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{{For|transportation network mathematical graph theory|Flow network}}
{{Network Science}}
A '''transport network''', or '''transportation network''' is
Examples include but are not limited to [[road network]]s, [[railways]], [[Airway (aviation)|air routes]], [[Pipeline transport|pipelines]], [[Navigable aqueduct|aqueducts]], and [[power lines]]. The digital representation of these networks, and the methods for their analysis, is a core part of [[spatial analysis]], [[geographic information system]]s, [[Public utility|public utilities]], and [[transport engineering]]. Network analysis is an application of the theories and algorithms of [[Graph theory]] and is a form of [[proximity analysis]].
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Network analysis requires detailed data representing the elements of the network and its properties. The core of a network dataset is a [[Vector graphics|vector]] layer of polylines representing the paths of travel, either precise geographic routes or schematic diagrams, known as ''edges''. In addition, information is needed on the [[network topology]], representing the connections between the lines, thus enabling the transport from one line to another to be modeled. Typically, these connection points, or ''nodes'', are included as an additional dataset.
Both the edges and notes are attributed with properties related to the movement or flow:
* ''Capacity'', measurements of any limitation on the volume of flow allowed, such as the number of lanes in a road, telecommunications bandwidth, or pipe diameter.
* ''Impedance'', measurements of any resistance to flow or to the speed of flow, such as a speed limit or a forbidden turn direction at a street intersection
* ''Cost'' accumulated through individual travel along the edge or through the node, commonly elapsed time, in keeping with the principle of [[friction of distance]]. For example, a node in a street network may require a different amount of time to make a particular left turn or right turn. Such costs can vary over time, such as the pattern of travel time along an urban street depending on diurnal cycles of traffic volume.
* ''Flow volume'', measurements of the actual movement taking place. This may be specific time-encoded measurements collected using [[sensor network]]s such as [[traffic counter]]s, or general trends over a period of time, such as [[Annual average daily traffic]] (AADT).
==Analysis Methods==
Transport network analysis is used to determine the [[Traffic flow|flow of vehicles]] (or people) through a transport network, typically using [[flow network|mathematical graph theory]]. It may combine different [[Mode of transport|modes of transport]], for example, walking and car, to model multi-modal journeys. Transport network analysis falls within the field of [[transport engineering]]. Traffic has been studied extensively using statistical physics methods.<ref>{{Cite journal|last=Helbing|first=D|date=2001|title=Traffic and related self-driven many-particle systems|journal=Reviews of Modern Physics|volume=73|issue=4|pages=1067–1141|arxiv=cond-mat/0012229|bibcode=2001RvMP...73.1067H|doi=10.1103/RevModPhys.73.1067|s2cid=119330488}}</ref><ref>{{Cite book|title=The Physics of Traffic : Empirical Freeway Pattern Features, Engineering Applications, and Theory|last=S.|first=Kerner, Boris|date=2004|publisher=Springer Berlin Heidelberg|isbn=9783540409861|___location=Berlin, Heidelberg|oclc=840291446}}</ref><ref>{{Cite book|last1=Wolf|first1=D E|last2=Schreckenberg|first2=M|last3=Bachem|first3=A|title=Traffic and Granular Flow|date=June 1996|journal=Traffic and Granular Flow|pages=1–394|language=en-US|publisher=WORLD SCIENTIFIC|doi=10.1142/9789814531276|isbn=9789810226350}}</ref>
Recently a real transport network of Beijing was studied using a network approach and percolation theory.
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