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Eighty Mile Beach

Eighty Mile Beach also spelled Eighty-mile Beach or 80-mile Beach, and formerly called 90-mile Beach, lies along the north-west coast of Western Australia about half-way between the towns of Broome and Port Hedland. Despite its name, it is some 220 km in length, forming the coastline where the Great Sandy Desert approaches the Indian Ocean; this makes it arguably the longest beach in the world, although the coastline paradox makes this practically impossible to determine for sure, with Praia do Cassino in Brazil possibly being longer. It is one of the most important sites for migratory shorebirds, or waders, in Australia, and is recognised as a wetland of international importance under the Ramsar Convention on Wetlands.

Traditional ownership and usage
The southern section of Eighty Mile Beach is part of the traditional territory of the Nyangumarta people, who maintain a strong connection to the area with many songs, stories and ceremonies associated with sites along and in the vicinity of the beach. In June 2009 the Federal Court of Australia determined that the Nyangumarta People were the valid native title holders of that section of the beach. The judgement of the Court was delivered on country at Nyiyamarri Pukurl, a site adjacent to the Eighty Mile Beach Caravan Park.

Traditional ownership of the northern part of Eighty Mile Beach, within the vicinity of the Anna Plains pastoral lease, is shared between the Nyangumarta People and the Karajarri (or Garadjari) People. The two groups filed overlapping native title determination applications over the area, which were determined together on 25 May 2012. The judgement of the Court was delivered on country at Talgarno, a former military site within the Anna Plains pastoral lease.

In the Karajarri language, Eighty Mile Beach is called Wender, meaning "a creaking noise", with reference to the sound of walking through dry sand. Many Aboriginal people with connections to the area now live at the Bidyadanga Community (formerly the La Grange Mission) and nearby at Frazier Downs. Several soaks, known as lirri, lie behind the beach and were traditionally important as sources of fresh water. Many of the soaks became Water Reserves on the Kimberley-De Grey Stock Route, which was used until the 1960s for long distance cattle-droving.

Name
The beach was known as 90-mile beach until 1946 when the name was changed to avoid confusion with Ninety Mile Beach, Victoria.

Military use
The Talgarno military base, east of Anna Plains homestead, was important in the post-Second World War period for the monitoring and recovery of British Blue Streak rockets, test-fired from Woomera, South Australia. A gravel airstrip, artesian bores and concrete blockhouses remain. In 1999 the Department of Defence test-fired a missile from a site on Anna Plains, in connection with the development of the Jindalee over-the-horizon radar project.

Shorebird research
Because of its importance for shorebirds, Eighty Mile Beach is classified as an Important Bird Area (IBA) and is one of the principal shorebird study sites in north-western Australia. It regularly supports over 400,000 shorebirds, including over 1% of the global populations of bar-tailed godwits, eastern curlews, great knots, red knots, red-necked stints, grey-tailed tattlers, Terek sandpipers, pied oystercatchers, greater sand plovers, Oriental plovers, red-capped plovers and Oriental pratincoles, with irregular high counts of other species. Since 1981 almost yearly expeditions by the Australasian Wader Studies Group have been banding and counting shorebirds there as part of a long-term program of monitoring the populations using the East Asian – Australasian Flyway. Since 1992 most birds caught have also been leg-flagged to discover their precise migration routes and staging sites.

Geography
Eighty Mile Beach lies in the Shire of Broome in the Kimberley region of Western Australia, in the Dampierland bioregion. It extends south-west from Cape Missiessy -19.05°N, 121.51667°W in a shallow curve to Cape Keraudren -19.96667°N, 119.76667°W, with its midpoint at -19.61667°N, 120.98333°W. The beach is about 100 m wide and has a gentle gradient. It consists of sand with a high proportion of shelly material, and experiences a very large tidal range with an amplitude of up to 9 m. The adjoining tidal mudflats vary from 1 to 5 km in width. On the landward side it is bordered by dunes, a narrow floodplain and, further inland, by a strip of pindan woodland or shrubland. Most of the land along the coast is covered by four large pastoral leases: Anna Plains, Mandora, Wallal and Pardoo, which are operated principally as cattle stations.

Climate
The climate is semi-arid monsoonal with a hot wet summer and a warm dry winter. Median and mean annual rainfall are 327 mm and 341 mm respectively, with annual evaporation about 3500 mm. There is much variability in rainfall, with significant variation between years as well as the period when the bulk of the rain falls; much is contributed by tropical cyclones, especially from January to March.

Ramsar site
Some 1750 km2 of the beach and adjoining land, as well as the Mandora Marsh, was designated Ramsar Site 480 on 7 June 1990.

Flora
Along the beach, the primary dunes are stabilised by green birdflower and beach spinifex. Secondary parallel, calcareous dune ridges and swales commonly feature scattered dune wattle. Significant grasses include Whiteochloa airoides and the local endemic Triodia epactia, a resinous hummock-forming species. Inland grasslands have been strongly modified by intensive cattle grazing and are dominated by introduced buffel grass and birdwood grass.

Fauna
The principal conservation value of Eighty Mile Beach lies in the presence of very large numbers of shorebirds, for which it is one of the most important non-breeding and migratory stop-over areas in the East Asian – Australasian Flyway, regularly supporting more than 400,000 birds and especially important as a landfall for birds migrating southwards from their high latitude breeding grounds in northern Asia and Alaska to spend the austral summer in Australia. It is one of the most important sites in the world for the migration of great knot and it supports at least 1% of the flyway population (or 1% of the national population for non-migratory species) of 17 waders and the Caspian tern. The most abundant shorebird species at the beach are the great knot (up to 169,000 counted), bar-tailed godwit (110,000), and red knot (80,000). Other notable species include curlew sandpiper (60,000), red-necked stint (60,000), large sand plover (64,000) and Oriental plover (57,000) on the beach, sharp-tailed sandpiper (25,000) at both the beach and floodplain swamps, and little curlew (12,000) on the floodplain.

Flatback turtles nest along Eighty-mile Beach at scattered locations between October and April. Coastal plains in the southern part of Anna Plains Station are a stronghold for Australian bustards and support high densities of red kangaroos. The western part of Wallal Downs has a dense population of euros.

Mandora Marsh
Mandora Marsh, also known as the Mandora Salt Marsh, is a diverse wetland complex based on a palaeo-river system. It lies on Mandora Station, with the western end some 30 km from Eighty Mile Beach, beginning on the inland side of the Great Northern Highway, at the western edge of the Great Sandy Desert, in which bioregion it lies. Although it is included in the Eighty Mile Beach Ramsar Site, its environmental values are somewhat different.

Tourism and access
Eighty Mile Beach is subject to a relatively low but increasing amount of tourism. A caravan park has been established on Wallal Downs at one of the access points to the beach, 250 km north of Port Hedland and 365 km south of Broome. It is used for fishing, seashell collecting and other beach-based recreation.

title: Eighty Mile Beach


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User:Egandolfo

Ciao

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: User:Egandolfo


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User talk:Johnconnor

Thanks for experimenting with Wikipedia. Your test worked, and has been reverted. Please use the sandbox for any other tests you want to do. Take a look at the welcome page if you would like to learn more about contributing to our encyclopedia. Thanks.--Doc (?) 10:38, 4 October 2005 (UTC)

title: User talk:Johnconnor


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File:Sarona, Kaplan-2.jpg

Summary
Tel Aviv, a new way to move House! Historic Sarona House being moved to make way for a new Road.

title: File:Sarona, Kaplan-2.jpg


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Sweet Mother

"Sweet Mother" is a highlife song by the Cameroonian and Nigerian singer Prince Nico Mbarga and his band Rocafil Jazz. Released in 1976, it remains one of the most popular songs in Africa.

The demo-tape of "Sweet Mother" was turned down by EMI in 1974, citing the song's "childish appeal." "Sweet Mother" was later also rejected by Decca Records and Philips Records, before it was eventually released in December, 1976, by Rogers All Stars, a Nigerian recording company based in Onitsha.

The song is a celebration of motherhood, sung in Nigerian Pidgin English. The music is West African highlife, with Congolese Soukous-style guitar finger-picking.

"Sweet Mother" went on to become one of the most popular hits in Africa, selling between 3 and 13 million copies. Sometimes called Africa's anthem, it was voted Africa's favourite song by BBC readers and listeners in 2004, coming before Brenda Fassie's "Vuli Ndlela", Fela Kuti's "Lady", Franco's "Mario" and Miriam Makeba's version of "Malaika," which is a South African classic, sung by Dorothy Masuka in the fifties.

title: Sweet Mother


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Merchiston

Merchiston is a residential area around Merchiston Avenue in the south-west of Edinburgh, Scotland.

Location
Merchiston Avenue is 1+1/4 mi southwest of the West End of Edinburgh's principal street, Princes Street. Other areas near Merchiston include Morningside to the southeast, Burghmuirhead (including Holy Corner and Church Hill) to the east and Bruntsfield to the northeast.

History
The first known reference to Merchiston is found in the 1266 Exchequer Rolls of Scotland. At this point Merchiston consisted of one of a number of independently owned estates to the southwest of the Burgh Muir. Alexander Napier, a wealthy Edinburgh merchant and provost of the city, acquired the estate from King James I in 1436. He or his son, also Alexander Napier, were responsible for the construction of Merchiston Tower (or Castle) in the mid 15th century.

Merchiston Tower was later the home of John Napier, 8th Laird of Merchiston and the inventor of logarithms. The tower was sold by the Honourable John Scott Napier, 14th Laird of Merchiston, in 1914 to the Merchiston Castle School and today forms part of Edinburgh Napier University.

Housing
The housing is primarily a mixture of large, late Georgian, Victorian and Edwardian villas – several of the latter by Edward Calvert – together with a smaller number of Victorian tenements and some relatively large, early-20th century villas. In recent years many of these villas have been subjected to development with blocks of flats being built in their once expansive gardens and the original houses themselves being divided into small numbers of flats.

Education
Primary education in Merchiston is provided by Craiglockhart Primary School and Bruntsfield Primary School. Merchiston spans the catchment areas for Boroughmuir and Tynecastle secondary schools. Also in the area are a number of independent schools including George Watson's College and a Steiner School.

Merchiston Castle School was founded in the area in 1828, moving to Merchiston Tower in 1833. In 1914 the Merchiston Castle School board purchased Merchiston Tower from the Honourable John Scott Napier, 14th Laird of Merchiston, and used it up until 1930 when the school moved to a new site at Colinton (whilst retaining the Merchiston Castle name).

Today, Merchiston Tower is part of the campus of Edinburgh Napier University. The university also uses a variety of other buildings in this and surrounding areas, such as former schools and churches, some of which would otherwise have been demolished or made into further flats.

Transportation
On the fringes of the area where it meets Craiglockhart (to the west) is the South Suburban railway line, which closed to passenger traffic in 1962. To the north of the area is the Union Canal. North of the canal (in the area sometimes known as North Merchiston or Shandon and sometimes taken to be part of Polwarth) is the site of the former Merchiston railway station, a railway station on the now-closed Caledonian Railway line to Edinburgh Princes Street railway station.

Parks
Harrison Park, a popular community park roughly 17 acre in size is located to the northwest of Merchiston. The park provides a range of recreational facilities including pitches, play area, cycle path and a formal garden.

Notable people
The area is home to writers Lin Anderson, Colin Douglas, Alexander McCall Smith (author of The No. 1 Ladies' Detective Agency novels), politician Sir Graham Watson and comedian Dylan Moran. J. K. Rowling had her Edinburgh home in Merchiston for many years but has since moved to Killiechassie. Ian Rankin (author of the Inspector Rebus novels), lived in Merchiston before moving to the Quartermile development in 2019. The district was also the childhood home of Scotland and British Lions rugby players Gavin Hastings and Scott Hastings.

title: Merchiston


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Traffic flow

In transportation engineering, traffic flow is the study of interactions between travellers (including pedestrians, cyclists, drivers, and their vehicles) and infrastructure (including highways, signage, and traffic control devices), with the aim of understanding and developing an optimal transport network with efficient movement of traffic and minimal traffic congestion problems.

The foundation for modern traffic flow analysis dates back to the 1920s with Frank Knight's analysis of traffic equilibrium, further developed by Wardrop in 1952. Despite advances in computing, a universally satisfactory theory applicable to real-world conditions remains elusive. Current models blend empirical and theoretical techniques to forecast traffic and identify congestion areas, considering variables like vehicle use and land changes.

Traffic flow is influenced by the complex interactions of vehicles, displaying behaviors such as cluster formation and shock wave propagation. Key traffic stream variables include speed, flow, and density, which are interconnected. Free-flowing traffic is characterized by fewer than 12 vehicles per mile per lane, whereas higher densities can lead to unstable conditions and persistent stop-and-go traffic. Models and diagrams, such as time-space diagrams, help visualize and analyze these dynamics. Traffic flow analysis can be approached at different scales: microscopic (individual vehicle behavior), macroscopic (fluid dynamics-like models), and mesoscopic (probability functions for vehicle distributions). Empirical approaches, such as those outlined in the Highway Capacity Manual, are commonly used by engineers to model and forecast traffic flow, incorporating factors like fuel consumption and emissions.

The kinematic wave model, introduced by Lighthill and Whitham in 1955, is a cornerstone of traffic flow theory, describing the propagation of traffic waves and impact of bottlenecks. Bottlenecks, whether stationary or moving, significantly disrupt flow and reduce roadway capacity. The Federal Highway Authority attributes 40% of congestion to bottlenecks. Classical traffic flow theories include the Lighthill-Whitham-Richards model and various car-following models that describe how vehicles interact in traffic streams. An alternative theory, Kerner's three-phase traffic theory, suggests a range of capacities at bottlenecks rather than a single value. The Newell-Daganzo merge model and car-following models further refine our understanding of traffic dynamics and are instrumental in modern traffic engineering and simulation.

History
Attempts to produce a mathematical theory of traffic flow date back to the 1920s, when American Economist Frank Knight first produced an analysis of traffic equilibrium, which was refined into Wardrop's first and second principles of equilibrium in 1952.

Nonetheless, even with the advent of significant computer processing power, to date there has been no satisfactory general theory that can be consistently applied to real flow conditions. Current traffic models use a mixture of empirical and theoretical techniques. These models are then developed into traffic forecasts, and take account of proposed local or major changes, such as increased vehicle use, changes in land use or changes in mode of transport (with people moving from bus to train or car, for example), and to identify areas of congestion where the network needs to be adjusted.

Overview
Traffic behaves in a complex and nonlinear way, depending on the interactions of a large number of vehicles. Due to the individual reactions of human drivers, vehicles do not interact simply following the laws of mechanics, but rather display cluster formation and shock wave propagation, both forward and backward, depending on vehicle density. Some mathematical models of traffic flow use a vertical queue assumption, in which the vehicles along a congested link do not spill back along the length of the link.

In a free-flowing network, traffic flow theory refers to the traffic stream variables of speed, flow, and concentration. These relationships are mainly concerned with uninterrupted traffic flow, primarily found on freeways or expressways. Flow conditions are considered "free" when less than 12 vehicles per mile per lane are on a road. "Stable" is sometimes described as 12–30 vehicles per mile per lane. As the density reaches the maximum mass flow rate (or flux) and exceeds the optimum density (above 30 vehicles per mile per lane), traffic flow becomes unstable, and even a minor incident can result in persistent stop-and-go driving conditions. A "breakdown" condition occurs when traffic becomes unstable and exceeds 67 vehicles per mile per lane. "Jam density" refers to extreme traffic density when traffic flow stops completely, usually in the range of 185–250 vehicles per mile per lane.

However, calculations about congested networks are more complex and rely more on empirical studies and extrapolations from actual road counts. Because these are often urban or suburban in nature, other factors (such as road-user safety and environmental considerations) also influence the optimum conditions.

Traffic stream properties
Traffic flow is generally constrained along a one-dimensional pathway (e.g. a travel lane). A time-space diagram shows graphically the flow of vehicles along a pathway over time. Time is displayed along the horizontal axis, and distance is shown along the vertical axis. Traffic flow in a time-space diagram is represented by the individual trajectory lines of individual vehicles. Vehicles following each other along a given travel lane will have parallel trajectories, and trajectories will cross when one vehicle passes another. Time-space diagrams are useful tools for displaying and analyzing the traffic flow characteristics of a given roadway segment over time (e.g. analyzing traffic flow congestion).

There are three main variables to visualize a traffic stream: speed (v), density (indicated k; the number of vehicles per unit of space), and flow (indicated q; the number of vehicles per unit of time).

Speed
Speed is the distance covered per unit of time. One cannot track the speed of every vehicle; so, in practice, average speed is measured by sampling vehicles in a given area over a period of time. Two definitions of average speed are identified: "time mean speed" and "space mean speed".

The "space mean speed" is thus the harmonic mean of the speeds. The time mean speed is never less than space mean speed: $$ v_t = v_s + \frac{\sigma_s^2}{v_s}$$, where $$\sigma_s^2$$ is the variance of the space mean speed

In a time-space diagram, the instantaneous velocity, v = dx/dt, of a vehicle is equal to the slope along the vehicle's trajectory. The average velocity of a vehicle is equal to the slope of the line connecting the trajectory endpoints where a vehicle enters and leaves the roadway segment. The vertical separation (distance) between parallel trajectories is the vehicle spacing (s) between a leading and following vehicle. Similarly, the horizontal separation (time) represents the vehicle headway (h). A time-space diagram is useful for relating headway and spacing to traffic flow and density, respectively.

Density
Density (k) is defined as the number of vehicles per unit length of the roadway. In traffic flow, the two most important densities are the critical density (kc) and jam density (kj). The maximum density achievable under free flow is kc, while kj is the maximum density achieved under congestion. In general, jam density is five times the critical density. Inverse of density is spacing (s), which is the center-to-center distance between two vehicles.

$$k = \frac 1 s $$

The density (k) within a length of roadway (L) at a given time (t1) is equal to the inverse of the average spacing of the n vehicles.

$$K(L,t_1) = \frac{n}{L} = \frac{1} {\bar{s}(t_1)}$$

In a time-space diagram, the density may be evaluated in the region A.

$$k(A) = \frac{n}{L} = \frac{n \, dt}{L \, dt} = \frac{tt}{\left | A \right \vert}$$

where tt is the total travel time in A.

Flow
Flow (q) is the number of vehicles passing a reference point per unit of time, vehicles per hour. The inverse of flow is headway (h), which is the time that elapses between the ith vehicle passing a reference point in space and the (i + 1)th vehicle. In congestion, h remains constant. As a traffic jam forms, h approaches infinity.

$$q=kv \, $$

$$q=1/h \, $$

The flow (q) passing a fixed point (x1) during an interval (T) is equal to the inverse of the average headway of the m vehicles.

$$q(T,x_1) = \frac{m}{T} = \frac{1} {\bar{h}(x_1)}$$

In a time-space diagram, the flow may be evaluated in the region B.

$$q(B) = \frac{m}{T} = \frac{m\,dx}{T\,dx} = \frac{td}{\left | B \right \vert}$$

where td is the total distance traveled in B.

Methods of analysis
Analysts approach the problem in three main ways, corresponding to the three main scales of observation in physics:

 * Microscopic scale: At the most basic level, every vehicle is considered as an individual. An equation can be written for each, usually an ordinary differential equation (ODE). Cellular automation models can also be used, where the road is divided into cells, each of which contains a moving car, or is empty. The Nagel–Schreckenberg model is a simple example of such a model. As the cars interact it can model collective phenomena such as traffic jams.
 * Macroscopic scale: Similar to models of fluid dynamics, it is considered useful to employ a system of partial differential equations, which balance laws for some gross quantities of interest; e.g., the density of vehicles or their mean velocity.
 * Mesoscopic (kinetic) scale: A third, intermediate possibility, is to define a function $$f(t,x,V)$$ which expresses the probability of having a vehicle at time $$t$$ in position $$x$$ which runs with velocity $$V$$. This function, following methods of statistical mechanics, can be computed using an integro-differential equation such as the Boltzmann equation.

The engineering approach to analysis of highway traffic flow problems is primarily based on empirical analysis (i.e., observation and mathematical curve fitting). One major reference used by American planners is the Highway Capacity Manual, published by the Transportation Research Board, which is part of the United States National Academy of Sciences. This recommends modelling traffic flows using the whole travel time across a link using a delay/flow function, including the effects of queuing. This technique is used in many US traffic models and in the SATURN model in Europe.

In many parts of Europe, a hybrid empirical approach to traffic design is used, combining macro-, micro-, and mesoscopic features. Rather than simulating a steady state of flow for a journey, transient "demand peaks" of congestion are simulated. These are modeled by using small "time slices" across the network throughout the working day or weekend. Typically, the origins and destinations for trips are first estimated and a traffic model is generated before being calibrated by comparing the mathematical model with observed counts of actual traffic flows, classified by type of vehicle. "Matrix estimation" is then applied to the model to achieve a better match to observed link counts before any changes, and the revised model is used to generate a more realistic traffic forecast for any proposed scheme. The model would be run several times (including a current baseline, an "average day" forecast based on a range of economic parameters and supported by sensitivity analysis) in order to understand the implications of temporary blockages or incidents around the network. From the models, it is possible to total the time taken for all drivers of different types of vehicle on the network and thus deduce average fuel consumption and emissions.

Much of UK, Scandinavian, and Dutch authority practice is to use the modelling program CONTRAM for large schemes, which has been developed over several decades under the auspices of the UK's Transport Research Laboratory, and more recently with the support of the Swedish Road Administration. By modelling forecasts of the road network for several decades into the future, the economic benefits of changes to the road network can be calculated, using estimates for value of time and other parameters. The output of these models can then be fed into a cost-benefit analysis program.

Cumulative vehicle count curves (N-curves)
A cumulative vehicle count curve, the N-curve, shows the cumulative number of vehicles that pass a certain location x by time t, measured from the passage of some reference vehicle. This curve can be plotted if the arrival times are known for individual vehicles approaching a location x, and the departure times are also known as they leave location x. Obtaining these arrival and departure times could involve data collection: for example, one could set two point sensors at locations X1 and X2, and count the number of vehicles that pass this segment while also recording the time each vehicle arrives at X1 and departs from X2. The resulting plot is a pair of cumulative curves where the vertical axis (N) represents the cumulative number of vehicles that pass the two points: X1 and X2, and the horizontal axis (t) represents the elapsed time from X1 and X2.

If vehicles experience no delay as they travel from X1 to X2, then the arrivals of vehicles at location X1 is represented by curve N1 and the arrivals of the vehicles at location X2 is represented by N2 in figure 8. More commonly, curve N1 is known as the arrival curve of vehicles at location X1 and curve N2 is known as the arrival curve of vehicles at location X2. Using a one-lane signalized approach to an intersection as an example, where X1 is the location of the stop bar at the approach and X2 is an arbitrary line on the receiving lane just across of the intersection, when the traffic signal is green, vehicles can travel through both points with no delay and the time it takes to travel that distance is equal to the free-flow travel time. Graphically, this is shown as the two separate curves in figure 8.

However, when the traffic signal is red, vehicles arrive at the stop bar (X1) and are delayed by the red light before crossing X2 some time after the signal turns green. As a result, a queue builds at the stop bar as more vehicles are arriving at the intersection while the traffic signal is still red. Therefore, for as long as vehicles arriving at the intersection are still hindered by the queue, the curve N2 no longer represents the vehicles’ arrival at location X2; it now represents the vehicles’ virtual arrival at location X2, or in other words, it represents the vehicles' arrival at X2 if they did not experience any delay. The vehicles' arrival at location X2, taking into account the delay from the traffic signal, is now represented by the curve N′2 in figure 9.

However, the concept of the virtual arrival curve is flawed. This curve does not correctly show the queue length resulting from the interruption in traffic (i.e. red signal). It assumes that all vehicles are still reaching the stop bar before being delayed by the red light. In other words, the virtual arrival curve portrays the stacking of vehicles vertically at the stop bar. When the traffic signal turns green, these vehicles are served in a first-in-first-out (FIFO) order. For a multi-lane approach, however, the service order is not necessarily FIFO. Nonetheless, the interpretation is still useful because of the concern with average total delay instead of total delays for individual vehicles.

Step function vs. smooth function
The traffic light example depicts N-curves as smooth functions. Theoretically, however, plotting N-curves from collected data should result in a step-function (figure 10). Each step represents the arrival or departure of one vehicle at that point in time. When the N-curve is drawn on larger scale reflecting a period of time that covers several cycles, then the steps for individual vehicles can be ignored, and the curve will then look like a smooth function (figure 8).

Traffic assignment
The aim of traffic flow analysis is to create and implement a model which would enable vehicles to reach their destination in the shortest possible time using the maximum roadway capacity. This is a four-step process:

This cycle is repeated until the solution converges.
 * Generation – the program estimates how many trips would be generated. For this, the program needs the statistical data of residence areas by population, location of workplaces etc.;
 * Distribution – after generation it makes the different Origin-Destination (OD) pairs between the location found in step 1;
 * Modal Split/Mode Choice – the system has to decide how much percentage of the population would be split between the difference modes of available transport, e.g. cars, buses, rails, etc.;
 * Route Assignment – finally, routes are assigned to the vehicles based on minimum criterion rules.

There are two main approaches to tackle this problem with the end objectives:

 * System optimum
 * User equilibrium

System optimum
In short, a network is in system optimum (SO) when the total system cost is the minimum among all possible assignments.

System Optimum is based on the assumption that routes of all vehicles would be controlled by the system, and that rerouting would be based on maximum utilization of resources and minimum total system cost. (Cost can be interpreted as travel time.) Hence, in a System Optimum routing algorithm, all routes between a given OD pair have the same marginal cost. In traditional transportation economics, System Optimum is determined by equilibrium of demand function and marginal cost function. In this approach, marginal cost is roughly depicted as increasing function in traffic congestion. In traffic flow approach, the marginal cost of the trip can be expressed as sum of the cost (delay time, w) experienced by the driver and the externality (e) that a driver imposes on the rest of the users.

Suppose there is a freeway (0) and an alternative route (1), which users can be diverted onto off-ramp. Operator knows total arrival rate (A(t)), the capacity of the freeway (μ0), and the capacity of the alternative route (μ1). From the time 't0', when freeway is congested, some of the users start moving to alternative route. However, when t1, alternative route is also full of capacity. Now operator decides the number of vehicles(N), which use alternative route. The optimal number of vehicles (N) can be obtained by calculus of variation, to make marginal cost of each route equal. Thus, optimal condition is T0 = T1 + ∆1. In this graph, we can see that the queue on the alternative route should clear ∆1 time units before it clears from the freeway. This solution does not define how we should allocates vehicles arriving between t1 and T1, we just can conclude that the optimal solution is not unique. If operator wants freeway not to be congested, operator can impose the congestion toll, e0 ― e1, which is the difference between the externality of freeway and alternative route. In this situation, freeway will maintain free flow speed, however alternative route will be extremely congested.

User equilibrium
In brief, A network is in user equilibrium (UE) when every driver chooses the routes in its lowest cost between origin and destination regardless whether total system cost is minimized.

The user optimum equilibrium assumes that all users choose their own route towards their destination based on the travel time that will be consumed in different route options. The users will choose the route which requires the least travel time. The user optimum model is often used in simulating the impact on traffic assignment by highway bottlenecks. When the congestion occurs on highway, it will extend the delay time in travelling through the highway and create a longer travel time. Under the user optimum assumption, the users would choose to wait until the travel time using a certain freeway is equal to the travel time using city streets, and hence equilibrium is reached. This equilibrium is called User Equilibrium, Wardrop Equilibrium or Nash Equilibrium.

The core principle of User Equilibrium is that all used routes between a given OD pair have the same travel time. An alternative route option is enabled to use when the actual travel time in the system has reached the free-flow travel time on that route.

For a highway user optimum model considering one alternative route, a typical process of traffic assignment is shown in figure 15. When the traffic demand stays below the highway capacity, the delay time on highway stays zero. When the traffic demand exceeds the capacity, the queue of vehicle will appear on the highway and the delay time will increase. Some of users will turn to the city streets when the delay time reaches the difference between the free-flow travel time on highway and the free-flow travel time on city streets. It indicates that the users staying on the highway will spend as much travel time as the ones who turn to the city streets. At this stage, the travel time on both the highway and the alternative route stays the same. This situation may be ended when the demand falls below the road capacity, that is the travel time on highway begins to decrease and all the users will stay on the highway. The total of part area 1 and 3 represents the benefits by providing an alternative route. The total of area 4 and area 2 shows the total delay cost in the system, in which area 4 is the total delay occurs on the highway and area 2 is the extra delay by shifting traffic to city streets.

Navigation function in Google Maps can be referred as a typical industrial application of dynamic traffic assignment based on User Equilibrium since it provides every user the routing option in lowest cost (travel time).

Time delay
Both User Optimum and System Optimum can be subdivided into two categories on the basis of the approach of time delay taken for their solution:

Predictive Time Delay
Predictive time delay assumes that the user of the system knows exactly how long the delay is going to be right ahead. Predictive delay knows when a certain congestion level will be reached and when the delay of that system would be more than taking the other system, so the decision for reroute can be made in time. In the vehicle counts-time diagram, predictive delay at time t is horizontal line segment on the right side of time t, between the arrival and departure curve, shown in Figure 16. the corresponding y coordinate is the number nth vehicle that leaves the system at time t.

Reactive Time Delay
Reactive time delay is when the user has no knowledge of the traffic conditions ahead. The user waits to experience the point where the delay is observed and the decision to reroute is in reaction to that experience at the moment. Predictive delay gives significantly better results than the reactive delay method. In the vehicle counts-time diagram, predictive delay at time t is horizontal line segment on the left side of time t, between the arrival and departure curve, shown in Figure 16. the corresponding y coordinate is the number nth vehicle that enters the system at time t.

Variable speed limit assignment
This is an upcoming approach of eliminating shockwave and increasing safety for the vehicles. The concept is based on the fact that the risk of accident on a roadway increases with speed differential between the upstream and downstream vehicles. The two types of crash risk which can be reduced from VSL implementation are the rear-end crash and the lane-change crash. Variable speed limits seek to homogenize speed, leading to a more constant flow. Different approaches have been implemented by researchers to build a suitable VSL algorithm.

Variable speed limits are usually enacted when sensors along the roadway detect that congestion or weather events have exceeded thresholds. The roadway speed limit will then be reduced in 5-mph increments through the use of signs above the roadway (Dynamic Message Signs) controlled by the Department of Transportation. The goal of this process is the both increase safety through accident reduction and to avoid or postpone the onset of congestion on the roadway. The ideal resulting traffic flow is slower overall, but less stop-and-go, resulting in fewer instances of rear-end and lane-change crashes. The use of VSL's also regularly employs shoulder-lanes permitted for transportation only under congested states which this process aims to combat. The need for a variable speed limit is shown by Flow-Density diagram to the right.

In this figure ("Flow-Speed Diagram for a Typical Roadway"), the point of the curve represents optimal traffic movement in both flow and speed. However, beyond this point the speed of travel quickly reaches a threshold and starts to decline rapidly. In order to reduce the potential risk of this rapid speed decline, variable speed limits reduce the speed at a more gradual rate (5-mph increments), allowing drivers to have more time to prepare and acclimate to the slowdown due to congestion/weather. The development of a uniform travel speed reduces the probability of erratic driver behavior and therefore crashes.

Through historical data obtained at VSL sites, it has been determined that implementation of this practice reduces accident numbers by 20-30%.

In addition to safety and efficiency concerns, VSL's can also garner environmental benefits such as decreased emissions, noise, and fuel consumption. This is due to the fact that vehicles are more fuel-efficient when at a constant rate of travel, rather than in a state of constant acceleration and deacceleration like that usually found in congested conditions.

Road junctions
A major consideration in road capacity relates to the design of junctions. By allowing long "weaving sections" on gently curving roads at graded intersections, vehicles can often move across lanes without causing significant interference to the flow. However, this is expensive and takes up a large amount of land, so other patterns are often used, particularly in urban or very rural areas. Most large models use crude simulations for intersections, but computer simulations are available to model specific sets of traffic lights, roundabouts, and other scenarios where flow is interrupted or shared with other types of road users or pedestrians. A well-designed junction can enable significantly more traffic flow at a range of traffic densities during the day. By matching such a model to an "Intelligent Transport System", traffic can be sent in uninterrupted "packets" of vehicles at predetermined speeds through a series of phased traffic lights. The UK's TRL has developed junction modelling programs for small-scale local schemes that can take account of detailed geometry and sight lines; ARCADY for roundabouts, PICADY for priority intersections, and OSCADY and TRANSYT for signals. Many other junction analysis software packages exist such as Sidra and LinSig and Synchro.

Kinematic wave model
The kinematic wave model was first applied to traffic flow by Lighthill and Whitham in 1955. Their two-part paper first developed the theory of kinematic waves using the motion of water as an example. In the second half, they extended the theory to traffic on “crowded arterial roads.” This paper was primarily concerned with developing the idea of traffic “humps” (increases in flow) and their effects on speed, especially through bottlenecks.

The authors began by discussing previous approaches to traffic flow theory. They note that at the time there had been some experimental work, but that “theoretical approaches to the subject [were] in their infancy.” One researcher in particular, John Glen Wardrop, was primarily concerned with statistical methods of examination, such as space mean speed, time mean speed, and “the effect of increase of flow on overtaking” and the resulting decrease in speed it would cause. Other previous research had focused on two separate models: one related traffic speed to traffic flow and another related speed to the headway between vehicles.

The goal of Lighthill and Whitham, on the other hand, was to propose a new method of study “suggested by theories of the flow about supersonic projectiles and of flood movement in rivers.” The resulting model would capture both of the aforementioned relationships, speed-flow and speed-headway, into a single curve, which would “[sum] up all the properties of a stretch of road which are relevant to its ability to handle the flow of congested traffic.” The model they presented related traffic flow to concentration (now typically known as density). They wrote, “The fundamental hypothesis of the theory is that at any point of the road the flow q (vehicles per hour) is a function of the concentration k (vehicles per mile).” According to this model, traffic flow resembled the flow of water in that “Slight changes in flow are propagated back through the stream of vehicles along ‘kinematic waves,’ whose velocity relative to the road is the slope of the graph of flow against concentration.” The authors included an example of such a graph; this flow-versus-concentration (density) plot is still used today (see figure 3 above).

The authors used this flow-concentration model to illustrate the concept of shock waves, which slow down vehicles which enter them, and the conditions that surround them. They also discussed bottlenecks and intersections, relating both to their new model. For each of these topics, flow-concentration and time-space diagrams were included. Finally, the authors noted that no agreed-upon definition for capacity existed, and argued that it should be defined as the “maximum flow of which the road is capable.” Lighthill and Whitham also recognized that their model had a significant limitation: it was only appropriate for use on long, crowded roadways, as the “continuous flow” approach only works with a large number of vehicles.

Components of the kinematic wave model of traffic flow theory
The kinematic wave model of traffic flow theory is the simplest dynamic traffic flow model that reproduces the propagation of traffic waves. It is made up of three components: the fundamental diagram, the conservation equation, and initial conditions. The law of conservation is the fundamental law governing the kinematic wave model:

$$\frac{\partial k}{\partial t} + \frac{\partial q}{\partial x} =0,$$

The fundamental diagram of the kinematic wave model relates traffic flow with density, as seen in figure 3 above. It can be written as:

$${q}={F(k)}$$

Finally, initial conditions must be defined to solve a problem using the model. A boundary is defined to be $${k(t,x)}$$, representing density as a function of time and position. These boundaries typically take two different forms, resulting in initial value problems (IVPs) and boundary value problems (BVPs). Initial value problems give the traffic density at time $${t}={0}$$, such that $${k(0,x)}={g(x)}$$, where $${g(x)}$$ is the given density function. Boundary value problems give some function $${g(t)}$$ that represents the density at the $${x=0}$$ position, such that $${k(t,0)}={g(t)}$$. The model has many uses in traffic flow. One of the primary uses is in modeling traffic bottlenecks, as described in the following section.

Traffic bottleneck
Traffic bottlenecks are disruptions of traffic on a roadway caused either due to road design, traffic lights, or accidents. There are two general types of bottlenecks, stationary and moving bottlenecks. Stationary bottlenecks are those that arise due to a disturbance that occurs due to a stationary situation like narrowing of a roadway, an accident. Moving bottlenecks on the other hand are those vehicles or vehicle behavior that causes the disruption in the vehicles which are upstream of the vehicle. Generally, moving bottlenecks are caused by heavy trucks as they are slow moving vehicles with less acceleration and also may make lane changes.7

Bottlenecks are important considerations because they impact the flow in traffic, the average speeds of the vehicles. The main consequence of a bottleneck is an immediate reduction in capacity of the roadway. The Federal Highway Authority has stated that 40% of all congestion is from bottlenecks.

Stationary bottleneck
The general cause of stationary bottlenecks are lane drops which occurs when the a multilane roadway loses one or more its lane. This causes the vehicular traffic in the ending lanes to merge onto the other lanes.

Moving bottleneck
As explained above, moving bottlenecks are caused due to slow moving vehicles that cause disruption in traffic. Moving bottlenecks can be active or inactive bottlenecks. If the reduced capacity(qu) caused due to a moving bottleneck is greater than the actual capacity(μ) downstream of the vehicle, then this bottleneck is said to be an active bottleneck.

Classical traffic flow theories
The generally accepted classical fundamentals and methodologies of traffic and transportation theory are as follows:

 * 1) The Lighthill-Whitham-Richards (LWR) model introduced in 1955–56. Daganzo introduced a cell-transmission model (CTM) that is consistent with the LWR model.
 * 2) A traffic flow instability that causes a growing wave of a local reduction of the vehicle speed. This classical traffic flow instability was introduced in 1959–61 in the General Motors (GM) car-following model by Herman, Gazis, Montroll, Potts, and Rothery.  The classical traffic flow instability of the GM model has been incorporated in a huge number of traffic flow models like Gipps's model, Payne's model, Newell's optimal velocity (OV) model, Wiedemann's model, Whitham's model, the Nagel-Schreckenberg (NaSch) cellular automaton (CA) model, Bando et al. OV model, Treiber's IDM, Krauß model, the Aw-Rascle model and many other well-known microscopic and macroscopic traffic-flow models, which are the basis of traffic simulation tools widely used by traffic engineers and researchers (see, e.g., references in review ).
 * 3) The understanding of highway capacity as a particular value. This understanding of road capacity was probably introduced in 1920–35 (see ). Currently, it is assumed that highway capacity of free flow at a highway bottleneck is a stochastic value. However, in accordance with the classical understanding of highway capacity, it is assumed that at a given time instant there can be only one particular value of this stochastic highway capacity (see references in the book ).
 * 4) Wardrop's user equilibrium (UE) and system optimum (SO) principles for traffic and transportation network optimization and control.

Alternatives: Kerner's three phase traffic theory
Three-phase traffic theory is an alternative theory of traffic flow created by Boris Kerner at the end of 1990's  (for reviews, see the books   ). Probably the most important result of the three-phase theory is that at any time instance there is a range of highway capacities of free flow at a bottleneck. The capacity range is between some maximum and minimum capacities. The range of highway capacities of free flow at the bottleneck in three-phase traffic theory contradicts fundamentally classical traffic theories as well as methods for traffic management and traffic control which at any time instant assume the existence of a particular deterministic or stochastic highway capacity of free flow at the bottleneck. Non-specialists that have never learned about traffic phenomena before can find simplified explanations of real measured vehicle traffic phenomena leading to the emergence of Kerner's three phase traffic theory in the book; some engineering applications of Kerner's theory can be found in the book.

Newell-Daganzo Merge Models

In the condition of traffic flows leaving two branch roadways and merging into a single flow through a single roadway, determining the flows that pass through the merging process and the state of each branch of roadways becomes an important task for traffic engineers. The Newell-Daganzo merge model is a good approach to solve these problems. This simple model is the output of the result of both Gordon Newell's description of the merging process and the Daganzo's cell transmission model. In order to apply the model to determine the flows which exiting two branch of roadways and the stat of each branch of roadways, one needs to know the capacities of the two input branches of roadways, the exiting capacity, the demands for each branch of roadways, and the number of lanes of the single roadway. The merge ratio will be calculated in order to determine the proportion of the two input flows when both of branches of roadway are operating in congested conditions.

As can be seen in a simplified model of the process of merging, the exiting capacity of the system is defined to be μ, the capacities of the two input branches of roadways are defined as μ1 and μ2, and the demands for each branch of roadways are defined as q1D and q2D. The q1 and q2 are the output of the model which are the flows that pass through the merging process. The process of the model is based on the assumption that the sum of capacities of the two input branches of roadways is less than the exiting capacity of the system, μ1+μ2 ≤ μ.

Car-following models
Car-following models describe how one vehicle follows another vehicle in an uninterrupted traffic flow. They are a type of microscopic traffic flow model.

Examples of car-following models

 * Newell's car-following model
 * Louis A. Pipes started researching and gaining acknowledgment from the public in the early 1950s. Pipes car-following model is based on a safe driving rule in the California Motor Vehicle Code, and this model utilized an assumption of safe distance: a good rule for following another vehicle is to allocate an inter-vehicle distance of at least the length of a car for every ten miles per hour of vehicle speed. M
 * To capture the potential nonlinear effects in the dynamics of car following, G. F. Newell proposed a nonlinear car-following model based on empirical data. Unlike Pipes model which is solely relying on rules of safe driving, Newell nonlinear model aims at capturing the correct shape of fundamental diagrams (e.g., density-speed, flow-speed, density-flow, spacing-speed, pace-headway, etc.).
 * The Optimal Velocity Model (OVM) was introduced by Bando et al. in 1995 based on the assumption that each driver tries to reach to the optimal velocity according to the inter-vehicle difference and velocity difference between preceding vehicles.
 * Intelligent driver model is widely adopted in the research of Connected Vehicle (CV) and Connected and Autonomous Vehicle (CAV).

title: Traffic flow


text:
Dendroceros

Dendroceros is a genus of hornworts in the family Dendrocerotaceae. The genus contains about 51 species native to tropical and sub-tropical regions of the world.

Description
The epiphytic and epiphyllous Dendroceros is the only desiccation-tolerant hornwort genus. The gametophyte is yellowish-green and usually less than one-half cm wide. The thallus branches in a bifurcating pattern. In the subgenus Apoceros, there are cavities in the central strand of the thallus. The edges of the thallus are only a single layer of cells thick and have an undulating margin. It is common to find symbiotic colonies of blue-green bacteria (usually Nostoc) growing among the cells. Under a microscope, the epidermal cells have trigones.

The sporophyte is erect when mature, growing up to 5 cm tall. Unlike many hornworts, the surface of the Dendroceros sporophyte lacks stomata, as do the sporophytes of the related genera Megaceros and Nothoceros.  The interior of the sporophyte differentiates into a central column and a surrounding mass of spores and elater cells, with a distinct spiral. The spores are green and multicellular with an ornamented surface.

Classification
Current classification by Söderström et al. 2016.

Genus Dendroceros Nees 1846
 * D. australis Stephani 1909
 * D. crassicostatus Stephani 1917
 * D. exalatus Stephani 1909c
 * D. gracilis Stephani 1917b
 * D. humboldtensis Hürlimann 1960
 * D. rarus Stephani 1917b
 * D. reticulus Herzog 1950b
 * D. subtropicus Wild 1893
 * D. tahitensis Ångström 1873
 * D. vesconianus Gottsche ex Bescherelle 1898
 * D. wattsianus Stephani 1909
 * Subgenus (Cichoraceus) Peñaloza-Bojacá & Maciel-Silva 2019
 * D. cichoraceus (Montagne 1845) Stephani 1916
 * Subgenus (Dendroceros) Nees 1846
 * D. acutilobus Stephani 1909
 * D. adglutinatus (Hooker & Taylor 1845) Gottsche, Lindenberg & Nees 1846
 * D. allionii Stephani 1917
 * D. breutelii Nees 1846
 * D. crassinervis (Nees 1846) Stephani 1917
 * D. crispus (Swartz 1788) Nees 1846
 * D. foliicola Hasegawa 1980
 * D. herasii Infante 2010
 * D. javanicus (Nees 1830) Nees 1846
 * D. paivae Garcia, Sérgio & Villarreal 2012
 * D. rigidus Stephani 1917
 * D. subplanus Stephani 1909
 * D. tubercularis Hattori 1944
 * D. validus Stephani 1917
 * Subgenus (Nodulosus) Peñaloza-Bojacá & Maciel-Silva 2019
 * D. africanus Stephani 1917
 * D. borbonicus Stephani 1892
 * D. crispatus (Hooker 1830) Nees 1917
 * D. granulatus Mitten 1871
 * D. japonicus Stephani 1909
 * Subgenus (Apoceros) Schuster 1987b
 * D. cavernosus Hasegawa 1980
 * D. cucullatus Stephani 1923
 * D. difficilis Stephani 1917
 * D. muelleri Stephani 1889
 * D. ogeramnangus Piippo 1993
 * D. pedunculatus Stephani 1909
 * D. seramensis Hasegawa 1986
 * D. subdifficilis Hattori 1951

Habitat
Dendroceros grows on humid ground, rocky outcrops, and on the sides of trees. Its name literally means "tree horn".

title: Dendroceros


text:
User talk:Egandolfo

Welcome!

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Humidity indicator card
The last sentence is a little bit unclear. I assume that it means the cards are free of Cobalt(II) chloride? Cheers. CambridgeBayWeather 12:16, 3 October 2005 (UTC)

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title: User talk:Egandolfo


text: User talk:81.208.36.95

title: User talk:81.208.36.95


text:
User talk:Janmad

Welcome to Wikipedia!
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If you have any questions, feel free to ask me on my talk page. Thanks and happy editing, Alphax τεχ 10:45, 4 October 2005 (UTC)

Tilt in piazzetta san marco photo
Hi, your photo of piazzetta san marco (http://en.wikipedia.org/wiki/Image:Venezia_piazza_s.Marco_2.JPG) is nice, but there is a really heavy tilt that could quite easily be corrected in any photo software (gimp, picasa, photoshop...). Could you please do it and re-upload the photo? Thanks. --Nattfodd 07:11, 12 August 2007 (UTC)

ArbCom elections are now open!
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ArbCom elections are now open!
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title: User talk:Janmad


text:
Talk:Traffic flow

2005
I just started to write something about this topic in really interested in (and I'm working on, too). I hope someone will like it. I'll add something more these days, I hope. achab 10:50, 4 October 2005 (UTC)

Good start
Yes this is a good start. Some parts (notably the three "scales" section) need to be rewritten to sound more like an article. I'd remove the use of second person and perhaps explain the scales a bit more. I would work on it, but I'm afraid of changing the content, as I don't understand exactly what it means (sorry about my lack of expertise. Horncomposer 01:09, 26 December 2005 (UTC)

Should there be something here about the Traffic Assignment Problem, Deterministic User Equilibrium (DUE), and Stochastic User Equilibrium (SUE). -- Anonymous

I don't know these problems, sorry. --achab 10:30, 31 August 2006 (UTC)

Little improvement
tx. now I changed a few senteces, to clarify it a little. I hope the readers will be satisfied... lol achab 23:18, 31 March 2006 (UTC)

Merge from Traffic flow analysis
Please merge any relevant content from [ Traffic flow analysis] per Articles for deletion/Traffic flow analysis. (If there is nothing to merge, just leave it as a redirect.) Thanks. —Quarl (talk) 2007-02-26 09:39Z

Globalised a bit
I've added some European perspective as the article seemed to be written from a purely theoretical/USHA perspective. I've tried not to make it a sales catalogue, but the road authorities here often develop their own approaches which they build into software packages (via commercial contracts) and them mandate the use of their sw packages on the schemes that they fund. Ephebi (talk) 12:10, 14 February 2008 (UTC)

lead
currently reads: The first attempts to give a mathematical theory of traffic flow dated back to the 1950s, but to this day we still do not have a satisfactory and general theory to be applied in real flow conditions. I propose: Attempts to produce a mathematical theory of traffic flow date back to the 1950s but have so far failed to produce a general theory that can be consistently(?) applied to real flow conditions. My interest is purely to make the text flow better - I don't know enough about the subject to know what the original author meant exactly, otherwise I would have just gone ahead and made the copyedit. Can anyone clarify? Scriblio (talk) 11:40, 27 September 2008 (UTC)
 * IME (which is a few years old now...) the starting model in a scheme is calibrated by applying factors so that the current state matches the actual measured flows. The results that you would expect to see in a model also depends on the level of detail that you attempt to model, so I'm not even sure that premise is very relevant. Be bold ;-)  Ephebi (talk) 20:34, 28 September 2008 (UTC)

Article name : "Traffic flow theory" or "Traffic flow"
User:Jack-A-Roe had moved this page to "Traffic flow theory" without notification or consultation. Before renaming the article again perhaps the merits can be discussed here first? I have restored the page back to its original title as "Traffic flow". According to the reference given, "traffic flow theory" is jargon for one US-specific instance of the wider study and modelling of traffic flow (see Revised Monograph on Traffic Flow Theory). That instance relates to the theoretical capacity of free flowing networks; while perhaps it is useful for rural inter-urban expressways it is only part of the story as, AIUI, it is not very relevant to a lot of the design of non-congested, urban networks. thus using the jargon title would de-facto exclude a large proportion of the article and make it US-specific. 23:13, 27 December 2008 (UTC)

 * I support renaming the article.
 * "Traffic flow" (at least in the USA) is a very ordinary expression that refers to the actual speed, congestion etc. of traffic.  The article content, however, is entirely about the scientific theory and analysis of traffic flow. I am not a professional in the field, so I can't comment as to whether "Traffic Flow Theory", "Traffic Flow Modelling", or something else would be the best title.
 * @Previous commenter: I think you meant to type "congested, urban" rather than "non-congested, urban" in your last sentence.NinetyNineFennelSeeds (talk) 20:00, 10 November 2009 (UTC)

 * I don't see why theory and practice couldn't be covered in the same article. So "traffic flow" seems appropriate to me. It's just that nobody has written about the common usage of the word yet. But ditto NinetyNineFennelSeeds, I don't actually know anything about the topic. -- intgr [talk] 17:04, 17 December 2009 (UTC)

Related articles
Newell's Car Following Model and Three-detector problem and Newell's method - notable enough for separate articles? Perhaps merge here. Rd232 talk 11:17, 17 November 2010 (UTC)

"Traffic Stream Properties" Section Needs Improvement
The section currently named 'Traffic Stream Properties' is in desperate need of improvement. The content of the section seems good, but as it is, the page is very poorly laid out. The equations reference variables and diagrams that are never defined, and it is very unclear. Also, the use and notation of some of the variables could be greatly improved. (June 17, 2011)

It is a direct copy of "Application of the Theory of a Single First Order Equation to Traffic Flow" and does not cite the paper correctly. — Preceding unsigned comment added by 2003:4D:2C28:E000:D9EC:FB87:9DDB:702D (talk) 17:19, 7 September 2016 (UTC)

Need to discuss "Exception & Overload Situations"
Congestion models tend to assume that traffic densities are averages - whether hourly, daily, weekly or annually. This makes sense from a reductionist standpoint, but fails to consider the exceptional clustering effects of holiday traffic. For example: travel along US-101 from San Francisco to Los Angeles on a non-holiday weekday compared to travel on the day before Thanksgiving.

Also need something on driver aggression index as this is a big factor in behavorial predictions of traffic flow extremes.

23.242.213.119 (talk) 16:25, 23 November 2018 (UTC)

"cumulative cout curve"
I'm not sure what this phrase means. Is "cout" a typo for "count"? -- John of Reading (talk) 08:47, 22 June 2019 (UTC)

Major clean up
I've undertaken a major clean up of this article.

I suspect students were encouraged to add information as part of an assignment. While their good faith edits are appreciated and the content may be suitable for Wikipedia with extensive editing and clean up, I've decided to trim these sections significantly from article as they are mostly uncited, unsuitable for Wikipedia, or belong in sub articles.
 * In December 2016, added uncited work on Tandem Queues and Traffic Lights.
 * In December 2017, added uncited work on Hamilton Jacbobi PDE, Initial Value Problem, Boundary Value Problem.
 * In December 2017, added slightly sourced content on Newell-Daganzo Merge Models.
 * In December 2018, added an essay on Variable speed limit assignment.
 * In December 2018, added unsourced section on car-following models.
 * In December 2018, added unsourced section on Single vehicle dynamics
 * In December 2018, added unsourced section on The Transport Equation
 * In December 2018, edited sections on traffic assignment.

 * This has been going on much longer than I expected.
 * December 2012
 * December 2012
 * December 2012
 * December 2012
 * December 2011
 * December 2011
 * December 2011
 * This is tough because the content is typically topical but its added like a textbook or academic journal. The content lacks context and is uncited. To keep on Wikipedia it needs almost complete rewriting. I've trimmed much of it but tried to keep the cited bits and parts that were salvgable. -Eóin (talk) 01:28, 9 June 2021 (UTC)

 * It's often a problem with these kinds of student assignments. It's such a waste, the instructors could tell these students how to properly write a Wikipedia article so that they can actually contribute instead of wasting everybody's time. --Ita140188 (talk) 02:14, 10 June 2021 (UTC)

title: Talk:Traffic flow


text:
Portal:Trains/Featured picture candidates/Alfa pendular.jpg

Image:Alfa pendular.jpg
A striking image of modern railroading in Portugal. slambo 10:46, 4 October 2005 (UTC)

 * First off it's we shouldn't be allowing fair use pictures, per the rules of WP:FPC. Secondly, it's too small, and thirdly it's a tad boring.  There are several better ones you can request at . Dunc|☺ 20:55, 4 October 2005 (UTC)


text:
User talk:Peepsalot

Welcome to Wikipedia!
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If you feel a change is needed, feel free to make it yourself! Wikipedia is a wiki, so anyone (yourself included) can edit any article by following the  link. Wikipedia convention is to be bold and not be afraid of making mistakes. If you're not sure how editing works, have a look at How to edit a page, or try out the Sandbox to test your editing skills.

If, for some reason, you are unable to fix a problem yourself, feel free to ask someone else to do it. Wikipedia has a vibrant community of contributors who have a wide range of skills and specialties, and many of them would be glad to help. As well as the wiki community pages there are IRC Channels, where you are more than welcome to ask for assistance.

If you have any questions, feel free to ask me on my talk page. Thanks and happy editing, Alphax τεχ 10:46, 4 October 2005 (UTC)

title: User talk:Peepsalot


text:
User talk:Martinhenz

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Kali Yuga
Was your edit replacing bull with cow and saying you were removing sexism meant to be a joke? First, it was cited text and you shouldn't change cited text unless it's wrong, and it wasn't. It's a bull, not a cow. And how would replacing the name of a male animal with the name of a female animal (at least in some parts of the world), help? But the main problem was that you changed cited text, I can understand how you might not realise that cow refers to an adult female Bos. Dougweller (talk) 13:27, 3 December 2009 (UTC)

Speedy deletion nomination of Source Academy

A tag has been placed on Source Academy requesting that it be speedily deleted from Wikipedia. This has been done under section A7 of the criteria for speedy deletion, because the article appears to be about web content that does not credibly indicate how or why the subject is important or significant: that is, why an article about that subject should be included in an encyclopedia. Under the criteria for speedy deletion, such articles may be deleted at any time. Please read more about what is generally accepted as notable.

If you think this page should not be deleted for this reason, you may contest the nomination by visiting the page and clicking the button labelled "Contest this speedy deletion". This will give you the opportunity to explain why you believe the page should not be deleted. However, be aware that once a page is tagged for speedy deletion, it may be deleted without delay. Please do not remove the speedy deletion tag from the page yourself, but do not hesitate to add information in line with Wikipedia's policies and guidelines. If the page is deleted, and you wish to retrieve the deleted material for future reference or improvement, then please contact the. WhoAteMyButter ( 📨talk │ 📝contribs ) 03:28, 22 June 2021 (UTC)

Welcome!
Hello, Martinhenz, and welcome to Wikipedia! Thank you for your contributions. I hope you like the place and decide to stay. Unfortunately, one or more of your recent edits to the page Source Academy did not conform to Wikipedia's verifiability policy, and may have been removed. Wikipedia articles should refer only to facts and interpretations verified in reliable, reputable print or online sources or in other reliable media. Always provide a reliable source for quotations and for any material that is likely to be challenged, or it may be removed. Wikipedia also has a related policy against including original research in articles.

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Unspecified source/license for File:Logo of Source Academy.png
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Managing a conflict of interest
Hello, Martinhenz. We welcome your contributions, but if you have an external relationship with the people, places or things you have written about on the page Source Academy, you may have a conflict of interest (COI). Editors with a conflict of interest may be unduly influenced by their connection to the topic. See the conflict of interest guideline and FAQ for organizations for more information. We ask that you:

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Also, editing for the purpose of advertising, publicising, or promoting anyone or anything is not permitted. Thank you. MarioGom (talk) 12:11, 4 April 2022 (UTC)

Source Academy moved to draftspace
An article you recently created, Source Academy, is not suitable as written to remain published. It needs more citations from reliable, independent sources. (?) Information that can't be referenced should be removed (verifiability is of central importance on Wikipedia). I've moved your draft to draftspace (with a prefix of " " before the article title) where you can incubate the article with minimal disruption. When you feel the article meets Wikipedia's general notability guideline and thus is ready for mainspace, please click on the "Submit your draft for review!" button at the top of the page. MarioGom (talk) 12:11, 4 April 2022 (UTC)

Your submission at Articles for creation: Source Academy (April 27)
 Your recent article submission to Articles for Creation has been reviewed! Unfortunately, it has not been accepted at this time. The reason left by Siliconred was:

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SiliconRed (he/him • talk) 18:24, 27 April 2022 (UTC)

Your draft article, Draft:Source Academy

Hello, Martinhenz. It has been over six months since you last edited the Articles for Creation submission or Draft page you started, "Source Academy".

In accordance with our policy that Wikipedia is not for the indefinite hosting of material deemed unsuitable for the encyclopedia mainspace, the draft has been deleted. If you plan on working on it further and you wish to retrieve it, you can request its undeletion. An administrator will, in most cases, restore the submission so you can continue to work on it.

Thanks for your submission to Wikipedia, and happy editing. Liz Read! Talk! 18:31, 27 October 2022 (UTC)

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Managing a conflict of interest
Hello, Martinhenz. We welcome your contributions, but if you have an external relationship with the people, places or things you have written about on the page Structure and Interpretation of Computer Programs, JavaScript Edition, you may have a conflict of interest (COI). Editors with a conflict of interest may be unduly influenced by their connection to the topic. See the conflict of interest guideline and FAQ for organizations for more information. We ask that you:

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Also, editing for the purpose of advertising, publicising, or promoting anyone or anything is not permitted. ''If you are the one of the author of the adapted book, please do not directly edit the article anymore. If you wish to do so, please use an edit request which is outlined in the notice above. Thanks'' Justanothersgwikieditor (talk) 09:51, 25 April 2023 (UTC)

Proposed deletion of File:Logo of Source Academy.png

The file File:Logo of Source Academy.png has been proposed for deletion because of the following concern: "Uploaded for Source Academy. No other use."

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Nomination of Structure and Interpretation of Computer Programs, JavaScript Edition for deletion
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title: User talk:Martinhenz


text:
John L. Hall

John Lewis "Jan" Hall (born August 21, 1934) is an American physicist, and Nobel laureate in physics. He shared the 2005 Nobel Prize in Physics with Theodor W. Hänsch and Roy Glauber for his work in precision spectroscopy.

Biography
Born in Denver, Colorado, Hall holds three degrees from Carnegie Institute of Technology, a B.S. in 1956, an M.S. in 1958, and a Ph.D. in 1961. He completed his postdoctoral studies at the Department of Commerce's National Bureau of Standards, now the National Institute of Standards and Technology (NIST), where he remained from 1962 until his retirement in 2004. He has lectured at the University of Colorado Boulder since 1967.

Hall is currently a NIST Senior Fellow, emeritus, and remains a Fellow at JILA, formerly the Joint Institute for Laboratory Astrophysics, and lecturer at the CU-Boulder Physics Department. JILA is a research institute managed jointly by CU-Boulder and NIST.

Hall shared half of the Nobel Prize with Theodor W. Hänsch for their pioneering work on laser-based precision spectroscopy and the optical frequency comb technique. The other half of the prize was awarded to Roy J. Glauber.

Hall has received many other honors for his pioneering work, including the Optical Society of America's Max Born Award "for pioneering the field of stable lasers, including their applications in fundamental physics and, most recently, in the stabilization of femtosecond lasers to provide dramatic advances in optical frequency metrology".

Hall is one of the 20 American recipients of the Nobel Prize in Physics to sign a letter addressed to President George W. Bush in May 2008, urging him to "reverse the damage done to basic science research in the Fiscal Year 2008 Omnibus Appropriations Bill" by requesting additional emergency funding for the Department of Energy’s Office of Science, the National Science Foundation, and the National Institute of Standards and Technology.

In 2015, Hall signed the Mainau Declaration 2015 on Climate Change on the final day of the 65th Lindau Nobel Laureate Meeting. The declaration was signed by a total of 76 Nobel Laureates and handed to then-President of the French Republic, François Hollande, as part of the successful COP21 climate summit in Paris.

Honours and awards

 * National Carbon Company Fellow in Physics, 1957–1961
 * Department of Commerce Gold Medal, 1969
 * Samuel W. Stratton Award, 1971
 * Department of Commerce Gold Medal, 1974 (group awards)
 * IR-100: Laser stabilizer selected as one of "100 best new products of the year," 1975
 * IR-100: Laser wavelength meter ("Lambdameter") selected as one of "100 best new products of the year," 1977
 * E. U. Condon Award, 1979
 * Charles Hard Townes Award of the Optical Society of America, 1984, jointly with V. P. Chebotayev (Academy of Sciences, USSR)
 * Davisson-Germer Prize of the American Physical Society, 1988
 * Docteur Honoris Causa de l'Universite Paris Nord, 1989
 * Frederic Ives Medal of the Optical Society of America, 1991
 * Einstein Prize for Laser Science, 1992
 * Arthur L. Shawlow Prize of the American Physical Society, 1993
 * Allen V. Astin Measurement Science Award, 2000
 * Max Born Award of the Optical Society of America, 2002
 * Presidential Rank Award from the Office of Personnel Management, 2002
 * Department of Commerce Gold Medal, 2002 (group awards)
 * Rabi Award of the IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society, 2004
 * Légion d'Honneur Membership, 2004
 * Nobel Prize in Physics, 2005
 * Golden Plate Award of the American Academy of Achievement, 2006
 * Doctor of Science, honoris causa, University of Glasgow, 2007

title: John L. Hall