EP1056063A1 - Method and device for the determination of the traffic conditions of a roadsegment - Google Patents

Abstract

The method involves using a Kalman filter based on a model that can take into account the length of the traffic section and measured levels of traffic at the entry to and exit from the section. A transport-oriented model is selected in which a vector i formed contg. a correction value and a traffic level value for each of at least two segments using the length of the traffic sec and the sampled levels of traffic at the entry to and exit from the section. The vector is corrected using the Kalman filter to provide a precise estimate of the exit traffic level and of the traffic state. An Independent claim is also included for an arrangement for determining the traffic state of a section of traffic.

Description

The present invention relates to a method and a device according to the preamble of the claim 1 or 6.

Measuring devices are often used for automatic monitoring and control of road traffic. which measure the traffic volume or the number of vehicles that are within a certain Time pass a measuring point. Based on the measurement results, however, only a useful statement about the local traffic volume can be made. About the condition It is not possible to specify the traffic in the traffic section before the measurement point mentioned.

A determination is also based on the measurement of the vehicle speed or speed tendency of the traffic condition possible. Traffic disturbances are due to the speed measurements However, it can only be determined so late that appropriate measures to regulate traffic can no longer counteract the disturbance.

To monitor a traffic segment, traffic measurements are therefore made at its entrance and exit serving sensors are provided. By comparing the measurement results of the two sensors can make a statement about the traffic condition within the monitored traffic segment be made. Because the measured traffic volume shows strong fluctuations, the stochastic Faults around a "true" value occur, the measured values are filtered.

Delays occur when filtering using classic low-pass, band-pass or high-pass filters, which prevent a quick statement about the traffic condition from being made. It is known that measured values are filtered practically without delay using a Kalman filter can.

Kalman filters or so-called observers use a model as well as the input and output information of the object whose condition is to be determined or tracked. Basically, the The more information, the faster and more accurately the actual object states can be determined of the object can be used for the model, which shows the relationships between the inputs and Output variables should reflect correctly. From [1] the use of a model is known that is based on the use of eight parameters, some of which are difficult to determine. A part the parameter also depends on the weather. The results achieved with this system, Despite the increased effort, therefore, deviate significantly from the results, which ideally let achieve.

The present invention is therefore based on the object of a method and an apparatus for to quickly determine the state of a traffic segment.

This object is achieved by those specified in the characterizing part of patent claim 1 and 6, respectively Measures solved. Advantageous embodiments of the invention are specified in further claims.

According to the invention, a Kalman filter with a model of the monitored traffic section is used. By the advantageous choice of a simple model for the trouble-free case in connection The traffic condition can be determined quickly and precisely using a Kalman KF filter. In particular Faults are recognized almost without delay, which means that the necessary measures are taken (Traffic jam warning, etc.) can be issued quickly. Further advantages of the invention or of the selected model are explained below.

Models differ in their input and output sizes and their interdependency as well as the choice of state variables that reflect the "inner state" of the model. Out For reasons of measurement technology there is a location and time discretization of the continuous traffic instead of. Measured values are only available at certain locations and at certain times (sampling times) Available. According to the invention, a transport-oriented model is used for the state assessment. The monitored traffic segment is accordingly regarded as a transport medium, which, analogous to a conveyor belt, elements or vehicles are transported at a certain speed or let it happen. To simplify matters, it is provided that all vehicles are in the traffic section moving at a constant speed. Despite the simple structure of the used transport model enables the inventive method with the help of a Kalman filter a quick and reliable determination of traffic disruptions and other parameters from strongly fluctuating measurements of traffic volume q and speed v.

It is essential that the model used describes the conditions of an "ideal" street, the one has infinite capacity. This property in particular enables faults to be very quickly and safely recognizable, since measured values in the event of a fault differ from the "predicted" values of the model Differentiate values strongly. However, the model is not suitable for simulating road traffic. The use of this model is therefore only useful in connection with a Kalman filter. Because of however, the linearity of the transport model is optimally suited for this purpose.

A major advantage of the method according to the invention is that all parameters of the Model results in a simple manner from the geometric arrangement of the measuring points. An expensive one Identification of model parameters is therefore no longer necessary. In particular, the parameters depend on the used Model does not depend on weather influences such as wetness or smoothness, which means that it can be adapted to different weather conditions Ambient conditions are superfluous.

Fig. 1

ein Verkehrsmodell mit Ein- und Ausgangsgrössen q1, q2,

Fig. 2

das Verkehrsmodell gemäss Fig. 1 für einen Verkehrsabschnitt, den Fahrzeuge mit einer bestimmten Gruppenlaufzeit durchfahren,

Fig. 3

den Verlauf der Verkehrsstärke q2 am Ausgang des Verkehrsabschnittes als Reaktion eines synthetischen Verlaufs der Verkehrsstärke q1 am Eingang des Verkehrsabschnittes für das kontinuierliche Modell,

Fig. 4

das Verkehrsmodell mit virtueller Ein- und Ausfahrt,

Fig. 5

das verwendete Kalman-Filter KF mit einem erweiterten diskreten Modell MOD,

Fig. 6

den Verlauf der Verkehrsstärke q2 am Ausgang des Verkehrsabschnittes als Reaktion eines synthetischen Verlaufs der Verkehrsstärke q1 am Eingang des Verkehrsabschnittes für das diskrete Modell und

Fig. 7

den vom Kalman-Filter KF geschätzten Verlauf der Verkehrsstärke q2_e (verzögerungsfreie Glättung von q2) sowie die Verkehrsstärke q2_d auf den virtuellen Ein- und Ausfahrten beim Auftreten einer Störung (Wert q2_d durch das Kalman-Filter KF geschätzt, wird mit q2_de bezeichnet).

The invention is explained in more detail below using a drawing, for example. It shows:

Fig. 1

a traffic model with input and output variables q1, q2,

Fig. 2

1 for a traffic segment that vehicles with a certain group transit time are traveling through,

Fig. 3

the course of the traffic volume q2 at the exit of the traffic section in response to a synthetic course of the traffic strength q1 at the entrance of the traffic section for the continuous model,

Fig. 4

the traffic model with virtual entry and exit,

Fig. 5

the Kalman filter KF used with an extended discrete model MOD,

Fig. 6

the course of the traffic volume q2 at the exit of the traffic section in response to a synthetic course of the traffic intensity q1 at the entrance of the traffic section for the discrete model and

Fig. 7

the course of the traffic intensity q2 _e estimated by the Kalman filter KF (instantaneous smoothing of q2) and the traffic intensity q2 _d on the virtual entrances and exits when a fault occurs (value q2 _d estimated by the Kalman filter KF is estimated with q2 _de designated).

To begin with, the terms used are listed in a table:

1 shows a traffic model corresponding to a traffic segment, to which the traffic volume q1 measured at the entrance to the traffic segment and the speed v1 measured at the entrance to the traffic segment as well as the naturally constant length I _{s of} the traffic segment are supplied as an input variable. The model provides an output quantity q2 _m , which, taking into account the calculated running time T _{L of} the vehicles Fz, indicates the traffic volume at the exit of the traffic section. For the sake of simplicity, it is assumed that all vehicles in the section travel at the same constant speed v1.

The change in traffic volume q is thus analogous to the transport of material (e.g. sand) an assembly line moving at a constant speed v. The material appears after a time delay that depends on the transport speed and the length of the conveyor belt dependent, unchanged at the point of delivery. There are no differences in the transit times of the transported Material particles. Therefore, the vehicles F1, F2 and F3 appear according to the idealized one shown in FIG. 1 Model without differences in transit time, at the same mutual distance at the exit of the traffic section.

A transport process of this kind corresponds to the partial differential equation (1):

exakt der Veglauf von q1, d.h. es gilt: q2(t) = q1(t-TL).At constant speed v, the following is repeated at the end of the conveyor belt after the running time:

the exact run of q1, ie: q2 (t) = q1 (tT L ) .

This fact does not do justice to the arrest of the vehicles, since the vehicles that are in measured at a time interval do not have the same speed and consequently as in FIG. 2 shown to pass the second measurement cross-section after different running times.

The intended use of a Kalman filter also requires a description of the process in Form of an ordinary and not a partial differential equation, which partial derivatives an unknown function with multiple variables (in relation to the difference from ordinary and partial differential equations see [3], page 435).

According to the invention, an ordinary differential equation is obtained by only considering discrete locations in the traffic section. For this purpose, the traffic section is divided into n segments s1, s2, ..., s _n , the inner limits of which do not have sensors or sensors. The speed is assumed to be constant in each of these segments.

If the behavior of one of the n subsegments is described by a discrete 1st order delay, the entire segment behaves like a runtime element le, the time constant of which is as follows:

The larger the number n of segments, the more precisely the behavior can be described. The transport process is thus mapped by connecting n first order delays:

This measure results in a location discretization for selected segments s1, s2, ... (see FIG. 1), whereby an ordinary differential equation is achieved. The first value of the trowel q _{1, 0} is the value q1 at the input q1; the last value of the q _{1, n} is identical to the q2 value at the exit of the model.

If the values q _{1, i are combined} to form a state vector X describing the internal state of the system, the system of differential equations can be stated in the form of a state. A system consisting of four segments also has T ie = 4 / T L following representation:

The individual matrices are as follows:

3 shows the traffic volume q2 in response to a synthetic course of q1 for n = 4 segments (or delays). It can be seen that some of the vehicles now arrive before the time T _{L has} elapsed, while another part requires more time due to a somewhat lower speed (see FIG. 2).

The selected model is now used to describe the transport process and thus the undisturbed traffic suitable in a traffic segment. This shape is also very suitable for a Kalman filter because it is linear in the state variables. The model serves as a building block for a traffic section with two Measuring points. With the help of this module, models for arbitrarily complex topologies can be easily set up, which have entrances, exits, branches and junctions.

The measured values are available as discrete-time values for a specific measuring interval (sampling time Ts). Therefore, the discrete version of the model is chosen, which results from the continuous model as follows (the matrix C does not change):

von der gemessenen Geschwindigkeit abhängen und somit nicht konstant sind. Die Diskretisierung kontinuierlicher Systeme ist u.a. auch aus [4], Seiten 74-80, Kapitel 3.1 (Diskretisierung der Regelstrecke) bekannt. Für das diskrete Modell gehen somit die Gleichungen : x(k + 1) = A(k) xe (k) + B(k) q1(k), q2 m (k) = C 1 x(k) + D(k) q1(k). The infinite series converges very quickly due to the factorial in the denominator, so that very high computing power is not necessary. However, the discretization must be carried out anew in every measuring interval k, since the matrices

depend on the measured speed and are therefore not constant. The discretization of continuous systems is also known from [4], pages 74-80, chapter 3.1 (discretization of the control system). The equations for the discrete model are therefore: x ( k + 1) = A ( k ) x e ( k ) + B ( k ) q 1( k ), q 2nd m ( k ) = C. 1 x ( k ) + D ( k ) q 1( k ).

To simplify matters, the scanning step k was written as an index. The index k denotes the interval for the time t kT S ≤ t <( k + 1) T S

According to the invention, all values are assumed to be constant in one interval, which makes simple calculation possible. It should be noted that the parameters of the process model are not constant. After each sampling interval T _S , there is a new value for the speed v1 measured at the entrance of the traffic section, with which, according to the assumption, all vehicles move in the monitored section.

The deviations between the predicted values of the model q2 _m and the actually measured values q2 are corrected by the Kalman filter, which calculates the estimated values q2 _e , in the undisturbed case. However, if malfunctions occur that were not taken into account in the modeling, the Kalman filter also supplies incorrect values. The accident is now taken into account by extending the model, as shown in FIG. 4, by virtual entrances and exits to a “parking space with infinite capacity”.

The traffic flows on the virtual exits, which are designated q2 _d , are a measure of the effects of a disruption that has occurred. The Kalman filter estimates the values q2 _d (value q2 _de ).

Since the model has an unlimited capacity, as mentioned at the beginning, the value q2 _d cannot be concluded from the input information q1 and the measured value q2. Since the Kalman filter can only make optimal estimates based on a model, the value q2 _d must also be taken into account by the model. Since the model is designed for trouble-free operation, it is consequently assumed that the initial value of q2 _d is zero and then behaves as follows from sampling period to sampling period: q2 d (k + 1) = q2 d (k) . As modeled, the model therefore does not provide for any disturbance, so that the value q2d is constant or can only be changed with the Kalman filter KF at every interval k ( q2 d (k + 1) = q2 d (k) + corr KF (k) ). For this purpose, the state vector x provided for four segments s1, s2, s3, s4 is expanded as follows:

In the undisturbed case, there are no virtual traffic flows (see FIG. 4). If malfunctions occur, the Kalman filter can use the virtual traffic flows to compare measured values and estimated values of the model. The vector x (k) is corrected by the Kalman filter KF (see FIG. 5), which results in the vector x _e (k) with corrected values x _1e , ..., x _4e and x _de . The corrected (internal) state values x _4e plus x _de or x _de thus correspond to the estimated values q2e and q2 _{de of} the Kalman filter KF. The values q2 _de are an immediate measure of the degree of disturbance that has occurred in the monitored section (see FIG. 7).

The basis of the Kalman filter KF shown in FIG. 5 is the expanded discrete model MOD described above, which satisfies the conditions in the ideal state. To adapt to the real conditions, it is additionally assumed that process and sensor faults p and s at the points shown in FIG. 5 are effective as follows (assumption D (k) = [0]): x ( k + 1) = A ( k ) x ( k ) + B ( k ) q 1( k ) + G ( k ) p ( k ), q 2 * m ( k ) = C. ( k ) x ( k ) + D ( k ) q 1( k ) + s ( k ) ≅ q 2 ( k )

The corrected model value q2 * _m (k) thus corresponds to the value q2 (k) actually measured in the interval k. The assumed disturbances result from the traffic process and correspond to the form of a frequency-independent noise with a Gaussian-distributed amplitude spectrum. If one looks at measured courses of traffic intensity q, one can see that the strong fluctuations can be represented quite well as a noise process around an average. The Kalman KF filter is an optimal filter that measures the variance of the estimation error x err ( k ) ≅ x ( k ) - x e ( k ) minimized. The "true" state vector is not known. Therefore x _e (k) is used as an estimate for the unknown state vector x (k).

entsteht, gibt an, wie sich die (unbekannten) Störgrössen auf die internen Zustandsgrössen des Prozesses verteilen. Im einfachsten Fall nimmt man an, dass diese Störgrössen gleichmässig auf alle internen Zustandsgrössen einwirken. Durch die Matrix G(k) wird daher angegeben wo nicht aber mit welcher Intensität die Störgrössen auf das Modell einwirken. Die Intensität der Störungen wird durch die nachstehend beschriebenen Kovarianzmatrizen Q und R beschrieben. Sofern zum Beispiel drei Zustandsgrössen und zwei Störgrössen vorhanden sind und die erste Störgrösse nur auf die erste Zustandsgrösse und die zweite Störgrösse nur auf die beiden folgenden Zustandsgrössen einwirkt, würde dieser Sachverhalt mit einer Matrix G(k) der nachstehend angegebenen Form berücksichtigt:

The matrix G (k), which is exactly like the matrices A (k), B (k) by discretization

arises, indicates how the (unknown) disturbance variables are distributed among the internal state variables of the process. In the simplest case, it is assumed that these disturbance variables have an even effect on all internal state variables. The matrix G (k) therefore indicates where, but not with what intensity, the disturbance variables affect the model. The intensity of the disturbances is described by the covariance matrices Q and R described below. If, for example, there are three state variables and two disturbance variables and the first disturbance variable only affects the first state variable and the second disturbance variable only affects the following two state variables, this would be taken into account with a matrix G (k) of the form given below:

According to the invention, the matrix G (k) is selected in such a way that process disturbances p all sizes of the state vector influence x (k) in the same way.

The control parameters of the Kalman filter KF are the covariance matrices Q and R of the (assumed) Noise processes that relate to the process itself or to the measured values output by the sensors act. The matrix Q describes the intensity of the process disturbances p. The matrix R describes the Intensity of sensor disturbances see Although the matrices Q and R may theoretically be time-variant, assumed that the fluctuations in the measured values regardless of the traffic situation and thus are independent of time. The matrices Q and R thus take on constant values. The elements of the matrices Q and R are the design parameters of the Kalman KF filter, with which the settling time and sensitivity to interference of the Kalman filter KF can be set.

With the initialization values P (0) = G (0) QG T (0) and x (0) = 0 the following equations must be calculated. A distinction is made between the equations for determining the optimal estimated values and the equations for calculating the subsequent state.

The equations for determining the optimal estimates are as follows (I is the unit matrix):

The equations for calculating the subsequent state are as follows: x ( k + 1) = A ( k ) x e ( k ) + B ( k ) q 1( k ), P ( k + 1) = A ( k ) P e ( k ) A T ( k ) + G ( k ) QG T ( k )

The above equations are calculated for each scan step. The order in which the Equations should not be mixed up because the results are partly different depend.

The signal flow in the Kalman filter KF according to the invention is shown in FIG. 5. For each interval, the products of the input value q1 (k) times matrix B (k) and the estimated state vector x _e (k) times matrix A (k) are added in the addition stage ADD1. After the time shift, the sum results in the new uncorrected internal state vector x (k) which is multiplied by the matrix C ₁ = [00011], whereby the sum of the values x ₄ plus x _{d is} formed ( q2 m = x 4th + x d ). The difference level DIFF is then used to form the difference between the traffic intensity q2 _m (k) newly determined by the model and the actually measured traffic intensity q2 (k) (at the exit of the traffic segment). The resulting difference (q2 _m (k) - q2 (k)) is multiplied by the Kalman matrix L (k), whereby values are formed with which the state vector x (k) is corrected using the addition stage ADD3 or in the estimated values State vector x _e (k) is converted. The estimated values x4 _e and x _de are read out using the matrices C ₁ = [00011] and C ₂ = [0000-1]. Using C ₁ , the sum of the estimated values x4 _e and x _{de is} formed, which gives the estimated value q2 _e (k). As already mentioned, the matrices C ₁ and C _{2 are} constant. The last element of the matrix C ₂ is negative, so that a positive value for q2 _de (k) is obtained in the event of a jam. Of course, all arithmetic operations can be carried out by a computer system known to the person skilled in the art (for example: processor, signal processor).

FIG. 7 shows the traffic intensity q2 _e estimated by the Kalman filter KF and the estimated traffic intensity q2 _de on the virtual entrance and exit, influenced by a disturbance that occurs at a time 11 and during the time T _dist up to one Time t2 continues. Up to time t1, the vehicles can pass through the traffic section unhindered, so that the traffic volumes determined at the entrance and exit correspond to the value q1, neglecting the travel time differences. At time t1, a disturbance occurs, which creates a bottleneck within the traffic section, which only allows traffic to pass with the reduced traffic volume q2 _dist . In order for corrective and safety measures to be taken in good time, the fault in the traffic control center must be recognized immediately. FIG. 7 also shows a typical course for the measured variables q1 and q2. It can be seen that the measured variables q1, q2 have large statistical fluctuations even in the undisturbed case and that the effect of a disturbance within the traffic section can only be determined after a considerable delay. The device according to the invention and the method by means of which input and output variables q1, q2 are processed using the Kalman filter KF now allow the occurrence of the fault to be detected with practically no delay. The value q2 _de starts up immediately after the fault occurs. This corresponds to an increase in the estimated traffic volume q2 _de on the entrance to the virtual parking lot. After the disturbance has been eliminated at time t2, the traffic volume q2 _{de increases again} from the virtual parking lot back into the traffic section and then falls back to zero when the traffic volumes q1 and q2 are adjusted at the entrance and exit of the traffic section. By comparing the estimated traffic volumes q2 _de between the virtual parking lot and the monitored traffic section with positive and negative threshold values thp; Therefore, thm can quickly determine whether a fault has occurred or has been remedied.

The threshold values are chosen according to the size of the fault to be detected. Preferably several threshold values are provided which correspond to the states "light traffic obstruction" ,, "slow traffic" or "traffic jam". When the corresponding conditions occur therefore the appropriate measures (e.g. traffic jam warning) are initialized.

In addition, different road sections can be connected to each other. The models different Street sections can easily be strung together, again for each model Length, output and input variables of the relevant traffic section must be taken into account. Measured values at the exit of a traffic segment can therefore also serve as input variables for the subsequent traffic section can be used. By connecting several traffic sections there is an instantaneous smoothing effect.

Based on the knowledge gained about the traffic levels, the expected ones can also be expected Calculate travel times quickly and precisely. This can also be optimal for road users Itineraries to be set.

bibliography

[1] Michael Cremer, The flow of traffic on expressways, Springer Verlag, Berlin 1979

[2] Kai Müller, draft robust regulations, Teubner Verlag, Stuttgart 1996

[3] Lothar Papula, mathematics for engineers and scientists, Vieweg Verlag, Wiesbaden 1997, 8th edition

[4] Jürgen Ackermann, scanning control, Springer Verlag, 3rd edition, Berlin 1988

Claims (10)

Method for determining the traffic condition within a traffic segment using a Kalman Fifter (KF) based on a model (MOD), by means of which the length l _{s of} the traffic segment and the traffic volumes q1 and q2 measured at the entrance and exit of the traffic segment are taken into account, characterized in that a transport-oriented model (MOD) is selected in which for at least two segments (s1; ...; s _n ) of the traffic section based on the length l _s and the traffic intensity q1 measured at the entrance of the traffic section and the vehicle speed v1 ( k) at sampling intervals k, a vector x (k) containing a correction value (x _d ) and for each segment (s1; ...; s _n ) a traffic intensity value (x1; ...; x _n ) is formed, which is formed by Comparison with the traffic volume q2 measured at the exit of the traffic section is corrected using the Kalman filter (KF), so that the corrected vector x _e (k) makes the estimated value more precise e q2 _e for the traffic intensity at the exit of the traffic segment and an estimate q2 _de for a differential traffic intensity q2 _d representing the inner state of the traffic segment can be gathered. Method according to Claim 1, characterized in that a new vector x (k + 1) on the basis of matrices A (k) and B (k) of the extended traffic model (MOD) newly defined for each sampling interval k as a function of the speed v1 (k) is calculated as follows: x ( k + 1) = A ( k ) x e ( k ) + B ( k ) q 1( k ), whereby the old vector x (k) is converted into the corrected vector x _e (k) by the Kalman filter (KF):

by multiplying the difference between the measured value q2 (k) and the sum of the values x _n (k) and x _d (k) by a Kalman matrix L (k) newly defined for each sampling interval. Method according to Claim 1 or 2, characterized in that the estimated values q2 _de for the differential traffic volumes q2 _d representing the inner state of the traffic section have at least one threshold value thp; thm are compared, after which a change in state within the traffic section is determined. Method according to Claim 3, characterized in that, depending on the polarity and amplitude of the estimated values q2, _de gradual reductions and / or increases in the capacity of the traffic section are determined and, depending on this, measures are taken to inform the traffic participants and / or to control the traffic. Method according to one of claims 1-4, characterized in that several traffic sections are linked in a model (MOD) and monitored by a Kalman filter (KF) and / or that travel times and / or optimal traffic routes are calculated depending on the determined conditions . Device for determining the traffic condition within a traffic segment with a Kalman filter (KF) based on a model (MOD), by which the length l _s and the traffic volumes q1 and q2 measured by means of sensors at the entrance and exit of the traffic segment are taken into account, characterized in that a transport-oriented model (MOD) is provided, in which for at least two segments (s1; ...; s _n ) of the traffic section based on the length l _s and the traffic intensity q1 measured at the entrance of the traffic section and the vehicle speed v1 ( k) at sampling intervals k a vector x (k) containing a correction value (x _d ) and for each segment (s1; ...; s _n ) a traffic intensity value (x1; ...; x _n ) can be generated Comparison with the traffic volume q2 measured at the exit of the traffic section can be corrected using the Kalman filter (KF), so that the corrected vector x _e (k) makes the estimated value more precise e q2 _e for the traffic intensity at the exit of the traffic segment and an estimate q2 _de for a differential traffic intensity q2 _d representing the inner state of the traffic segment can be gathered. Apparatus according to claim 6, characterized in that a new vector x (k + 1) on the basis of matrices A (k) and B (k) of the extended traffic model (MOD) newly defined for each sampling interval k as a function of the speed v1 (k) can be calculated as follows: x ( k + 1) = A ( k ) x e ( k ) + B ( k ) q 1( k ), the old vector x (k) can be converted into the corrected vector x _e (k) by the Kalman filter (KF)

in that the difference between the measured value q2 (k) and the sum of the values x _n (k) and x _d (k) can be multiplied by a Kalman matrix L (k) newly defined for each sampling interval. Apparatus according to claim 6 or 7, characterized in that the estimated values q2 _de for the differential traffic volumes q2 _d representing the inner state of the traffic section with at least one threshold value thp; thm are comparable, after which a change in state within the traffic section can be determined. Apparatus according to claim 8, characterized in that depending on the polarity and amplitude of the estimated values q2 _de gradual reductions and / or increases in the capacity of the traffic section can be determined and, depending on this, measures can be implemented to inform the traffic participants and / or to control the traffic. Device according to one of claims 6-9, characterized in that several traffic sections are linked in a model (MOD) and monitored by a Kalman filter (KF) and / or that travel times and / or optimal traffic routes can be calculated depending on the determined conditions .

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