There are a number of specific circumstances which cause or aggravate congestion; most of them reduce the capacity of a road at a given point or over a certain length, or increase the number of vehicles required for a given volume of people or goods.
About half of U.S. traffic congestion is recurring, and is attributed to sheer weight of traffic; most of the rest is attributed to traffic incidents, road works and weather events.
Traffic research still cannot fully predict under which conditions a “traffic jam” (as opposed to heavy, but smoothly flowing traffic) may suddenly occur. It has been found that individual incidents (such as accidents or even a single car braking heavily in a previously smooth flow) may cause ripple effects (a cascading failure) which then spread out and create a sustained traffic jam when, otherwise, normal flow might have continued for some time longer.
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 effects”), such as an abrupt steering maneuver by a single motorist.
Traffic scientists liken such a situation to the sudden freezing of supercooled fluid. However, unlike a fluid, traffic flow is often affected by signals or other events at junctions that periodically affect the smooth flow of traffic.
Alternative mathematical theories exist, such as Boris Kerner’s three-phase traffic theory.
Because of the poor correlation of theoretical models to actual observed traffic flows, transportation planners and highway engineers attempt to 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 randomising 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. It is now claimed that equations can predict these in detail: Phantom jams can form when there is a heavy volume of cars on the road.
In that high density of traffic, small disturbances (a driver hitting the brake too hard, or getting too close to another car) can quickly become amplified into a full-blown, self-sustaining traffic jam…
A team of MIT mathematicians has developed a model that describes how and under what conditions such jams form, which could help road designers minimize the odds of their formation. The researchers reported their findings May 26 in the online edition of Physical Review E. Key to the new 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.
India’s economic surge has resulted in a massive increase in the number of private vehicles on its roads, overwhelming the transport infrastructure. Congested roads can be seen as an example of the tragedy of the commons.
Because roads in most places are free at the point of usage, there is little financial incentive for drivers not to over-use them, up to the point where traffic collapses into a jam, when demand becomes limited by opportunity cost. Privatization of highways and road pricing have both been proposed as measures that may reduce congestion through economic incentives and disincentives.
Congestion can also happen due to non-recurring highway incidents, such as a crash or road works, which may reduce the road’s capacity below normal levels. Economist Anthony Downs, in his books Stuck in Traffic (1992) and Still Stuck in Traffic (2004), argues that rush hour traffic congestion is inevitable because of the benefits of having a relatively standard work day.
In a capitalist economy, goods can be allocated either by pricing (ability to pay) or by queueing (first-come first-serve); congestion is an example of the latter. Instead of the traditional solution of making the “pipe” large enough to accommodate the total demand for peak-hour vehicle travel (a supply side solution), either by widening roadways or increasing “flow pressure” via automated highway systems, Downs advocates greater use of road pricing to reduce congestion (a demand-side solution, effectively rationing demand), in turn plowing the revenues generated therefrom into public transportation projects.
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Thanks for sharing, long may you ride Andrew-(Black Devil) from Moscow, Russia
Always Enjoy The Show
Well now that’s one way through traffic, you can put away the slide rule.