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<body lang=EN-US link=blue vlink=purple style='tab-interval:.5in'>
<div class=Section1>
<p class=MsoNormal><a name=Top></a><a name="_top"></a><b style='mso-bidi-font-weight:
normal'><span style='font-size:16.0pt'>SONG: Simulator of Network Growth 1.0—Help
file<o:p></o:p></span></b></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a
href="#Introduction">Introduction</a><o:p></o:p></b></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a href="#Simulation">Simulation
Models</a><o:p></o:p></b></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a href="#Interface">Interface</a><o:p></o:p></b></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a href="#Parameters">Parameters
List</a><o:p></o:p></b></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a href="#Statistics">Statistics
Outputs</a><o:p></o:p></b></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a
href="#Instructions">User Instructions</a><o:p></o:p></b></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><a href="#FAQs">FAQs</a></b></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Click <b style='mso-bidi-font-weight:normal'><u><span
style='color:blue'><a href="HelpFileSONG1.0.pdf">here</a></span></u></b> for a
PDF version of this file. </p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=Heading1><a name=Introduction><b style='mso-bidi-font-weight:normal'><span
style='font-size:14.0pt'>Introduction<o:p></o:p></span></b></a></p>
<span style='mso-bookmark:Introduction'></span>
<p class=Heading1>-----------</p>
<p class=MsoNormal>SONG 1.0 is a simulator designed for simulating the process
of transportation network growth. The growth or decline of transportation
networks is normally treated as the result of top-down decision making in the
long-range planning efforts of metropolitan planning organizations (MPOs).<span
style='mso-spacerun:yes'> </span>However, changes to transportation
networks are essentially the results of numerous small decisions by property
owners, firms, developers, towns, cities, counties, state department of
transportation districts, MPOs, and states in response to market conditions and
policy initiatives. These system behaviors demonstrate the characteristics of decentralized
systems, where organized patterns and structures can emerge not because of
centralized control, but because of the interactions among decentralized system
components. <span style='mso-spacerun:yes'> </span>This phenomenon of
decentralization and self-organization occurs in many circumstances, such as
bird flocks, ant colonies, life evolutions, neural networks. Increasingly research
is applying decentralized models in different domains as a new way of viewing
the world. </p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>In SONG 1.0, transportation networks are treated as
decentralized systems that demonstrate self-organizing patterns. By design, the
simulator models behavior of individual system components and small decisions, and
then demonstrates the patterns resulting from interactions of component models.
<span style='mso-spacerun:yes'> </span>The idea is that it is the actions
of the components, not the whole that are controlled by the designer; the
resulting patterns or structures, which often behave very differently than the
elements that compose them, are not designed.<span
style='mso-spacerun:yes'> </span></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>It is worth to notice that SONG 1.0 shows a different kind
of simulation, called “soft simulation”, which “provides qualitative
understanding of a complex system by constructing a simple one that shares the
same principle (Papert, 1992).” <span style='mso-spacerun:yes'> </span>By developing
the simulator, the focus is not on perfect reproductions of the real world, but
rather to help people explore the “microworld” of transportation network
systems and to stimulate new ways of thinking about the network growth process.
</p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=Heading1><a href="#_top">Top</a></p>
<p class=Heading1><o:p> </o:p></p>
<b style='mso-bidi-font-weight:normal'><span style='font-size:14.0pt;
font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-ansi-language:
EN-US;mso-fareast-language:ZH-CN;mso-bidi-language:AR-SA'><br clear=all
style='page-break-before:always'>
</span></b>
<p class=Heading1><a name=Simulation><b style='mso-bidi-font-weight:normal'><span
style='font-size:14.0pt'>Simulation</span></b></a><b style='mso-bidi-font-weight:
normal'><span style='font-size:14.0pt'> Models<o:p></o:p></span></b></p>
<p class=Heading1>-----------</p>
<p class=MsoNormal>SONG 1.0 simulates behaviors of individual links to explore
the patterns of resulting network forms. In particular, each link is treated as
autonomous agent and the simulation models individual link behaviors based on
certain rules that determine link revenue, costs and investment decision. The
model flowchart in Figure 1 provides a global view of the model structure;
detailed descriptions of components models are followed after the flowchart. </p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>Figure 1: Simulation flowchart</p>
<p class=MsoNormal><o:p> </o:p></p>
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<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><i style='mso-bidi-font-style:
normal'>1. Land Use and Network Settings<o:p></o:p></i></b></p>
<p class=MsoNormal>The land use and network are treated as initial conditions
input into the model. The land use is modeled as a layer of a grid of land
blocks called land use cells. Transportation networks are modeled as directed
graphs overlaying a land user layer. </p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>The main objective of setting initial land use is to provide
input to transportation models. Information contained in the land use layer
includes: location, population density, and market density. It is assumed in
the simulation model that trips produced from a land use cell are directly
proportional to population density and trips attracted to a land use cell are
directly proportional to market density.<span style='mso-spacerun:yes'>
</span>The model considers three land use patterns:<span
style='mso-spacerun:yes'> </span>(1) random initial land use, where trips
produced and attracted are randomly distributed; (2) uniform initial land use,
where trips are uniformed distributed, and it is a conceptual condition
designed to control the effect of land use on the formation of network; and (3)
bell-shaped land use, which is designed to replicate the distribution of land
use patterns with a Central Business District (CBD). <span
style='mso-spacerun:yes'> </span><o:p></o:p></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p> </o:p></b></p>
<p class=MsoNormal>The transportation network is represented as a directed
graph that connects nodes with directional arcs, links. The simulator applies a
grid network structure. </p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><i style='mso-bidi-font-style:
normal'>2. Travel Demand Model<o:p></o:p></i></b></p>
<p class=MsoNormal>The travel demand model converts land use information
(population and employment data) into link flows on a given transportation
network through trip generation, trip distribution and route assignment models.
</p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>The trip generation model divides geographical area into
zones centered on network nodes and converts land use properties of the zones
into trips produced and attracted. The land use layer is modeled as a square,
and it is assumed that the land use layer is static throughout the
simulation.<span style='mso-spacerun:yes'> </span></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal>The trip distribution model matches the generated trips by
purpose between origin (O) and destination (D). Trips produced are differentiated
according to destination; trips attracted to a zone are differentiated
according to origins.</p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal style='text-align:justify;text-justify:inter-ideograph'>The
traffic assignment model predicts the travelers’ choices of routes by the
relationship between flow and travel time. In particular, it converts the OD
matrix, from the trip distribution step, into flows on the links depending on
the way travelers choose routes.<span style='mso-spacerun:yes'> </span>By
assumption, every traveler tries to optimize his travel time by choosing the
quickest route to reach the destination. </p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><b style='mso-bidi-font-weight:normal'><i style='mso-bidi-font-style:
normal'>3. Revenue Model<o:p></o:p></i></b></p>
<p class=MsoNormal>The network revenue model determines the revenue road
providers can collect by providing the road services, depending on link speed,