optimal means to transport people and goods around within city clusters to help reduce the overhead of the transit system.