Abstract:
Numerous mobile devices report their respective locations to a server which collects the location reports to provide real-time pedestrian traffic information, e.g., as a map that shows locations of individual mobile devices. Distribution of mobile devices in a geographic area more closely represents overall population locations and densities. Each device is identified by a digital fingerprint, formed from a number of system and device configuration parameters. Each mobile device is configured to periodically report its location, e.g., at a predetermined time interval and/or whenever the mobile device determines its location for another reason. A known location of a wireless network access point to which a mobile device is connected can serve as an estimated location of the mobile device.

Description:
[0001]    This application claims priority to U.S. Provisional Application 61/771,610, filed Mar. 1, 2013, which is fully incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention relates generally to computer data processing and, more particularly, methods of and systems for monitoring and analyzing pedestrian traffic. 
         [0004]    2. Description of the Related Art 
         [0005]    There are countless occasions and studies in which knowing where people are at any given time is valuable. Some occasions are quite serious. For example, emergency services—such as fire departments and paramedics—need to get to places quickly. If areas of a city are blocked by public gatherings, public demonstrations, or the city&#39;s celebration of a sports team winning a national championship, emergency response vehicles can be significantly delayed, sometimes with very serious consequences. In addition, knowing whether and where people are in a burning building is highly valuable information for a fire department. 
         [0006]    Analysis of pedestrian traffic patterns can help significantly with urban planning. Knowing where people tend to go within a city can help with planning a mass transit system. Knowing places that people tend to avoid can identify instances of urban blight. And, once the urban blight is removed, knowing how many people tend to go to that location can provide empirical evidence of the efficacy of the blight removal. Monitoring pedestrian traffic can also determine if and how often people enter environmentally sensitive areas. 
         [0007]    Businesses also value information regarding where people are and where they go. Knowing approximately how many people are in a store at any given time is valuable. Knowing patterns regarding how many people are in the store at different times of day and different days of the week and during clearance sales and holidays is very valuable. Also valuable is knowing from where people travel to come to the store and what other stores they go to before and after shopping in the store. 
         [0008]    What is needed is a way to monitor and analyze pedestrian traffic. 
       SUMMARY OF THE INVENTION 
       [0009]    In accordance with the present invention, numerous mobile devices report their respective locations to a server which collects the location reports to provide real-time pedestrian traffic information. The result can be as simple as a map that shows locations of individual mobile devices. As more and more people own and carry mobile computing devices, such as smart phones, tablet computers, and laptop computers, distribution of mobile devices in a geographic area more closely represents overall population locations and densities. 
         [0010]    Real-time pedestrian traffic maps and reports can be used by emergency response vehicles to avoid areas blocked by demonstrations, celebrations, or other mass gatherings that can block various routes. In addition, during mass emergencies such as hurricanes and earthquakes, emergency response personnel can identify locations of large dispossessed populations or can determine whether people are in locations of particular danger. 
         [0011]    In addition to reporting real time locations of people in an area, the location reports gathered over time can be statistically regressed to provide information on personal traffic flows. For example, large sporting events can wreak havoc on transportation systems. Tracking individual mobile devices to and from such a large sporting event can provide information regarding how many people travel along which paths to the sporting event, allowing a municipality to modify mass transit or other traffic management systems accordingly. Adapting mass transit to actual, observed human traffic patterns provides optimum service at minimal cost. 
         [0012]    Furthermore, commercial interests—particularly retail—benefit from being able to determine a population of customers within a store at any given time and even more so from being able to determine where customers tend to come from before shopping in the retail location and where they tend to go to afterwards. 
         [0013]    To ensure privacy and convenience of the individuals, the movement of each is tracked anonymously by monitoring the location of a portable personal computing device. Each device is identified by a digital fingerprint, formed from a number of system and device configuration parameters. Thus, each device can be accurately and securely identified without identification of the person carrying the device. 
         [0014]    A client device requests personal traffic information from the server. The request can include a number of attributes of the traffic information requested. For example, for a current, real-time map, the attributes can specify the geographic boundaries of the map. For a map of personal movement over time, the attributes can specify both the geographic boundaries of the map and a range of time over which personal movement is to be mapped. For a statistical regression of personal location data, the attributes include the specifics of the statistical regression to be performed and can also include a format in which the results of statistical regression are to be represented. 
         [0015]    Each mobile device is configured to periodically report its location, e.g., at a predetermined time interval and/or whenever the mobile device determines its location for another reason. A known location of a wireless network access point to which a mobile device is connected can serve as an estimated location of the mobile device. The location of the wireless network access point can be roughly determined from other location reports that include locations of respective mobile device that have been connected to the wireless network access point at the time location was reported. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0016]    Other systems, methods, features and advantages of the invention will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. Component parts shown in the drawings are not necessarily to scale, and may be exaggerated to better illustrate the important features of the invention. In the drawings, like reference numerals may designate like parts throughout the different views, wherein: 
           [0017]      FIG. 1  is a diagram showing a server computer that gathers and analyzes location data from a number of mobile devices through a computer network to provide personal traffic information to a client device in accordance with one embodiment of the present invention. 
           [0018]      FIG. 2  is a map showing personal traffic information received from the mobile device of  FIG. 1 . 
           [0019]      FIG. 3  is a transaction flow diagram showing reporting of current location by a mobile device of  FIG. 1 . 
           [0020]      FIG. 4  is a logic flow diagram showing the manner in which a mobile device determines its location in an illustrative embodiment of the present invention. 
           [0021]      FIG. 5  is a block diagram of a WiFi data record used by the server of  FIG. 1  to determine the geographic location of a wireless network access point. 
           [0022]      FIG. 6  is logic flow diagram illustrating the processing by the server of  FIG. 1  of a request for traffic information. 
           [0023]      FIG. 7  is a block diagram of a location data record used by the server of  FIG. 1  to represent a location report received from a mobile device. 
           [0024]      FIG. 8  is a block diagram showing a mobile device of  FIG. 1  in greater detail. 
           [0025]      FIG. 9  is a block diagram showing the client device of  FIG. 1  in greater detail. 
           [0026]      FIG. 10  is a block diagram showing the server of  FIG. 1  in greater detail. 
       
    
    
     DETAILED DESCRIPTION 
       [0027]    In accordance with the present invention, a server  106  ( FIG. 1 ) gathers location information from user devices  102 A-D, provides instantaneous maps of current pedestrian traffic such as map  200  ( FIG. 2 ), and generates statistical analysis of such location information to provide detailed information regarding typical pedestrian traffic patterns. 
         [0028]    Map  200  is a very simple example of a pedestrian traffic map yet provides valuable information. For example, emergency services can immediately see that the Southeast intersection appears to be blocked by pedestrians and can reroute vehicles in response. The number of people using the park can be determined, both those using the park immediately and how many people use the park on average for various days, weeks, months, and seasons. The number of people using the parking garage at the South of map  200  can be determined, both immediate use and usage patterns. The number of people visiting any store location shown on map  200  can be seen. In addition, patterns of pedestrian behavior can be identified, such as how many pedestrians visit a particular sequence of stores vs. other sequences of the same or other stores. Thus, a store manager can determine that a significant number of customers visit her store and then immediate visit a competitor around the block. 
         [0029]    User devices  102 A-D ( FIG. 1 ) each can be any of a number of types of networked computing devices, including smartphones, tablets, netbooks, laptop computers, and desktops computers, though location information is expected to be more useful for devices that are portable. Each of user devices  102 A-D serves as a location proxy for a single user. In other words, the location of each of devices  102 A-D is presumed to be the location of a single user. In addition, each of user devices  102 A-D communicates with server  106  through a network  108 , which is the Internet in this illustrative embodiment. Network  108  can also be a mobile telephony network. User devices  102 A-D are analogous to one another and description of user device  102 A is equally applicable to user devices  102 B-D unless otherwise noted herein. It should also be noted that, while four (4) user devices are shown in this illustrative example, many more than four (4) user devices will report locations to make maps such as map  200  more representative of pedestrian traffic generally. 
         [0030]    Client device  104  is a device for which server  106  provides access to pedestrian traffic information such as map  200  ( FIG. 2 ) through network  108 . 
         [0031]    Transaction flow diagram  300  ( FIG. 3 ) illustrates the reporting by device  102 A of its location to server  106 . 
         [0032]    In step  302 , device  102 A its globally unique device identifier to server  106  along with data indicating an intent to report the current location of device  102 A. 
         [0033]    In step  304 , server  106  sends a digital fingerprint challenge to device  102 A. Digital fingerprints and digital fingerprint challenges are known and described in U.S. Patent Application Publication 2011/0093503 for “Computer Hardware Identity Tracking Using Characteristic Parameter-Derived Data” by Craig S. Etchegoyen (filed Apr. 21, 2011) and that description is incorporated herein in its entirety by reference. 
         [0034]    Digital fingerprints offer the advantage of being more stable and less amenable to spoofing that are IP addresses and MAC addresses and, of particular significance here, require no user intervention. Accordingly, location reporting in transaction flow diagram  300  is secure, reliable (no device spoofing), and requires no action on the part of the user. 
         [0035]    To avoid frequent communication of digital fingerprints through network  108 , device identification and authentication uses only part of the digital fingerprints of user devices  102 A-D. A digital fingerprint challenge specifies one or more parts of a digital fingerprint and a manner in which the parts are combined and cryptographically obscured. In addition, the digital fingerprint challenge can change each time device identification and authentication is needed. Accordingly, each time a given device sends its digital fingerprint in response to a different digital fingerprint challenge, the digital fingerprint sent is different. Any digital fingerprint intercepted within network  108  or any network will not authenticate properly if used in response to a different digital fingerprint challenge. 
         [0036]    In step  306 , user device  102 A determines its geographic location. Step  306  is described in greater detail below in conjunction with logic flow diagram  306  ( FIG. 4 ). 
         [0037]    In step  308 , user device  102 A produces a responsive digital fingerprint data using the challenge received in step  304  and digital fingerprint  822  ( FIG. 8 ). 
         [0038]    In step  310  ( FIG. 3 ), user device  102 A cryptographically combines the responsive digital fingerprint data produced in step  308  with data representing the location of user device  102 A determined in step  308 . By cryptographically combining the responsive digital fingerprint data and the location data, user device  102 A makes the data tamper-evident and obscured. 
         [0039]    In step  312 , user device  102 A sends the combined data to server  106 . In step  314 , server  106  parses the responsive digital fingerprint data and location data and stores the location of user device  102 A in location data  1040  ( FIG. 10 ). 
         [0040]    After step  314  ( FIG. 3 ), processing according to transaction logic diagram  300  completes. In one embodiment, user device  102 A is configured to report its location according to transaction logic diagram  300  at regular time intervals. In an alternative embodiment, user device  102 A is configured to report its location according to transaction logic diagram  300  in response to a number of triggering events, including for example, determination of the location of user device  102 A for reasons independent of reporting of the location to server  106  and connecting to a wireless networking access point. Thus, any time the user of user device  102 A uses GPS circuitry of user device  102 A resulting in determination of the location of user device  102 A, user device  102 A reports the location to server  106  in the manner described above. 
         [0041]    As described above, step  306 —in which user device  102 A determines its location—is shown in greater detail as logic flow diagram  306  ( FIG. 4 ). In logic flow diagram  306 , priority is given to the most accurate of location data available to user device  102 A and data identifying any wireless data network to which user device  102 A is included when available to help server  106  in determining the location of devices for which a connected wireless data network is the only location data available. 
         [0042]    In test step  402 , user device  102 A determines whether a GPS (Global Positioning System) location of user device  102 A is available. User device  102 A may not include GPS circuitry, in which case the GPS location of user device  102 A is not available. Even if user device  102 A includes GPS circuitry, the GPS location of user device  102 A may not be available if the GPS circuitry is disabled or unable to receive adequate signals from GPS satellites. 
         [0043]    If the GPS location of user device  102 A is available, processing transfers to step  404  in which user device  102 A retrieves its GPS location. Otherwise, processing transfers from test step  402  to test step  406 . 
         [0044]    In test step  404 , user device  102 A determines whether a coarse location of user device  102 A is available. Coarse location of user device  102 A can be determined in a number of known techniques involving measurement of distances of user device  102 A from a number of wireless telephone network base stations and triangulation. Coarse location of user device  102 A may not be available if telephony network circuitry is disabled or unable to receive adequate signals from base stations. 
         [0045]    If the coarse location of user device  102 A is available, processing transfers to step  408  in which user device  102 A retrieves its coarse location. Otherwise, processing transfers from test step  406  to test step  410 . 
         [0046]    In test step  410 , user device  102 A determines whether user device  102 A is connected to a wireless data network (WiFi) through an access point. If user device  102 A is connected to a wireless data network through an access point, processing transfers to step  412  in which user device  102 A retrieves a globally unique identifier of the access point and determines a signal strength from the access point. Otherwise, processing transfers from test step  410  to test step  416 . The globally unique identifier (GUID) of the access point can be a digital fingerprint of the access point or a canonical combination of readily available information of the access point, such as MAC address and ESSID, for example. 
         [0047]    In step  416 , user device  102 A identifies its nearest wireless telephone network base station. 
         [0048]    After either step  404  or step  408 , processing by user device  102 A transfers to test step  414  in which user device  102 A determines whether user device  102 A is connected to a wireless data network in the manner described above with respect to test step  410 . If user device  102 A is connected to a wireless data network through an access point, processing transfers to step  412  in which user device  102 A retrieves a globally unique identifier of the access point as described above. 
         [0049]    From step  416 , step  412 , or test step  414  if user device  102 A is not connected to a wireless data network through an access point, processing transfers to step  418  in which user device  102 A reports the retrieved location information as its best estimate for its location. 
         [0050]    Server  106  can estimate the location of user devices  102 A-D for which the only location data is a WiFi access point GUID if server  106  can estimate the location of the WiFi access point itself, since WiFi access points have limited range. Accordingly, user device  102 A reports any WiFi access point to which user device  102 A is connected even if user device  102 A can determine its location by a more accurate technique. 
         [0051]    In response to a location report that includes WiFi access point data in addition to location information independent of the WiFi access point data, server  106  stores the location information in a location report  504  ( FIG. 5 ) within a WiFi data record  500  associated with the WiFi access point. WiFi network GUID  502  is the GUID of the WiFi access point, which is sometimes referred to as the subject access point in the context of  FIG. 5 . Each location report for a user device connected to the subject access point is represented in a location report  504 . Location  506  represents the location of the reporting user device. Signal strength  508  represents the signal strength of the subject access point as measured by the reporting user device. Time stamp  510  represents the date and time at which the reporting user device was connected to the subject access point and can be used to determine whether the subject access point has been moved. If locations  506  before a given date and time do not correlate well with locations  506  after the given date and time, it can be determined that locations  506  before the date and time represent a prior location of the subject access point can be disregarded. 
         [0052]    When a user device reports its location with only a GUID of a WiFi access point, server  106  estimates the location of the subject access point by finding a center of locations  506  weighted by signal strengths  508 . Server  106  uses the weighted center as an estimated location of the reporting user device. 
         [0053]    As described above, server  106  stores the location of user device  102 A in step  312  ( FIG. 3 ). In particular, server  106  stores the location of user device  102 A in a location data record  700  ( FIG. 7 ). Device digital fingerprint  702  is the digital fingerprint by which user device  102 A is identified and authenticated. A number of location reports  704  identify the location of user device  102 A at a given date and time. Location  706  represents the location of user device  102 A, and time stamp  708  represents the date and time. 
         [0054]    Logic flow diagram  600  ( FIG. 6 ) illustrates processing by server  106  ( FIG. 1 ) in response to a request for pedestrian traffic information from client device  104 . In step  602  ( FIG. 6 ), server  106  gathers location information of numerous location data records  700  ( FIG. 7 ). 
         [0055]    In step  604  ( FIG. 6 ), server  106  performs statistical analysis of the location data. For a simple request such as that represented in map  200  ( FIG. 2 ), the statistical analysis can be as simple as gathering all device locations within the geographic boundaries of map  200  and a predetermined recency, e.g., reported within the last twenty (20) minutes. Other statistical analysis can be more complex, involving a variety of statistical regressions and relationships between multiple devices and/or device locations over time. 
         [0056]    User device  102 A is a personal computing device and is shown in greater detail in  FIG. 8 . User device  102 A includes one or more microprocessors  802  (collectively referred to as CPU  802 ) that retrieve data and/or instructions from memory  804  and execute retrieved instructions in a conventional manner. Memory  804  can include generally any computer-readable medium including, for example, persistent memory such as magnetic and/or optical disks, ROM, and PROM and volatile memory such as RAM. 
         [0057]    CPU  802  and memory  804  are connected to one another through a conventional interconnect  806 , which is a bus in this illustrative embodiment and which connects CPU  802  and memory  804  to one or more input devices  808 , output devices  810 , and network access circuitry  812 . Input devices  808  can include, for example, a keyboard, a keypad, a touch-sensitive screen, a mouse, a microphone, and one or more cameras. Output devices  310  can include, for example, a display—such as a liquid crystal display (LCD)—and one or more loudspeakers. Network access circuitry  812  sends and receives data through computer networks such as network  108  ( FIG. 1 ). GPS circuitry  814  determines the location of user device  102 A in a conventional manner. 
         [0058]    A number of components of user device  102 A are stored in memory  804 . In particular, device tracking logic  820  is all or part of one or more computer processes executing within CPU  802  from memory  804  in this illustrative embodiment but can also be implemented using digital logic circuitry. As used herein, “logic” refers to (i) logic implemented as computer instructions and/or data within one or more computer processes and/or (ii) logic implemented in electronic circuitry. Device tracking logic  820  causes user device  102 A to report its location in the manner described above. 
         [0059]    Digital fingerprint  822  is persistent data stored in memory  804 . 
         [0060]    Client device  104  is a personal computing device and is shown in greater detail in  FIG. 9 . Client device  104  includes one or more microprocessors  902  (collectively referred to as CPU  902 ), memory  904 , an interconnect  806 , input devices  908 , output devices  910 , and network access circuitry  912  that are analogous to CPU  802  ( FIG. 8 ), memory  804 , interconnect  806 , input devices  808 , output devices  810 , and network access circuitry  812 , respectively. 
         [0061]    A number of components of client device  104  ( FIG. 9 ) are stored in memory  904 . In particular, web browser logic  920  is all or part of one or more computer processes executing within CPU  902  from memory  904  in this illustrative embodiment but can also be implemented using digital logic circuitry. Web browser  920  is conventional. Digital fingerprint  922  is persistent data stored in memory  904 . 
         [0062]    Server computer  106  is shown in greater detail in  FIG. 10 . Server  106  includes one or more microprocessors  1002  (collectively referred to as CPU  1002 ) that retrieve data and/or instructions from memory  1004  and execute retrieved instructions in a conventional manner. Memory  1004  can include generally any computer-readable medium including, for example, persistent memory such as magnetic and/or optical disks, ROM, and PROM and volatile memory such as RAM. 
         [0063]    CPU  1002  and memory  1004  are connected to one another through a conventional interconnect  1006 , which is a bus in this illustrative embodiment and which connects CPU  1002  and memory  1004  to network access circuitry  1012 . Network access circuitry  1012  sends and receives data through computer networks such as wide area network  104  ( FIG. 1 ). 
         [0064]    A number of components of server  106  are stored in memory  1004 . In particular, web server logic  1020  and web application logic  1022 , including location analysis logic  1024 , are each all or part of one or more computer processes executing within CPU  1002  from memory  1004  in this illustrative embodiment but can also be implemented using digital logic circuitry. Location monitoring logic  1026  and location analysis logic  1028  are also each all or part of one or more computer processes executing within CPU  1002  from memory  1004  in this illustrative embodiment but can also be implemented using digital logic circuitry. 
         [0065]    Web server logic  1020  is a conventional web server. Web application logic  1022  is content that defines one or more pages of a web site and is served by web server logic  1020  to client devices such as client device  104 . Location analysis logic  1024  specifies the behavior of server  106  in providing location analysis services in the manner described above. For example, location analysis logic  1024  provides a user interface through which a user of client device  104  can specify attributes of the statistical analysis of location data desired. In addition, location analysis logic  1024  analyzes location data  1040 , invoking location analysis logic  1028  in some embodiments. 
         [0066]    Location monitoring logic  1026  specifies the behavior of server  106  in receiving location reports in the manner described above. Location analysis logic  1028  specifies the behavior of server  106  in analyzing location data  1040  in the manner described herein. 
         [0067]    Location data  1040  is data persistently stored in memory  1004  and is organized as one or more databases in this illustrative embodiment. Location data  1040  includes WiFi data records such as WiFi data record  500  ( FIG. 5 ) and location data records such as location data record  700  ( FIG. 7 ). 
         [0068]    The above description is illustrative only and is not limiting. The present invention is defined solely by the claims which follow and their full range of equivalents. It is intended that the following appended claims be interpreted as including all such alterations, modifications, permutations, and substitute equivalents as fall within the true spirit and scope of the present invention.