HK40000303A - Method for determining and comparing users' paths in a building - Google Patents

Description

Method for determining and comparing paths of users in a building

The application is a divisional application of a Chinese patent application with the application number of 201380013489.X and the name of invention creation of 'method for determining and comparing paths of users in a building', which is submitted on 8/2/2013.

Technical Field

The invention relates to a method and a system for determining a path of a user. Furthermore, the invention relates to a method and a system for determining the position of a user. More particularly, the present invention relates to a method and system for tracking users in indoor or outdoor areas such as hospitals, university campuses, stadiums or airports. The invention also relates to a method and system for determining a path for a user for operation and planning purposes, in particular to a method and system for determining a dwell time of a user within an area.

The invention also applies to providing real-time information to passengers as well as service schedules so that airport authorities can cope with any aggregation of passengers in critical areas such as security checks, inbound inspections, baggage, etc.

Background

In the past, it was difficult for airports to obtain historical and real-time information about the behavior of passengers within and near airports.

One solution to this problem is to use Bluetooth (Bluetooth is a registered trademark of Bluetooth SIG, Inc.) or Radio Frequency Identification (RFID) tags. However, these solutions have the following limitations:

passengers do not normally carry RFID tags and therefore cannot use them without specifically issuing them to them.

Bluetooth is a short range protocol limited to a small area at airports.

Bluetooth is normally not activated in the passenger's smartphone device, limiting the accuracy of any measurements.

Bluetooth relies on a bluetooth access point at a fixed location. It is relatively complex and time consuming if they need to be repositioned.

Another solution is a smartphone that uses WiFi triangulation to track passengers. WiFi uses a wireless connection between a user device and an access point to transfer data between the user device and the access point. WiFi is a registered trademark of San Jose, the WiFi Union, USA. Typically, an access point has a wired connection to a Local Area Network (LAN). However, this method has a problem in that the WiFi device does not transmit a continuous data stream. This is because the device will only be detected when the user is actually using the airport WiFi infrastructure.

This means that for any given installation, it can only be detected sporadically throughout the airport. For example, devices may be detected during a time when a passenger is using their telephone at a cafe or gate, but may not be detected during a time when the passenger is traveling from a check-in area to a security check-in area. This is of course problematic for real-time dwell time measurements, as sporadic data does not represent what is actually happening in the airport.

Embodiments of the present invention seek to address the above problems by using WiFi signals transmitted by passenger smartphones and other devices to provide location data that can be used to locate, track and measure passenger behavior throughout the airport. The location data is processed to remove bad data, and the remaining data is used to determine the passenger's path and associated dwell time information. This data can then be used to provide real-time measurements for any part of the airport.

Embodiments of the invention, which may be referred to as an indoor anonymous residence time tracking system, are multi-component services that:

1. allowing airport personnel to define any area in the airport.

2. Triangulation of WiFi signal strength is used to locate the device.

3. The devices are associated with an area in an airport.

4. The paths of the devices in these spaces are plotted.

5. A real-time set of consecutive device detections is maintained for devices detected in the airport.

6. The area and device path data is used to determine the dwell time in any area throughout the airport.

Embodiments of the present invention improve upon existing RFID systems because the tracked passengers/consumers do not need to be carried with RFID tags, and they do not need to be informed that their actions are being tracked, or they can subconsciously change behavior.

Embodiments of the present invention improve upon bluetooth systems because WiFi covers the entire airport rather than only a small specific area. It is thus possible to provide a fine measurement such as "display the current waiting time of an inbound passenger departing at international arrival". Improvements are also made to the bluetooth system because the area being measured is arbitrary and does not directly depend on the location of the access point. In this regard, in a bluetooth system, if an airport wishes to modify the area being measured, it is necessary to physically move the bluetooth sensor. In the present invention, airport personnel need only use the google map application to configure a new area.

Embodiments of the present invention improve upon basic WiFi triangulation in that the present invention can maintain a real-time device path, store all previous regions traversed by the device, and use this data to determine whether the data is suitable for real-time dwell time measurements.

Disclosure of Invention

According to a first aspect of the present invention there is provided a system for determining a path taken by a user through one or more areas. The system may include: a location server arranged to receive location data indicative of a location of a communication device associated with a user, the location data defining locations of the communication device at a plurality of different points in time, the location server further arranged to receive sequence data associated with the location data indicative of an order in which the location data is determined; path determining means for determining a path for the user through the area, the path for the user being defined by at least a portion of the received location data; a comparator for comparing the determined path of the user with one or more predetermined user paths. The location server processes the received location data based on the result of the comparison. Preferably, the location server corrects the determined user path with the processed location data.

According to another aspect of the present invention there is provided a system for processing user location data, the system comprising: a location server arranged to receive location data for a communication device associated with a user, the location data defining locations of the communication device at a plurality of different points in time, the location server further arranged to receive sequence data associated with the location data indicating an order in which the location data is determined; a path determination device for determining a path of the user defined by the received location data and associated sequence data; a comparator for comparing the determined path of the user with one or more predetermined user paths.

Preferably, each predetermined path is defined by further location data and associated sequence data indicating the order of the further location data. The location server is configured to process the received location data based on a result of the comparison. Typically, the location data is determined based on signal strength data typically received by the access point from the mobile device.

In yet another aspect of the present invention, a method for processing user location data is provided. The method comprises receiving, with a receiver, location data for a communication device associated with a user, the location data defining locations at which the communication device was detected at a plurality of different points in time, and receiving, with the receiver, sequence data associated with the location data indicating an order in which the location data was determined; determining, using a processor, a path of the user through a point defined by the received location data and associated sequence data; comparing, using a processor, the determined path of the user with one or more predetermined user paths; and processing the received location data using a processor based on a result of the comparison. Preferably, the determined user path is corrected or updated using the processed location data.

Drawings

Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of the major functional components that implement the present invention;

FIG. 2 is a screen shot of a zone editor embodying the invention that may be used as a means to select, create, edit or delete different zones in an airport to track a user;

FIG. 3 is a screen shot of an editor embodying the invention in which high quality airport device pathways are visualized in a particular area;

FIG. 4 is a screen shot of an editor implementing the invention in which lower quality airport device paths are visualized in a particular area;

FIG. 5 is a histogram illustrating real-time dwell time data acquired by an embodiment of the present invention; and

FIG. 6 is a flow chart showing the main steps performed by an embodiment of the present invention.

Detailed Description

The following describes a system used in the aeronautical industry, but this is exemplary and other applications of the invention will be discussed. For example, the system may be used in any indoor or outdoor area, such as a hospital, university campus, stadium, etc., where users carry WiFi enabled devices.

Referring now to FIG. 1, shown is a schematic diagram of a system 100 in accordance with an embodiment of the present invention. The operation of the different components will be explained in detail below.

The system 100 may be directed to tracking a user in an airport 101, the airport 101 having a plurality of WiFi access points 101a-101d that provide at least a portion of a WiFi infrastructure in the airport 101. Each access point 101a-101d may be located at a different location within the airport 101.

As the skilled person will appreciate, the WiFi infrastructure may use real time positioning system (RTLS) triangulation to locate the passenger's smartphone device or other mobile communication device.

In the embodiment shown in FIG. 1, location server 107 is communicatively coupled with airport WiFi infrastructures 101a-101 d. Location server 107 may be coupled with the airport infrastructure via a wireless link or a wired link. The server 107 may run within an airport data center or may run as a cloud service on several servers, typically located at different locations.

The server 107 may communicate with the airport network by means of an Application Programming Interface (API) provided by a WiFi vendor or operator. The server 107 may use a vendor-specific API to obtain raw location data from the airport.

The location server 107 may comprise a history data storage 103 for storing the determined movements of the apparatus in the airport. The historical data storage may be provided as part of the location server as a hard disk or solid state memory or other local storage. Alternatively, the historical data store 103 may be a separate storage device, such as a hard disk or solid state memory or other remote storage device, located at a different location than the location server 107. The historical data storage device may store historical reference data and data identifying devices belonging to airport personnel or infrastructure. In either case, the historical data store 103 is communicatively coupled to the regional data component 102, which can be part of the location server 107. This may use a wired or wireless connection. An area refers to a region of space in an airport where residence time measurements are to be made. Examples of regions are: a security check-in area, a baggage area, an entry area, a retail area, or a check-in area.

The area data component 102 can store definitions of areas of interest in an airport, such as: free side, land side, security check, luggage, retail, etc. The data component 102 can store the region definitions on a hard disk or solid state memory or in other storage devices. The air side portion of an airport is typically the portion of the airport that can only be accessed after passengers have taken a boarding card and passed X-ray security. The land-side portion of an airport is typically the portion before passengers have taken a boarding card and passed X-ray security. It is also noted that arriving passengers emerge from the luggage lobby and enter the landside area.

In the example shown in fig. 2 and described in further detail below, there are three terminal buildings T1, T2, and T3. Each terminal includes a plurality of zones. For example, terminal building T1 may include the following areas: void-one layer, void-bottom layer, and land area. Terminal building T2 may include areas for duty free, food, retail, free, check-in, side-by-side, and side-by-floor. Further, terminal T3 may include baggage and check-in areas, although it is defined above which areas are associated with which terminal, but this is exemplary only.

The region is defined as a virtual space. The user may define each region by dragging out polygons on a mapping application, such as an internet browser mapping application running on management interface 106. One example of a suitable internet browser mapping application is the google mapping application. Defining an area is similar to creating a polygon in microsoft PowerPoint. Google is a registered trademark of google, usa, and microsoft and PowerPoint are registered trademarks of microsoft, usa.

It is only necessary to define the area in the region where the access point is present in the airport. However, one or more physical access points may be added to or removed from a particular area without modifying the virtual area. Furthermore, the virtual area may be redefined without modifying any airport infrastructure. This solves one of the problems identified above with respect to bluetooth, as embodiments of the present invention may allow for changing the measured or monitored zone without physically moving the access point.

In fig. 1, the arrow labeled B, pointing from the management interface to the zone data component, schematically shows how the management interface sends data defining a particular zone to the zone data component. The data defining the respective areas may be data defining a polygon and preferably relevant data defining the position of each access point in the polygon.

The server may also include an in-memory cache 104. The cache 104 stores data indicating the currently active devices (active devices) being tracked in the airport, including their current location and area. The cache may also store in-memory representations of the paths taken by the device as it moves through the airport. The path may be defined by a curve or shape linking or connecting a series of data points indicating the location of the device. The sequence of position data is typically ordered chronologically.

Examples of this representation are shown in fig. 3 and 4 of the drawings, and are described in detail below. Fig. 3 is an example of a device path that is considered a high quality device path. There is a large number of device detections with high accuracy in the desired measurement area. Fig. 4 is an example of a device path that is considered to be of lower quality than the device path shown in fig. 3. A less frequent detection of a device path than a higher quality one is shown in fig. 4. The paths shown in fig. 4 are examples of the types of problematic data quality that the present invention seeks to address.

The server 107 may also include an Application Programming Interface (API) 105. The API provides a way to access data stored in the in-memory cache 104.

In the embodiment shown in fig. 1, API 105 is communicatively coupled with management interface 106 and in-memory cache 104. The management interface will be described in detail below. Further, the in-memory cache 104 is communicatively coupled with the region data component 102 and the API 105. Further, in the embodiment illustrated in fig. 1, regional data component 102 is communicatively coupled to both historical data store 103 and airport WiFi infrastructure 101.

The management interface 106 uses the API 105 to access the memory cache data 104. The management interface sends requests to access the memory cache data 104 via the API 105. Arrows E and F shown in fig. 1 represent that the memory cache data 104 is sent to the management interface 106 in response to the request.

As previously described, the system 100 may include a management interface 106 tool. The tools may be used to define regions and display data returned from the API. The administrative interface tool 106 is typically located on a server separate or distinct from the location server 107, however, in theory they could be located on a single server. In either case, the management interface 106 is communicatively coupled with the area data component 102 and the API 105 within the location server 107.

The various steps performed by embodiments of the present invention will be described in further detail below with reference to the flow chart shown in fig. 6. In some embodiments, not all of the steps shown in fig. 6 will be performed, and the steps need not be performed in the order shown in fig. 6.

At step 201, an airport operator uses the management interface 106 to define an area in the airport. A region may be defined as a polygon having a plurality of lines connected by a number of vertices. Typically, the polygon is a closed shape such that the user must cross one of the lines or zone boundaries when leaving the zone.

The region data can be stored in the region data component 102. Furthermore, the region data may also be stored offline in the history data storage 103, but storage in a single storage is sufficient for the region. An example of a baggage area is shown in fig. 2 as a filled black polygon area.

At step 203, the location server 107 polls the airport WiFi infrastructure via a third party server. Typically, the airport WiFi infrastructure is provided by a third party, so the location server 107 polls the third party server, which in turn requests data such as location data associated with all devices that may have moved since the last polling request. Typically, the third party server polls the airport WiFi infrastructure in a periodic or regular manner. The server may poll the WiFi infrastructure approximately every 15 seconds. However, while this polling may be periodic, the received location data for each device is typically irregular in nature. This is because the airport WiFi infrastructure has no control as to whether it receives signals from the devices. For example, if a device is temporarily turned off, the location server will not receive location data for the device when the device is turned off.

When the device uses a WiFi network, the third party server performs triangulation of the device. The third party server performs triangulation of the device by well known triangulation methods familiar to the skilled person. The third party server sends the location data for each mobile device that has been detected by the third party server to the location server. This allows the location server 107 to receive data associated with all devices that have moved since the last polling request. The arrow labeled a shown in fig. 1 indicates that the data is sent from the third party location server to location server 105.

The quality of the data received from the third party server may be determined based on an accuracy value provided by the third party vendor. The data quality may be determined based on signal strength or the number of access points at which each device is detectable, or both.

Further, the location data may be time stamped. This provides additional data indicating when the location data for a particular device is determined. The system may determine the detection frequency by comparing the time stamps of successive location data messages received by location server 107.

The third party server then sends the raw, or in other words, unprocessed, location data to the location server 107. The raw location data may include absolute location data for each device, i.e., the latitude and longitude of each device in the airport, or in other words, the x and y coordinates of the device. In general, the received location data for each device is independent of the location of the access point with which each device is communicating. Thus, the received location data for each device may be independent of the location of each access point.

At step 205, the location server 105 receives data from a third party triangulation server that determines the location of all mobile devices active within the airport. This may be performed every 15 seconds, but may be performed more or less frequently. After the server receives the location data at step 205, the location server may determine a path for the user defined by the received location data and associated sequence data defining a plurality of points on the path at step 207. At step 209, the location server compares the determined user path with one or more predetermined user paths. In step 211, the location server processes the received location data based on the comparison result. In step 213, the location server corrects or updates the determined user path based on the comparison.

In some embodiments, the raw data received by location server 107 may be combined or associated with the region data to derive its context. This is done by determining whether each device is within the boundaries that define a particular area. If it is determined that a particular device is located in a particular area, the location data associated with the device is also associated with the area in which the device is located.

For example, location server 107 may compare location data (i.e., coordinates of the device) to coordinates defining an area. If a device is determined to be within the boundaries of a polygon that defines the area under consideration, the location server 107 associates the area with a data structure for the device.

The combined or associated data may be referred to as context data. This data is combined by the server 107 and then stored in an in-memory (preferably database) data structure. There is one data structure for each detected device. The data structure contains coordinates of the device, e.g., an abscissa (e.g., x-coordinate) and an ordinate (e.g., y-coordinate).

In other words, each device is associated with an area at each detected location. Typically, each area is associated with a plurality of devices. In the example shown in fig. 3, 5522 device paths are found, waiting for an operator to check.

For example, there may be many people waiting in a security check area or baggage area during peak hours. If 100 people wait and the server receives data that detects about 10% of them, there will be 10 devices activated in the security check area or baggage area.

Although not necessary for all embodiments, the environmental location data may be stored in historical data storage 103.

The environmental location data may also be updated in a memory cache 104 as shown in fig. 3 that stores a real-time representation of the movement of all devices in the airport.

The environmental data may be stored in the storage device 103 and memory cache 104 so that a) any airport personnel may be automatically identified during nighttime processing because they are longer at the airport than passengers typically stay at, and b) so that the airport may use the data for historical comparisons.

Each time data is polled, a real-time dwell time may be determined for each device within each zone. This will be described in further detail below with reference to a particular real-time dwell time algorithm.

For each communication device, the dwell time may be determined by determining (before the user moves to a different zone) the time the user was first detected within the zone and the time the user was last detected within the zone. The dwell time may be calculated as the time difference between the last detection time within the zone and the first detection time within the zone. In the histogram shown in fig. 5, the number of devices within the security check area is determined as a function of the waiting time or dwell time: 5 devices had residence times of less than 1 minute; 6 devices had a residence time of 1 minute; another 6 devices had a residence time of 2 minutes; another 6 devices had a residence time of 3 minutes; 2 devices had a residence time of 4 minutes; the other 2 devices had a residence time of 5 minutes; finally, 2 devices were determined to have a 6 minute dwell time in the security area. Steps 203, 205, 207, 209, 211, and 213 may be repeated when location server 107 receives the updated location data.

The dwell time is the amount of time a passenger spends in a particular zone (i.e., area) of the airport. The term is interchangeable with waiting time. The dwell time is used in areas of the airport, such as retail areas, food halls, etc., where passengers want to be. The waiting time is used in areas of airports, such as check-in areas, security areas, baggage claim stations, etc., where passengers do not want to be.

The data may be accessed by a third party via the API. In other words, the third party may access data stored in the historical data store 103 (and the in-memory cache 104). The real-time data is stored in the memory 104 and the historical data is stored in the historical data storage 103. As shown in fig. 4, the data (real-time or historical or both) may be viewed using the management interface 106, although this step is actually optional.

The processing of raw WiFi location data for each mobile device within the airport using the algorithm will now be described in further detail. The algorithm uses the received location data to determine the real-time dwell time of each mobile device within the airport. The algorithm may be executed each time the location server 107 refreshes the location of a device in the airport.

When measuring real-time dwell time for a given area, such as a security check area, the following data quality issues need to be taken into account:

1. employee WiFi devices and static WiFi devices (e.g., employee Personal Computers (PCs)) of the security check area should be filtered out.

The sporadic and periodic inaccurate nature of WiFi data means that devices that are close to, rather than passing through, a security check area may be incorrectly reported as being in the security check area.

3. The number, accuracy and frequency of detections will vary for the device.

Embodiments of the present invention address these data quality issues in a number of different ways.

Staff handling device

The location server 107 maintains a dynamic list of employees and infrastructure at the airport (the above-mentioned historical data 103). The list is automatically generated by monitoring such devices: devices in airports that are long in time and that may symbolize employees working there, or devices that frequently appear in airports and that may symbolize employees working for five days of the week.

The devices in the security check area are then compared to the list and filtered from the results.

Handling inaccurate or partial paths

Inaccurate WiFi data can be smoothed or eliminated by using the typical path of a passenger departing through an airport. The following example is referenced to the departing passenger because it is described in conjunction with a security checkpoint where only the departing passenger is subject to security checks. Nonetheless, the process steps are equally applicable to other passenger types, such as arriving passengers.

The device path may be used to describe the passenger as a departure, arrival or transfer passenger. Furthermore, the device path may be described as an airport employee or a welcome (i.e., an interviewer/receptionist).

The typical path of the departing passenger is given by the following sequence of regions:

air side retail gate for checking check-in on land side

That is, passengers arrive at an airport in a landside area, check in, and then arrive at an empty side area through a security check. Passengers typically remain in retail areas until they are ready to board an aircraft and then arrive at a gate.

To measure the dwell time at the security check-in area, any path that includes a past check-in or check-in, and does not include an empty side/retail/gate, may be considered a well-represented path.

Examples of inaccurate paths caused by the sporadic nature of WiFi data are:

1. security check [ dormancy ] boarding gate

This is an example of a path where a device is first detected in a security check area and then enters a sleep mode no longer detected by the WiFi infrastructure, detected at a gate after a long period of time. This is a bad path because a) it is not known how long the device was in the security check area before it was first detected, b) it is not known how long it stayed in the security check area before it went to the empty side because it entered the sleep mode and could not be detected.

1. Empty side boarding gate luggage security check land side

This is an example of arriving passengers who arrive at the gate to the empty side, walk to a baggage claim to pick up their baggage. Passengers are detected briefly (and incorrectly) in the security check area before traveling to the landside because of poor WiFi quality. The device path must therefore be eliminated from the dwell time measurement.

Because the complete device paths remain in memory, these poor quality paths can be filtered or removed by specifying filter conditions in the algorithm. The filtering conditions vary according to the area being measured and must therefore be configurable for the area under consideration. The following is an example of filter conditions for two regions.

1. Security area

a. The device must be in the security area or the device must just be transferred from the security area to the empty side

b. The device must never be on the air side

c. The device must have been on the land side before

d. The device path must match the starting passenger's description

2. Luggage area

a. The device must be in the luggage area or the device must just be transferred from the luggage area to the land side

b. The device must never be on the land side

c. The device must have been previously on the air side

d. The device path must match the description of the arriving passenger

Handling the number, accuracy and frequency of detections

The real-time dwell time algorithm may take into account the number, accuracy and frequency of detections to determine the quality of any given device path for use. These three factors are important because:

1. generally, the more a particular device path is detected, the better the quality of that device path. Closely related to this is the accuracy and frequency of these detections.

2. Due to environmental factors at airports, the accuracy may vary from test to test. The higher the accuracy, the more reliable the data.

3. The detection frequency varies along the device path (typically due to whether the passenger is using the device). Infrequent detection is a problem because if a device is not detected within, say, 2 minutes, it is not possible to know whether the device is still in the security area or has left the security area. Fig. 2 and 3 show examples of device paths with high and low frequencies.

The algorithm takes these three parameters into account when assigning quality values to the paths. Of particular importance is the frequency and accuracy of detection when the device transitions from check-in to security and from security to empty. If the determination can be made with high accuracy when the device enters/leaves the security check, embodiments of the present invention can determine with high accuracy how long the device spends in the security check area.

The device path must meet a quality threshold that is available for regional dwell time measurements.

Thus, embodiments of the present invention combine arbitrary region definitions, device path descriptions, device path filtering through history, and device detection quality descriptions, such that variations and sporadic nature of WiFi signals can be handled. This allows for determining the real-time (and historical) dwell time of the user in any part of the airport.

In some embodiments, a system for processing user location data is provided. The system comprises:

a. a location server arranged to receive location data for a communication device associated with a user, the location data defining locations at which the communication device was detected at a plurality of different points in time, the location server further arranged to receive sequence data associated with the location data indicating an order in which the location data was determined;

b. path determining means for determining a path of the user through a point defined by the received location data and associated sequence data; and

c. a comparator for comparing the determined path of the user with one or more predetermined user paths;

wherein the location server processes the received location data based on a result of the comparison.

In some embodiments, a method for processing user location data is provided. The method comprises the following steps:

a. receiving, with a receiver, location data for a communication device associated with a user, the location data defining locations of the communication device detected at a plurality of different points in time, and receiving, with the receiver, sequence data associated with the location data indicating an order in which the location data was determined;

b. determining, using a processor, a path of the user through a point defined by the received location data and associated sequence data; and

c. comparing, using a processor, the determined path of the user with one or more predetermined user paths; and

processing, using a processor, the received location data based on a result of the comparison.

Claims (20)

1. A system for describing a user, comprising:

a. a server (107) arranged to receive location data of a communication device associated with the user, the location data defining locations of the communication device detected at a plurality of different points in time, the server (107) further arranged to receive sequence data associated with the location data indicating an order in which the location data is determined, the server (107) further arranged to compare the received location data with area data defining a plurality of areas and to associate the received location data with one of the plurality of areas; and

b. path determining means for determining a path of the user through a first sequence of regions;

wherein the server (107) is further arranged to describe the user as one or more predetermined user types based on the determined path.

2. A system according to claim 1 wherein the path determination means is arranged to determine a path based on region data associated with the location data and the sequence data.

3. The system of claim 1, further comprising a comparator to compare the determined path of the user with one or more predetermined paths, each predetermined path defining a second sequence of regions, and to describe the user based on the comparison.

4. The system according to any one of claims 1 to 3, wherein the areas are associated with indoor or outdoor areas, in particular with one or more of hospitals, campuses, stadiums and airports, and wherein each area is preferably associated with one or more of a landside area, a vacant area, a check-in area, a security check-in area, a retail area, a luggage area, an inbound area, a duty free area, a food area, a retail area and a gate.

5. The system of any preceding claim, further comprising a management interface (106), the management interface (106) for defining each of the regions.

6. The system according to any one of the preceding claims, wherein the server (107) is further arranged to associate the user with one or more predetermined user types including a departure passenger type, an arrival passenger type, a transfer passenger type, an airport staff type or a welcome people type based on the determined first sequence of zones.

7. A system according to any preceding claim, wherein the regional data is associated with a plurality of different terminals of an airport, and wherein the regional data associated with each terminal preferably comprises a plurality of different regions on different levels.

8. The system of any preceding claim, wherein each region is defined based on a polygon, and wherein the data defining each region preferably comprises correlation data defining the position of access points in the polygon.

9. The system of any preceding claim, further comprising storing data defining the plurality of regions in a region data component (102).

10. The system according to any of the preceding claims, wherein the server (107) is further configured to poll other servers to request location data associated with devices that have moved since a last polling request, wherein the polling requests are preferably periodic and associated with location data received at irregular times.

11. The system according to any of the preceding claims, wherein the location data further comprises time stamp data, and wherein the user is preferably associated with one or more predetermined user types based on the time stamp data.

12. A system according to claim 11 wherein the system is arranged to determine a period of time for which a user is located within one or more areas, wherein the system associates the user with one or more predetermined user types, preferably if the period of time is greater than a predetermined value, and wherein the one or more predetermined user types preferably comprise airport staff user types.

13. The system according to claim 11 or 12, wherein the server (107) is further arranged to determine a dwell time associated with one or more areas based on a difference between timestamp data a user was first detected within an area and timestamp data a user was last detected within the area.

14. The system according to claim 13, wherein the server (107) is further arranged to associate the user with one or more predetermined user types based on the dwell time.

15. A method for describing a user, comprising:

a. receiving location data for a communication device associated with the user, the location data defining locations of the communication device detected at a plurality of different points in time;

b. receiving sequence data associated with the location data indicating an order in which the location data was determined;

c. comparing the received location data with area data defining a plurality of areas;

d. associating the received location data with one of the plurality of regions;

e. determining a path of the user through a first sequence of regions; and

f. describing the user as one or more predetermined user types based on the determined path.

16. The method of claim 15, further comprising comparing the determined path of the user to one or more predetermined paths, wherein each predetermined path defines a second sequence of regions, and wherein the user is described based on the comparison.

17. A method according to claim 15 or 16, further comprising polling other servers to request location data associated with devices that have moved since the last polling request, wherein the polling requests are preferably periodic and associated with location data received at irregular times.

18. The method of any of claims 15 to 17, wherein the location data further comprises timestamp data, wherein the user is associated with one or more predetermined user types based on the timestamp data, and the method further comprises: determining a time period during which the user is located within one or more zones; and wherein the method preferably associates the user with one or more predetermined user types if the period of time is greater than a predetermined value, and wherein the one or more predetermined user types preferably comprise airport staff user types.

19. The method of any of claims 15 to 18, wherein the location data further comprises timestamp data, wherein the user is associated with one or more predetermined user types based on the timestamp data, the method further comprising determining a dwell time associated with one or more zones based on a difference between timestamp data when a user was first detected within a zone and timestamp data when a user was last detected within the zone.

20. The method of claim 19, further comprising associating the user with one or more predetermined user types based on the dwell time.