Traffic model

A traffic model is a mathematical model of real-world traffic, usually, but not restricted to, road traffic. Traffic modeling draws heavily on theoretical foundations like network theory and certain theories from physics like the kinematic wave model. The interesting quantity being modeled and measured is the traffic flow, i.e. the throughput of mobile units (e.g. vehicles) per time and transportation medium capacity (e.g. road or lane width). Models can teach researchers and engineers how to ensure an optimal flow with a minimum number of traffic jams.

Traffic models often are the basis of a traffic simulation.[1]

In recent articles, percolation theory has been applied to model and study traffic congestion in a city. The quality of the global traffic in a city at a given time can be characterized 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. Above this threshold one can travel only within a relatively small clusters (neighborhoods). The method is able to identify repetitive traffic bottlenecks.[2] Critical exponents characterizing the cluster size distribution of good traffic are similar to those of percolation theory.[3] A method to identify functional clusters of spatio-temporal streets that represent fluent traffic flow in a city has been developed by Serok et al.[4] An empirical study regarding the size distribution of traffic jams has been performed recently by Zhang et al.[5] They found an approximate universal power law for the jam sizes distribution. A simulation model for urban traffic can be found in ref.[6]

Types
Microscopic traffic flow model
Traffic flow is assumed to depend on individual mobile units, i.e. cars, which are explicitly modeled
Macroscopic traffic flow model
Only the mass action or the statistical properties of a large number of units is analyzed

Examples

See also

References
  1. Mahmud, Khizir; Town, Graham E. (June 2016). "A review of computer tools for modeling electric vehicle energy requirements and their impact on power distribution networks". Applied Energy. 172: 337–359. doi:10.1016/j.apenergy.2016.03.100.
  2. D. Li, B. Fu, Y. Wang, G. Lu, Y. Berezin, H.E. Stanley, S. Havlin (2015). "Percolation transition in dynamical traffic network with evolving critical bottlenecks". PNAS. 112: 669.CS1 maint: multiple names: authors list (link)
  3. G Zeng, D Li, S Guo, L Gao, Z Gao, HE Stanley, S Havlin Proceedings of the (2019). "Switch between critical percolation modes in city traffic dynamics". National Academy of Sciences. 116 (1): 23–28.CS1 maint: multiple names: authors list (link)
  4. Nimrod Serok, Orr Levy, Shlomo Havlin, Efrat Blumenfeld-Lieberthal (2019). "Unveiling the inter-relations between the urban streets network and its dynamic traffic flows: Planning implication". SAGE Publications. 46 (7): 1362.CS1 maint: multiple names: authors list (link)
  5. Limiao Zhang, Guanwen Zeng, Daqing Li, Hai-Jun Huang, H Eugene Stanley, Shlomo Havlin (2019). "Scale-free resilience of real traffic jams". Proceedings of the National Academy of Sciences. 116 (18): 8673–8678.CS1 maint: multiple names: authors list (link)
  6. F. Wang, D. Li, X. Xu, R. Wu, S. Havlin (2015). "Percolation properties in a traffic model". Europhys. Lett. 112: 380001.CS1 maint: multiple names: authors list (link)
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