Editing Traffic congestion (section)

==Classification and modeling==
Qualitative classification of traffic is often done in the form of a six-letter A–F [[Level of service (transportation)|level of service]] (LOS) scale defined in the [[Highway Capacity Manual]], a US document used (or used as a basis for national guidelines) worldwide. While this system generally uses delay as the basis for its measurements, the particular measurements and statistical methods vary depending on the facility being described. For instance, while the percent time spent following a slower-moving vehicle figures into the LOS for a rural two-lane road, the LOS at an urban intersection incorporates such measurements as the number of drivers forced to wait through more than one signal cycle.<ref>''Traffic Engineering'', Third Edition.  Roger P. Roess, Elana S. Prassas, and William R. McShane.  {{ISBN|0-13-142471-8}}</ref>

Another classification schema of traffic congestion is associated with some [[Traffic congestion: Reconstruction with Kerner's three-phase theory|common spatiotemporal features of traffic congestion]] found in measured traffic data. Common spatiotemporal empirical features of traffic congestion are those features, which are qualitatively the same for different highways in different countries measured during years of traffic observations. Common features of traffic congestion are independent{{what|date=February 2025}} on [[weather]], road conditions and road infrastructure, vehicular technology, driver characteristics, day time, etc. Examples of common features of traffic congestion are the features [J] and [S] for, respectively, the ''wide moving jam'' and ''synchronized flow'' traffic phases found in [[Boris Kerner]]'s [[three-phase traffic theory]]. The common features of traffic congestion can be reconstructed in space and time with the use of the [[Traffic congestion: Reconstruction with Kerner's three-phase theory|ASDA and FOTO]] models.

[[File:Speed-flow horseshoe diagram traffic congestion.svg|thumb|300px|right|Speed-flow diagram for a highway, scales omitted. When the volume of vehicles per hour reaches 75%-100% of the road capacity, traffic flow shifts from free-flowing (green) to congested (gray) and both volume and speeds are reduced. The red ellipse represents rush-hour traffic.<ref>{{citation|author=Asaf Hazut |date=April 2019 |contribution=Speed-volume curve for the [[Tel Aviv Savidor Central railway station|Arlozorov]]-[[Tel Aviv HaShalom railway station|Hashalom]] section of the [[Highway 20 (Israel)|Ayalon Highway]] on weekdays, May 2017 |title=Annual convention of the Israeli Society for Transportation Research |publisher=[[Hebrew University of Jerusalem]]}}</ref><ref>{{citation |url=https://cityobservatory.org/what-covid-19-teaches-us-about-how-to-fix-freeways/ |title= What Covid-19 teaches us about how to fix freeways |author=Joe Cortright |date=April 22, 2020 |website=City Observatory}}</ref><ref>{{citation |url=https://www.nzta.govt.nz/roads-and-rail/ramp-signals/sh1-northbound-at-green-lane-what-happens-here/ |title=SH1 Northbound at Green Lane – What happens here? |publisher=Waka Kotahi NZ Transport Agency |year=2016}}</ref>]]

[[File:Motorcycles on Civic Boulevard 20080918.jpg|thumb|Congestion on a street in [[Taipei]] consisting primarily of [[motorcycles]]]]
Some traffic engineers have attempted to apply the rules of [[fluid dynamics]] to traffic flow, likening it to the flow of a fluid in a pipe. Congestion simulations and real-time observations have shown that in heavy but free flowing traffic, jams can arise spontaneously, triggered by minor events ("[[butterfly effect]]s"), such as an abrupt steering maneuver by a single motorist. Traffic scientists liken such a situation to the sudden freezing of [[supercooling|supercooled fluid]].<ref name="ball">''Critical Mass'' – [[Philip Ball|Ball, Philip]], {{ISBN|0-09-945786-5}}</ref>

Because of the poor correlation of theoretical models to actual observed traffic flows, transportation planners and highway engineers attempt to [[traffic flow|forecast traffic flow]] using empirical models. Their working traffic models typically use a combination of macro-, micro- and mesoscopic features, and may add matrix [[entropy]] effects, by "platooning" groups of vehicles and by randomizing the flow patterns within individual segments of the network. These models are then typically calibrated by measuring actual traffic flows on the links in the network, and the baseline flows are adjusted accordingly.

A team of MIT mathematicians has developed a model that describes the formation of "phantom jams", in which small disturbances (a driver hitting the brake too hard, or getting too close to another car) in heavy traffic can become amplified into a full-blown, self-sustaining traffic jam. Key to the study is the realization that the mathematics of such jams, which the researchers call "jamitons", are strikingly similar to the equations that describe detonation waves produced by explosions, says Aslan Kasimov, lecturer in MIT's Department of Mathematics. That discovery enabled the team to solve traffic-jam equations that were first theorized in the 1950s.<ref>{{cite web|url=https://www.sciencedaily.com/releases/2009/06/090608151550.htm|title=Mathematicians Take Aim At 'Phantom' Traffic Jams|work=ScienceDaily|access-date=October 5, 2014|archive-date=July 23, 2018|archive-url=https://web.archive.org/web/20180723163613/https://www.sciencedaily.com/releases/2009/06/090608151550.htm|url-status=live}}</ref>