Patent Publication Number: US-6988032-B2

Title: Generating vehicle traffic data from raw location data for mobile units

Description:
BACKGROUND OF THE INVENTION 
   An Intelligent Transportation System (ITS) is a system that provides information to assist travelers and operators, respectively, to make intelligent decisions while driving and to control traffic on road networks. It can be considered as an adaptive/feedback system from a control point of view. An ITS can increase efficiency, safety, productivity, energy savings, and environmental quality (e.g., by pollution level reduction associated with easing of traffic congestion). An ITS makes use of computing resources (hardware/software), control devices, sensors, and communication networks, as well as other technologies. 
   To control traffic on roadways efficiently, the Background Art has collected traffic data traditionally by using sensors installed on roads or at roadsides. The traffic data can then be transmitted to a control and command center that has systems to process the data and control guidance devices, e.g., traffic lights and/or dynamic roadway signage. For providing a more efficient public transportation system, locations and load information of public vehicles can be fed into yet another control system for scheduling and providing arrival information to awaiting passengers. Automatic electronic toll systems installed in highways are also considered part of an ITS. These systems not only can reduce the line at toll booths (hence increasing roadway efficiency), but also can provide convenience to the travelers. 
   SUMMARY OF THE INVENTION 
   The invention provides a method of gathering/harvesting vehicular traffic data based on information collected from wireless mobile units and a related enhanced intelligent traffic system (EITS) that augments the existing traffic data with the mobile-unit-derived traffic data. In the invention, existing location techniques for locating a wireless mobile unit may be used to generate position information for the mobile unit over time. A portion of the mobile units typically move with (are being carried by) vehicles, so the position information for the wireless mobile units can be treated as a form of raw data that includes possible vehicle position information. 
   According to the invention, through the use of filtering techniques, wireless mobile units that are moving with vehicles can be discriminated and their data treated as the corresponding data of the vehicles, respectively. As a byproduct of the filtering techniques, the speed of the vehicles may be determined. An aggregate of such vehicle data may be treated as traffic data by (and stored in) the EITS. This additional traffic data increases the amount of information upon which traffic control and traffic information reports are based. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention will become more fully understood from the detailed description given below and the accompanying drawings, wherein like elements are represented by like reference numerals, which are given by way of illustration only and thus are not limiting on the invention and wherein: 
       FIG. 1  is a block diagram of an enhanced intelligent traffic system; and 
       FIG. 2  is a flowchart of data harvesting performed by the enhanced intelligent traffic system. 
   

   DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
     FIG. 1  is a block diagram of an enhanced intelligent traffic system (EITS)  100 . The system  100  includes a wireless network  104  that collects raw location data for mobile units, such as the mobile units  106  and  110  shown in  FIG. 1 , currently being served by the wireless network  104 . For example, the mobile units  106  and  110  may include a global positioning system (GPS) sensor and send geolocation information (e.g., longitude or latitude coordinates or longitude, latitude and altitude coordinates) generated by the GPS sensor to the wireless network  104 . However, any well-known mobile-unit-location-determining technique may be used to generate the location data. The raw location data may further include a time stamp indicating a time at which the location data was obtained. As will be described in greater detail below, the time stamp can facilitate a determination of the speed or velocity of a mobile unit. 
   The mobile-unit  106  and/or  110  can be a wireless telephone, a wireless personal data assistant (PDA) (with or without telephony capability), etc. The mobile unit  110  is representative of vehicle-born mobile units and is shown on a vehicle  108  to convey that it moves with the vehicle  108 . Vehicle-born mobile units may be hard-wired into the vehicle  108 , carried by a driver or passenger, etc. The mobile unit  106  is representative of non-vehicle born mobile units. It is to be understood that typically there are many units  106  and  110  served by the wireless network  104 . In  FIG. 1 , only one mobile unit  106  and one mobile unit  110  have been shown for simplicity. 
   A harvesting unit  102  receives the raw location data from the wireless network  104 , and harvests/generates vehicle traffic data from the raw location data supplied by a wireless network  104 . Specifically, the harvesting unit  102  filters the raw location data to determine which of the raw location data does and does not correspond to vehicle-born mobile units. The harvesting unit  102  filters out the raw location data corresponding to non-vehicle-born mobile units  106  but retains the raw location data corresponding to the vehicle-born mobile units  110 . 
   Because the mobile units  110  move with the vehicles  108 , the raw location data for the mobile units  110  can be treated as vehicle location data for the vehicles  108 . Each vehicle is separately identified, e.g., based on an identifier of the mobile unit with which the associated position data is tagged. In addition to this vehicle location data, the harvesting unit  102  may generate speed or velocity data by determining the change in vehicle location data for a vehicle (identified by the associated mobile unit) over time. Traffic data can be formed by aggregating (for plural vehicles) the resulting vehicle location data and, optionally, the speed or velocity data for the vehicle. The operation or the harvesting unit  102  will be described in greater detail below. 
   The harvesting unit  102  may provide the vehicle traffic data to a traffic management and information system (TMIC) unit  116 . The TMIC unit  116  can incorporate the vehicle traffic data into a traffic database  118 . Alternatively (as indicated by a communication path depicted as a phantom line), the harvesting unit  102  may directly incorporate the vehicle traffic data into the traffic database  118 . 
   As shown in  FIG. 1 , the harvesting unit  102  includes a vehicle discriminating unit (VDU)  120  (e.g., software hosted by a typical computer) and a filter database  122  of vehicle-relevant areas. The filter database may include a “roadway” database  124  and a “footpath” database  126 . The roadway database  124  provides position information on the known vehicular traffic areas, e.g., the system of roads, in the geographic region served by the wireless network  104 . The footpath database  126  provides position information on the known pedestrian traffic areas, e.g., sidewalks and footpaths, in the geographic region served by the wireless network  104 . An example of an unsophisticated footpath database  126  is merely the reciprocal of the roadway database  124 , i.e., any part of the geographic region served by the wireless network  104  that is not part of the roadway database  124  is assumed to be for pedestrian traffic. 
   It will be appreciated from the forgoing and following disclosure that the filter database  122  may include several additional databases such as a railway database, building database, etc. It will also be understood that the databases included in the filter database  122  are a matter of design choice. For example, in one example embodiment, the filter database only includes the roadway database. 
   Next, the operation of the harvesting unit  102  will be described in detail with respect to  FIG. 2 . The flowchart of  FIG. 2  illustrates a method of gathering vehicle traffic data according to one embodiment of the invention. For simplicity,  FIG. 2  is couched in terms of one mobile unit. But it is to be understood that the same is applied to however many mobile units are represented in the raw location data received from the wireless network  104 . 
   As shown, in step S 204 , the VDU  120  receives the raw location data from the wireless network  104 . Then, in step S 206 , the VDU  120  determines the speed of a mobile unit from the raw location data. 
   As an example implementation of step S 206 , the VDU  120  can determine the speed of a mobile unit  106 / 110  based upon two positions of the vehicle, e.g., successive positions. The distance between the two successive positions of a mobile unit  106 / 110  is derived. Then the difference is divided by the time elapsed between the two successive position determinations. The VDU  120  can recognize position data pertaining to a particular mobile unit because position data for each mobile unit is tagged with an identification based upon the mobile unit. Also, the position data is typically tagged with a time stamp. If data for several positions of a vehicle are available, the time stamps can be compared to determine, e.g., the two most recent positions. From the coordinates for the two most recent positions, the VDU  120  can derive the distance that the mobile unit has moved, i.e., the distance between the two most recent positions. From the time stamps for the two most recent positions, the VDU  120  can determine the elapsed time. Then the VDU  120  can calculate the speed by dividing the distance-moved by the elapsed time. 
   Optionally, the accuracy of the speed determined by step S 206  can be improved by averaging or integrating multiple speed values. 
   In step S 208 , the VDU  120  performs speed-based filtering of the speed data for the mobile unit. Recognizing that vehicle-born mobile units  110  typically move at a much greater speed than mobile units  106  (which are typically carried by pedestrians, i.e., are pedestrian-born), the speed-based filtering can include: comparing the speed data against a predetermined reference value; and treating data for the mobile unit as representing data for the vehicle if the result of the comparison indicates that the mobile unit is moving with the vehicle. 
   As an example implementation of step S 208 , the predetermined reference value can be a minimum speed (SMIN) for a typical vehicle. The VDU  120  compares the speed data of the mobile unit against SMIN. If the speed (S) satisfies S&gt;SMIN or S≧SMIN, then the VDU  120  treats the mobile unit as representing a vehicle. 
   As another example implementation of step S 208 , the predetermined reference value can be a maximum speed (SMAX) for a typical pedestrian. Again, the VDU  120  compares the speed data of the mobile unit against SMAX. If the speed (S) satisfies S&gt;SMAX or S≧SMAX, then the VDU  120  treats the mobile unit as representing a vehicle. 
   If it is determined in step S 208  that the mobile unit represents a vehicle, then step S 208  can further include the VDU  120  generating vehicle data as follows. The VDU  120  can: assign an identifier to the vehicle; and adopt, as the vehicle&#39;s position coordinates and time stamps, the position coordinates and time stamps, respectively, of the mobile unit. Additionally, the VDU  120  can incorporate the speed data derived for the vehicle (see step S 206  above) as part of the vehicle data. The vehicle identifier assigned to the vehicle can be based upon the identifier for the mobile unit, e.g., the vehicle identifier can be the same as the mobile unit identifier; alternatively, the vehicle identifier does not have to be based upon the mobile unit identifier. 
   There are circumstances in which the speed-based filtering of step S 208  might not recognize a mobile unit that is actually moving with a vehicle. Such a mobile unit might be a mobile unit  110  whose vehicle  108  is caught in slow traffic or a traffic-jam situation. The vehicle data harvested from such a mobile unit  110  can be important to an EITS  100 , so a position-based filtering (step S 210 ) is provided to identify such mobile units, i.e., vehicles. It should be noted that if step S 208  determines that the mobile unit is moving with a vehicle, then step S 210  can be skipped. For the purposes of discussion, it will be assumed that it has not been determined in step S 208  that the mobile unit is moving with a vehicle. 
   In step S 210 , the VDU  120  performs position-based filtering of the position data for the mobile unit. Step S 210  is based upon the assumption that a mobile unit  110  whose vehicle  108  is caught in slow traffic or a traffic-jam situation will be found in a vehicular-traffic area, not in a pedestrian-traffic area. The VDU  120  can filter based upon position by comparing the position data for the mobile unit against the content of the filter database  122  of vehicle-relevant areas. 
   As an example implementation of step S 210 , let the filter database  122  of vehicle-relevant areas be the road database  124 . The VDU  120  can compare the position of the mobile unit against the road database  124 . If the mobile unit&#39;s position is on or within a predetermined distance of a vehicular traffic area listed in the road database  124 , then the VDU  120  can treat the mobile unit as being a vehicle-born mobile unit  110  whose position and speed data represent that of the corresponding vehicle  108 . 
   As another example implementation of step  210 , let the filter database  122  of vehicle-relevant areas be the footpath database. The VDU  120  can compare the position of the mobile unit against the footpath database  126 . If the mobile unit&#39;s position is not on, or not within a predetermined distance of, a pedestrian traffic area listed in the footpath database  126 , then the VDU  120  can treat the mobile unit as being a vehicle-born mobile unit  110  whose position and speed data represent that of the corresponding vehicle  108 . 
   If it is determined in step S 210  that the mobile unit represents a vehicle, then step S 210  can further include generating corresponding vehicle data, as discussed above. Again, the VDU  120  can: assign an identifier to the vehicle; adopt, as the vehicle&#39;s position coordinates and time stamps, the position coordinates and time stamps, respectively, of the mobile unit; and (optionally) incorporate the speed data derived for the vehicle (see step S 206  above) as part of the vehicle data. A vehicle identifier can be assigned to the vehicle as discussed above (see step S 208 ). 
   At step S 212 , assuming one of step S 2308  and S 210  determines that the mobile unit is vehicle-born, then the VDU  120  outputs the vehicle data. Again,  FIG. 2  is couched in terms of one mobile unit for simplicity, but the same is applied to however many mobile units are represented in the raw location data. An aggregate of the vehicle data for all of the mobile units determined to be vehicle-born represents the traffic data outputted by the VDU  120 . Step S 212  sends the vehicle traffic data to the TMIC  116  and/or traffic database  118 . 
   While the embodiment of the method for gathering vehicle traffic data discussed above with respect to  FIG. 2  describes speed-based filtering being performed before the position-based filtering, it will be understood that the position-based filtering could be performed before the speed-based filtering, or either of the speed-based or position-based filtering could be eliminated. The elimination of the speed-based filtering is shown in  FIG. 2  as a path (in phantom lines) from step S 204  directly to step S 210 . 
   As an alternative, the step S 206  could determine velocity (speed and heading) rather than just speed. The step S 208  could become a velocity-based filtering, or just the speed component of the velocity could be used for the speed-based filtering of step S 208 . 
   As another alternative to being speed-based, steps S 206 –S 208  can be based upon a distance moved by the mobile unit. In more detail, step S 206  can derive the distance (D) moved by the mobile unit between two successive instances of position data. This alternative assumes that a predetermined amount of time (or delta) elapses between successive geolocation determinations, the delta being sufficiently short so that only a vehicle  108  should be capable of moving a reference distance (or greater). Correspondingly, step S 208  can be a distance-based filtering, e.g., comparing the distances against the reference distance, etc. Example implementations of step S 208  include: the predetermined reference distance being a minimum distance (DMIN) moved by a typical vehicle such that if D&gt;DMIN or D≧DMIN, then the mobile unit represents a vehicle; or the predetermined reference value being a maximum distance (DMAX) moved by a typical pedestrian such that if D&gt;DMAX or D≧DMAX, then the mobile unit represents a vehicle. 
   Further in the alternative, additional filtering can be added to the flowchart  200  of  FIG. 2 . For example, the step S 210  can include a second type of position-based filtering to recognize the situation in which a mobile unit is being carried on a train and/or an airplane. In the train situation, a train database of known train, subway, etc., areas can be provided. The position-based filtering can compare the position data for the mobile unit against the train database, etc. In the airplane situation, the altitude component of three-dimensional position data can be compared against a maximum height reference value, etc. 
   Another example of additional filtering that can be provided is historical filtering. More specifically, e.g., if the speed-based filtering of a mobile unit does not indicate the mobile unit as vehicle-born in step S 208 , the VDU  120  can determine if data for the mobile unit was previously treated as being vehicle-born (such that data for the mobile unit was treated as representing data for the vehicle). If so, this can indicate several possibilities such as: the mobile unit is no longer vehicle-born; or the vehicle is caught in slow traffic or a traffic-jam situation; or the vehicle has been parked; or the vehicle has stopped off the road but not in a parking area (possibly due to an accident or some other unusual situation), etc. The VDU  120  can then look at data for other vehicles proximal to the presently-considered vehicle to determine if they likewise have slowed down or are stopped, which would be indicative of a traffic slow-down or a traffic jam and/or do the position-based types of filtering (step S 210 ), etc. 
   Where position-data is only two-dimensional, there can be situations in which the footpath database  126  overlaps the road database  124 . For example, there can be a pedestrian overpass across a roadway. If a mobile unit  110  is in a vehicle  108  moving slowly, or that is stopped, underneath the overpass, then two-dimensional position data could yield indeterminate results indicating that the mobile unit  110  was both on the road and on a footpath (the overpass). The altitude component of three-dimensional position-data can be used to resolve such indeterminacy. 
   The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the invention.