DE102016220400B3 - Method and device for determining a traffic condition at a traffic node with at least two traffic arms - Google Patents

Abstract

The invention relates to a method for determining a traffic state at a traffic node (61), comprising the following steps: detection of C2X information in the vicinity of the traffic node (61), detection of individual inquiry messages (20, 21) by mobile wireless communication units (9 ) in a predetermined detection area (7, 7-1, 7-2, 7-3, 7-4), wherein the wireless communication units (9) are each identified by a unique identifier, estimating a speed (12) of a detected wireless communication unit (9 ) from a time difference between a reception of a first inquiry message (20) of this wireless communication unit (9) and a reception of a subsequent inquiry message (21) of this wireless communication unit (9) taking into account dimensions (11) of the predetermined coverage area (7, 7 -1, 7-2, 7-3, 7-4), merging the C2X information and the estimated speed (12) using a Bayesian network s (30) to a speed probability distribution (14) for said wireless communication unit (9), estimating a vehicle number (15), wherein the number of detected wireless communication units (9) is corrected by at least one correction factor (16), outputting the estimated vehicle number (15) and the velocity probability distributions (14). Furthermore, the invention relates to an associated device (1).

Description

The invention relates to a method and a device for determining a traffic condition at a traffic node having at least two traffic arms.

For controlling the traffic at a traffic junction, for example an intersection, traffic light installations are usually used. In order to be able to regulate the traffic in the best possible way, controllers of the traffic light systems require information about the vehicles which pass through the traffic light system. Here, a distinction is made between directly measurable and indirectly derivable parameters. The directly measurable parameters are in accordance with the directive for traffic signal systems (RiLSA, 2010) u. a. the occupancy rate and the time gap between vehicles; The derivable information includes traffic counts and speeds.

The degree of occupancy relates to the presence of vehicles at a certain point or distance in front of the traffic signal. The counting information relates to the number of vehicles, which in the course of a certain time, for. B. while the traffic signal "red" signaled, enter the section before the traffic signal.

The speed refers to the time it takes the vehicles to pass through a particular stretch of road.

Newer methods for traffic signal control, such. In DE 10 2009 033 431 A1 described. use the so-called loss time of road users for optimal control of traffic flows at a transport hub. Loss time is the difference between the time a road user needs under ideal conditions to pass the light signal and the time it actually takes under real conditions. The complete determination of loss times would require the determination of a movement line (trajectory) for 100% of all road users at the traffic junction. This can be done, for example, if all vehicles are equipped with C2X transceivers (Car-to-X). In this case, the traffic light system must be equipped with a receiver for C2X information.

In practice, there is the difficulty that at present, and also in the future, can not be expected with a equipment and detection rate of 100% of all vehicles.

In DE 10 2011 107 663 B4 Therefore, a possibility is shown to determine the average traffic loss of the road users already at equipment rates with C2X from 20%. Here, however, there is still the problem that in the medium term only with equipment rates of vehicles with C2X from 1 to 3% is to be expected. This value deviates by a power of ten from the required equipment rate of 20%.

From the DE 10 2009 055 337 A1 Furthermore, a method for determining a traffic situation in a traffic network is known. This includes steps of detecting a plurality of traffic data, the traffic data being from multiple data sources and having different spatial and temporal availability, determining correlation maps between the traffic data from the plurality of data sources, determining interpolated traffic data based on associated traffic data, and the determined correlation maps, and determining the traffic situation based on the interpolated traffic data. However, the method requires that there be a sufficiently strong correlation between the directly measurable characteristics used by the traffic signal controller. This is currently not the case.

The invention is based on the technical problem of providing a method and a device for determining a traffic situation at a traffic junction with at least two traffic arms, in which a traffic condition can be improved even with a low equipment level of the vehicles with C2X.

The technical problem is solved by a method with the features of claim 1 and a device having the features of claim 10. Advantageous embodiments of the invention will become apparent from the dependent claims.

In particular, a method for determining a traffic condition at a traffic node having at least two traffic arms is provided, comprising the following steps: detecting C2X information in the vicinity of the traffic node by means of a C2X RSU (Road Side Unit), detecting individual inquiry messages of mobile wireless communication units by means of at least one wireless transceiver having a predetermined coverage area, the wireless communication units each being identified by a unique identifier, estimating a speed of a detected wireless communication unit from a time difference between receiving a first inquiry message from that wireless communication unit and receiving one subsequent Inquiry message this wireless communication unit, taking into account dimensions of the predetermined detection range means controlling, by means of the controller, fusing the C2X information and the estimated speed using a Bayesian network into a speed probability distribution for that wireless communication unit, estimating a vehicle number based on a number of wireless communication units detected simultaneously or at a time interval, the number of detected Wireless communication units is corrected by means of at least one correction factor, outputting the estimated number of vehicles and the speed probability distributions by means of an output device.

Furthermore, an apparatus is provided for determining a traffic condition at a traffic node having at least two traffic arms, comprising a C2X-RSU for receiving C2X information in the vicinity of the traffic node, at least one wireless transceiver having a predetermined detection area for receiving signals from wireless communication units , a controller, and
an output device, wherein the C2X-RSU is designed to detect C2X information in the vicinity of the traffic node, and wherein the at least one wireless transceiver is configured to detect individual inquiry messages from mobile wireless communication units in the predetermined coverage area, wherein the wireless communication units are each identified by a unique identifier, and wherein the controller is configured, a speed for a detected wireless communication unit from a time difference between a reception of a first inquiry message of this wireless communication unit and a reception of a subsequent inquiry message of this wireless communication unit under consideration estimate the dimensions of the predetermined detection area, the C2X information and the estimated speed using a Bayesian network to a speed probability distribution g, for estimating a vehicle number based on a number of wireless communication units detected simultaneously or at a time interval, wherein the number of detected wireless communication units is corrected by at least one correction factor, and wherein the output means is configured to estimate the estimated number of vehicles and to output the velocity probability distributions.

The basic idea of the invention is to use periodically sent inquiry messages from mobile wireless communication units, which are installed in large numbers in mobile terminals, such as smartphones, wireless headphones or wireless hands-free devices etc., to determine a traffic condition at a transport node. Such inquiry messages are sent to prepare for establishing connections with other wireless communication units or stations in the environment. For this purpose, the wireless communication units transmit a unique identifier, via which they can be uniquely identified. In order to derive the traffic condition from the inquiry messages, at least one wireless transceiver is used. The at least one wireless transmitting / receiving device has a precisely predetermined or at least exactly known detection range. Such a detection area may ideally be a directional "reception lobe", for example a directional antenna provided for this purpose, which selectively detects only the traffic arm of the traffic node to be detected. In this predetermined detection range, the times of the received inquiry messages of a wireless communication unit are detected. From the detected times of the individual inquiry messages, a speed of the mobile wireless communication unit is estimated depending on the dimensions of the predetermined detection area. The speed estimated in this way is then fused in a Bayesian network, taking into account the characteristics of the sensors used (here the wireless transceiver and the C2X-RSU) to a velocity probability distribution. This also includes the C2X data, which may have been recorded in the same period as the Inquiry Messages from the C2X-RSU. Further, a vehicle number is estimated. This is done on the basis of the acquired inquiry messages, assuming that a detected wireless communication unit corresponds to at least one vehicle on the associated traffic arm. Estimating the number of vehicles takes place taking into account at least one correction factor.

The advantage of the described method and apparatus is that a traffic condition at a traffic node can be determined improved. Currently available detectors and traffic control systems are expensive and require the detection of as many vehicles as possible in order to control the traffic efficiently and in particular traffic-dependent. The described method, which is based on the future C2X technology used in vehicles, as well as widespread wireless communication units (for example directed standard Bluetooth transceivers), can save a large part of the costs for the measurement and control technology. long-term existing infrastructure can be dismantled so that resources can be spared in this way. This will pave the way to network-wide traffic detection and control by cost-effectively equipping transportation hubs that previously could not be equipped.

Wireless communication units are intended to be mobile terminals which have a standardized wireless communication protocol. By way of example, such mobile terminals may be smartphones, headphones, laptops or hands-free devices, etc. The wireless communication can be done for example via Bluetooth, WLAN, WiMAX, etc. Here, however, only communication protocols or modes of the wireless communication units are considered, which are accompanied by a periodic transmission of inquiry messages, so that they can be received by a corresponding wireless transceiver. In particular, the wireless communication units are able to uniquely identify, for example via a Media Access Control (MAC) address.

In a particularly preferred embodiment, it is provided that the mobile wireless communication units are Bluetooth units and the at least one wireless transceiver is a Bluetooth transceiver.

The estimated number of vehicles and the speed probability distributions are output by the output device, for example, as an analog or digital traffic condition signal, for example in the form of a data packet. The output traffic condition signal can then be further processed by a traffic signal system or by a centralized or decentralized traffic control system.

wobei r_BT die Ausdehnung des Erfassungsbereichs entlang des Verkehrsarmes, t_First der Zeitpunkt der ersten und t_Last der Zeitpunkt der letzten Detektion einer Inquiry-Nachricht der Drahtloskommunikationseinheit bezeichnen. Hierbei muss beachtet werden, dass beim Inquiry-Vorgang periodisch nach Drahtloskommunikationseinheiten gesucht wird und üblicherweise ein- und dieselbe Drahtloskommunikationseinheit mehrfach detektiert wird. Das bedeutet: je weniger häufig ein solches Gerät detektiert wird (bzw. je schneller es den Erfassungsbereich der entsprechenden Drahtlos-Sende-Empfangseinrichtung durchfährt), desto größer sind eine geschätzte Geschwindigkeit und ein resultierender Fehler der geschätzten Geschwindigkeit. Wird ein Fahrzeug in dem Erfassungsbereich hingegen häufiger detektiert (d. h. das Fahrzeug ist langsam unterwegs), so sind die geschätzte Geschwindigkeit und der resultierende Fehler kleiner.The speed V _{BT of} a wireless communication unit is determined as follows with known dimensions of the detection range of the associated wireless transceiver:

where r _{BT denotes} the extent of the detection area along the traffic arm, t _First the time of the first and t _Last the time of the last detection of an Inquiry message of the wireless communication unit. It should be noted that the Inquiry process is searched periodically for wireless communication units and usually one and the same wireless communication unit is detected multiple times. That is, the less often such a device is detected (or the faster it traverses the detection range of the corresponding wireless transceiver), the greater are an estimated speed and a resulting estimated speed error. On the other hand, if a vehicle is detected more frequently in the detection area (ie, the vehicle is traveling slowly), the estimated speed and resulting error are smaller.

The dimensions of the detection area may change depending on the weather and the development. It may be provided to take this into account when estimating the speed accordingly.

In the event that there is only a single detection of a wireless communication unit, V _BT can not be determined. Here it has proven useful in practice to start from a predetermined speed, z. B. V _BT = V _max , since any further detection anyway leads to a reduction of V _BT .

The error of the above-described wireless communication unit-based estimation of speeds is dependent on the speed of the wireless communication unit (s) itself. Since the inquiry process itself lasts for a certain time and is searched for in-range wireless communication units in that time, the probability of Detection process-related high when a wireless communication unit is long in the detection range of the wireless transceiver, so moves only slowly. Conversely, the less time the wireless communication unit spends in the detection area, the lower the probability of detection, in particular when the vehicle is moving rapidly. As a consequence, there is a high probability that, at low speeds, most of the wireless communication units in the reception area will also be detected. At high speeds, on the other hand, the opposite applies. H. there is a reciprocal proportionality between the probability of measuring a certain speed and the speed V of the wireless communication unit.

In one embodiment, it is therefore provided that a correction factor is a detection probability of the wireless communication units. Such a correction factor then makes it possible to estimate a number of vehicles in the detection area more accurately.

In particular, it can be provided that the correction factor depends on the estimated Speed of the wireless communication unit (s) is. Each detection of a wireless communication unit in the predetermined detection area corresponds in this case to at least one wireless communication unit or a vehicle that passes through this detection area. By way of this, an a-priori probability for a number of vehicles can be determined empirically as a function of their actual speed. In this way, it is possible to estimate vehicle numbers even with low equipment levels of vehicles with C2X.

In order to take into account the equipment level of the vehicles with corresponding wireless communication units, it is further provided in a preferred further embodiment that a correction factor is an equipment level of the vehicles with wireless communication units. The correction factor may, for example, represent the equipment level of the vehicles with Bluetooth units if the wireless communication units are Bluetooth units.

At low speeds of the wireless communication units or vehicles, the number of vehicles then corresponds on average to the reciprocal of the equipment level of the vehicles with wireless communication units, that is N ≈ number of detected wireless communication units / equipment level · C (V) where N is the vehicle number and C is the speed-dependent correction factor that maps the above-described reciprocity between the detection probability and the speed of the wireless communication unit or vehicle.

It has been found useful to apply this equation to equally distributed wireless communication units and equipment levels ranging between about 30% and 100%. As a rule, this is to be assumed, for example, when Bluetooth units are provided as wireless communication units.

By contrast, this equation can not be used for lower equipment levels and low detection ranges. In addition, the equation is no longer applicable even at high speeds. Maximum speeds of approx. 50 km / h at 30 m and approx. 150 km / h at 100 m detection range have proven to be expedient. A corresponding functional relationship between the speed V, the number of vehicles N and the maximum speed is to be deposited as a priori knowledge, z. B. as a probability or as a polynomial function.

According to the assumption made above, that extremely low levels of C2X (maximum 1 to 3%) and moderate levels of wireless communication units, for example, 20 to 40% for Bluetooth, can be expected, a fusion process capable of doing so is needed to deal with incomplete and unsafe data. The fusion of the optionally additionally acquired C2X information and the speed estimated from the inquiry messages of the wireless communication units into a speed probability distribution for the corresponding wireless communication unit is carried out according to the invention by means of a Bayesian network in the controller. In such a Bayesian network, both the traffic process (physical speeds and vehicle counts) and the measurement process (measured / estimated speeds and vehicle counts) are considered as random processes and modeled as corresponding nodes of the Bayesian network. Uncertainty is quantified with the calculation of conditional probabilities. In general, the Bayesian network makes it possible to merge incomplete, incomplete data from sensors in a suitable manner and to determine the quantities sought from them. However, this requires knowledge and modeling of the accuracy and properties of the sensors used.

In this Bayesian network, a parent node V corresponds to the actual physical speed and the two child nodes V _BT and V _C2X correspond to the speeds of the detected vehicles measured via the wireless communication units or via C2X and P (V) to the a priori probability of the underlying process and P (V _BT | V) and P (V _C2X | V) the conditional probabilities representing the characteristics of the "sensors", ie the sensor likelihoods. In this Bayesian network the a-posteriori probability P (V | V _BT , V _C2X ) is now to be determined, which gives information about what speed V is to be expected when the velocities V _BT and V _{C2X have been} measured. The fusion equation is given by α as a normalization constant to: P (V | V _BT , V _C2X ) = α · P (V) · P (V _BT | V) · P (V _C2X | V).

The calculation of this equation is always possible if measured values are available at the same time. If the measurements are temporally asynchronous, then there is a need for synchronization. If only measured values of a sensor are present, for example a Bluetooth transceiver device designed as a wireless transceiver device, then the equation is reduced too P (V | V _BT ) = α ₁ .P (V) .P (V _BT | V).

If only C2X information is available, the equation is reduced accordingly P (V | V _C2X ) = α _{2 .P} (V) _.P (V _C2X | V).

If, however, no wireless communication unit is detected in a detection area of a wireless transceiver, it is assumed that no vehicle has also passed the coverage area.

In one embodiment, it is provided that the wireless communication units are detected on each of the at least two traffic arms by means of at least one own wireless transceiver with predetermined detection ranges assigned to the respective traffic arm. This has the advantage that each traffic arm can be monitored with its own predetermined detection range.

In a further embodiment it is further provided that direction information of the wireless communication units is derived by means of the control on the basis of the inquiry messages detected by different wireless transceivers for different traffic arms. This has the advantage that, in addition to a speed distribution and a number of vehicles, a direction and / or a mean direction of the wireless communication units or vehicles can also be determined. If a traffic junction has, for example, four traffic arms and if each of the four traffic arms is monitored by means of its own wireless transceiver with a detection area assigned to the respective traffic arm, it is possible to deduce a direction of travel of the wireless communication unit from the chronological sequence of detection of a wireless communication unit in two of the coverage areas be, so that from this direction information can be generated. For example, it may be determined in this way that a wireless communication unit on the first of the four traffic arms has traveled in the direction of the traffic node and has moved away beyond the third of the four traffic arms, such as that from the traffic node. This thus makes it possible to determine a traffic flow in addition to a number of vehicles and a speed distribution of the vehicles. In this way it can be determined both qualitatively and quantitatively which of the traffic arms are driven in which direction (s).

It can generally be assumed that more than one vehicle is in the detection area and the vehicles have the same or only slowly changing statistical properties (stationarity condition in the signal processing). Assuming that the speed estimates of the vehicles in the detection area can be combined appropriately, the "overall estimate" can be improved. This can be done by simple averaging or probabilistic approaches.

In a further embodiment, it is provided that speeds which have been estimated from successively acquired inquiry messages of at least one wireless communication unit are merged assuming a stationarity condition. It is assumed that speed and number of vehicles will change slowly over time. If, for example, n vehicles (or wireless communication units, for example Bluetooth units) are located in the detection range r _{BT of} a wireless transceiver at time t, then it can be assumed for the time step t + 1 that there are still n vehicles (or wireless communication units) ) are in the detection area and that the wireless communication units are moving at a similar speed and this is changing slowly. These assumptions lead to the stationarity of the considered traffic condition.

• – Die betrachteten Zeitschritte sind klein genug zu wählen, so dass sich Änderungen schnell auswirken können. Als praktisch sinnvoll haben sich beispielsweise Zeitschritte von 0,1 s erwiesen.
• – Der Erfassungsbereich ist groß genug zu wählen, so dass Änderungen von Geschwindigkeit und Fahrzeuganzahl klein sind. Gleichzeitig ist der Erfassungsbereich klein genug zu wählen, so dass Veränderungen des Verkehrsprozesses, z. B. ein sich ausbildender Stau, identifiziert werden können. Ein Erfassungsbereich von beispielsweise 30 bis 100 m hat sich hierbei als praktisch sinnvoll erwiesen.

The two assumptions lead to requirements for the size of the time steps and the coverage area:

• - The considered time steps are small enough to be chosen, so that changes can affect quickly. For example, time steps of 0.1 s have proven to be practically useful.
• - The detection range is large enough to choose, so that changes in speed and number of vehicles are small. At the same time the coverage is small enough to choose, so that changes in the transport process, eg. As a training congestion can be identified. A detection range of, for example, 30 to 100 m has proved to be practically useful.

If the requirements are met, the inquiry messages of the wireless communication units can be captured within a time window of a certain size and combined into an overall message. It should be noted that this time window must be so large that stationarity is guaranteed. But it should not be much larger, since otherwise essential properties related to the traffic process will be smoothed out. Time windows with a size of about 5 to 10 s have proved to be practically useful.

It can also be provided here, more accurate, the time of day and / or depending to adjust the time period of the traffic supplement for further improvement.

To explain the fusing under the assumption of stationarity, it is now assumed for the sake of simplification that at time t = t ₁ exactly one vehicle with a wireless communication unit was detected in the detection area, ie two inquiry messages were detected at different times, so that V _BT = {V _{BT, 0} } is. The a posteriori probability is then determined in the first step as follows: P (V | V _{BT, 0} ) = α ₀ .P (V) .P (V _{BT, 0} | V)

In the time step t = t ₂ (t ₂ > t ₁ ), the wireless communication unit is detected a second time, ie a further inquiry message is detected and, together with the first acquired inquiry message, a speed is estimated, so that now V _BT applies = {V _{BT, 0} , V _{BT, 1} } and P (V | _{BT, 0} , V _{BT, 1} ) = α ₁ .P (V) .P (V _{BT, 0} | V) .P (V _{BT, 1} | V) = α ₂ .P (V | V _{BT, 0} ) · P (V _{BT, 1} | V).

Finally, after T time steps, the following equation applies, with V _BT = {V _{BT, 0} ,..., V _{BT, T} } corresponding to the formulation of the stationarity assumption: P (V | _{BT, 0: T} ) = α _{0: T} × P (V) × P (V _{BT, T} | V) × P (V | _{BT, 0: T-1} ).

This equation expresses that the a posteriori probability P (V | _{BT, 0; T} ) estimated at time T is _calculated from the product of the a posteriori probability P (V | V _{BT0; T-1} ) and the sensor likelihood P (V _{BT, T} | V) associated with the instant _T and the a-priori probability P (V). It can be shown that the distribution P (V | _{BT, 0; T} ) becomes more and more acute with each additional time step in which a further inquiry message is detected and from which a velocity is determined, ie the underlying, true statistical one Average speed manifested.

Without limiting the generality, the stationarity assumption can also be extended to other wireless communication units and their detection. Without describing the derivation here in detail, this then results for all N wireless communication units: P (V | {V _{BT, 0: T} } _N ) = α _{0: T} · P (V) · Π _iεN P ({V _{BT, T} } _i | V) · P (V | {V _{BT, 0: T-1} } _i ).

Finally, the fusion equation to be solved taking into account the stationarity assumption can be formulated as follows: P (V | {V _{BT, 0: T} } _N , V _C2X ) = α · P (V) · P (V _C2X | V) · Π _iεN P ({V _{BT, T} } _i | V) P (V | {V _{BT, 0: T-1} } _i .

In order to obtain a reliable fusion result, the essential task now is to model the a-priori probability P (V) and the sensor likelihoods P (V _BT | V) and P (V _C2X | V). The a-priori probability P (V) characterizes the statistically verified, expected physical speed. This can be viewed temporally (at a fixed location) and / or spatially (eg at a section of track) and determined by extensive measurements with accurate reference sensors. It is important that P (V) be determined for the detection ranges of each sensor in each traffic branch present at the traffic node.

Furthermore, the sensors, ie the wireless communication transceiver unit and the C2X-RSU, must be modeled in the Bayesian network, ie the associated conditional probabilities must be determined. The conditional probabilities of nodes V _BT and V _C2X characterize the accuracies of the "Bluetooth detector" (ie, the measurement method used) and the C2X-RSU that must be determined via a learning process using reference sensors. In order to determine the probability P (V _BT | V) and P (V _C2X | V), reference _{records of} the speeds are _required when the "true" physical speed of the considered wireless _{communication units or vehicles coinciding therewith} is known. Since the actual speeds are not known, these are to be determined with the help of an accurate reference sensor. It is important here that the accuracies of the wireless transceivers can be determined with statistical certainty. If the "behavior" of the sensors is known, P (V _BT | V) and P (V _C2X | V) can be modeled accordingly by an empirical distribution _density function.

Since not only vehicles but also pedestrians and cyclists, passengers of suburban trains, buses and other means of transport can be equipped with wireless communication units, in particular Bluetooth units, these wireless communication units are erroneously detected. If the speed is estimated on the basis of this, then a systematic error arises. This can be taken into account by preceding the velocity estimation according to the above equation with traffic mode detection that separates the different traffic modes.

In a further embodiment, it is therefore provided that, on the basis of the acquired inquiry messages and the estimated number of vehicles and / or the estimating velocity probability distributions, the particular traffic mode is subsequently considered in estimating the velocity probability distributions and the number of vehicles. For example, heuristics or pattern recognition methods can be used to determine the traffic mode. Traffic mode describes, for example, whether a road user on foot, by suburban train, bus or motor vehicle moves.

In particular, it is provided in one embodiment that the speed probability distributions are combined with respect to the estimated number of vehicles to an overall speed distribution. In this way, a speed probability distribution with respect to the estimated number of vehicles is provided.

The invention will be explained in more detail with reference to preferred embodiments with reference to the figures. Hereby show:

1 a schematic representation of an embodiment of the device for determining a traffic condition at a transport node;

2 a schematic representation of an embodiment of the device for determining a traffic condition at a traffic junction with four traffic arms;

3 a schematic representation of the Bayesian network, which is used to estimate the velocity probability distribution in the controller;

4 a schematic representation of a correction factor for correcting a number of vehicles based on a detection probability;

5 a schematic flow diagram of an embodiment of the method for determining a traffic condition at a transport node.

In 1 is a schematic representation of an embodiment of the device 1 for determining a traffic condition at a traffic node. For the sake of clarity, this is merely a traffic arm 60 shown. The device 1 includes a C2X RSU 2 , a wireless transceiver 3 , here a Bluetooth transceiver 4 , a controller 5 and an output device 6 , The traffic arm 60 is from the Bluetooth transceiver 4 in a given detection area 7 captured, with Inquiry messages 20 . 21 of mobile wireless communication units 9 , here a bluetooth unit 10 , which are detected in particular in vehicles 50 are located. In a larger coverage 8th If necessary, additional C2X information from the C2X-RSU 2 (Road Side Unit).

The captured inquiry messages 20 . 21 are from the Bluetooth transceiver 4 to the controller 5 forwarded. If C2X information has been received, it will be sent from the C2X RSU 2 also to the controller 5 forwarded.

The control 5 is now designed in such a way, a speed 12 for the detected wireless communication unit 9 from a time difference between a time t _First of receipt of a first Inquiry message 20 this wireless communication unit 9 and a time t _Last of receiving a subsequent Inquiry message 21 this wireless communication unit 9 considering dimensions 11 of the specified detection range 7 to estimate the C2X information and the estimated speed 12 using a Bayesian network 13 to a velocity probability distribution 14 for this wireless communication unit, and a vehicle count 15 based on a number of wireless communication units detected simultaneously or in a time interval 9 to estimate the number of wireless communication units detected 9 by means of at least one correction factor 16 is corrected. Such a correction factor 16 especially takes into account a detection probability of the wireless communication units 9 depending on the estimated speed (s) 12 and a degree of equipment of the vehicles 50 with wireless communication units 9 ,

In particular, it can be provided that speeds 12 consisting of successively collected inquiry messages 20 . 21 a wireless communication unit 9 estimated to be fused assuming a stationarity condition. This allows all of the wireless transceiver 3 received inquiry messages 20 . 21 to a single estimate of the speed 12 this wireless communication unit 9 summarize, so the estimate of the speed 12 overall is improved. Will only be a single inquiry message 20 . 21 detected, then the speed is estimated over a predetermined maximum value. Becoming further Inquiry messages 20 . 21 detected, the estimated speed decreases 12 As a rule, and always strives for the value of the speed that the wireless communication unit 9 actually has.

The estimated number of vehicles 15 and the velocity probability distributions 14 and / or a derived total velocity distribution from the output device 6 for example, as an analog or digital traffic condition signal 17 , for example in the form of a data packet. The issued traffic condition signal 17 can then be processed by a traffic signal system or by a centralized or decentralized traffic control system and used to control the traffic.

In 2 is a schematic representation of an embodiment of the device 1 for determining a traffic condition at a traffic node 61 with four traffic arms 60-1 . 60-2 . 60-3 . 60-4 shown from a bird's-eye view. On the traffic arms 60-1 . 60-2 . 60-3 . 60-4 there are vehicles 50 (For the sake of clarity, not all are provided with their own reference numeral), which are equipped with wireless communication units, such as smart phones, wireless headphones, hands-free devices, laptops, etc. The device 1 for determining the traffic condition at the traffic node 61 comprises four wireless transceivers, each with its own predetermined detection range 7-1 . 7-2 . 7-3 . 7-4 , The individual coverage areas 7-1 . 7-2 . 7-3 . 7-4 are each one of the traffic arms 60-1 . 60-2 . 60-3 . 60-4 assigned. The four wireless transceivers are, for example, Bluetooth transceivers which detect inquiry messages from Bluetooth units. In a larger coverage 8th C2X information is from a C2X RSU of the device 1 detected. As already for in the 1 described embodiment, determines the device 1 at the four traffic arms 60-1 . 60-2 . 60-3 . 60-1 each a number of vehicles and a speed probability distribution.

In particular, it can be provided that additionally a direction information 18 the wireless communication units or the vehicles 50 is determined. This is done by evaluating a chronological sequence of detecting the respectively identical wireless communication unit in different traffic arms 60-1 . 60-2 . 60-3 . 60-4 , Such direction information 18 is exemplary of a trajectory of a vehicle 50 from traffic arm 60-2 in the direction of the traffic arm 60-3 shown. The advantage is that in addition to a number of vehicles and a speed probability distribution can also be determined where the wireless communication units or the vehicles 50 move.

Deriving the direction information 18 can be improved even if, for example, different tracks of the traffic arms 60-1 . 60-2 . 60-3 . 60-4 can be detected individually.

3 shows a schematic representation of the Bayesian network 30 which is used to estimate the velocity probability distribution in the controller. In such a Bayesian network, both the traffic process (physical speeds and vehicle counts) and the measurement process (measured / estimated speeds and vehicle numbers) are considered to be random processes and nodes 31 . 32 . 33 the Bayesian network 30 modeled. In general, it allows the Bayesian network 30 to merge incomplete, incomplete data from sensors (in this case the acquired C2X information and the speeds estimated from the acquired inquiry messages) and to determine the quantities searched here, here the velocity probability distribution. However, this requires knowledge and modeling of the accuracy and properties of the sensors used, in particular the conditional probabilities P (V _BT | V) and P (V _C2X | V), which describe how likely it is that there is a velocity V if the speeds V _BT and V _{C2X were determined} by a wireless transceiver or from the C2X information. Furthermore, the knowledge of the probability P (V) is necessary, which describes the process itself, ie how probable it is to find a wireless communication unit or a vehicle with a certain speed V in a considered detection area or traffic arm.

In 4 is a schematic course of a speed-dependent correction factor 40 for correcting a vehicle number based on a detection probability P (D). The correction factor is clear 40 by which value must be divided by when a single wireless communication unit is detected in a detection area. When the speed V of the wireless communication unit approaches zero, the likelihood that this wireless communication unit is approaching 1. based on detected inquiry messages from this wireless communication unit. However, the faster the wireless communication unit moves through the coverage area, the lower the probability P (D), that the wireless communication unit is detected. However, if a total of enough wireless communication units are present, which can be detected on the basis of the inquiry messages, a reliable estimate of the number of vehicles can nevertheless be made by means of the correction factor.

When a wireless communication unit is detected, its speed is estimated and the value of the correction factor is then determined based on the estimated speed. The number of vehicles is then divided by this value. The exact course of the correction factor 40 can be determined for example by means of empirical measurements of reference sensors. The illustrated curve of the correction factor 40 is only schematic. In practice, it has been shown that the values for the correction factor 40 between 0.5 and 2, ie a single detection would correspond to a speed of a real number of vehicles from 1 to about 2 depending on the speed.

In particular, when estimating the number of vehicles, the degree of equipment of the vehicles with wireless communication units can and should also be taken into account. For Bluetooth, the equipment level is currently between 20% and 40%. In particular, the equipment level may also depend on the type of road on which the vehicles are traveling. In vehicles driving on a highway, more wireless hands-free devices are usually activated than in urban areas.

In 5 FIG. 3 is a schematic flow diagram of an embodiment of the method for determining a traffic condition at a traffic node. FIG.

After the start 100 of the method are in a first step 101 C2X information collected in the traffic node environment using a C2X-RSU. At the same time in the process step 102 detects individual inquiry messages from mobile wireless communication units, for example Bluetooth units, by means of at least one wireless transceiver with a predetermined coverage area.

In the process step 103 For example, a speed of a detected wireless communication unit is estimated from a time difference between a reception of a first inquiry message of this wireless communication unit and a reception of a subsequent inquiry message of that wireless communication unit taking into consideration dimensions of the predetermined detection area by means of a controller. If further wireless communication units are detected, a speed is also estimated for each of them.

It can be done in one step 104 provided that, assuming stationarity of the traffic condition, a plurality of speeds determined for a wireless communication unit are fused to a velocity probability distribution.

In the process step 105 Subsequently, the detected C2X information, if present, and the respective estimated speed are fused by means of a Bayesian network to a speed probability distribution for the respective wireless communication unit by means of the controller.

In the process step 106 For example, a number of vehicles based on a number of wireless communication units detected simultaneously or in a time interval is estimated by the controller, wherein the number of detected wireless communication units is corrected by means of at least one correction factor.

The correction factor is in this case, for example, in the method step 107 from the in process step 103 estimated speed of the wireless communication unit (s) (see also 4 ). Furthermore, a degree of equipment of the vehicles with wireless communication units is considered as a further correction factor. If the wireless communication units, as preferred, for example, are Bluetooth units and, on average, every other vehicle has a Bluetooth unit (activated, ie sending inquiry messages), the equipment level is 50%. If, on the other hand, only one in five vehicles has such a Bluetooth unit, the equipment level is only 20%.

It can additionally in one process step 108 be provided that a traffic mode on the basis of the number of vehicles and the speed probability distribution is determined. For example, heuristics or pattern recognition methods can be used here.

It can also be in one process step 109 be provided that an overall speed distribution of the individual speed distributions and the number of vehicles is determined. In this way, a distribution is generated by number of vehicles at a certain speed.

In the last procedural step 110 For example, the speed probability distribution and the number of vehicles are output in the form of a traffic condition signal by means of an output device. The traffic condition signal can then be used, for example, by a controller of a traffic signal system to regulate the traffic at the traffic node. Then the process is finished 111 ,

It is envisaged that the method is repeated cyclically, so that a current traffic condition is always determined and corresponding data is provided thereon.

LIST OF REFERENCE NUMBERS

1

contraption

2

C2X RSU

3

Wireless transceiver

4

Bluetooth transceiver

5

control

6

output device

7

predetermined detection range

7-1

predetermined detection range

7-2

predetermined detection range

7-3

predetermined detection range

7-4

predetermined detection range

8th

larger detection area

9

Wireless communication unit

10

Bluetooth unit

11

dimension

12

speed

13

Bayesian network

14

Speed probability distribution

15

number of vehicles

16

correction factor

17

Traffic condition signal

18

directional information

20

Inquiry message

21

Inquiry message

30

Bayesian network

31

node

32

node

33

node

40

correction factor

50

vehicle

60

little traffic

60-1

little traffic

60-2

little traffic

60-3

little traffic

60-4

little traffic

61

transportation hub

100-111

steps

Claims (13)

Method for determining a traffic condition at a traffic node ( 61 ) with at least two traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ), comprising the following steps: acquiring C2X information in the environment of the traffic node ( 61 ) by means of a C2X-RSU ( 2 ), Capturing individual inquiry messages ( 20 . 21 ) of mobile wireless communication units ( 9 ) by means of at least one wireless transceiver device ( 3 ) with a predetermined detection range ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ), wherein the wireless communication units ( 9 ) are each identified by a unique identifier, estimating a speed ( 12 ) of a detected wireless communication unit ( 9 ) from a time difference between a receipt of a first inquiry message ( 20 ) of this wireless communication unit ( 9 ) and a receipt of a subsequent inquiry message ( 21 ) of this wireless communication unit ( 9 ) taking into account dimensions ( 11 ) of the given detection range ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ) by means of a controller ( 5 ), Merging the C2X information and the estimated speed ( 12 ) by means of a Bayesian network ( 30 ) to a velocity probability distribution ( 14 ) for this wireless communication unit ( 9 ) by means of the controller ( 5 ), Estimating a number of vehicles ( 15 ) based on a number of wireless communication units (simultaneously or in a time interval) ( 9 ) by means of the controller ( 5 ), wherein the number of detected wireless communication units ( 9 ) by means of at least one correction factor ( 16 ), outputting the estimated number of vehicles ( 15 ) and the velocity probability distributions ( 14 ) by means of an output device. Method according to claim 1, characterized in that a correction factor ( 16 ) a detection probability of the wireless communication units ( 9 ). Method according to one of the preceding claims, characterized in that a correction factor ( 16 ) an equipment level of the vehicles ( 50 ) with wireless communication units ( 9 ). Method according to one of the preceding claims, characterized in that the wireless communication units ( 9 ) at each of the at least two traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) by means of in each case at least one own wireless transceiver device ( 3 ) with the respective traffic arm ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) assigned predetermined detection areas ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ). Method according to claim 4, characterized in that on the basis of the different wireless transceivers ( 3 ) for different traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) received Inquiry messages ( 20 . 21 ) by means of the controller ( 5 ) a direction information ( 18 ) of the wireless communication units ( 9 ) is derived. Method according to one of the preceding claims, characterized in that speeds ( 12 ), which consists of successively acquired inquiry messages ( 20 . 21 ) at least one wireless communication unit ( 9 ) estimated were fused, assuming a stationarity condition. Method according to one of the preceding claims, characterized in that on the basis of the acquired inquiry messages ( 20 . 21 ) and the estimated number of vehicles ( 15 ) and / or the estimated velocity probability distributions ( 14 ) a traffic mode is determined, wherein the determined traffic mode is subsequently used in estimating the speed probability distributions ( 14 ) and the number of vehicles ( 15 ) is taken into account. Method according to one of the preceding claims, characterized in that the velocity probability distributions ( 14 ) in relation to the estimated number of vehicles ( 15 ) are summarized to a total velocity distribution. Method according to one of the preceding claims, characterized in that the mobile wireless communication units ( 9 ) Bluetooth units ( 10 ) and the at least one wireless transceiver ( 3 ) a Bluetooth transceiver ( 4 ). Contraption ( 1 ) for determining a traffic condition at a traffic node ( 61 ) with at least two traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) comprising: a C2X RSU ( 2 ) for receiving C2X information in the environment of the traffic node ( 61 ), at least one wireless transceiver ( 3 ) with a predetermined detection range ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ) for receiving signals from wireless communication units ( 9 ), a controller ( 5 ), and an output device ( 6 ), where the C2X-RSU ( 2 ) is designed such that C2X information in the environment of the traffic node ( 61 ), and wherein the at least one wireless transceiver device ( 3 ) is designed such that individual inquiry messages ( 20 . 21 ) of mobile wireless communication units ( 9 ) in the given coverage area ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ), wherein the wireless communication units ( 9 ) are each identified by a unique identifier, and wherein the controller ( 5 ) is designed such that it has a speed ( 12 ) for a detected wireless communication unit ( 9 ) from a time difference between a receipt of a first inquiry message ( 20 ) of this wireless communication unit ( 9 ) and a receipt of a subsequent inquiry message ( 21 ) of this wireless communication unit ( 9 ) taking into account dimensions ( 11 ) of the given detection range ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ), the C2X information and the estimated speed ( 12 ) by means of a Bayesian network ( 30 ) to a velocity probability distribution ( 14 ) for this wireless communication unit ( 9 ) and a number of vehicles ( 15 ) based on a number of wireless communication units (simultaneously or in a time interval) ( 9 ), wherein the number of detected wireless communication units ( 9 ) by means of at least one correction factor ( 16 ), and wherein the output device ( 6 ) is configured such that the estimated number of vehicles ( 16 ) and the velocity probability distributions ( 14 ). Contraption ( 1 ) according to claim 10, characterized in that the device ( 1 ) for each of the at least two traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) of the traffic node ( 61 ) each have their own wireless transceiver device ( 3 ), wherein the respective predetermined detection range ( 7 . 7-1 . 7-2 . 7-3 . 7-4 ) of the wireless transceiver ( 3 ) one of the at least two traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) assigned. Contraption ( 1 ) according to claim 10 or 11, characterized in that the controller ( 5 ) is formed on the basis of the different wireless transceiver devices ( 9 ) for different traffic arms ( 60 . 60-1 . 60-2 . 60-3 . 60-4 ) received Inquiry messages ( 20 . 21 ) a direction information ( 18 ) of the wireless communication units ( 18 ). Contraption ( 1 ) according to one of claims 10 to 12, characterized in that the at least one wireless transceiver device ( 9 ) a Bluetooth transceiver ( 4 ) configured to receive signals from Bluetooth mobile units ( 10 ) to recieve.

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