Integrated rail efficiency and safety support system

Abstract

Embodiments of an integrated rail efficiency and safety support system and method are shown comprising a server operable to receive a plurality of sets of collected information, each of the sets of collected information comprising a consist physical location and weather conditions and rail temperatures in an area of the respective consist and to process a predictive rail temperature algorithm for predicting a rail temperature and/or a trend in rail temperature for a selected section of track; and wherein the predictive rail temperature algorithm factors the information provided to it and provides a predicted rail temperature and/or trend in rail temperature for the selected section of track.

1 . A system, comprising:
 a server configured for communication with at least one rail vehicle, wherein the server:   (a) receives sensed data over a network, said sensed data comprising at least one of observed weather conditions for a location and rail temperatures in a location;   (b) wherein the server is operable to process an algorithm for predicting a rail temperature and/or a trend in rail temperature for a selected section of track;   (c) wherein the algorithm factors the information provided to it and determines an instruction calculated to influence the rail temperature for the selected section of track; and   (d) wherein the server sends, over the network, the instruction to a user.   
     
     
         2 . The system of  claim 1 , wherein the algorithm combines the sensed data with at least one of the following: data from other trains, current and forecasted weather information, historical data and trends, and track profiles to create specialized prediction models for rail conditions. 
     
     
         3 . The system of  claim 1 , wherein the algorithm applies predictive modeling based on the following variables: the type and composition of the ties and rail bed along the section of track, the likely sun exposure of the tracks in the section of track in view of nearby sun blocking features, the length and weight of a selected train passing over the section of track, and expected and/or actual traffic from other rail vehicles passing over the section of track. 
     
     
         4 . The system of  claim 1 , wherein the algorithm applies predictive modeling based on accumulated historical data to make rail temperature determinations purely upon historically and currently measured rail temperatures. 
     
     
         5 . The system of  claim 1 , wherein the algorithm determines for a selected section of track and a selected period of time at least one of an average, mean, median, lowest, or highest temperature of the rail, and compares the actual rail temperature against an at least one of an average, mean, median, lowest, or highest ambient temperature over the time period to determine a delta value. 
     
     
         6 . The system of  claim 5 , wherein the algorithm may be made more sophisticated by adding a third variable, which is at least one of an average, mean, median, lowest or highest rail traffic over the selected period of time. 
     
     
         7 . The system of  claim 1 , wherein the algorithm factors in the effects of natural events, such as actual or potential flooding, snow depth, and drifting of snow. 
     
     
         8 . The system of  claim 1 , wherein the algorithm predicts the impact of selected hypothetical train routing or dispatch scenarios on track conditions. 
     
     
         9 . The system of  claim 1 , wherein the algorithm compares current images with historic images of objects and, based on the comparison, identifies alarm conditions. 
     
     
         10 . The system of  claim 1 , wherein the algorithm uses historical and subsequent data to refine prediction models. 
     
     
         11 . A method, comprising:
 (a) providing a server configured for communication with at least one rail vehicle;   (b) receiving sensed data over a network and by the server, said sensed data comprising at least one of observed weather conditions for a location and rail temperatures in a location;   (c) processing, by the server, an algorithm for predicting a rail temperature and/or a trend in rail temperature for a selected section of track;   (d) determining an instruction calculated to influence the rail temperature for the selected section of track; and   (e) sending, by the server and over the network, the instruction to a user.   
     
     
         12 . The method of  claim 11 , wherein the algorithm combines the sensed data with at least one of the following: data from other trains, current and forecasted weather information, historical data and trends, and track profiles to create specialized prediction models for rail conditions. 
     
     
         13 . The method of  claim 11 , wherein the algorithm applies predictive modeling based on the following variables: the type and composition of the ties and rail bed along the section of track, the likely sun exposure of the tracks in the section of track in view of nearby sun blocking features, the length and weight of a selected train passing over the section of track, and expected and/or actual traffic from other rail vehicles passing over the section of track. 
     
     
         14 . The method of  claim 11 , wherein the algorithm applies predictive modeling based on accumulated historical data to make rail temperature determinations purely upon historically and currently measured rail temperatures. 
     
     
         15 . The method of  claim 11 , wherein the algorithm determines for a selected section of track and a selected period of time at least one of an average, mean, median, lowest, or highest temperature of the rail, and compares the actual rail temperature against an at least one of an average, mean, median, lowest, or highest ambient temperature over the time period to determine a delta value. 
     
     
         16 . The method of  claim 15 , wherein the algorithm may be made more sophisticated by adding a third variable, which is at least one of an average, mean, median, lowest or highest rail traffic over the selected period of time. 
     
     
         17 . The method of  claim 11 , wherein the algorithm factors in the effects of natural events, such as actual or potential flooding, snow depth, and drifting of snow. 
     
     
         18 . The method of  claim 11 , wherein the algorithm predicts the impact of selected hypothetical train routing or dispatch scenarios on track conditions. 
     
     
         19 . The method of  claim 11 , wherein the algorithm compares current images with historic images of objects and, based on the comparison, identifies alarm conditions. 
     
     
         20 . The method of  claim 11 , wherein the algorithm uses historical and subsequent data to refine prediction models.