System and method for synthesizing rail vehicle event information

• US 9,902,410 B2
• Filed: 01/08/2015
• Issued: 02/27/2018
• Est. Priority Date: 01/08/2015
• Status: Active Grant

• Current Assignee: SmartDrive Systems, Inc. (Solera Holdings Incorporated)
• Original Assignee: SmartDrive Systems, Inc. (Solera Holdings Incorporated)
• Inventors: Palmer, Jason, Sljivar, Slaven, Freitas, Mark, Deninger, Daniel A., Ravari, Shahriar
• Primary Examiner(s): Kuhfuss, Zachary L
• Application Number:
  US14/592,446
• Publication Number:
  US 20160200331A1
• Time in Patent Office: 1,146 Days
• Field of Search: 701 19
• CPC Class Codes: B61K 9/08 Measuring installations for surveying permanent way applications of measuring apparatus or devices for track building purposes E01B35/00; measuring techniques G01
• CPC Class Codes: B61L 15/0072 On-board train data handling
• CPC Class Codes: B61L 15/0081 On-board diagnosis or maintenance
• CPC Class Codes: B61L 25/02 Indicating or recording positions or identities of vehicles or trains
• CPC Class Codes: B61L 27/40 Handling position reports or trackside vehicle data
• CPC Class Codes: B61L 27/57 for vehicles or trains, e.g. trackside supervision of train conditions

Abstract

This disclosure relates to a system configured to identify geolocations in a rail network where rail vehicle events are likely to occur. In some implementations, the system may include one or more of a processor, a computing system, electronic storage, external resources, and/or other components. The system may be configured to illustrate the geolocations in the rail network where rail vehicle events are likely to occur on a map of the rail network, predict geolocations in the rail network where rail vehicle events will likely occur, generate coaching information based on the identified geolocations, and/or perform other actions.

Claims

1. A system configured to identify geolocations in a rail network where rail vehicle events are likely to occur based on prior rail vehicle events that occurred to rail vehicles, wherein the rail vehicles include a first rail vehicle and a second rail vehicle, wherein the first rail vehicle includes a first sensor and a first rail vehicle event recorder configured to detect rail vehicle events based on output signals generated by the first sensor, wherein the second rail vehicle includes a second sensor and a second rail vehicle event recorder configured to detect rail vehicle events based on output signals generated by the second sensor, the system comprising one or more physical computer processors configured by computer readable instructions to:

obtain information that characterizes rail vehicle parameters during prior rail vehicle events involving the rail vehicles, wherein the obtained information includes a first set of information regarding the rail vehicle events detected by the first rail vehicle event recorder and a second set of information regarding the rail vehicle events detected by the second rail vehicle event recorder, the rail vehicle parameters including geolocations of the rail vehicles during the prior rail vehicle events; and

identify individual combinations of rail vehicle parameters associated with apparently elevated likelihoods of occurrences of rail vehicle events that are similar to the prior rail vehicle events, wherein the identified individual combinations include the rail vehicle parameters that indicate shared geolocations where the prior rail vehicle events have occurred, and wherein the identified individual combinations are based on the first and second sets of information.

2. The system of claim 1, wherein the one or more physical computer processors are further configured to effectuate communication of instructions to operators of rail vehicles approaching the shared geolocations.

3. The system of claim 1, wherein the one or more physical computer processors are further configured, based on the identification of the identified individual combinations, to analyze common features of the shared geolocations and further to predict other geolocations in the rail network where rail vehicle events are likely to occur based on the common features.

4. The system of claim 1, wherein the one or more physical computer processors are further configured to normalize the rail vehicle parameters in the identified individual combinations based on one or more of a frequency of rail vehicles passing through the shared geolocations where the prior rail vehicle events have occurred or a severity of the prior rail vehicle events at the shared geolocations.

5. The system of claim 1, wherein the one or more physical computer processors are further configured to indicate the shared geolocations where the prior rail vehicle events have occurred on a map of the rail network.

6. The system of claim 1, wherein the one or more physical computer processors are further configured to generate coaching information based on the shared geolocations where the prior rail vehicle events have occurred for presentation to one or more of a rail vehicle operator or a non-rail vehicle operator user during a coaching session.

7. The system of claim 1, wherein the one or more physical computer processors are further configured to:

obtain information that characterizes observations associated with the rail vehicle events made by rail vehicle event reviewers; and

identify individual combinations of observations associated with apparently elevated likelihoods of rail vehicle events, wherein the individual combinations include observations that indicate the shared geolocations where rail vehicle events have occurred.

8. The system of claim 1, wherein the one or more physical computer processors are further configured such that:

the obtained information that characterizes rail vehicle parameters during rail vehicle events includes times of day of the rail vehicle events and days of the week the rail vehicle events occurred on; and

the shared geolocations where rail vehicle events have occurred are categorized based on one or more of the time of day or the day of the week.

9. The system of claim 1, wherein the one or more processors are configured such that the prior rail vehicle events are related to one or more of a collision, a near collision, passing a red over red, passing a signal bar, a deadman, distracted operation of the rail vehicle by a rail vehicle operator, a penalty stop, slingshotting, excessive braking, an improper stop at a station, inappropriate language used by the rail vehicle operator, an intercom call, an intercom response, or activation of an ATP bypass.

10. The system of claim 1, wherein the one or more physical computer processors are configured such that the information that characterizes the geolocations of rail vehicles during the rail vehicle events is generated by one or more system location sensors that are coupled with the rail vehicles and/or one or more non-system location sensors that are not coupled with the rail vehicles.

11. A method for identifying geolocations in a rail network where rail vehicle events are likely to occur based on prior rail vehicle events that occurred to rail vehicles, wherein the rail vehicles include a first rail vehicle and a second rail vehicle, wherein the first rail vehicle includes a first sensor and a first rail vehicle event recorder configured to detect rail vehicle events based on output signals generated by the first sensor, wherein the second rail vehicle includes a second sensor and a second rail vehicle event recorder configured to detect rail vehicle events based on output signals generated by the second sensor, the method comprising:

obtaining information that characterizes rail vehicle parameters during prior rail vehicle events involving the rail vehicles, wherein the obtained information includes a first set of information regarding the rail vehicle events detected by the first rail vehicle event recorder and a second set of information regarding the rail vehicle events detected by the second rail vehicle event recorder, the rail vehicle parameters including geolocations of the rail vehicles during the prior rail vehicle events; and

identifying individual combinations of rail vehicle parameters associated with apparently elevated likelihoods of occurrences of rail vehicle events that are similar to the prior rail vehicle events, wherein the identified individual combinations include the rail vehicle parameters that indicate shared geolocations where the prior rail vehicle events have occurred, and wherein the identified individual combinations are based on the first and second sets of information.

12. The method of claim 11, further comprising effectuating communication of instructions to operators of rail vehicles approaching the shared geolocations.

13. The method of claim 11, further comprising analyzing, based on the identification of the identified individual combinations, common features of the shared geolocations;

and

predicting other geolocations in the rail network where rail vehicle events are likely to occur based on the common features.

14. The method of claim 11, further comprising normalizing the rail vehicle parameters in the identified individual combinations based on one or more of a frequency of rail vehicles passing through the shared geolocations where the prior rail vehicle events have occurred or a severity of the prior rail vehicle events at the shared geolocations.

15. The method of claim 11, further comprising indicating the shared geolocations where the prior rail vehicle events have occurred on a map of the rail network.

16. The method of claim 11, further comprising generating coaching information based on the shared geolocations where the prior rail vehicle events have occurred for presentation to one or more of a rail vehicle operator or a non-rail vehicle operator user during a coaching session.

17. The method of claim 11, further comprising:

obtaining information that characterizes observations associated with the rail vehicle events made by rail vehicle event reviewers; and

identifying individual combinations of observations associated with apparently elevated likelihoods of rail vehicle events, wherein the individual combinations include observations that indicate the shared geolocations where rail vehicle events have occurred.

18. The method of claim 11, wherein:

the obtained information that characterizes rail vehicle parameters during rail vehicle events includes times of day of the rail vehicle events and days of the week the rail vehicle events occurred on; and

the shared geolocations where rail vehicle events have occurred are categorized based on one or more of the time of day or the day of the week.

19. The method of claim 11, wherein the prior rail vehicle events are related to one or more of a collision, a near collision, passing a red over red, passing a signal bar, a deadman, distracted operation of the rail vehicle by a rail vehicle operator, a penalty stop, slingshotting, excessive braking, an improper stop at a station, inappropriate language used by the rail vehicle operator, an intercom call, an intercom response, or activation of an ATP bypass.

20. A system configured to identify geolocations in a rail network where rail vehicle events are likely to occur based on prior rail vehicle events that occurred to rail vehicles, wherein the rail vehicles include a first rail vehicle and a second rail vehicle, the system comprising:

a first rail vehicle event recorder configured to detect first rail vehicle events based on output signals generated by a first accelerometer carried by the first rail vehicle;

a second rail vehicle event recorder configured to detect second rail vehicle events based on output signals generated by a second accelerometer carried by the second rail vehicle;

a third rail vehicle event recorder configured to detect third rail vehicle events based on output signals generated by a third accelerometer carried by the third rail vehicle;

one or more physical computer processors configured by computer readable instructions to;

receive information from the first rail vehicle that characterizes first rail vehicle parameters, wherein the first rail vehicle parameters are based on the detected first rail vehicle events, and wherein the received information from the first rail vehicle includes geographic locations and time-of-day of the detected first rail vehicle events;

receive information from the second rail vehicle that characterizes second rail vehicle parameters, wherein the second rail vehicle parameters are based on the detected second rail vehicle events, and wherein the received information from the second rail vehicle includes geographic locations and time-of-day of the detected second rail vehicle events;

receive information from the third rail vehicle that characterizes third rail vehicle parameters, wherein the third rail vehicle parameters are based on the detected third rail vehicle events, and wherein the received information from the third rail vehicle includes geographic locations and time-of-day of the detected third rail vehicle events;

identify individual combinations of first, second, and third rail vehicle parameters associated with apparently elevated likelihoods of occurrences of rail vehicle events that are similar to previously detected rail vehicle events, wherein the identified individual combinations include the geographic locations of the previously detected rail vehicle events; and

effectuate communication of instructions to operators of rail vehicles approaching the geographic locations of the previously detected rail vehicle events.