Traffic congestion is a condition in transport that is characterized by slower speeds, longer trip times, and increased vehicular queueing. Traffic congestion on urban road networks has increased substantially since the 1950s. When traffic demand is great enough that the interaction between vehicles slows the traffic stream, this results in congestion. While congestion is a possibility for any mode of transportation, this article will focus on automobile congestion on public roads.
As demand approaches the capacity of a road (or of the intersections along the road), extreme traffic congestion sets in. When vehicles are fully stopped for periods of time, this is known as a traffic jam or (informally) a traffic snarl-up or a tailback.
Drivers can become frustrated and engage in road rage. Drivers and driver-focused road planning departments commonly propose to alleviate congestion by adding another lane to the road. This is ineffective: increasing road capacity induces more demand for driving.
Mathematically, traffic is modeled as a flow through a fixed point on the route, analogously to fluid dynamics.
Causes
Traffic congestion occurs when a volume of traffic generates demand for space greater than the available street capacity; this point is commonly termed saturation. Several specific circumstances can 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 work and weather events. In terms of traffic operation, rainfall reduces traffic capacity and operating speeds, thereby resulting in greater congestion and road network productivity loss.
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 crashes 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, the normal flow might have continued for some time longer.
Separation of work and residential areas
People often work and live in different parts of the city. Many workplaces are located in a central business district away from residential areas, resulting in workers commuting. According to a 2011 report published by the United States Census Bureau, a total of 132.3 million people in the United States commute between their work and residential areas daily.
Movement to obtain or provide goods and services
People may need to move about within the city to obtain goods and services, for instance to purchase goods or attend classes in a different part of the city. Brussels, a Belgian city with a strong service economy, has one of the worst traffic congestion in the world, wasting 74 hours in traffic in 2014.
Mathematical theories
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 (see also spatiotemporal reconstruction of traffic congestion).
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 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.