Patent Description:
Inferring road traffic conditions in real-time is important for several different applications. Firstly, an example would be that, knowing traffic congestion spots can assist optimal route planning, something that is becoming increasingly important for ride-hailing, ridesharing, food delivery, and courier services, as they are heavily dependent on estimated time of arrival (ETA). Non-optimal routes lead to unnecessary increases in fuel consumption and wear-and-tear (e.g. burning of rubber tires due to friction and more rubber and road particles in the air), both of which induce significant financial cost on transport operators, as well as leading to both exhaust emissions, such as carbon dioxide (CO2), nitrogen oxide (NOx), particles, and non-exhaust emissions, the latter becoming ever more important due to uptake of electric vehicles (EVs), which are heavier than petroleum based ones. Secondly, knowing various traffic metrics in real-time, such as traffic flow, average speed, and traffic density, opens up for more dynamic public transport infrastructure optimizations, such as dynamically adjusting traffic light timing based on current traffic flow.

The current methods of inferring traffic conditions are either based on manual traffic counting, automatic traffic counting, or by collecting telemetry from smartphones and in-vehicle sensors such as GPS navigation systems and dedicated sensors in trams and buses. Manual traffic counting involves human observers who visually count traffic and report the tally either on a sheet or via an app or specialized handheld device. Manual methods also involve individuals reporting traffic incidents by contacting the local traffic authority. Automatic traffic counting includes installation of both permanent as well as temporary traffic counting devices. These electronic devices often involve sensors (pneumatic tubes, piezo-electric sensors, and inductive loops) placed on the road surface; changes in electric charge due to mechanical stress (e.g. by a passing vehicle) are then used to detect and tally traffic. Off-road sensors can also be used, such as infrared beams, radar, and cameras.

When it comes to smartphone and in-vehicle sensor data collection for traffic reporting purposes, it is generally done using GPS coordinates tracking of users and vehicles. In the case of smartphones, by estimating the speed at which the user is moving, combined with GPS coordinates of known roads, one can infer whether the user is inside a moving vehicle.

<CIT> relates to a hierarchical floating car data network which comprises a central server, an egress point network, and a participating vehicle network. The egress point network is in communication with the central server. The egress point network comprises a plurality of egress points. The participating vehicle network comprises a plurality of participating vehicles. Some of the plurality of participating vehicles are in direct communication with each other and with at least some of the plurality of egress points. Furthermore, at least some of the plurality of participating vehicles may be in communication with the central server. A geographic database is formed from content communicated between the elements of the hierarchical floating car data network.

<CIT> relates to systems and methods for detecting the presence of a body in a network without fiducial elements, using signal absorption, and signal forward and reflected backscatter of Radio Frequency waves caused by the presence of a biological mass in a communications network.

<CIT> relates to systems and methods of using a machine learning model to detect physical characteristics of an environment based on radio signal data. A system uses a radio signal receiver for collecting noise floor signal data comprising radio signal data from an environment within a predetermined proximity of the radio signal receiver. The system implements a trained deep machine learning classifier that is trained to classify one or more physical characteristics of the environment based on the radio signal data.

<CIT> discloses methods and apparatus for extracting the features of either a set of power spectrum density measurements or a set of pre-processed frequency domain real and imaginary portions of uplink (UL) Sounding Reference Signals (SRSs) measurements and feed the features to an Artificial Intelligence (AI) classifier for User Equipment (UE) speed estimation.

An object of the invention is to enable dynamic traffic flow management, such as variable speed limit and traffic light timing, by enabling real-time traffic counts, for example, per vehicle category.

The invention will now be described more in detail hereinafter with reference to the accompanying drawings, in which certain embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided as examples of embodiments within the claimed scope.

It is to be understood that the singular form "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It will be further understood that the terms, "comprises" "comprising", "includes" and/or "including" when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, step, operations, elements, components, and/or groups thereof.

An advantage of embodiments of the invention will be the enabling of monitoring of traffic in areas without the need to provide specific infrastructure components or excessive human resources to accomplish such tasks. It also enables the ability to have an overview of traffic flow, what type of traffic the traffic comprises of, and movements of traffic in specific areas. This will enable insights into traffic management systems, including visualizations of traffic hotspots, enable ease of heavy-traffic burdened areas and allows the user of the invention to supply further detailed information such as real-time optimal route planning, and future traffic predictions due to the continues generation and analysis of real time data.

According to the invention, a machine learning model is trained. The training is done by using a set of training data. The machine learning model can use several sets of training data to reinforce the machine learning model training. The set of training data is representative of a radio frequency characteristic or a performance management counter. A variety of input parameters, from none, one or a plurality, that can be controlled for, may be used by the machine learning model to account for any difference between use cases of the machine learning model. The machine learning model can be a Bayesian model. The Bayesian model may be such that one can encode a model using prior real-world knowledge corresponding to various parts of the model.

As used herein, examples of input parameters that can be controlled for may be, traffic information, congestion information, communal traffic route, communal traffic timetable, and the base station's cell configuration data, e.g. a cell id, a cell name, a bandwidth, a longitude, a latitude, a bearing, an angle, a radius, a cell range, a cell capacity, a downlink channel bandwidth, an uplink channel bandwidth, a band, a downlink frequency, and an uplink frequency.

An area is represented using one or more parameters where the parameter is associated with an, or several, environmental categorizations of the area. The parameter can be a numerical value, a text, a symbol, binary code, or other type of information display, representing for example a vehicle flow rate, a vehicle count, and a vehicle type. The term "environmental categorization," as used herein, refers to a categorization of a physical environment dependent upon attributes attributed to a one or more conditions of the physical environment. The environmental categorization of the area is any one or a combination of the following, a high-rise area, suburban houses, a park, a garage, a congested area, high traffic, low traffic, medium traffic, no traffic, many pedestrians, no pedestrians, dense wood, sparse tree, open field, lake, mountainous, hilly, rocky, fallow farmland, farmland, crops, grazing pasture, subway, fast traffic flow, and slow traffic flow or other reasonable representation of a categorization of the area. The term environmental categorization embodies a way to infer a characteristic of the area at a specific point in time, such as the state of the area at the current point in time. In one embodiment the specified point in time is offset from the current time, by a time period selected from any of: a number of minutes, a number of hours, a number of days, a number of weeks, a number of months and a number of years, in the past. According to another embodiment the represented point in time is set in the future, offset from the current time, by a time period selected from any of: a number of hours, a number of days, a number of weeks, a number of months and a number of years. Alternatively, in another embodiment, the time is a specified time interval spanning the range of a number of minutes, a number of hours, a number of days, a number of weeks, a number of months and a number of years, or variants thereof. This entails a continuum of points in time; either past, present or future is possible for the area.

<FIG> and <FIG> illustrate a computer system <NUM>. The computer system <NUM> comprises a machine learning model <NUM> and one or more network nodes <NUM>. The machine learning model <NUM> refers to a mathematical model based on sample data, computational neural network-based decisions, or supervised learning system. <FIG> and <FIG> show the machine learning model <NUM> as located as data and software stored and processed within the network node <NUM>. As illustrated in <FIG>, the network node <NUM> is a physical network device, such as in a radio access network device, a radio core network device, a distributed networks system node and, in <FIG>, a distributed computing platform (e. g a cloud computing platform) node <NUM> such as a server, or a virtual network node which can be implemented in a cloud. The two figures further illustrate embodiments where another network node, a radio network node <NUM>, e.g. in the form of a radio base station (e.g. <NUM> as mentioned below) in the form of a standardized base station such as nodeB or evolved NodeB (eNB), for Long Term Evolution or gNodeB for New Radio (NR), is transmitting a radio frequency characteristic, <NUM>, to the computer system <NUM>. It will be appreciated by the person skilled in the art that the radio frequency characteristic <NUM> can be exposed in a performance management counter <NUM>. In other words, the radio frequency characteristic <NUM> may be a part of the performance management counter <NUM>. In other words, <FIG> and <FIG> illustrate the computer system <NUM> as receiving the radio frequency characteristic <NUM> or the performance management counter <NUM>. According to one embodiment illustrated in <FIG>, the radio frequency characteristic <NUM> is derived from a transmission from a communication device <NUM>. For example, the communication device <NUM> transmits radio signals <NUM> to a radio base station <NUM>, which is the radio network node <NUM>. The radio base station <NUM> then performs at least any one of: to capture, to measure and to record, the radio frequency characteristic <NUM> of the transmitted radio signals <NUM> in the performance management counter <NUM> of an operations support system (OSS). The radio frequency characteristic <NUM> or the performance management counter <NUM> are then subsequently sent by the radio base station <NUM>,. g as JSON encoded HTTP POST message, and received by the computer system <NUM> where they are processed according to a method illustrated in the flowcharts in either <FIG> and <FIG>, which are described in more detail below. In other words, an embodiment comprises gathering of information at the radio base station <NUM>, sending out the information from the radio base station, and receiving the information by the computer system <NUM>. The radio base station <NUM> provides radio connectivity to a plurality of wireless terminals or communication devices such as, for example, the communication device <NUM>, and a second communication device <NUM>.

The network node <NUM> may in another embodiment be the radio network node <NUM> as indicated with a dashed line in <FIG>. It will be appreciated that the radio network node <NUM> may refer to a base station as mentioned above, a base transceiver station, an access point, a network control node such as a network controller, a radio network controller, a base station controller, and the like or some combination thereof. The network node <NUM> may, in an embodiment, be a modem, hub, bridge, switch or other data communication equipment, or a data terminal equipment such as a host computer.

As used herein, the term radio frequency characteristic <NUM>, represents a characteristic of a radio wave frequency. This radio frequency characteristic <NUM> can be a value of or can be derived from, the effects of reflection, refraction, polarization, diffraction, free-space loss, aperture-medium coupling loss, absorption, or others. The radio frequency characteristic <NUM> can also be based upon the effect that occurs, such as pathloss, uplink-pathloss, path attenuation, received power, transmitted power, transmitter gain, transmitter losses, receiver gain, receiver losses, Doppler shifts, or Doppler shift spreads. As used herein, the term, performance management counter <NUM> may be data received from a network element in a mobile network operators' network or support system. The performance management counter <NUM> or radio frequency characteristic <NUM> may also be received from a telecommunications service provider's business support system. The performance management counter <NUM> may further contain information such as the timing advance (TA) index and number of successful radio resource control (RRC) connections counted. As an example of where one or more radio frequency characteristics are captured is in a radio access network (RAN), is the radio base station, with the radio base stations comprises such as mentioned above. These can be exposed via performance management counts on the north-bound interface to the radio access network's OSS.

As used herein the term communication device <NUM>, which may be known as a "wireless terminal" or a "User Equipment" (UE), which may further refer to a mobile phone, a cellular phone, a Personal Digital Assistant (PDA), equipped with radio communications capabilities, a smart phone, iPAD, USB dongle e.g. with a radio modem, a laptop or personal computer, PC, equipped with an internal or external mobile broadband modem, a tablet PC with radio communication capabilities, laptop embedded equipment, a laptop mounted equipment, a device to device UE, a machine type UE or UE capable of machine to machine communications, customer premises equipment, a portable electronic radio communication device, a sensor device equipped with radio communication capabilities, a telematics unit within a vehicle or the like. In particular, the term "communication device" should be interpreted as non-limiting terms comprising any type of wireless device communicating with a radio network node in a cellular or a mobile communication system.

The information exchange between the network node <NUM> and the computer system <NUM> can be facilitated by one or more service architectures including but not limited to Simple Object Access Protocol (SOAP), Representational State Transfer (REST) and Remote Procedure Call (RPC). The service architectures utilize a number of application layer protocols including Hypertext Transfer Protocol (HTTP), Simple Mail Transfer Protocol (SMTP), Secure Shell (SSH), and File Transfer Protocol (FTP). Data encoding and representation can take form of JavaScript Objection Notation (JSON), Extensible Markup Language (XML), as well as binary data encoding including but not limited to Protocol Buffers (Protobuf) as well as FlatBuffers.

For example, initial configuration management data related to <NUM>/<NUM> cell configuration (e.g. cell-id, frequency, bandwidth, longitude, altitude, etc) is sent from a mobile network operator to the computer system <NUM> as JSON encoded objects via HTTP POST messages.

The computer system uses the received configuration management data from the mobile network operator together with a suitable radio wave propagation model (e.g. Okumura-Hata) as well as auxiliary data from mapping services (e.g. OpenStreetMap) and traffic authorities to create an internal representation of a cell and its coverage area. From the computer systems point of view a cell will now be considered as a traffic counter or a sensor, responsible for counting traffic on the roads within its estimated coverage area.

Performance management data such as uplink path loss are then periodically extracted from the performance management data stored in the OSS and sent from the mobile network operator to the computer system <NUM> as JSON encoded objects via HTTP POST messages. The computer system maps the Performance Management counters to their respective cells (internal cell/sensor representation), and applies the machine learning model to infer traffic characteristics such as vehicle flow rate and vehicle counts. The estimated traffic characteristics can in some embodiments be stored internally in the computer systems data storage.

An insight consumer, such as a city municipality, can then request traffic data by sending a HTTP GET message to the computer system's REST endpoint. The computer system responds by sending JSON encoded data containing traffic flow information.

Further, <FIG> and <FIG> illustrate the information being transmitted or sent from the computer system <NUM> comprising the machine learning model <NUM> to any one, more than one, or all the following: a second communication device <NUM>, a display <NUM> of e.g. a third-party outside of the computer system <NUM>, and a storage device <NUM>, the storage is in some embodiments localized within the computer system. In an embodiment the information is a parameter associated with the environmental categorization of an area.

<FIG> and <FIG> illustrate embodiments of a method performed by the computer system <NUM>. <FIG> illustrates an embodiment for determining a representation of at least one area; training, S31, the machine learning model <NUM>; receiving, S32, information, where the information is the radio frequency characteristic <NUM> or the performance management counter <NUM>; and obtaining, S33, the parameter of the environmental categorization, in one example, the obtaining is performed from a machine learning model comprised within the computer system, the machine learning model is applied on the received information to infer the parameters, another example is where the computer system access the machine learning model by sending the received information to a machine learning model located outside the computer system, which is then applied on the information and then the inferred parameters are obtained. Further, <FIG> illustrates the method further comprising, sending, S34, the parameter of the obtained environmental categorization e.g. as JSON encoded data in a HTTP POST message. In some embodiments illustrated in <FIG>, the method comprises training S41 the machine learning model <NUM>; receiving S42 information, which is the radio frequency characteristic <NUM> or the performance management counter <NUM>, and obtaining S43 the parameter of the environmental categorization. Further illustrated in <FIG>, is that the embodiment of the method here also comprises initiating, S44, the sending of the information. To train, S31, S41, the machine learning model <NUM>, the computer system <NUM> uses a set of training data. The training data is representative of radio frequency characteristics and/or information contained within performance management counters. Further, the training of the machine learning model <NUM> can be an iterative process, with the training of the machine learning model <NUM> occurring across several dependent training steps. In other words, the machine learning model <NUM> is trained, in one or several stages, to interpret and act upon information that the computer system <NUM> has access to, by using data representative of the information or other complementing information, enabling an interpretation/inference of a location's physical characteristics.

In certain embodiments the information is received, S32, S42, from the radio network node <NUM>. The information that is received, S32, S42, may contain, data traffic, the performance management counter <NUM>, an environmental categorization, a forecast of the environmental categorization, i.e. a forecast of the parameters representing the environmental categorization, the radio frequency characteristic <NUM>, and/or any other suitable information such as a country id, an operator id, a node id, a measurement time or a timestamp, a measurement period, a cell id, a cell name, a counter e.g. of vehicle types, a number of successful RRC connections established, and a pathloss value e.g. a pathloss distribution in decibels.

The information exchange may be facilitated by the several service architectures explained above. To obtain, S33, S43, the parameter the received information is processed using the machine learning model <NUM>. In other words, the parameter that is associated with the representation of the environmental categorization, is obtained by processing received information with the machine learning model <NUM>. The obtained parameter is transmitted or sent S34 by the computer system <NUM>. The sending or transmission may be to the second communication device <NUM>, the storage <NUM> or a processing node outside of the computer system or a network node of the computer system (not illustrated).

<FIG> illustrates embodiments wherein the communication device <NUM>, and at least the second communication device <NUM> are shown as being present at a location. Two exemplary areas, <NUM> and <NUM>, are illustrated between the communication devices <NUM>, <NUM>, and a receiver of radio signals, which in this case is the radio network node <NUM>, which can be the base station <NUM>. Both trafficked areas, <NUM>, <NUM> environmental conditions are determined in the form of the environmental categorization, examples of which will be described further with reference to <FIG>. The radio frequency characteristics <NUM> of the signals transmitted from the communications devices <NUM>,<NUM> are captured in the radio network node <NUM> and further transmitted to the computer system <NUM> embodied as a single network node, or as illustrated in <FIG>, a distributed computing platform, <NUM>.

The areas may be others than those represented in <FIG> and it is merely for illustrative purposes to highlight various areas that may be comprised in the embodiments of the disclosure. The area, representing a geographical area represented by e.g. geographical coordinates or a name or a word or a numerical value, may be the vehicular trafficked area <NUM> where such an area could be or comprise one or more of at least a road, a highway, a sidewalk, a garage, a bridge, a car park, and other terrain and areas traversed by vehicular traffic. The area is in an embodiment the pedestrian trafficked area <NUM>. The pedestrian area <NUM> may be or comprise at least one or more of at least a sidewalk, a park, a forest path, a road, a shopping mall, a subway station, and other area traversed by a pedestrian. The pedestrian trafficked area is an area traversed by a human, which can be an area traversed using a car such as a road, parking area, and gas station. The area could further be areas traversed by a human using others means such as bicycle or walking. In other words, the area may also comprise a walkway, a crosswalk, a bicycle path, a housing building, a park, and further variants thereof. The area in yet another embodiment, is an area such as a landscape area, which may be a lake, a forest, a mountain, and a field, and variants thereof. The area is located near a site/device that can receive and, in some embodiments, process signals, which in an embodiment represented in <FIG>, is the radio network node <NUM>.

As used herein, the area, could further be any region of a landscape, where the region of the area could be any size, but commonly the area may be the region within range of a radio network node. The range can be several hundreds of meters, up to <NUM> meters, with the range further being specified to be between <NUM>-<NUM> meters. The range is dependent on the carrier frequency with more precise measurement at smaller distance using higher frequencies. The person skilled in the art will appreciate that with new technologies the effective range can be expanded to beyond <NUM> meters.

Another embodiment of the invention is where the environmental categorization parameter, in <FIG> indicated as <NUM>, indicates vehicular traffic congestion or pedestrian traffic congestion in the area. Examples of what can be indicated in the environmental categorization parameter <NUM> is further illustrated in <FIG>. The environmental categorization parameter <NUM> is in some embodiments related specifically to details regarding traffic congestion activities, which may be the numbers of vehicles or pedestrians, the flow rate of such objects, which could be as a rate of change in the area across time, or a count per unit time. Further, the flow rate may also be defined descriptively such as high rate of traffic, high traffic, medium amount of traffic, and/or low traffic flow, slow traffic, or variants thereof. Further information is also the types of objects moving in the area. A non-exhaustive but representative list of what objects of the embodiment may be is any one of the following: car, van, bus, lorry, tram, bicycle, moped, motorcycle, caravan, tanker, trailer, construction vehicle, cyclist, all-terrain-vehicle, four-wheeler, train, scooter, electric scooter, pedestrian. These objects may be described in numerical terms where the parameters are, for example; "four cars, three pedestrians, one bus, two mopeds", and is a representation of the area.

Another embodiment, partially illustrated in <FIG>, is a computer system <NUM>, consisting of a multitude of network nodes <NUM> as described above where the training S31, S41 of the machine learning model <NUM> is performed in a singular network node or, in another embodiment, across many network nodes. The receiving, S32, S42, of the radio frequency characteristic <NUM> or the performance management counter <NUM> is performed in a first network node <NUM> or across a plurality of network nodes. It will be appreciated by one skilled in the art that the receiving of the radio frequency characteristic <NUM> or the performance management counter <NUM> can be the receiving of information inferred from radio frequency characteristic <NUM> or exposed as part of the performance management counter <NUM>. The obtaining, S33, S43, of the environmental categorization parameter (i. e the parameter associated with an environmental categorization), is performed in a second network node <NUM> or across many network nodes and the initiation S44 or the sending S34 of the value obtained is performed in any of: the first network node, the second network node, a third network node <NUM>, and several distributes network nodes within the computer system. The network node <NUM>, <NUM>, <NUM>, <NUM> is in some embodiments a virtual network node.

<FIG> illustrates a block diagram of an embodiment of the computer system <NUM>, which comprises a computer (e.g. in the form of a server host) <NUM> which includes one or more processors <NUM>, e.g. any one of, a general purpose microprocessor, and one or more data processing circuits, such as an application specific integrated circuits, and field-programmable gate arrays. The computer system <NUM> in some embodiments further includes a network interface <NUM> to connect to a network. The network interface is in some embodiments used in communicating with network nodes of the computer system outside of the computer, or any other connections. The computer system <NUM> also comprises a transceiver <NUM> coupled to an antenna for wirelessly communicating with another network node or with a communication device outside of the computer system <NUM>. The computer system <NUM> further includes a computer program product <NUM> in the form of a data storage for storing information, which may include one or more computer readable storage medium <NUM> in the form of non-volatile memories, and/or one or more volatile memories such as random-access memory. The computer program product <NUM> comprises a computer program <NUM>, which comprises computer instructions or code <NUM>. The computer readable storage medium <NUM> can be a non-transitory computer readable medium such as, a magnetic media where a hard disk is an example thereof, optical media, such as a DVD, and a flash memory The computer instructions <NUM> of the computer program are configured such that when executed by computer the computer instructions cause the computer to perform some or all of the functions and operations described herein.

The various embodiments described herein may be implemented in a recording medium readable by a computer or its similar device by employing for example, software, hardware or combinations thereof.

A software implementation of the embodiments described may be implemented as procedures and functions that may be implemented in separate modules and/or computer program parts, each of which is written to cause a computer system to perform one or more of the functions and operations described herein. Software codes may be implemented using a software application written in any suitable programming language.

Claim 1:
A method performed by a computer system (<NUM>) comprising one or more network nodes (<NUM>) for determining at least one parameter associated with an environmental categorization (<NUM>) of an area, the environmental categorization representing the area at a specified point in time or a specified time interval, the method being:
training (S31, S41) a machine learning model (<NUM>) using a set of training data representing a first set of radio frequency characteristics or a performance management counter;
receiving (S32, S42) at least one radio frequency characteristic (<NUM>) or a performance management counter (<NUM>), wherein the received radio frequency characteristic (<NUM>) is from radio frequency characteristics of radio waves of signals (<NUM>) propagated from at least one wireless communication device (<NUM>, <NUM>) to a radio base station (<NUM>), and wherein information contained within the received performance management counter (<NUM>) is derived from radio frequency characteristics of radio waves of signals (<NUM>) propagated from at least one wireless communication device (<NUM>, <NUM>) to a radio base station (<NUM>);
obtaining (S33, S43) the parameter associated with the environmental categorization (<NUM>), by using the machine learning model (<NUM>) after the training on the received radio frequency characteristic (<NUM>) or the received performance management counter (<NUM>); and
sending (S34) or initiating the sending of (S44) the parameter associated with the environmental categorization (<NUM>) of the area obtained using the machine learning model (<NUM>) to any one of: an information storage (<NUM>), a communication device (<NUM>), and a network node of the computer system (<NUM>) wherein the parameter associated with the environmental categorization (<NUM>) of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion and/or pedestrian traffic congestion in the area and the traffic congestion parameter comprises at least any one of a vehicle flow rate, a vehicle count, and a vehicle type.