Transport network
































Network science


Internet_map_1024.jpg



  • Theory



  • Graph

  • Complex network

  • Contagion

  • Small-world

  • Scale-free

  • Community structure

  • Percolation

  • Evolution

  • Controllability

  • Graph drawing

  • Social capital

  • Link analysis

  • Optimization

  • Reciprocity

  • Closure

  • Homophily

  • Transitivity

  • Preferential attachment

  • Balance theory

  • Network effect

  • Social influence

Network types


  • Informational (computing)

  • Telecommunication

  • Transport

  • Social

  • Scientific collaboration

  • Biological

  • Artificial neural

  • Interdependent

  • Semantic

  • Spatial

  • Dependency

  • Flow

  • on-Chip

Graphs











Features


  • Clique

  • Component

  • Cut

  • Cycle

  • Data structure

  • Edge

  • Loop

  • Neighborhood

  • Path

  • Vertex

  • Adjacency list / matrix

  • Incidence list / matrix

Types


  • Bipartite

  • Complete

  • Directed

  • Hyper

  • Multi

  • Random

  • Weighted



  • Metrics

  • Algorithms




  • Centrality

  • Degree

  • Betweenness

  • Closeness

  • PageRank

  • Motif

  • Clustering

  • Degree distribution

  • Assortativity

  • Distance

  • Modularity

  • Efficiency

Models











Topology


  • Random graph

  • Erdős–Rényi

  • Barabási–Albert

  • Fitness model

  • Watts–Strogatz

  • Exponential random (ERGM)

  • Random geometric (RGG)

  • Hyperbolic (HGN)

  • Hierarchical

  • Stochastic block

  • Maximum entropy

  • Soft configuration

  • LFR Benchmark

Dynamics


  • Boolean network

  • agent based


  • Epidemic/SIR



  • Lists

  • Categories




  • Topics

  • Software

  • Network scientists



  • Category:Network theory

  • Category:Graph theory

  • v

  • t

  • e


For transportation network mathematical graph theory, see Flow network.

A transport network, or transportation network is a realisation of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity.[1][2]
Examples include but are not limited to road networks, railways, air routes, pipelines, aqueducts, and power lines.



Methods


Transport network analysis is used to determine the flow of vehicles (or people) through a transport network, typically using mathematical graph theory. It may combine different 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.[3][4][5]



Percolation traffic networks

Fig. 1: Percolation traffic networks.


Recently a real transport network of Beijing was studied using network approach and percolation theory.[6] The research showed that one can characterize the quality of traffic in a city at each time in the day using percolation threshold, see Fig. 1.



See also



  • Braess' paradox

  • Flow network

  • Heuristic routing

  • Interplanetary Transport Network

  • Network science

  • Percolation theory



References





  1. ^ Barthelemy, Marc (2010). "Spatial Networks". Physics Reports. 499 (1–3): 1–101. arXiv:1010.0302. Bibcode:2011PhR...499....1B. doi:10.1016/j.physrep.2010.11.002..mw-parser-output cite.citation{font-style:inherit}.mw-parser-output .citation q{quotes:"""""""'""'"}.mw-parser-output .citation .cs1-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .citation .cs1-lock-limited a,.mw-parser-output .citation .cs1-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Lock-gray-alt-2.svg/9px-Lock-gray-alt-2.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .citation .cs1-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/a/aa/Lock-red-alt-2.svg/9px-Lock-red-alt-2.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration{color:#555}.mw-parser-output .cs1-subscription span,.mw-parser-output .cs1-registration span{border-bottom:1px dotted;cursor:help}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Wikisource-logo.svg/12px-Wikisource-logo.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output code.cs1-code{color:inherit;background:inherit;border:inherit;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;font-size:100%}.mw-parser-output .cs1-visible-error{font-size:100%}.mw-parser-output .cs1-maint{display:none;color:#33aa33;margin-left:0.3em}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration,.mw-parser-output .cs1-format{font-size:95%}.mw-parser-output .cs1-kern-left,.mw-parser-output .cs1-kern-wl-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right,.mw-parser-output .cs1-kern-wl-right{padding-right:0.2em}


  2. ^ Boeing, G. (2017). "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks". Computers, Environment and Urban Systems. 65: 126–139. arXiv:1611.01890. doi:10.1016/j.compenvurbsys.2017.05.004. Retrieved 2017-08-26.


  3. ^ Helbing, D (2001). "Traffic and related self-driven many-particle systems". Reviews of Modern Physics. 73 (4): 1067–1141. arXiv:cond-mat/0012229. Bibcode:2001RvMP...73.1067H. doi:10.1103/RevModPhys.73.1067.


  4. ^ S., Kerner, Boris (2004). The Physics of Traffic : Empirical Freeway Pattern Features, Engineering Applications, and Theory. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN 9783540409861. OCLC 840291446.


  5. ^ Wolf, D E; Schreckenberg, M; Bachem, A (June 1996). Traffic and Granular Flow. Traffic and Granular Flow. WORLD SCIENTIFIC. p. 1. doi:10.1142/9789814531276. ISBN 9789810226350.


  6. ^ Li, Daqing; Fu, Bowen; Wang, Yunpeng; Lu, Guangquan; Berezin, Yehiel; Stanley, H. Eugene; Havlin, Shlomo (2015-01-20). "Percolation transition in dynamical traffic network with evolving critical bottlenecks". Proceedings of the National Academy of Sciences. 112 (3): 669–672. Bibcode:2015PNAS..112..669L. doi:10.1073/pnas.1419185112. ISSN 0027-8424. PMC 4311803. PMID 25552558.












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