Abstract:
A database schema is disclosed that can significantly reduce the quantity of data required to describe the geometry of a train track and the geo-locations of features (e.g., grade crossings, mileposts, signals, platforms, switches, spurs, etc.) along the track. In accordance with the illustrative embodiment, a railroad track is represented as a plurality of partitions, each of which has its geometry contained within unique track point elements. Multiple track partitions are then joined together by common track point elements at their boundaries to create continuous rail networks. A compact table schema is employed that enables continuous sections of three-dimensional track splines to be rendered accurately in the track database, irrespective of the location of vertical and horizontal curvature along track segments. The data representation scheme also enables efficient storage of the geo-locations of features along a track, as well as the direct reconstitution of accurate three-dimensional track splines.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is related to U.S. patent application Ser. No. ______ entitled “Validation of Track Databases” (Attorney Docket: 711-178us) and U.S. patent application Ser. No. ______ entitled “Updating Track Databases After Track Maintenance” (Attorney Docket: 711-179us), both of which were filed on the same day as the present application. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates to databases in general, and, more particularly, to a database for efficiently storing railroad track geometry and feature location information. 
       BACKGROUND OF THE INVENTION 
       [0003]    In many instances it would be desirable to have a database that captures the geometry of a train track, as well as the geo-locations of various features (e.g., grade crossings, mileposts, signals, platforms, switches, spurs, etc.) along the train track. For example, such a database would be useful in train motion and path-taken navigation algorithms, predictive braking algorithms, and locomotive fuel management algorithms. 
         [0004]    If a track database become very large, however, a variety of issues can mitigate the database&#39;s utility. For example, a large database typically requires greater effort to build and maintain, is more susceptible to data errors, and has less portability. Moreover, high data-rate links might be required to transmit data to locomotives if the track database is too large. 
       SUMMARY OF THE INVENTION 
       [0005]    The present invention provides a data representation scheme that can significantly reduce the quantity of data required to describe track geometry and feature geo-locations. In accordance with the illustrative embodiment, a compact table schema is employed that enables continuous sections of three-dimensional track splines to be rendered accurately in the track database, irrespective of the location of vertical and horizontal curvature along track segments. The data representation scheme also enables efficient storage of the geo-locations of features along a track, as well as the direct reconstitution of accurate three-dimensional track splines. 
         [0006]    In addition to compressing the size of the track database while maintaining high accuracy, the illustrative embodiment also enables:
       computation of track feature three-dimensional coordinates in terms of simple trackline offsets,   derivation of track feature geographic coordinates from simple track offset parameters and track geometry coefficients, and   reconstitution of the original track centerline database from track point parameters.       
 
         [0010]    The illustrative embodiment comprises: obtaining a set of geo-locations from a survey of a train track; converting the geo-locations from latitude/longitude/altitude to points expressed in three-dimensional Earth-Centered Earth-Fixed Cartesian coordinates; generating, based on the three-dimensional coordinates of the set of points, a piecewise polynomial spline that is defined in terms of a proper subset of the set of points; populating a first data structure that stores for each point P in the subset: (i) the three-dimensional Earth-Centered Earth-Fixed Cartesian coordinates of the point P, (ii) an estimate of the heading of the train track at the point P, (iii) an estimate of the grade of the train track at the point P, and (iv) an estimate of the curvature of the train track at the point P; and populating a second data structure that stores for each of a non-empty set of features along the train track: (i) an indicium of a point in the subset, and (ii) an offset. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  depicts a segment of illustrative railroad track partition  100  and illustrative features  101 - 1  and  101 - 2 , in accordance with the illustrative embodiment of the present invention. 
           [0012]      FIG. 2  depicts railroad track partition  100  and illustrative centerline geo-locations  201 - 1  through  201 - 39  that are obtained from a survey, in accordance with the illustrative embodiment of the present invention. 
           [0013]      FIG. 3  depicts illustrative piecewise polynomial spline  300  that is fit to a subset of centerline geo-locations  201 - 1  through  201 - 39 , in accordance with the illustrative embodiment of the present invention. 
           [0014]      FIG. 4  depicts data store  400  for storing track geometry data and feature locations, in accordance with the illustrative embodiment of the present invention. 
           [0015]      FIG. 5  depicts the structure of table  401 , as shown in  FIG. 4 , in accordance with the illustrative embodiment of the present invention. 
           [0016]      FIG. 6  depicts the structure of table  402 - i , where i is an integer between 1 and N, in accordance with the illustrative embodiment of the present invention. 
           [0017]      FIG. 7  depicts a flowchart of a method for populating tables  401  and  402 - 1  through  402 -N, in accordance with the illustrative embodiment of the present invention. 
           [0018]      FIG. 8  depicts a flowchart of a method for determining the geo-location of a feature based on the contents of data store  400 , in accordance with the illustrative embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]      FIG. 1  depicts a segment of illustrative railroad track partition  100  and illustrative features  101 - 1  and  101 - 2 , in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 1 , a feature might be located either on or abreast railroad track partition  100 , depending on the particular type of feature. For example, feature  101 - 1 , which is located on railroad track partition  100 , might be a switch, while feature  101 - 2 , which is abreast railroad track partition  100 , might be a platform. 
         [0020]      FIG. 2  depicts railroad track partition  100  and illustrative centerline geo-locations  201 - 1  through  201 - 39  that are obtained from a survey, in accordance with the illustrative embodiment of the present invention. As will be appreciated by those skilled in the art, the fact that there happen to be 39 centerline geo-locations depicted in  FIG. 2  is merely illustrative, and does not represent the actual number or spacing of centerline geo-locations obtained in a track survey. 
         [0021]    In accordance with the illustrative embodiment, centerline geo-locations  201 - 1  through  201 - 39  are expressed in latitude/longitude/altitude, and might be obtained from readings of a Global Positioning System (GPS) unit traveling along railroad track partition  100 , from an aerial survey, etc. As indicated above, a key objective of the present invention is the construction of a database of relatively small size that accurately captures the track geometry, as opposed to a database that stores all of the track centerline geo-locations, which would require a significantly larger amount of memory. 
         [0022]      FIG. 3  depicts illustrative piecewise polynomial spline  300  that is fit to a subset of centerline geo-locations  201 - 1  through  201 - 39 , in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 3 , each point of piecewise polynomial spline  300  corresponds to a particular centerline geo-location in this subset (e.g., centerline geo-location  201 - 1 , centerline geo-location  201 - 39 , etc.). The manner in which piecewise polynomial spline  300  is represented and constructed is described in detail below and with respect to  FIGS. 7 and 8 . 
         [0023]      FIG. 4  depicts data store  400  for storing track geometry data and feature locations, in accordance with the illustrative embodiment of the present invention. In accordance with the illustrative embodiment, data store  400  is a relational database; as will be appreciated by those skilled in the art, however, some other embodiments of the present invention might employ another kind of data store (e.g., an alternative type of database such as an object-oriented database or a hierarchical database, one or more unstructured “flat” files, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments of data store  400 . 
         [0024]    As shown in  FIG. 4 , data store comprises table  401  and tables  402 - 1  through  402 -N, where N is a positive integer. Table  401  is a data structure for storing track geometry information, and in particular, data derived from piecewise polynomial spline  300 , as described in detail below and with respect to  FIG. 5 . Tables  402 - 1  through  402 -N are data structures for storing the locations of features along railroad track partition  100 , as described in detail below and with respect to  FIG. 6 . 
         [0025]    In accordance with the illustrative embodiment, each table  402 - i , where i is an integer between 1 and N inclusive, corresponds to a respective feature class (e.g., mileposts, traffic signals, platforms, etc.), so that, for example, table  402 - 1  might store the locations of all mileposts along railroad track partition  100 , table  402 - 2  might store the locations of all traffic signals along railroad track partition  100 , and so forth. Moreover, in accordance with the illustrative embodiment tables  402 - 1  through  402 -N are organized into physical track locations. 
         [0026]    As will be appreciated by those skilled in the art, some other embodiments of the present invention might organize the data stored in data store  400  in an alternative manner (e.g., the locations of all types of features might be stored in a single table, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. Moreover, it will be appreciated by those skilled in the art that in some other embodiments data store  400  might employ alternative types of data structures in lieu of tables (e.g., objects in an object-oriented database, trees in a hierarchical database, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such embodiments. 
         [0027]      FIG. 5  depicts the structure of table  401  in accordance with the illustrative embodiment of the present invention. Each row in table  401  corresponds to a particular point of piecewise polynomial spline  300 , which in turn corresponds to centerline geo-location  201 - i  that is included in (e.g.,  201 - 1 ,  201 - 39 , etc.). As shown in  FIG. 5 , table  401  comprises columns  501  through  507 . 
         [0028]    Columns  501 ,  502 , and  503  store x, y, and z coordinates, respectively, for each point of piecewise polynomial spline  300 . As described below and with respect to  FIG. 7 , these coordinates are Earth-Centered Earth-Fixed Cartesian coordinates and are derived from the latitude, longitude, and altitude of the corresponding centerline geo-locations. 
         [0029]    Columns  504 ,  505 , and  506  store estimates of the heading, grade, and curvature of railroad track partition  100 , respectively, at each point of piecewise polynomial spline  300 . In accordance with the illustrative embodiment, column  504  stores the heading in degrees, column  505  stores the grade as a percentage, and column  506  represents track curvature in units of degrees/meter. 
         [0030]    For the feature offsets, there is no corresponding track point element specifically located at that feature. The feature can lie between two track point elements. For feature offsets, however, one needs to have the corresponding track partition&#39;s track point elements (shown in  FIG. 5 ) which do describe the geometry of that section of track. 
         [0031]      FIG. 6  depicts the structure of table  402 - i , where i is an integer between 1 and N, in accordance with the illustrative embodiment of the present invention. Each row in table  402 - i  corresponds to a feature of class i (e.g., mileposts, traffic signals, etc.) along railroad track partition  100 . As shown in  FIG. 6 , table  402 - i  comprises columns  601 - i ,  602 - i , and  603 - i . Column  601 - i  stores an identifier that uniquely identifies each feature; column  602 - i  stores an indicium of a particular point of piecewise polynomial spline  300  (e.g., its row number in table  401 , etc.), and  603 - i  stores an estimated distance offset from the point specified by column  602 - i . In this way, the exact location of each feature can be derived from piecewise polynomial spline  300  and the corresponding entries of columns  602 - i  and  603 - i.    
         [0032]    For example, a row in table  402 - i  for feature  101 - 1  would specify an indicium of the point corresponding to centerline geo-location  201 - 8  (which is the point of spline  300  that immediately precedes feature  101 - 1 , as shown in  FIG. 3 ), and an estimated distance offset along railroad track partition  100  from this point (say, 205.3 centimeters). As will be appreciated by those skilled in the art, column  602 - i  is not required to determine the three-dimensional coordinates of a track-centric feature at a specific offset into the partition. The computations required to recover the geo-location of a feature from the information stored in tables  401  and  402 - i  are described in detail below and with respect to  FIG. 8 . 
         [0033]      FIG. 7  depicts a flowchart of a method for populating tables  401  and  402 - 1  through  402 -N, in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which tasks depicted in  FIG. 7  can be performed simultaneously or in a different order than that depicted. 
         [0034]    At task  710 , track centerline geo-locations  201  and feature geo-locations  101  are obtained from a survey of railroad track partition  100 . 
         [0035]    At task  720 , geo-locations  201  are converted from latitude/longitude/altitude to points expressed in Earth-Centered Earth-Fixed Cartesian coordinates, in well-known fashion. 
         [0036]    At task  730 , the heading, grade, and curvature of railroad track partition  100  are estimated at each of the points determined at task  720 , in well-known fashion. 
         [0037]    At task  740 , piecewise polynomial spline  300  is generated, where the spline is defined in terms of a proper subset of the points determined at task  720 . 
         [0038]    At task  750 , columns  501  through  506  of table  401  are populated with the coordinates and heading/grade/curvature estimates for each of the subset of points determined at task  740 . In addition, column  507  is populated with a distance offset from the first point in the table (i.e., the point corresponding to geo-location  201 - 1 ), so that the entries in column  507  are monotonically increasing, starting with zero in the first row. The distance offset is particularly useful in determining the locations, extents, magnitude, and nature of horizontal and vertical railroad track curves. 
         [0039]    At task  760 , tables  402 - 1  through  402 -N are populated with data corresponding to features  101  of railroad track partition  100 , where, as described above, each feature corresponds to a row in one of these tables, and the corresponding row comprises: a feature identifier, an indicium of the point on piecewise polynomial spline  300  that immediately precedes the feature, and an estimated distance offset from this point. As will be appreciated by those skilled in the art, the distance offset can be estimated via elementary Calculus from the coordinates of the feature, the coordinates of the preceding point, and the equation of piecewise polynomial spline  300 . As mentioned above, the geo-locations of features  101  can be recovered from these data via the method described below and with respect to  FIG. 8 . 
         [0040]      FIG. 8  depicts a flowchart of a method for determining the geo-location of a feature based on the contents of data store  400 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which tasks depicted in  FIG. 8  can be performed simultaneously or in a different order than that depicted. 
         [0041]    At task  810 , the Cartesian coordinates of preceding point A on piecewise polynomial spline  300  (i.e., A is the point on spline  300  that immediately precedes the feature) is converted to latitude/longitude/altitude, in well-known fashion. 
         [0042]    At task  820 , coefficient α is computed using Equation 1: 
         [0000]    
       
         
           
             
               
                 
                   α 
                   = 
                   
                     
                       
                         1 
                         2 
                       
                        
                       
                         
                           ca 
                           2 
                         
                          
                         
                           ( 
                           
                             1 
                             - 
                             
                               
                                 1 
                                 12 
                               
                                
                               
                                 c 
                                 2 
                               
                                
                               
                                 a 
                                 2 
                               
                             
                           
                           ) 
                         
                       
                        
                       cos 
                        
                       
                           
                       
                        
                       
                         ψ 
                         A 
                       
                     
                     + 
                     
                       
                         a 
                          
                         
                           ( 
                           
                             1 
                             - 
                             
                               
                                 1 
                                 6 
                               
                                
                               
                                 c 
                                 2 
                               
                                
                               
                                 a 
                                 2 
                               
                             
                           
                           ) 
                         
                       
                        
                       sin 
                        
                       
                           
                       
                        
                       
                         ψ 
                         A 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Eq 
                     . 
                     
                         
                     
                      
                     1 
                   
                   ) 
                 
               
             
           
         
       
     
         [0000]    where a is an offset from point A, c is curvature, and ψ A  is the heading at point A. 
         [0043]    At task  830 , coefficient β is computed using Equation 2: 
         [0000]    
       
         
           
             
               
                 
                   β 
                   = 
                   
                     
                       
                         1 
                         2 
                       
                        
                       
                         
                           ca 
                           2 
                         
                          
                         
                           ( 
                           
                             
                               
                                 1 
                                 12 
                               
                                
                               
                                 c 
                                 2 
                               
                                
                               
                                 a 
                                 2 
                               
                             
                             - 
                             1 
                           
                           ) 
                         
                       
                        
                       sin 
                        
                       
                           
                       
                        
                       
                         ψ 
                         A 
                       
                     
                     + 
                     
                       
                         a 
                          
                         
                           ( 
                           
                             1 
                             - 
                             
                               
                                 1 
                                 6 
                               
                                
                               
                                 c 
                                 2 
                               
                                
                               
                                 a 
                                 2 
                               
                             
                           
                           ) 
                         
                       
                        
                       cos 
                        
                       
                           
                       
                        
                       
                         ψ 
                         A 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Eq 
                     . 
                     
                         
                     
                      
                     2 
                   
                   ) 
                 
               
             
           
         
       
     
         [0044]    At task  840 , vector  ρ   E  (α) is computed using Equation 3: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         ρ 
                         _ 
                       
                       E 
                     
                      
                     
                       ( 
                       a 
                       ) 
                     
                   
                   = 
                   
                     [ 
                     
                       
                         
                           
                             
                               
                                 s 
                                 L 
                               
                                
                               α 
                             
                             - 
                             
                               
                                 s 
                                 λ 
                               
                                
                               
                                 c 
                                 L 
                               
                                
                               β 
                             
                             + 
                             
                               
                                 c 
                                 λ 
                               
                                
                               
                                 c 
                                 L 
                               
                                
                               a 
                                
                               
                                   
                               
                                
                               θ 
                             
                           
                         
                       
                       
                         
                           
                             
                               
                                 - 
                                 
                                   c 
                                   L 
                                 
                               
                                
                               α 
                             
                             - 
                             
                               
                                 s 
                                 λ 
                               
                                
                               
                                 s 
                                 L 
                               
                                
                               β 
                             
                             + 
                             
                               
                                 c 
                                 λ 
                               
                                
                               
                                 s 
                                 L 
                               
                                
                               a 
                                
                               
                                   
                               
                                
                               θ 
                             
                           
                         
                       
                       
                         
                           
                             
                               
                                 c 
                                 λ 
                               
                                
                               β 
                             
                             + 
                             
                               
                                 s 
                                 λ 
                               
                                
                               a 
                                
                               
                                   
                               
                                
                               θ 
                             
                           
                         
                       
                     
                     ] 
                   
                 
               
               
                 
                   ( 
                   
                     Eq 
                     . 
                     
                         
                     
                      
                     3 
                   
                   ) 
                 
               
             
           
         
       
     
         [0000]    where θ is grade, s L  is shorthand for sine(longitude), s λ  is shorthand for sine(latitude), c L  is shorthand for cosine(longitude), c λ  is shorthand for cosine(latitude), and latitude is geodetic. Vector  ρ   E (α) is the Earth-Centered Earth-Fixed displacement vector to get to the centerline point at distance a beyond point A. 
         [0045]    At task  850 , Cartesian coordinate vector  r   E (α) is computed using Equation 4: 
         [0000]          r     E (α)=   r     A   E + ρ   E (α)  (Eq. 4) 
         [0000]    where  r   A   E  is the vector of Earth-Centered Earth-Fixed coordinates stored at point A. 
         [0046]    At task  860 , vector  r   E (α) is converted from Cartesian coordinates to latitude/longitude/altitude, in well-known fashion. After task  860 , the method of  FIG. 8  terminates. 
         [0047]    It is to be understood that the disclosure teaches just one example of the illustrative embodiment and that many variations of the invention can easily be devised by those skilled in the art after reading this disclosure and that the scope of the present invention is to be determined by the following claims.