Patent Publication Number: US-9410814-B2

Title: Passive crowd-sourced map updates and alternate route recommendations

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. Pat. No. 9,140,566, titled “Passive Crowd-Sourced Map Updates and Alternate Route Recommendations,” filed on Dec. 20, 2013, which is a continuation of U.S. Pat. No. 8,620,532, titled “Passive Crowd-Sourced Map Updates and Alternate Route Recommendations,” filed on Mar. 25, 2010, which claims the benefit of provisional patent application Ser. No. 61/163,091, filed Mar. 25, 2009, the disclosure of each of which is hereby incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates to crowd-sourced map updates and crowd-sourced alternate route recommendations. 
     BACKGROUND 
     Personal Navigation Devices (PNDs) often have maps that are out-of-date. Traditional mechanisms for updating the maps of PNDs are cumbersome and inconvenient. More specifically, traditionally companies such as NAVTEQ collect information regarding roads by driving every road using specially equipped cars. These companies then provide the collected information to PND providers for use in their maps. Recently, TomTom has introduced a service referred to as Map Share that enables users of TomTom® PNDs to manually make corrections to their maps and then share their corrections with other users of the TomTom® Map Share service. However, even though the TomTom® Map Share service provides some advantages, it is still cumbersome and burdensome on the users in that they must manually make corrections to their maps. As such, there is a need for a system and method for updating the maps of PNDs that places little, if any, burden on users of the PNDs. In addition, an improved system and method for providing alternate route recommendations to users is needed. 
     SUMMARY 
     Systems and methods for providing passive crowd-sourced alternate route recommendations are disclosed. In one embodiment, locations of users of a number of mobile location-aware devices are tracked over time. Upon receiving a request for alternate routes from a requestor, users of mobile location-aware devices that have traveled from or through a start location identified by the request to or through a stop location identified by the request are identified. Location histories for the identified users are analyzed to determine one or more routes taken by the users from the start location to the stop location. The one or more routes, or a select subset of the one or more routes, are then returned to the requestor as recommended alternate routes. In addition, one or more characteristics of the recommended alternate routes may be determined and returned to the requestor. For each recommended alternate route, the one or more characteristics may include, for example, an average travel time for the recommended alternate route, an average travel time for the recommended alternate route for a desired time window, a number of users that have previously traveled the recommended alternate route, or the like. 
     Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention. 
         FIG. 1  illustrates a system for providing crowd-sourced map updates and crowd-sourced alternate route recommendations according to one embodiment of the present disclosure; 
         FIG. 2  illustrates the operation of the system of  FIG. 1  to track the locations of users of the mobile location-aware devices according to one embodiment of the present disclosure; 
         FIG. 3  is a flow chart illustrating a process for providing crowd-sourced map updates according to one embodiment of the present disclosure; 
         FIG. 4  is a more detailed flow chart illustrating a process for providing crowd-sourced map updates according to one embodiment of the present disclosure; 
         FIG. 5  is a flow chart illustrating a process for detecting a pattern indicative of a new road according to one embodiment of the present disclosure; 
         FIG. 6  illustrates an exemplary bounding region utilized during the pattern detection process of  FIG. 5  according to one embodiment of the present disclosure; 
         FIG. 7  illustrates an exemplary Graphical User Interface (GUI) for presenting a new road and a degree of confidence for the new road to a user according to one embodiment of the present disclosure; 
         FIG. 8  illustrates the operation of the system of  FIG. 1  to provide crowd-sourced alternate route recommendations according to one embodiment of the present disclosure; 
         FIG. 9  is a flow chart illustrating a process for generating crowd-sourced alternate route recommendations according to one embodiment of the present disclosure; 
         FIG. 10  is a block diagram of the server of  FIG. 1  according to one embodiment of the present disclosure; 
         FIG. 11  is a block diagram of one of the mobile location-aware devices of  FIG. 1  according to one embodiment of the present disclosure; and 
         FIG. 12  is a block diagram of a computing device hosting the third-party map function of  FIG. 1  according to one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims. 
       FIG. 1  illustrates a system  10  for providing passive crowd-sourced map updates, passive crowd-sourced alternate route recommendations, or both according to one embodiment of the present disclosure. As illustrated, the system  10  includes a server  12  and a number of mobile location-aware devices  14 - 1  through  14 -N having associated users  16 - 1  through  16 -N. The server  12  and the mobile location-aware devices  14 - 1  through  14 -N are connected via a network  18 . The network  18  is preferably a publicly accessible distributed network such as the Internet. In addition, in this embodiment, the system  10  may also include a third-party map function  20 . 
     The server  12  is a physical server. Note, however, that while only a single server  12  is illustrated for clarity and ease of discussion, the system  10  may include multiple servers  12  that operate in a collaborative manner for purposes of load-sharing and/or redundancy. The server  12  includes a location tracking function  22 , a map updating function  24 , and an alternate route recommendation function  26 , each of which is preferably implemented in software but is not limited thereto. In addition, the server  12  includes a map data model  28  and a location tracking repository  30 . The location tracking function  22  generally operates to receive location updates from the mobile location-aware devices  14 - 1  through  14 -N defining locations of the users  16 - 1  through  16 -N over time and to store corresponding data in the location tracking repository  30 . Note that while the description herein refers to the tracking of the locations of the users  16 - 1  through  16 -N, as used herein, the locations of the users  16 - 1  through  16 -N is synonymous with the locations of the mobile location-aware devices  14 - 1  through  14 -N. The data stored in the location tracking repository  30  may include a location history for each of the users  16 - 1  through  16 -N or anonymized location histories that anonymously record the locations of the users  16 - 1  through  16 -N. Using the user  16 - 1  of the mobile location-aware device  14 - 1  as an example, for each location update received from the mobile location-aware device  14 - 1  for the user  16 - 1 , the location history for the user  16 - 1  includes the data from the location update (i.e., location and, optionally, a time-stamp, direction of travel, and/or speed of travel). Alternatively, the location history for the user  16 - 1  may include a number of vectors in the form of &lt;start, stop, time-stamp, direction, speed&gt; derived from the location updates received from the mobile location-aware device  14 - 1  for the user  16 - 1 . 
     In another embodiment, anonymized location histories are stored in the location tracking repository  30 . More specifically, again using the user  16 - 1  of the mobile location-aware device  14 - 1  as an example, the location history of the user  16 - 1  may be periodically persisted in the location tracking repository  30  as an anonymous location history. The anonymous location history is preferably a location history record or data object that has a new or unique identifier that is not tied back to the user  16 - 1  or the mobile location-aware device  14 - 1 . For example, at a desired periodic time interval (e.g., hourly, daily, weekly, or the like), the location history of the user  16 - 1  may be persisted as an anonymous location history that is not tied back to the user  16 - 1 . At the end of each periodic time interval, the location history of the user  16 - 1  is persisted as a new anonymous location history. Further, each time the location history of the user  16 - 1  is persisted as an anonymous location history, all of the location data (i.e., previous locations and, if any, time-stamps, directions of travel, and/or speed of travel) may be removed from the location history of the user  16 - 1 . 
     The map updating function  24  generally operates to analyze the data in the location tracking repository  30  that reflects the locations of the users  16 - 1  through  16 -N of the mobile location-aware devices  14 - 1  through  14 -N over time in order to detect patterns that are indicative of updates that should be made to a map defined by the map data model  28 . The map data model  28  is generally data that defines a map of a geographic area (e.g., North America, the United States of America, North Carolina, or the like). For instance, the map data model  28  may be Geographic Information Systems (GIS) data that defines a map for a geographic area. As discussed below in detail, in the preferred embodiment, the map updating function  24  operates to detect patterns of movement of the users  16 - 1  through  16 -N of the mobile location-aware devices  14 - 1  through  14 -N that are indicative of new roads that should be added to the map defined by the map data model  28 . However, in a similar manner, the map updating function  24  may additionally or alternatively detect other changes that should be made to the map such as, for example, temporary or permanent road closures. For instance, the absence of movement of the users  16 - 1  through  16 -N of the mobile location-aware devices  14 - 1  through  14 -N over a particular road in the map for at least a threshold amount of time may be used as a detection that the road is closed. 
     The alternate route recommendation function  26  generally operates to recommend alternate routes to the users  16 - 1  through  16 -N of the mobile location-aware devices  14 - 1  and  14 -N and the third-party map function  20  based on the data in the location tracking repository  30 . More specifically, as discussed below, the alternate route recommendation function  26  receives a request for alternate routes from a requestor, where the requestor may be one of the mobile location-aware devices  14 - 1  through  14 -N or the third-party map function  20 . The request identifies a desired start location and a desired stop location. The alternate route recommendation function  26  then uses the data in the location tracking repository  30  to identify a number of different routes previously taken by the users  16 - 1  through  16 -N of the mobile location-aware devices  14 - 1  through  14 -N from the desired start location to the desired stop location. One or more of the identified routes are then returned to the requestor as recommended alternate routes. 
     The mobile location-aware devices  14 - 1  through  14 -N are generally any type of user devices that are enabled to determine the locations of the users  16 - 1  through  16 -N and provide location updates for the users  16 - 1  through  16 -N to the server  12  via the network  18 . For example, each of the mobile location-aware devices  14 - 1  through  14 -N may be a personal navigation device permanently installed in an automobile, a portable personal navigation device similar to those manufactured and sold by Garmin or TomTom, a mobile smart phone providing personal navigation device functionality such as an Apple® iPhone having a software application providing personal navigation device functionality, or the like. As illustrated, the mobile location-aware devices  14 - 1  through  14 -N include personal navigation functions  32 - 1  through  32 -N, location reporting functions  34 - 1  through  34 -N, and Global Positioning System (GPS) receivers  36 - 1  through  36 -N. In addition, in this embodiment, the mobile location-aware devices  14 - 1  through  14 -N include map data models  38 - 1  through  38 -N. Each of the map data models  38 - 1  through  38 -N is a copy of the map data model  28  of the server  12  or a subset of the map data model  28  defining a portion of the map for a relevant geographic area. However, the present disclosure is not limited thereto. In an alternative embodiment, the mobile location-aware devices  14 - 1  through  14 -N obtain map data from the server  12  as needed. 
     Using the mobile location-aware device  14 - 1  as an example, the personal navigation function  32 - 1  may be implemented in software, hardware, or a combination thereof. The personal navigation function  32 - 1  generally operates in a manner similar to a traditional personal navigation device. More specifically, the personal navigation function  32 - 1  provides turn-by-turn directions to the user  16 - 1  in order to navigate the user  16 - 1  from a desired start location to a desired stop location. The personal navigation function  32 - 1  may also provide additional features such as Point-of-Interest (POI) lookup, current traffic conditions, or the like. The location reporting function  34 - 1  generally operates to provide location updates for the user  16 - 1  to the server  12 . 
     The third-party map function  20  may be implemented in hardware, software, or a combination thereof. For example, the third-party map function  20  may be a software application hosted by a physical server or farm of physical servers, a user device such as a personal computer, or the like. In general, the third-party map function  20  provides map-based services to users or entities. For example, the third-party map function  20  may be a web-based map service such as, or similar to, the Google® Maps service, the Bing® Maps service, the MapQuest® service, or the like. The third-party map function  20  may interact with the server  12  to obtain map updates and/or alternate routes. 
       FIG. 2  illustrates the operation of the system  10  of  FIG. 1  to track the locations of the users  16 - 1  through  16 -N according to one embodiment of the present disclosure. In this embodiment, tracking is performed passively by obtaining location updates for the users  16 - 1  through  16 -N and storing corresponding data at the server  12 . While this discussion uses the mobile location-aware device  14 - 1  and the user  16 - 1  as an example, this discussion is equally applicable to the other mobile location-aware devices  14 - 2  through  14 -N and the other users  16 - 2  through  16 -N. As illustrated, the mobile location-aware device  14 - 1 , and more specifically the location reporting function  34 - 1 , first gets a current location of the mobile location-aware device  14 - 1  (step  1000 ). In addition to the current location, the location reporting function  34 - 1  may get a time-stamp that defines the current time, a direction of travel of the mobile location-aware device  14 - 1 , and/or a speed of travel of the mobile location-aware device  14 . In the preferred embodiment, the location reporting function  34 - 1  gets the current location and, optionally, the current time, the direction of travel, and/or the speed of travel from the GPS receiver  36 - 1 . However, the GPS receiver  36 - 1  is exemplary. Any suitable technology for determining or otherwise obtaining the current location of the mobile location-aware device  14 - 1  may be used. Next, the location reporting function  34 - 1  of the mobile location-aware device  14 - 1  sends a location update to the server  12  (step  1002 ). The location update includes the current location of the user  16 - 1 , which is the current location of the mobile location-aware device  14 - 1  obtained from the GPS receiver  36 - 1 . In addition, the location update may obtain a time-stamp defining the time at which the current location was obtained (i.e., the current time), the direction of travel of the mobile location-aware device  14 - 1  as the direction of travel of the user  16 - 1 , and/or the speed of travel of the mobile location-aware device  14 - 1  as the speed of travel of the user  16 - 1 . 
     Upon receiving the location update, the location tracking function  22  stores data in the location tracking repository  30  corresponding to the location update (step  1004 ). In one embodiment, the location tracking repository  30  includes a location history for each of the users  16 - 1  through  16 -N. As such, in this embodiment, the location update, or more specifically the data included in the location update, is stored in a location history of the user  16 - 1  maintained in the location tracking repository  30 . Alternatively, the location update may be processed to provide a vector from the last location of the user  16 - 1  to the current location of the user  16 - 1 , where the vector may be &lt;start location, stop location, time-stamp, direction, speed&gt;. In another embodiment, as discussed above, the location tracking function  22  stores anonymized location histories. More specifically, the location tracking function  22  stores location histories for each of the users  16 - 1  through  16 -N. However, periodically (e.g., hourly, daily, weekly, or the like), the location tracking function  22  persists the location histories of the users  16 - 1  through  16 -N as anonymous location histories that are not tied back to the users  16 - 1  through  16 -N and removes the location data (i.e., the previous locations and/or corresponding time-stamps, speeds of travel, and/or directions of travel, or previous vectors) from the location histories of the users  16 - 1  through  16 -N. Anonymization may be performed as a background process. Alternatively, anonymization may be triggered by receipt of location updates. Thus, upon receiving the location update from the mobile location-aware device  14 - 1 , the location tracking function  22  may store the location update in the location history of the user  16 - 1  and then determine if it is time to anonymize the location history of the user  16 - 1 . If so, the location tracking function  22  removes the location updates from the location history of the user  16 - 1  and stores the location updates as an anonymous location history that is not tied back to the user  16 - 1  or the mobile location-aware device  14 - 1 . Note that the most recent location update, most recent vector, or current location of the user  16 - 1  may be retained in the location history of the user  16 - 1  after anonymization is performed. 
     In the same manner, the other mobile location-aware devices  14 - 2  through  14 -N get their current locations and send corresponding location updates to the server  12  (steps  1006  and  1008 ). In response, the location tracking function  22  stores corresponding data in the location tracking repository  30  for the users  16 - 2  through  16 -N (step  1010 ). As illustrated, this process continues such that the mobile location-aware devices  14 - 1  through  14 -N continue to send location updates for the users  16 - 1  through  16 -N to the server  12  over time and corresponding data is stored in the location tracking repository  30  (steps  1012  through  1022 ). 
       FIG. 3  illustrates the operation of the map updating function  24  of the server  12  according to one embodiment of the present disclosure. First, the map updating function  24  detects a travel pattern that is indicative of a new road that is not included on the map defined by the map data model  28  (step  2000 ). More specifically, the map updating function  24  analyzes the data in the location tracking repository  30  as compared to the map data model  28  to detect a pattern of movement of the users  16 - 1  through  16 -N that is indicative of a new road that is not included on the map defined by the map data model  28 . In general, a pattern indicative of a new road is a pattern of consistent and frequent travel of the users  16 - 1  through  16 -N, or more specifically at least a subset of the users  16 - 1  through  16 -N, in a manner that is consistent with travel along a road. In addition, the map updating function  24  may compute a degree of confidence for the new road. The degree of confidence is preferably a function of frequency of use and how recently the new road has been used. 
     Once the new road is detected, the map updating function  24  updates the map to include the new road (step  2002 ). More specifically, the map updating function  24  adds data defining the new road to the map data model  28 . In addition, the map updating function  24  may add the degree of confidence for the new road to the map data model  28 . At this point, in one embodiment, the map updating function  24  sends an update to the map data model  28  for the new road and the degree of confidence for the new road, if any, to one or more of the mobile location-aware devices  14 - 1  through  14 -N. Those mobile location-aware devices  14 - 1  through  14 -N that receive the update then add the update to their map data models  38 - 1  through  38 -N. In addition, the map updating function  24  may send the update for the map data model  28  to the third-party map function  20 . In an alternative embodiment, rather than immediately updating the map data model  28 , the map updating function  24  may flag the update or otherwise send an alert regarding the update to an owner or editor of the map represented by the map data model  28  for verification before the map is officially updated. 
       FIG. 4  is a more detailed flow chart illustrating the operation of the server  12  to update the map according to one embodiment of the present disclosure. In this embodiment, the location tracking function  22  receives a location update (step  3000 ). For this discussion, the location update is received from the mobile location-aware device  14 - 1  for the user  16 - 1 . In response, the location tracking function  22  generates and stores a vector from a previous location of the user  16 - 1  to a current location of the user  16 - 1  identified in the location update (step  3002 ). The previous location of the user  16 - 1  is the location of the user  16 - 1  identified in the immediately preceding location update received from the mobile location-aware device  14 - 1 . Again, the vector is preferably in the form of &lt;start location, stop location, time-stamp, direction, speed&gt; but is not limited thereto. “Start location” is the previous location of the user  16 - 1  identified by the immediately preceding location update for the user  16 - 1 , “stop location” is the current location of the user  16 - 1  identified in the location update, time-stamp is the timestamp from the corresponding location update, direction is the direction of travel from the location update, and speed is the speed of travel from the location update. 
     Next, the map updating function  24  determines whether the user  16 - 1  is currently on a crowd-sourced road (step  3004 ). As used herein, a crowd-sourced road is a road previously added to the map by the map updating function  24  based on detected patterns of travel, or movement, of the users  16 - 1  through  16 -N. Note, however, that a crowd-sourced road may be promoted to a permanent road in the map data model  28  when, for example, the crowd-sourced road is verified by an operator of the server  12  (i.e., a person) or the degree of confidence of the crowd-sourced road reaches a predefined threshold (e.g., 90% or 100%). The map updating function  24  determines whether the user  16 - 1  is currently on a crowd-sourced road by comparing the current location of the user  16 - 1  to the map data model  28 . If the user  16 - 1  is on a crowd-sourced road, the map updating function  24  updates the degree of confidence of the crowd-sourced road (step  3006 ). Again, the degree of confidence is preferably a function of frequency of use of the crowd-sourced road and how recently the crowd-sourced road has been used. The more frequently and recently the crowd-sourced road has been used by the users  16 - 1  through  16 -N, the higher the degree of confidence for the crowd-sourced road. At this point, the process returns to step  3000  and is repeated for the next received location update. 
     If the user  16 - 1  is not on a crowd-sourced road, the map updating function  24  determines whether the user  16 - 1  is currently on a permanent road (step  3008 ). As used herein, a permanent road is a road that was originally in the map or a crowd-sourced road added by the map updating function  24  that has been verified or that has a degree of confidence equal to or greater than a predefined threshold degree of confidence. If the user  16 - 1  is currently on a permanent road, the process returns to step  3000  and is repeated for the next received location update. If the user  16 - 1  is neither on a crowd-sourced road nor a permanent road, the map updating function  24  determines whether a predefined number (M) of location updates have been received for the user  16 - 1  since the user  16 - 1  was last determined to be on a road (i.e., a permanent road or a crowd-sourced road) (step  3010 ). The number M may be any integer greater than or equal to one (1). If less than M location updates have been received for the user  16 - 1  since the user  16 - 1  was last on a road, the process returns to step  3000  and is repeated for the next received location update. 
     If M location updates have been received for the user  16 - 1  since the user  16 - 1  was last on a road, the map updating function  24  performs a pattern detection process for the last M vectors in the location history of the user  16 - 1  (step  3012 ). Note that if vectors are not used, the pattern detection process is performed for the last M entries in the location history of the user  16 - 1 . In general, the map updating function  24  obtains the last M vectors from the location history of the user  16 - 1 . In addition, the map updating function  24  obtains other vectors from the location histories stored in the location tracking repository  30  that have start and stop locations in the same vicinity as the start and stop locations of one or more of the last M vectors for the user  16 - 1 . These vectors are then analyzed to determine whether there is a pattern of travel or movement that is indicative of a new road. If so, the map updating function  24  updates the map data model  28  with data defining the new road. In addition, map updates may be sent to one or more of the mobile location-aware devices  14 - 1  through  14 -N and/or the third-party map function  20 . At this point, the process returns to step  3000  and is repeated for the next received location update. 
       FIG. 5  is a flow chart illustrating step  3012  of  FIG. 4  in more detail according to one embodiment of the present disclosure. First, the map updating function  24  gets the last M vectors from the location history of the user  16 - 1  stored in the location tracking repository  30  (step  4000 ). The map updating function  24  then establishes a bounding region for the last M vectors (step  4002 ). The bounding region is generally a geographic region that encompasses the start and stop locations for the last M vectors for the user  16 - 1 . Preferably, the bounding region is established such that the bounding region is, or is approximately, a geographic region defined by a maximum distance (D) from the last M vectors of the user  16 - 1 , as illustrated in  FIG. 6 . 
     Returning to  FIG. 5 , the map updating function  24  then gets all known vectors from the location tracking repository  30  having start locations and stop locations located within the bounding region for the last M vectors of the user  16 - 1  (step  4004 ). Alternatively, the map updating function  24  may get a subset of all known vectors from the location tracking repository  30  having start locations and stop locations located within the bounding region for the last M vectors of the user  16 - 1 , such as all known vectors from the location tracking repository  30  having start locations and stop locations within the bounding region for the last M vectors of the user  16 - 1  that have time-stamps within a defined time window. The defined time window may be a relative time window such as, for example, the last month. 
     The map updating function  24  then analyzes the known vectors obtained in step  4004  and, optionally, the last M vectors for the user  16 - 1  to determine whether there is a pattern of travel or movement that is indicative of a new road (step  4006 ). For example, the known vectors may be filtered to remove those vectors having directions and, optionally, speeds that are inconsistent with the directions and speeds of the last M vectors for the user  16 - 1 . More specifically, for each known vector, the map updating function  24  may determine to filter the known vector if the direction and optionally speed of the known vector are more than a predefined amount of deviation from the direction and optionally speed of a nearest one of the last M vectors for the user  16 - 1  (i.e., the one of the last M vectors having a start location and/or stop location that is closest to the start location and/or stop location, respectively, of the known vector). If the direction and, if used, the speed of the known vector are within the predefined amount of deviation from the direction and, if used, the speed of the nearest one of the last M vectors for the user  16 - 1 , then the known vector is not filtered. Once filtering is complete, the remaining known vectors, which are referred to herein as the filtered vectors, are counted. If the number of filtered vectors is greater than a predefined threshold number of vectors, then a pattern is detected. Note that this process for detecting a pattern is exemplary and is not intended to limit the scope of the present disclosure. Any suitable pattern recognition technique may be used. 
     Once the analysis is complete, the map updating function  24  determines whether a pattern that is indicative of a new road has been detected (step  4008 ). If not, the process ends. If so, the map updating function  24  computes a path for the new road that corresponds to the detected pattern and, optionally, a confidence factor for the new road (step  4010 ). In one embodiment, the bounding region for the last M vectors for the user  16 - 1  is divided into a series of sub-regions. For example, each sub-region may include one of the last M vectors for the user  16 - 1 . Then, for each sub-region, the map updating function  24  may identify vectors from the filtered vectors that have start locations within that sub-region and then combine (e.g., average) the start locations for the identified vectors to provide a combined point for the sub-region. Once complete, the combined points for the sub-regions define the path for the new road. Again, the degree of confidence for the new road may be computed as a function of frequency of use by the users  16 - 1  through  16 -N and how recently the new road has been used by the users  16 - 1  through  16 -N. 
     In addition, the map updating function  24  may suggest a name for the new road. The map updating function  24  may suggest a name of the road based on detected patterns in the movement of users that have travelled the new road, surrounding roads in the map data model  28 , or a combination thereof. The detected patterns in movement may be, for example, an average speed of the users that have travelled the road, start and stop patterns, patterns indicating that the new road extends from an existing road, patterns indicating that the new road merges into an existing road, patterns indicating that the new road extends from and merges back into an existing road, or the like. For example, the average speed at which users have travelled the new road may be used to determine whether the new road is likely to be an Interstate Highway, a city street, or the like. Similarly, start and stop patterns may be used to determine that the new road is a city street. In addition or alternatively, the path of the new road may be analyzed with respect to surrounding roads to determine whether the new road is an extension of an existing road, an alternate version of an existing road (e.g., Alternate I-40 as an alternate for I-40). 
     Lastly, the map updating function  24  updates the map data model  28  to include data defining the new road (step  4012 ). In addition, a corresponding update may be provided to one or more of the mobile location-aware devices  14 - 1  through  14 -N and/or the third-party map function  20 . At this point, the process ends. Again, in an alternative embodiment, rather than immediately updating the map data model  28 , the map updating function  24  may flag the update or otherwise send an alert regarding the update to an owner or editor of the map represented by the map data model  28  for verification before the map is officially updated. 
       FIG. 7  illustrates an exemplary Graphical User Interface (GUI)  40  for presenting a map including a crowd-sourced map update provided by the map updating function  24  of the server  12  according to one embodiment of the present disclosure. As illustrated, the GUI  40  generally presents a map, which is preferably a portion of the map defined by the map data model  28  of the server  12 . A new road  42  detected by the map updating function  24  of the server  12  based on a detected travel pattern of the users  16 - 1  through  16 -N is shown in the GUI  40 . In one embodiment, an opacity of the new road  42  in the GUI  40  corresponds to a degree of confidence for the new road  42  computed by the map updating function  24 . In addition or alternatively, the GUI  40  may include a window providing information for the new road  42  such as, for example, the degree of confidence for the new road  42  and a likely, or suggested name, of the new road  42 . 
     In this example, since the new road  42  diverges from I-40 and rejoins I-40, the map updating function  24  determines that the new road  42  is likely an Alternate I-40. More specifically, based on the map data model  28 , the map updating function  24  knows that I-40 is an interstate and that characteristic speeds on I-40 are 55 to 80 mph. The map updating function  24  detects a large number of users diverging from I-40 onto the newly detected road at speeds that are characteristic of merging onto another highway. Then, ten miles later, the map updating function  24  detects a large number of users diverging from this newly detected road back onto I-40 at a speed that is characteristic of merging onto another highway. From these characteristic and passively detected inputs, the map updating function  24  is enabled to determine that the newly detected route is likely to be an “Alternate” or “Business Bypass” of I-40 and therefore suggest “Alternate I-40” as a name for the newly detected road. 
       FIG. 8  illustrates the operation of the system  10  to recommend alternate routes according to one embodiment of the present disclosure. As illustrated, first, the mobile location-aware device  14 - 1  sends an alternate route request to the server  12  (step  5000 ). Note that while the mobile location-aware device  14 - 1  is the requestor in this discussion, the requestor may alternatively be one of the other mobile location-aware devices  14 - 2  through  14 -N or the third-party map function  20 . The alternate route request identifies a desired start location and a desired stop location. More specifically, in one embodiment, the personal navigation function  32 - 1  sends the alternate route request to the server  12  either automatically in response to a request from the user  16 - 1  to be navigated from the desired start location to the desired stop location or in response to an explicit request for alternate routes from the user  16 - 1 . 
     In response to receiving the alternate route request, the alternate route recommendation function  26  of the server  12  generates one or more alternate routes from the desired start location to the desired stop location (step  5002 ). In general, the alternate route recommendation function  26  utilizes data in the location tracking repository  30  to identify routes previously taken by the users  16 - 1  through  16 -N from the desired start location to the desired stop location. The alternate route recommendation function  26  then selects one or more of the identified routes as alternate routes to recommend, and then returns the alternate routes to the mobile location-aware device  14 - 1  (step  5004 ). The personal navigation function  32 - 1  of the mobile location-aware device  14 - 1  then utilizes the alternate routes (step  5006 ). For example, the personal navigation function  32 - 1  may display the alternate routes to the user  16 - 1  and enable the user  16 - 1  to select one of the alternate routes to use. Note that, in an alternative embodiment, rather than immediately sending the alternate routes to the mobile location-aware device  14 - 1 , the recommended routes may be verified, such as by an owner or editor of the map represented by the map data model  28 , before the recommended routes are sent to the mobile location-aware device  14 - 1 . 
       FIG. 9  is a flow chart illustrating the operation of the alternate route recommendation function  26  of the server  12  in more detail according to one embodiment of the present disclosure. First, the alternate route recommendation function  26  receives an alternate route request that identifies a desired start location and a desired stop location (step  6000 ). In response, the alternate route recommendation function  26  identifies users from the users  16 - 1  through  16 -N that have traveled from the desired start location to the desired stop location (step  6002 ). Note that the users that have traveled from the desired start location to the desired stop location preferably include users that have started at the desired start location and ended at the desired stop location as well as users that have travelled from or through the desired start location to or through the desired stop location. Optionally, the identified users may be only those users that have traveled from the desired start location to the desired stop location during a desired time window. The desired time window may be a reoccurring time window corresponding to a current time of day (e.g., 10 AM to Noon), a current day of the week (Monday, Weekday, or Weekend), a combination of a current or defined time of day and day of week (e.g., Monday from 10 AM to Noon or Weekdays from 10 AM to Noon), or the like. 
     The alternate route recommendation function  26  then determines one or more different routes taken by the identified users from the desired start location to the desired stop location (step  6004 ). More specifically, for each of the identified users, the alternate route recommendation function  26  determines a route taken by the identified user from the desired start location to the desired stop location. The routes taken by the identified users are compared to one another to determine a number of different routes taken by the identified users from the desired start location to the desired stop location. 
     Next, the alternate route recommendation function  26  determines one or more characteristics for each of the different route(s) (step  6006 ). For each of the different routes, the one or more characteristics for that route may include, for example, a number of the identified users that took that route, an average travel time for that route, an average travel time for that route for desired time window, or the like. The average travel time for a route is determined based on actual travel times for that route for corresponding users determined based on the location histories of those users. Similarly, the average travel time for a route for the desired time window is determined based on actual travel times for that route for corresponding users that travelled that route during the desired time window. The desired time window may be a reoccurring time window corresponding to a current time of day (e.g., 10 AM to Noon), a current day of the week (Monday, Weekday, or Weekend), a combination of a current or defined time of day and day of week (e.g., Monday from 10 AM to Noon or Weekdays from 10 AM to Noon), or the like. The alternate route recommendation function  26  then returns the one or more different routes and the characteristics of the one or more different routes to the requestor as alternate route recommendations (step  6008 ). Note that either prior to step  6006  or before returning the recommendations in step  6008 , the different routes identified in step  6004  may be filtered or otherwise processed to remove unwanted routes. For example, filtering may be performed to remove a particular route that has already been provided to the user  16 - 1  (e.g., an optimal route that has already been generated by the personal navigation function  32 - 1  using a traditional route generation technique). 
       FIG. 10  is a block diagram of the server  12  according to one embodiment of the present disclosure. As illustrated, the server  12  includes a controller  46  connected to memory  48 , one or more secondary storage devices  50 , and a communication interface  52  by a bus  54  or similar mechanism. The controller  46  is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like. In this embodiment, the controller  46  is a microprocessor, and the location tracking function  22 , the map updating function  24 , and the alternate route recommendation function  26  are implemented in software and stored in the memory  48  for execution by the controller  46 . Further, the map data model  28  and the location tracking repository  30  may be stored in the one or more secondary storage devices  50 . The secondary storage devices  50  are digital data storage devices such as, for example, one or more hard disk drives. The communication interface  52  is a wired or wireless communication interface that communicatively couples the server  12  to the network  18  ( FIG. 1 ). For example, the communication interface  52  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, or the like. 
       FIG. 11  is a block diagram of the mobile location-aware device  14 - 1  according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile location-aware devices  14 - 2  through  14 -N. As illustrated, the mobile location-aware device  14 - 1  includes a controller  56  connected to memory  58 , a communication interface  60 , one or more user interface components  62 , and the GPS receiver  36 - 1  by a bus  64  or similar mechanism. The controller  56  is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller  56  is a microprocessor and the location reporting function  34 - 1  and, in some implementations, the personal navigation function  32 - 1  are implemented in software and stored in the memory  58  for execution by the controller  56 . In this embodiment, the GPS receiver  36 - 1  is a hardware component. The communication interface  60  is a wireless communication interface that communicatively couples the mobile location-aware device  14 - 1  to the network  18  ( FIG. 1 ). For example, the communication interface  60  may be a local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  62  include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof. 
       FIG. 12  is a block diagram of a computing device  66  that hosts the third-party map function  20  according to one embodiment of the present disclosure. As illustrated, the computing device  66  includes a controller  68  connected to memory  70 , one or more secondary storage devices  72 , a communication interface  74 , and one or more user interface components  76  by a bus  78  or similar mechanism. The controller  68  is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller  68  is a microprocessor, and the third-party map function  20  is implemented in software and stored in the memory  70  for execution by the controller  68 . The one or more secondary storage devices  72  are digital storage devices such as, for example, one or more hard disk drives. The communication interface  74  is a wired or wireless communication interface that communicatively couples the computing device  66  to the network  18  ( FIG. 1 ). For example, the communication interface  74  may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface, or the like. The one or more user interface components  76  include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof. 
     Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.