Three-phase traffic theory is a theory of traffic flow developed by Boris Kerner between 1996 and 2002.It focuses mainly on the explanation of the physics of traffic breakdown and resulting congested traffic on highways. Kerner describes three phases of traffic, while the classical theories based on the fundamental diagram of traffic flow have two phases: free flow and congested traffic. Kerner’s theory divides congested traffic into two distinct phases, synchronized flow and wide moving jam, bringing the total number of phases to three:
The word "wide" is used even though it is the length of the traffic jam that is being referred to.
A phase is defined as a state in space and time.
In free traffic flow, empirical data show a positive correlation between the flow rate (in vehicles per unit time) and vehicle density (in vehicles per unit distance). This relationship stops at the maximum free flow with a corresponding critical density . (See Figure 1.)
Data show a weaker relationship between flow and density in congested conditions. Therefore, Kerner argues that the fundamental diagram, as used in classical traffic theory, cannot adequately describe the complex dynamics of vehicular traffic. He instead divides congestion into synchronized flow and wide moving jams.
In congested traffic, the vehicle speed is lower than the lowest vehicle speed encountered in free flow, i.e., the line with the slope of the minimal speed in free flow (dotted line in Figure 2) divides the empirical data on the flow-density plane into two regions: on the left side data points of free flow and on the right side data points corresponding to congested traffic.
In Kerner's theory, the phases J and S in congested traffic are observed outcomes in universal spatial-temporal features of real traffic data. The phases J and S are defined through the definitions [J] and [S] as follows:
A so-called "wide moving jam" moves upstream through any highway bottlenecks. While doing so, the mean velocity of the downstream front is maintained. This is the characteristic feature of the wide moving jam that defines the phase J.
The term wide moving jam is meant to reflect the characteristic feature of the jam to propagate through any other state of traffic flow and through any bottleneck while maintaining the velocity of the downstream jam front. The phrase moving jam reflects the jam propagation as a whole localized structure on a road. To distinguish wide moving jams from other moving jams, which do not characteristically maintain the mean velocity of the downstream jam front, Kerner used the term wide. The term wide reflects the fact that if a moving jam has a width (in the longitudinal road direction) considerably greater than the widths of the jam fronts, and if the vehicle speed inside the jam is zero, the jam always exhibits the characteristic feature of maintaining the velocity of the downstream jam front (see Sec. 7.6.5 of the book). Thus the term wide has nothing to do with the width across the jam, but actually refers to its length being considerably more than the transition zones at its head and tail. Historically, Kerner used the term wide from a qualitative analogy of a wide moving jam in traffic flow with wide autosolitons occurring in many systems of natural science (like gas plasma, electron-hole plasma in semiconductors, biological systems, and chemical reactions): Both the wide moving jam and a wide autosoliton exhibit some characteristic features, which do not depend on initial conditions at which these localized patterns have occurred.
In "synchronized flow", the downstream front, where the vehicles accelerate to free flow, does not show this characteristic feature of the wide moving jam. Specifically, the downstream front of synchronized flow is often fixed at a bottleneck.
The term "synchronized flow" is meant to reflect the following features of this traffic phase: (i) It is a continuous traffic flow with no significant stoppage, as often occurs inside a wide moving jam. The term "flow" reflects this feature. (ii) There is a tendency towards synchronization of vehicle speeds across different lanes on a multilane road in this flow. In addition, there is a tendency towards synchronization of vehicle speeds in each of the road lanes (bunching of vehicles) in synchronized flow. This is due to a relatively low probability of passing. The term "synchronized" reflects this speed synchronization effect.
Measured data of averaged vehicle speeds (Figure 3 (a)) illustrate the phase definitions [J] and [S]. There are two spatial-temporal patterns of congested traffic with low vehicle speeds in Figure 3 (a). One pattern propagates upstream with almost constant velocity of the downstream front, moving straight through the freeway bottleneck. According to the definition [J], this pattern of congestion belongs to the "wide moving jam" phase. In contrast, the downstream front of the other pattern is fixed at a bottleneck. According to the definition [S], this pattern belongs to the "synchronized flow" phase (Figure 3 (a) and (b)). Other empirical examples of the validation of the traffic phase definitions [J] and [S] can be found in the booksand, in the article as well as in an empirical study of floating car data (floating car data is also called probe vehicle data).
In Sec. 6.1 of the bookhas been shown that the traffic phase definitions [S] and [J] are the origin of most hypotheses of three-phase theory and related three-phase microscopic traffic flow models. The traffic phase definitions [J] and [S] are non-local macroscopic ones and they are applicable only after macroscopic data has been measured in space and time, i.e., in an "off-line" study. This is because for the definitive distinction of the phases J and S through the definitions [J] and [S] a study of the propagation of traffic congestion through a bottleneck is necessary. This is often considered as a drawback of the traffic phase definitions [S] and [J]. However, there are local microscopic criteria for the distinction between the phases J and S without a study of the propagation of congested traffic through a bottleneck. The microscopic criteria are as follows (see Sec. 2.6 in the book ): If in single-vehicle (microscopic) data related to congested traffic the "flow-interruption interval", i.e., a time headway between two vehicles following each other is observed, which is much longer than the mean time delay in vehicle acceleration from a wide moving jam (the latter is about 1.3–2.1 s), then the related flow-interruption interval corresponds to the wide moving jam phase. After all wide moving jams have been found through this criterion in congested traffic, all remaining congested states are related to the synchronized flow phase.
Homogeneous synchronized flow is a hypothetical state of synchronized flow of identical vehicles and drivers in which all vehicles move with the same time-independent speed and have the same space gaps (a space gap is the distance between one vehicle and the one behind it), i.e., this synchronized flow is homogeneous in time and space.
Kerner’s hypothesis is that homogeneous synchronized flow can occur anywhere in a two-dimensional region (2D) of the flow-density plane (2D-region S in Figure 4(a)). The set of possible free flow states (F) overlaps in vehicle density the set of possible states of homogeneous synchronized flow. The free flow states on a multi-lane road and states of homogeneous synchronized flow are separated by a gap in the flow rate and, therefore, by a gap in the speed at a given density: at each given density the synchronized flow speed is lower than the free flow speed.
In accordance with this hypothesis of Kerner’s three-phase theory, at a given speed in synchronized flow, the driver can make an arbitrary choice as to the space gap to the preceding vehicle, within the range associated with the 2D region of homogeneous synchronized flow (Figure 4(b)): the driver accepts different space gaps at different times and does not use some one unique gap.
The hypothesis of Kerner’s three-phase traffic theory about 2D region of steady states of synchronized flow is contrary to the hypothesis of earlier traffic flow theories involving the fundamental diagram of traffic flow, which suppose a one-dimensional relationship between vehicle density and flow rate.
In Kerner’s three-phase theory, a vehicle accelerates when the space gap to the preceding vehicle is greater than a synchronization space gap , i.e., at (labelled by acceleration in Figure 5); the vehicle decelerates when the gap g is smaller than a safe space gap , i.e., at (labelled by deceleration in Figure 5).
If the gap is less than G, the driver tends to adapt his speed to the speed of the preceding vehicle without caring what the precise gap is, so long as this gap is not smaller than the safe space gap (labelled by speed adaptation in Figure 5). Thus the space gap in car following in the framework of Kerner’s three-phase theory can be any space gap within the space gap range .
In the framework of the three-phase theory the hypothesis about 2D regions of states of synchronized flow has also been applied for the development of a model of autonomous driving vehicle (called also automated driving, self-driving or autonomous vehicle).
In measured data, congested traffic most often occurs in the vicinity of highway bottlenecks, e.g., on-ramps, off-ramps, or roadwork. A transition from free flow to congested traffic is known as traffic breakdown. In Kerner’s three-phase traffic theory traffic breakdown is explained by a phase transition from free flow to synchronized flow (called as F →S phase transition). This explanation is supported by available measurements, because in measured traffic data after a traffic breakdown at a bottleneck the downstream front of the congested traffic is fixed at the bottleneck. Therefore, the resulting congested traffic after a traffic breakdown satisfies the definition [S] of the "synchronized flow" phase.
Kerner notes using empirical data that synchronized flow can form in free flow spontaneously (spontaneous F →S phase transition) or can be externally induced (induced F → S phase transition).
A spontaneous F →S phase transition means that the breakdown occurs when there has previously been free flow at the bottleneck as well as both up- and downstream of the bottleneck. This implies that a spontaneous F → S phase transition occurs through the growth of an internal disturbance in free flow in a neighbourhood of a bottleneck.
In contrast, an induced F → S phase transition occurs through a region of congested traffic that initially emerged at a different road location downstream from the bottleneck location. Normally, this is in connection with the upstream propagation of a synchronized flow region or a wide moving jam. An empirical example of an induced breakdown at a bottleneck leading to synchronized flow can be seen in Figure 3: synchronized flow emerges through the upstream propagation of a wide moving jam. The existence of empirical induced traffic breakdown (i.e., empirical induced F →S phase transition) means that a F → S phase transition occurs in a metastable state of free flow at a highway bottleneck. The term metastable free flow means that when small perturbations occur in free flow, the state of free flow is still stable, i.e., free flow persists at the bottleneck. However, when larger perturbations occur in free flow in a neighborhood of the bottleneck, the free flow is unstable and synchronized flow will emerge at the bottleneck.
Kerner explains the nature of the F → S phase transitions by a competition of "speed adaptation" and "over-acceleration". Speed adaptation is defined as the vehicle deceleration to the speed of a slower moving preceding vehicle. Over-acceleration is defined as the vehicle acceleration occurring even if the preceding vehicle does not drive faster than the vehicle and the preceding vehicle additionally does not accelerate. In Kerner’s theory, the probability of over-acceleration is a discontinuous function of the vehicle speed: At the same vehicle density, probability of over-acceleration in free flow is greater than in synchronized flow. When within a local speed disturbance speed adaptation is stronger than over-acceleration, an F → S phase transition occurs. Otherwise, when over-acceleration is stronger than speed adaptation the initial disturbance decays over time. Within a region of synchronized flow, a strong over-acceleration is responsible for a return transition from synchronized flow to free flow (S → F transition).
There can be several mechanisms of vehicle over-acceleration. It can be assumed that on a multi-lane road the most probable mechanism of over-acceleration is lane changing to a faster lane. In this case, the F → S phase transitions are explained by an interplay of acceleration while overtaking a slower vehicle (over-acceleration) and deceleration to the speed of a slower-moving vehicle ahead (speed adaptation). Overtaking supports the maintenance of free flow. "Speed adaptation" on the other hand leads to synchronized flow. Speed adaptation will occur if overtaking is not possible. Kerner states that the probability of overtaking is an interrupted function of the vehicle density (Figure 6): at a given vehicle density, the probability of overtaking in free flow is much higher than in synchronized flow.
Kerner’s explanation of traffic breakdown at a highway bottleneck by the F → S phase transition in a metastable free flow is associated with the following fundamental empirical features of traffic breakdown at the bottleneck found in real measured data: (i) Spontaneous traffic breakdown in an initial free flow at the bottleneck leads to the emergence of congested traffic whose downstream front is fixed at the bottleneck (at least during some time interval), i.e., this congested traffic satisfies the definition [S] for the synchronized flow phase. In other words, spontaneous traffic breakdown is always an F → S phase transition. (ii) Probability of this spontaneous traffic breakdown is an increasing function of the flow rates at the bottleneck. (iii) At the same bottleneck, traffic breakdown can be either spontaneous or induced (see empirical examples for these fundamental features of traffic breakdown in Secs. 2.2.3 and 3.1 of the book); for this reason, the F → S phase transition occurs in a metastable free flow at a highway bottleneck. As explained above, the sense of the term metastable free flow is as follows. Small enough disturbances in metastable free flow decay. However, when a large enough disturbance occurs at the bottleneck, an F → S phase transition does occur. Such a disturbance that initiates the F → S phase transition in metastable free flow at the bottleneck can be called a nucleus for traffic breakdown. In other words, real traffic breakdown (F → S phase transition) at a highway bottleneck exhibits the nucleation nature. Kerner considers the empirical nucleation nature of traffic breakdown (F → S phase transition) at a road bottleneck as the empirical fundamental of traffic and transportation science.
The empirical nucleation nature of traffic breakdown at highway bottlenecks cannot be explained by classical traffic theories and models. The search for explanation of the empirical nucleation nature of traffic breakdown (F → S phase transition) at a highway bottleneck has been the reason for the development of Kerner’s three-phase theory.
In particular, in two-phase traffic flow models in which traffic breakdown is associated with free flow instability, this model instability leads to the F → J phase transition, i.e. in these traffic flow models traffic breakdown is governed by spontaneous emergence of a wide moving jam(s) in an initial free flow (see Kerner’s criticism on such two-phase models as well as on other classical traffic flow models and theories in Chapter 10 of the bookas well as in critical reviews, ).
Kerner developed the three-phase theory as an explanation of the empirical nature of traffic breakdown at highway bottlenecks: a random (probabilistic) F → S phase transition that occurs in metastable state of free flow. Herewith Kerner explained the main prediction, that this metastability of free flow with respect to the F → S phase transition is governed by the nucleation nature of an instability of synchronized flow. The explanation is a large enough local increase in speed in synchronized flow (called a S → F instability), that is a growing speed wave of a local increase in speed in synchronized flow at the bottleneck. The development of the S → F instability leads to a local phase transition from synchronized flow to free flow at the bottleneck (S → F transition). To explain this phenomenon Kerner developed a microscopic theory of the S → F instability.
The basic result of the three-phase theory about the nucleation nature of traffic breakdown (F → S transition) shows that the three-phase theory is incommensurable with all earlier traffic flow theories and models (see explanations below).
As mentioned, the main reason of Kerner’s three-phase traffic theory is the explanation of the empirical nucleation nature of traffic breakdown (F → S transition) at the bottleneck. To reach this goal, in congested traffic a new traffic phase called synchronized flow has been introduced. The basic feature of the synchronized flow traffic phase formulated in the three-phase traffic theory leads to the nucleation nature of the F → S transition. In this sense, Kerner’s synchronized flow traffic phase that ensures the nucleation nature of the F → S transition at a highway bottleneck and Kerner’s three-phase traffic theory can be considered synonyms.
Initially developed for highway traffic, Kerner expanded the three phase theory for the description of city traffic in 2011–2014.
In three-phase traffic theory, traffic breakdown is explained by the F → S transition occurring in a metastable free flow. Probably the most important consequence of that is the existence of a range of highway capacities between some maximum and minimum capacities.
Spontaneous traffic breakdown, i.e., a spontaneous F → S phase transition, may occur in a wide range of flow rates in free flow. Kerner states, based on empirical data, that because of the possibility of spontaneous or induced traffic breakdowns at the same freeway bottleneck at any time instant there is a range of highway capacities at a bottleneck. This range of freeway capacities is between a minimum capacity and a maximum capacity of free flow (Figure 7).
There is a maximum highway capacity : If the flow rate is close to the maximum capacity , then even small disturbances in free flow at a bottleneck will lead to a spontaneous F → S phase transition. On the other hand, only very large disturbances in free flow at the bottleneck will lead to a spontaneous F → S phase transition, if the flow rate is close to a minimum capacity (see, for example, Sec. 17.2.2 of the book ). The probability of a smaller disturbance in free flow is much higher than that of a larger disturbance. Therefore, the higher the flow rate in free flow at a bottleneck, the higher the probability of the spontaneous F → S phase transition. If the flow rate in free flow is lower than the minimum capacity , there will be no traffic breakdown (no F →S phase transition) at the bottleneck.
The infinite number of highway capacities at a bottleneck can be illustrated by the meta-stability of free flow at flow rates with
Metastability of free flow means that for small disturbances free flow remains stable (free flow persists), but with larger disturbances the flow becomes unstable and a F → S phase transition to synchronized flow occurs.
Thus the basic theoretical result of three phase theory about the understanding of stochastic capacity of free flow at a bottleneck is as follows: At any time instant, there is the infinite number of highway capacities of free flow at the bottleneck. The infinite number of the flow rates, at which traffic breakdown can be induced at the bottleneck, are the infinite number of highway capacities. These capacities are within the flow rate range between a minimum capacity and a maximum capacity (Figure 7).
The range of highway capacities at a bottleneck in Kerner’s three-phase traffic theory contradicts fundamentally the classical understanding of stochastic highway capacity as well as traffic theories and methods for traffic management and traffic control which at any time assume the existence of a particular highway capacity. In contrast, in Kerner’s three-phase traffic theory at any time there is a range of highway capacities, which are between the minimum capacity and maximum capacity . The values and can depend considerably on traffic parameters (the percentage of long vehicles in traffic flow, weather, bottleneck characteristics, etc.).
The existence at any time instant of a range of highway capacities in Kerner’s theory changes crucially methodologies for traffic control, dynamic traffic assignment, and traffic management. In particular, to satisfy the nucleation nature of traffic breakdown, Kerner introduced breakdown minimization principle (BM principle) for the optimization and control of vehicular traffic networks.
A moving jam will be called "wide" if its length (in direction of the flow) clearly exceeds the lengths of the jam fronts. The average vehicle speed within wide moving jams is much lower than the average speed in free flow. At the downstream front the vehicles accelerate to the free flow speed. At the upstream jam front the vehicles come from free flow or synchronized flow and must reduce their speed. According to the definition [J] the wide moving jam always has the same mean velocity of the downstream front , even if the jam propagates through other traffic phases or bottlenecks. The flow rate is sharply reduced within a wide moving jam.
Kerner’s empirical results show that some characteristic features of wide moving jams are independent of the traffic volume and bottleneck features (e.g. where and when the jam formed). However, these characteristic features are dependent on weather conditions, road conditions, vehicle technology, percentage of long vehicles, etc.. The velocity of the downstream front of a wide moving jam (in the upstream direction) is a characteristic parameter, as is the flow rate just downstream of the jam (with free flow at this location, see Figure 8). This means that many wide moving jams have similar features under similar conditions. These parameters are relatively predictable. The movement of the downstream jam front can be illustrated in the flow-density plane by a line, which is called "Line J" (Line J in Figure 8). The slope of the Line J is the velocity of the downstream jam front .
Kerner emphasizes that the minimum capacity and the outflow of a wide moving jam describe two qualitatively different features of free flow: the minimum capacity characterizes an F → S phase transition at a bottleneck, i.e., a traffic breakdown. In contrast, the outflow of a wide moving jam determines a condition for the existence of the wide moving jam, i.e., the traffic phase J while the jam propagates in free flow: Indeed, if the jam propagates through free flow (i.e., both upstream and downstream of the jam free flows occur), then a wide moving jam can persist, only when the jam inflow is equal to or larger than the jam outflow ; otherwise, the jam dissolves over time. Depending on traffic parameters like weather, percentage of long vehicles, et cetera, and characteristics of the bottleneck where the F → S phase transition can occur, the minimum capacity might be smaller (as in Figure 8), or greater than the jam’s outflow .
In contrast to wide moving jams, both the flow rate and vehicle speed may vary significantly in the synchronized flow phase. The downstream front of synchronized flow is often spatially fixed (see definition [S]), normally at a bottleneck at a certain road location. The flow rate in this phase could remain similar to the one in free flow, even if the vehicle speeds are sharply reduced.
Because the synchronized flow phase does not have the characteristic features of the wide moving jam phase J, Kerner’s three-phase traffic theory assumes that the hypothetical homogeneous states of synchronized flow cover a two-dimensional region in the flow-density plane (dashed regions in Figure 8).
Wide moving jams do not emerge spontaneously in free flow, but they can emerge in regions of synchronized flow. This phase transition is called a S → J phase transition.
In 1998,Kerner found out that in real field traffic data the emergence of a wide moving jam in free flow is observed as a cascade of F → S → J phase transitions (Figure 9): first, a region of synchronized flow emerges in a region of free flow. As explained above, such an F → S phase transition occurs mostly at a bottleneck. Within the synchronized flow phase a further "self-compression" occurs and vehicle density increases while vehicle speed decreases. This self-compression is called "pinch effect". In "pinch" regions of synchronized flow, narrow moving jams emerge. If these narrow moving jams grow, wide moving jams will emerge labeled by S → J in Figure 9). Thus, wide moving jams emerge later than traffic breakdown (F → S transition) has occurred and at another road location upstream of the bottleneck. Therefore, when Kerner’s F → S → J phase transitions occurring in real traffic (Figure 9 (a)) are presented in the speed-density plane (Figure 9 (b)) (or speed-flow, or else flow-density planes), one should remember that states of synchronized flow and low speed state within a wide moving jam are measured at different road locations. Kerner notes that the frequency of the emergence of wide moving jams increases if the density in synchronized flow increases. The wide moving jams propagate further upstream, even if they propagate through regions of synchronized flow or bottlenecks. Obviously, any combination of return phase transitions (S → F, J → S, and J → F transitions shown in Figure 9) is also possible.
To further illustrate S → J phase transitions: in Kerner’s three-phase traffic theory the Line J divides the homogeneous states of synchronized flow in two (Figure 8). States of homogeneous synchronized flow above Line J are meta-stable. States of homogeneous synchronized flow below Line J are stable states in which no S → J phase transition can occur. Metastable homogeneous synchronized flow means that for small disturbances, the traffic state remains stable. However, when larger disturbances occur, synchronized flow becomes unstable, and a S → J phase transition occurs.
Very complex congested patterns can be observed, caused by F → S and S → J phase transitions.
A congestion pattern of synchronized flow (Synchronized Flow Pattern (SP)) with a fixed downstream and a not continuously propagating upstream front is called Localised Synchronized Flow Pattern (LSP).
Frequently the upstream front of a SP propagates upstream. If only the upstream front propagates upstream, the related SP is called Widening Synchronised Flow Pattern (WSP). The downstream front remains at the bottleneck location and the width of the SP increases.
It is possible that both upstream and downstream front propagate upstream. The downstream front is no longer located at the bottleneck. This pattern has been called Moving Synchronised Flow Pattern (MSP).
The difference between the SP and the wide moving jam becomes visible in that when a WSP or MSP reaches an upstream bottleneck the so-called "catch-effect" can occur. The SP will be caught at the bottleneck and as a result a new congested pattern emerges. A wide moving jam will not be caught at a bottleneck and moves further upstream. In contrast to wide moving jams, the synchronized flow, even if it moves as an MSP, has no characteristic parameters. As an example, the velocity of the downstream front of the MSP might vary significantly and can be different for different MSPs. These features of SP and wide moving jams are consequences of the phase definitions [S] and [J].
An often occurring congestion pattern is one that contains both congested phases, [S] and [J]. Such a pattern with [S] and [J] is called General Pattern (GP). An empirical example of GP is shown in Figure 9 (a).
In many freeway infrastructures bottlenecks are very close one to another. A congestion pattern whose synchronized flow covers two or more bottlenecks is called an Expanded Pattern (EP). An EP could contain synchronized flow only (called ESP: Expanded Synchronized Flow Pattern)), but normally wide moving jams form in the synchronized flow. In those cases the EP is called EGP (Expanded General Pattern) (see Figure 10).
One of the applications of Kerner’s three-phase traffic theory is the methods called ASDA/FOTO (Automatische StauDynamikAnalyse (Automatic tracking of wide moving jams) and Forecasting Of Traffic Objects). ASDA/FOTO is a software tool able to process large traffic data volumes quickly and efficiently on freeway networks (see examples from three countries, Figure 11). ASDA/FOTO works in an online traffic management system based on measured traffic data. Recognition, tracking and prediction of [S] and [J] are performed using the features of Kerner’s three-phase traffic theory.
Further applications of the theory are seen in the development of traffic simulation models, a ramp metering system (ANCONA), collective traffic control, traffic assistance, autonomous driving and traffic state detection, as described in the books by Kerner.
Rather than a mathematical model of traffic flow, Kerner’s three-phase theory is a qualitative traffic flow theory that consists of several hypotheses. The hypotheses of Kerner’s three-phase theory should qualitatively explain spatiotemporal traffic phenomena in traffic networks found out in real field traffic data, which was measured over years on a variety of highways in different countries. Some of the hypotheses of Kerner’s theory have been considered above. It can be expected that a diverse variety of different mathematical models of traffic flow can be developed in the framework of Kerner’s three-phase theory.
The first mathematical model of traffic flow in the framework of Kerner’s three-phase theory that mathematical simulations can show and explain traffic breakdown by an F → S phase transition in the metastable free flow at the bottleneck was the Kerner-Klenov model introduced in 2002.The Kerner–Klenov model is a microscopic stochastic model in the framework of Kerner’s three-phase traffic theory. In the Kerner-Klenov model, vehicles move in accordance with stochastic rules of vehicle motion that can be individually chosen for each of the vehicles. Some months later, Kerner, Klenov, and Wolf developed a cellular automaton (CA) traffic flow model in the framework of Kerner’s three-phase theory.
The Kerner-Klenov stochastic three-phase traffic flow model in the framework of Kerner’s theory has further been developed for different applications, in particular to simulate on-ramp metering, speed limit control, dynamic traffic assignment in traffic and transportation networks, traffic at heavy bottlenecks and on moving bottlenecks, features of heterogeneous traffic flow consisting of different vehicles and drivers, jam warning methods, vehicle-to-vehicle (V2V) communication for cooperative driving, the performance of self-driving vehicles in mixture traffic flow, traffic breakdown at signals in city traffic, over-saturated city traffic, vehicle fuel consumption in traffic networks (see references in Sec. 1.7 of a review).
Over time several scientific groups have developed new mathematical models in the framework of Kerner’s three-phase theory. In particular, new mathematical models in the framework of Kerner’s three-phase theory have been introduced in the works by Jiang, Wu, Gao, et al.,Davis, Lee, Barlovich, Schreckenberg, and Kim (see other references to mathematical models in the framework of Kerner’s three-phase traffic theory and results of their investigations in Sec. 1.7 of a review ).
The theory has been criticized for two primary reasons. First, the theory is almost completely based on measurements on the Bundesautobahn 5 in Germany. It may be that this road has this pattern, but other roads in other countries have other characteristics. Future research must show the validity of the theory on other roads in other countries around the world. Second, it is not clear how the data was interpolated. Kerner uses fixed point measurements (loop detectors), but draws his conclusions on vehicle trajectories, which span the whole length of the road under investigation. These trajectories can only be measured directly if floating car data is used, but as said, only loop detector measurements are used. How the data in between was gathered or interpolated, is not clear.
The above criticism has been responded to in a recent study of data measured in the US and the United Kingdom, which confirms conclusions made based on measurements on the Bundesautobahn 5 in Germany.Moreover, there is a recent validation of the theory based on floating car data. In this article one can also find methods for spatial-temporal interpolations of data measured at road detectors (see article’s appendixes).
Other criticisms have been made, such as that the notion of phases has not been well defined and that so-called two-phase models also succeed in simulating the essential features described by Kerner.
This criticism has been responded to in a reviewas follows. The most important feature of Kerner’s theory is the explanation of the empirical nucleation nature of traffic breakdown at a road bottleneck by the F → S transition. The empirical nucleation nature of traffic breakdown cannot be explained with earlier traffic flow theories including two-phase traffic flow models studied in.
The explanation of traffic breakdown at a highway bottleneck by a F → S transition in a metastable free flow at the bottleneck is the basic assumption of Kerner’s three-phase theory.However, none of earlier traffic-flow theories incorporates a F→S transition in a metastable free flow at the bottleneck. Therefore, none of the classical traffic flow theories is consistent with the empirical nucleation nature of real traffic breakdown at a highway bottleneck.
The F→S phase transition in metastable free flow at highway bottleneck does explain the empirical evidence of the induced transition from free flow to synchronized flow together with the flow-rate dependence of the breakdown probability. In accordance with the classical book by Kuhn,this shows the incommensurability of three-phase theory and the classical traffic-flow theories (for more details, see ):
The existence of these two phases F and S at the same flow rate does not result from the stochastic nature of traffic: Even if there were no stochastic processes in vehicular traffic, the states F and S do exist at the same flow rate. However, classical stochastic approaches to traffic control do not assume a possibility of an F→S phase transition in metastable free flow. For this reason, these stochastic approaches cannot resolve the problem of the inconsistence of classical theories with the nucleation nature of real traffic breakdown.
According to Kerner, this inconsistence can explain why network optimization and control approaches based on these fundamentals and methodologies have failed by their applications in the real world. Even several decades of a very intensive effort to improve and validate network optimization models have no success. Indeed, there can be found no examples where on-line implementations of the network optimization models based on these fundamentals and methodologies could reduce congestion in real traffic and transportation networks.
This is due to the fact that the fundamental empirical features of traffic breakdown at highway bottlenecks have been understood only during last 20 years. In contrast, the generally accepted fundamentals and methodologies of traffic and transportation theory have been introduced in the 50s–60s. Examples of this classical traffic flow theories are the Lighthill–Whitham–Richards (LWR) model,General Motors (GM) traffic-flow model of Herman, Gazis, Montroll, Potts, and Rothery, as well as Wardrop’s principles for optimization of transportation networks. Thus the scientists whose ideas led to these classical fundamentals and methodologies of traffic and transportation theory could not know the nucleation nature of real traffic breakdown. Many of the diverse driver behavioral characteristics related to real traffic as well as some of the mathematical approaches to traffic flow modeling, which have been discovered in classical approaches to traffic flow theory, are also used in three-phase traffic theory and associated microscopic traffic flow models (for more details, see Sec. 11 of a review ).
The term "incommensurability" mentioned above has been introduced by Kuhn in his classical bookto explain a paradigm shift in a scientific field. The paradigm shift in traffic and transportation science is the fundamental change in the meaning of stochastic highway capacity because the meaning of highway capacity is the basis for the development of any method for traffic control, management, and organization of a traffic network as well as applications of intelligent transportation systems . The paradigm of standard traffic and transportation theories is that at any time instant there is a value of stochastic highway capacity. When the flow rate at a bottleneck exceeds the capacity value at this time instant, traffic breakdown must occur at the bottleneck.
The new paradigm of traffic and transportation science following from the empirical nucleation nature of traffic breakdown (F → S transition) and Kerner's three-phase traffic theory changes fundamentally the meaning of stochastic highway capacity as follows. At any time instant there is a range of highway capacity values between a minimum and a maximum highway capacity, which are themselves stochastic values. When the flow rate at a bottleneck is inside this capacity range related to this time instant, traffic breakdown can occur at the bottleneck only with some probability, i.e., in some cases traffic breakdown occurs, in other cases it does not occur.
Traffic on roads consists of road users including pedestrians, ridden or herded animals, vehicles, streetcars, buses and other conveyances, either singly or together, while using the public way for purposes of travel.
A ramp meter, ramp signal, or metering light is a device, usually a basic traffic light or a two-section signal light together with a signal controller, that regulates the flow of traffic entering freeways according to current traffic conditions. Ramp meters are used at freeway on-ramps to manage the rate of automobiles entering the freeway. Ramp metering systems have proved to be successful in decreasing traffic congestion and improving driver safety.
Traffic engineering is a branch of civil engineering that uses engineering techniques to achieve the safe and efficient movement of people and goods on roadways. It focuses mainly on research for safe and efficient traffic flow, such as road geometry, sidewalks and crosswalks, cycling infrastructure, traffic signs, road surface markings and traffic lights. Traffic engineering deals with the functional part of transportation system, except the infrastructures provided.
Traffic congestion is a condition on transport that is characterised by slower speeds, longer trip times, and increased vehicular queueing. Traffic congestion on urban road networks has become increasingly problematic since the 1950s. When traffic demand is great enough that the interaction between vehicles slows the speed of the traffic stream, this results in some congestion. While congestion is a possibility for any mode of transportation, this article will focus on automobile congestion on public roads.
Level of service (LOS) is a qualitative measure used to relate the quality of motor vehicle traffic service. LOS is used to analyze roadways and intersections by categorizing traffic flow and assigning quality levels of traffic based on performance measure like vehicle speed, density, congestion, etc. In a more general sense, levels of service can apply to all services in asset management domain.
Gridlock is a form of traffic congestion where "continuous queues of vehicles block an entire network of intersecting streets, bringing traffic in all directions to a complete standstill". The term originates from a situation possible in a grid plan where intersections are blocked, preventing vehicles from either moving forwards through the intersection or backing up to an upstream intersection.
A transport network, or transportation network is a realisation of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include but are not limited to road networks, railways, air routes, pipelines, aqueducts, and power lines.
Traffic waves, also called stop waves, ghost jams, or traffic shocks, are traveling disturbances in the distribution of cars on a highway. Traffic waves travel backwards relative to the cars themselves. Relative to a fixed spot on the road the wave can move with, or against the traffic, or even be stationary. Traffic waves are a type of traffic jam. A deeper understanding of traffic waves is a goal of the physical study of traffic flow, in which traffic itself can often be seen using techniques similar to those used in fluid dynamics.
In mathematics and transportation engineering, traffic flow is the study of interactions between travellers and infrastructure, with the aim of understanding and developing an optimal transport network with efficient movement of traffic and minimal traffic congestion problems.
Boris S. Kerner is the pioneer of three phase traffic theory.
The fundamental diagram of traffic flow is a diagram that gives a relation between the traffic flux (vehicles/hour) and the traffic density (vehicles/km). A macroscopic traffic model involving traffic flux, traffic density and velocity forms the basis of the fundamental diagram. It can be used to predict the capability of a road system, or its behaviour when applying inflow regulation or speed limits.
A traffic bottleneck is a localized disruption of vehicular traffic on a street, road, or highway. As opposed to a traffic jam, a bottleneck is a result of a specific physical condition, often the design of the road, badly timed traffic lights, or sharp curves. They can also be caused by temporary situations, such as vehicular accidents.
In traffic flow theory, Newell’s car-following model is a method used to determine how vehicles follow one another on a roadway. The main idea of this model is that a vehicle will maintain a minimum space and time gap between it and the vehicle that precedes it. Thus, under congested conditions, if the leading car changes its speed, the following vehicle will also change speed at a point in time-space along the traffic wave speed, -w.
The Three-detector problem is a problem in traffic flow theory. Given is a homogeneous freeway and the vehicle counts at two detector stations. We seek the vehicle counts at some intermediate location. The method can be applied to incident detection and diagnosis by comparing the observed and predicted data, so a realistic solution to this problem is important. Newell G.F. proposed a simple method to solve this problem. In Newell's method, one gets the cumulative count curve (N-curve) of any intermediate location just by shifting the N-curves of the upstream and downstream detectors. Newell's method was developed before the variational theory of traffic flow was proposed to deal systematically with vehicle counts. This article shows how Newell's method fits in the context of variational theory.
Cell Transmission Model (CTM) is a popular numerical method proposed by Carlos Daganzo to solve the kinematic wave equation. Lebacque later showed that CTM is the first order discrete Godunov approximation.
In traffic flow theory, the Newell–Daganzo merge model describes a simple procedure on how to determine the flows exiting two branch roadways and merging to flow through a single roadway. The model is simple in that it does not consider the actual merging process between vehicles as the two branch roadways come together. The only information required to calculate the flows leaving the two branch roadways are the capacities of the two branch roadways and the exiting capacity, the demands into the system, and a value describing how the two input flows interact. This latter input term is called the split priority, or merge ratio, and is defined as the proportion of the two input flows when both are operating in congested conditions.
In traffic flow theory, the impact of freeway truck lane restrictions is an interesting topic. Intuitively, slow vehicles will cause queues behind them, but how it relates to the kinematic wave theory was not revealed until Newell. Leclercq et al did a complete review of Newell's theory. In addition to the simulation models developed by Laval and Daganzo on the basis of numerical solution methods for Newell's theory to capture the impacts of slow vehicle, Laval also mathematically derived the analytical capacity formulas for bottlenecks caused by single-type of trucks for multi-lane freeway segments.
Vehicular traffic can be either free or congested. Traffic occurs in time and space, i.e., it is a spatiotemporal process. However, usually traffic can be measured only at some road locations. For efficient traffic control and other intelligent transportation systems, the reconstruction of traffic congestion is necessary at all other road locations at which traffic measurements are not available. Traffic congestion can be reconstructed in space and time based on Boris Kerner’s three-phase traffic theory with the use of the ASDA and FOTO models introduced by Kerner. Kerner's three-phase traffic theory and, respectively, the ASDA/FOTO models are based on some common spatiotemporal features of traffic congestion observed in measured traffic data.
Kerner’s breakdown minimization principle is a principle for the optimization of vehicular traffic networks introduced by Boris Kerner in 2011.
Route capacity is the maximum number of vehicles, people, or amount of freight than can travel a given route in a given amount of time, usually an hour. It may be limited by the worst bottleneck in the system, such as a stretch of road with fewer lanes. Air traffic route capacity is affected by weather. For a metro or a light rail system, route capacity is generally the capacity of each vehicle, times the number of vehicles per train, times the number of trains per hour (tph). In this way, route capacity is highly dependent on headway. Beyond this mathematical theory, capacity may be influenced by other factors such as slow zones, single-tracked areas, and infrastructure limitations, e.g. to useful train lengths.