# Transport 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] Recently a real transport network of Beijing was studied using network approach and percolation theory. 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. In recent articles, percolation theory has been applied to study traffic congestion in a city. The quality of the global traffic in a city at a given time is by a single parameter, the percolation critical threshold. The critical threshold represent the velocity below which one can travel in a large fraction of city network. The method is able to identify repetitive traffic bottlenecks. [6] Critical exponents characterizing the cluster size distribution of good traffic are similar to those of percolation theory.[7]

An empirical study regarding the size distribution of traffic jams has been performed recently by Zhang et al. [8] They found an approximate universal power law for the jam sizes distribution.

## 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.
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.
7. Switch between critical percolation modes in city traffic dynamics G Zeng, D Li, S Guo, L Gao, Z Gao, HE Stanley, S Havlin Proceedings of the National Academy of Sciences 116 (1), 23-28 (2019) Scale-free resilience of real traffic jams
8. Scale-free resilience of real traffic jams Limiao Zhang, Guanwen Zeng, Daqing Li, Hai-Jun Huang, H Eugene Stanley, Shlomo Havlin Proceedings of the National Academy of Sciences 116(18), 8673-8678 (2019)