SYSTEMS OF METHODS FOR MANAGING OPERATIONS OF A CLASSIFICATION YARD

Abstract

Methods and systems for managing operations of a classification yard. In embodiments a release speed, coupling speed, and/or a predicted speed at one or more points of a route along which a cut is being routed is determined. A set of event messages of events that occurred during the traveling of the cut is generated. Real-world measurements associated with an actual speed of the cut at the one or more points of the route are obtained. Coefficients associated with the predicted speed of the cut at the one or more points are autotuned based on the real-world measurements, a status of one or more devices used to route the cut is determined based, at least in part, on thresholding analysis applied to the real-world measurements, and the set of event messages is stored in an event log for subsequent replaying in a graphical user interface (GUI).

Claims

1. A method of managing operations in a classification yard, comprising: determining, for a cut being routed to a destination train, one or more of a release speed, coupling speed, and a predicted speed of the cut at one or more points of a route along which the cut is traveling to reach the destination train, wherein each in a set of event messages represents an event that occurred during the traveling of the cut along the route to reach the destination train; obtaining real-world measurements associated with an actual speed of the cut at the one or more points of the route; autotuning one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route; determining a status of one or more devices used to route the cut to the destination train based, at least in part, on thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route; and storing the set of event messages in an event log for subsequent replaying in a graphical user interface (GUI).

2. The method of claim 1, wherein determining the one or more of a release speed, coupling speed, and a predicted speed of the cut at the one or more points of the route includes: generating the predicted speed of the cut at the one or more points using a production set of tuning parameters associated with the one or more points.

3. The method of claim 2, wherein autotuning the one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route includes: estimating a candidate set of tuning parameters associated with the one or more points based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route; generating a set of backoffice predictions of the speed of the cut at the one or more points using the candidate set of tuning parameters; comparing the predicted speed of the cut at the one or more points and set of backoffice predictions to determine which of the production set of tuning parameters or the candidate set of tuning parameters for the one or more points yields more accurate speed predictions; and determining to replace the production set of tuning parameters for one or more points with the candidate set of tuning parameters in response to a determination that the candidate set of tuning parameters yields more accurate speed predictions.

4. The method of claim 3, wherein estimating the candidate set of tuning parameters associated with the one or more points includes applying a regression algorithm to the real-world measurements associated with the actual speed of the cut at the one or more points of the route to obtain the candidate set of tuning parameters associated with the one or more points.

5. The method of claim 3, wherein comparing the predicted speed of the cut at the one or more points and the set of backoffice predictions includes: calculating a production absolute value average difference between the predicted speed of the cut at the one or more points and the actual speed of the cut at the one or more points; calculating a backoffice absolute value average difference between the set of backoffice predictions and the actual speed of the cut at the one or more points; comparing the production absolute value average difference and the backoffice absolute value average difference to determine which one of the production absolute value average difference and the backoffice absolute value average difference is smaller; determining that the production set of control parameters yields more accurate speed predictions for the one or more points than the candidate set of control parameters in response to a determination that the production absolute value average difference is smaller than the backoffice absolute value average difference; and determining that the candidate set of control parameters yields more accurate speed predictions for the one or more points than the production set of control parameters in response to a determination that the production absolute value average difference is not smaller than the backoffice absolute value average difference.

6. The method of claim 1, wherein the one or more points includes a hardware device, and wherein determining the status of the one or more devices based, at least in part, on the thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route includes: generating a set of deviation metrics between a set of predicted measurements at the hardware device and a set of actual measurements at the hardware device; and applying the thresholding analysis to the set of deviation metrics to determine a status of the hardware device.

7. The method of claim 6, wherein applying thresholding analysis to the set of deviation metrics applying one or more of a set of differential rules to the set of deviation metrics, wherein the set of differential rules includes one or more of: a first differential rule specifying that the status of the hardware device is based on whether a threshold percentage of the set of deviation metrics are outside of a range defined by plus or minus a measurement threshold; a second differential rule specifying that the status of the hardware device is based on whether a median or average of the set of deviation metrics is within a range defined by plus or minus an average threshold; a third differential rule specifying that the status of the hardware device is based on whether a spread range of deviation metrics values within a middle percentage of the set of deviation metrics is less than a spread threshold, wherein the middle percentage of the set of deviation metrics is defined by a range of deviation metrics values including a top percentile threshold of the deviation metrics values in the set of deviation metrics and a bottom percentile threshold of the deviation metrics values in the set of deviation metrics; and a combination differential rule that includes a weighted combination of the results of one or more of the first differential rule, the second differential rule, and the third differential rule.

8. The method of claim 1, further comprising: obtaining the set of event messages from the event log; generating, for each event message of the set of event messages, a visual representation of the event that occurred during the traveling of the cut along the route represented by each respective event message of the set of event messages; generating a graphical schematic diagram of the classification yard, wherein the graphical schematic diagram includes a graphical representation of one or more components of the classification yard; determining, for each event message of the set of event messages, a component of the one or more components of the classification yard with which each respective event message is associated; and replaying the set of event messages in the GUI, wherein replaying the set of event messages includes overlaying each visual representation of an event represented by a respective event message onto the graphical representation of a component determined to be associated with the respective event message.

9. The method of claim 8, wherein the graphical representation of the one or more components of the classification yard includes positioning each component of the one or more components on a location within the graphical schematic diagram of the classification yard corresponding to a generally relative physical location of each component of the one or more components on the physical layout of the classification yard.

10. The method of claim 8, wherein the visual representation of the event that occurred includes one or more of: a color-based indication of the respective event; a numerical indication of the respective event; and a size-based indication of the respective event.

11. A system for managing operations in a classification yard, comprising: at least one processor; and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations including: determining, for a cut being routed to a destination train, one or more of a release speed, coupling speed, and a predicted speed of the cut at one or more points of a route along which the cut is traveling to reach the destination train, wherein each in a set of event messages represents an event that occurred during the traveling of the cut along the route to reach the destination train; obtaining real-world measurements associated with an actual speed of the cut at the one or more points of the route; autotuning one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route; determining a status of one or more devices used to route the cut to the destination train based, at least in part, on thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route; and storing the set of event messages in an event log for subsequent replaying in a graphical user interface (GUI).

12. The system of claim 11, wherein determining the one or more of a release speed, coupling speed, and a predicted speed of the cut at the one or more points of the route includes: generating the predicted speed of the cut at the one or more points using a production set of tuning parameters associated with the one or more points.

13. The system of claim 12, wherein autotuning the one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route includes: estimating a candidate set of tuning parameters associated with the one or more points based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route; generating a set of backoffice predictions of the speed of the cut at the one or more points using the candidate set of tuning parameters; comparing the predicted speed of the cut at the one or more points and set of backoffice predictions to determine which of the production set of tuning parameters or the candidate set of tuning parameters for the one or more points yields more accurate speed predictions; and determining to replace the production set of tuning parameters for one or more points with the candidate set of tuning parameters in response to a determination that the candidate set of tuning parameters yields more accurate speed predictions.

14. The system of claim 13, wherein estimating the candidate set of tuning parameters associated with the one or more points includes applying a regression algorithm to the real-world measurements associated with the actual speed of the cut at the one or more points of the route to obtain the candidate set of tuning parameters associated with the one or more points.

15. The system of claim 13, wherein comparing the predicted speed of the cut at the one or more points and the set of backoffice predictions includes: calculating a production absolute value average difference between the predicted speed of the cut at the one or more points and the actual speed of the cut at the one or more points; calculating a backoffice absolute value average difference between the set of backoffice predictions and the actual speed of the cut at the one or more points; comparing the production absolute value average difference and the backoffice absolute value average difference to determine which one of the production absolute value average difference and the backoffice absolute value average difference is smaller; determining that the production set of control parameters yields more accurate speed predictions for the one or more points than the candidate set of control parameters in response to a determination that the production absolute value average difference is smaller than the backoffice absolute value average difference; and determining that the candidate set of control parameters yields more accurate speed predictions for the one or more points than the production set of control parameters in response to a determination that the production absolute value average difference is not smaller than the backoffice absolute value average difference.

16. The system of claim 11, wherein the one or more points includes a hardware device, and wherein determining the status of the one or more devices based, at least in part, on the thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route includes: generating a set of deviation metrics between a set of predicted measurements at the hardware device and a set of actual measurements at the hardware device; and applying the thresholding analysis to the set of deviation metrics to determine a status of the hardware device.

17. The system of claim 16, wherein applying thresholding analysis to the set of deviation metrics applying one or more of a set of differential rules to the set of deviation metrics, wherein the set of differential rules includes one or more of: a first differential rule specifying that the status of the hardware device is based on whether a threshold percentage of the set of deviation metrics are outside of a range defined by plus or minus a measurement threshold; a second differential rule specifying that the status of the hardware device is based on whether a median or average of the set of deviation metrics is within a range defined by plus or minus an average threshold; a third differential rule specifying that the status of the hardware device is based on whether a spread range of deviation metrics values within a middle percentage of the set of deviation metrics is less than a spread threshold, wherein the middle percentage of the set of deviation metrics is defined by a range of deviation metrics values including a top percentile threshold of the deviation metrics values in the set of deviation metrics and a bottom percentile threshold of the deviation metrics values in the set of deviation metrics; and a combination differential rule that includes a weighted combination of the results of one or more of the first differential rule, the second differential rule, and the third differential rule.

18. The system of claim 11, further comprising: obtaining the set of event messages from the event log; generating, for each event message of the set of event messages, a visual representation of the event that occurred during the traveling of the cut along the route represented by each respective event message of the set of event messages; generating a graphical schematic diagram of the classification yard, wherein the graphical schematic diagram includes a graphical representation of one or more components of the classification yard; determining, for each event message of the set of event messages, a component of the one or more components of the classification yard with which each respective event message is associated; and replaying the set of event messages in the GUI, wherein replaying the set of event messages includes overlaying each visual representation of an event represented by a respective event message onto the graphical representation of a component determined to be associated with the respective event message.

19. The system of claim 18, wherein the visual representation of the event that occurred includes one or more of: a color-based indication of the respective event; a numerical indication of the respective event; and a size-based indication of the respective event.

20. A computer-based tool for of managing operations in a classification yard, the computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising: determining, for a cut being routed to a destination train, one or more of a release speed, coupling speed, and a predicted speed of the cut at one or more points of a route along which the cut is traveling to reach the destination train, wherein each in a set of event messages represents an event that occurred during the traveling of the cut along the route to reach the destination train; obtaining real-world measurements associated with an actual speed of the cut at the one or more points of the route; autotuning one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route; determining a status of one or more devices used to route the cut to the destination train based, at least in part, on thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route; and storing the set of event messages in an event log for subsequent replaying in a graphical user interface (GUI).

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0024] FIG. 1 is a block diagram of an exemplary system configured with capabilities and functionality for managing operations of a classification yard in accordance with embodiments of the present disclosure.

[0025] FIG. 2 is a block diagram illustrating an example of a terminal process control (TPC) and a backoffice configured with capabilities and functionality for managing operations of a classification yard in accordance with embodiments of the present disclosure.

[0026] FIG. 3 is a block diagram illustrating exemplary operations of a speed control manager to predict arrival times and/or speeds of one or more cuts at various points along a route that the one or more cuts may travel along the marshalling tracks of a classification yard to reach their assigned destination train in accordance with embodiments of the present disclosure.

[0027] FIG. 4 show an example of a graphical user interface (GUI) configured to visualize and replay event messages in accordance with embodiments of the present disclosure.

[0028] FIG. 5 shows a high-level flow diagram of operations of a system for providing management of operations of a classification yard in accordance with embodiments of the present disclosure.

[0029] It should be understood that the drawings are not necessarily to scale and that the disclosed embodiments are sometimes illustrated diagrammatically and in partial views. In certain instances, details which are not necessary for an understanding of the disclosed methods and apparatuses or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the particular embodiments illustrated herein.

DETAILED DESCRIPTION

[0030] The disclosure presented in the following written description and the various features and advantageous details thereof, are explained more fully with reference to the non-limiting examples included in the accompanying drawings and as detailed in the description. Descriptions of well-known components have been omitted to not unnecessarily obscure the principal features described herein. The examples used in the following description are intended to facilitate an understanding of the ways in which the disclosure can be implemented and practiced. A person of ordinary skill in the art would read this disclosure to mean that any suitable combination of the functionality or exemplary embodiments below could be combined to achieve the subject matter claimed. The disclosure includes either a representative number of species falling within the scope of the genus or structural features common to the members of the genus so that one of ordinary skill in the art can recognize the members of the genus. Accordingly, these examples should not be construed as limiting the scope of the claims.

[0031] A person of ordinary skill in the art would understand that any system claims presented herein encompass all of the elements and limitations disclosed therein, and as such, require that each system claim be viewed as a whole. Any reasonably foreseeable items functionally related to the claims are also relevant. The Examiner, after having obtained a thorough understanding of the disclosure and claims of the present application has searched the prior art as disclosed in patents and other published documents, i.e., nonpatent literature. Therefore, the issuance of this patent is evidence that: the elements and limitations presented in the claims are enabled by the specification and drawings, the issued claims are directed toward patent-eligible subject matter, and the prior art fails to disclose or teach the claims as a whole, such that the issued claims of this patent are patentable under the applicable laws and rules of this country.

[0032] Various embodiments of the present disclosure are directed to systems and techniques that provide functionality for managing operations of a classification yard. Particular embodiments provide functionality for planning, controlling, tracking, and/or reporting the movement of cuts (e.g., train cars) from a hump rolling stock to a respectively assigned destination track/train. In embodiments, the functionality provided by the features described herein may represent an integrated, standardized, configurable, reliable, and efficient control system that may utilize a sustainable and maintainable software/hardware design to support operations of a classification yard.

[0033] FIG. 1 is a block diagram of an exemplary system 100 configured with capabilities and functionality for managing operations of a classification yard in accordance with embodiments of the present disclosure. As shown in FIG. 1, system 100 may include user terminal 130, classification yard 140, network 145, mapper 150, database 155, terminal process control (TPC) 160, and backoffice 165. These components, and their individual components, may cooperatively operate to provide functionality in accordance with the discussion herein.

[0034] It is noted that the functional blocks, and components thereof, of system 100 of embodiments of the present invention may be implemented using processors, electronics devices, hardware devices, electronics components, logical circuits, memories, software codes, firmware codes, etc., or any combination thereof. For example, one or more functional blocks, or some portion thereof, may be implemented as discrete gate or transistor logic, discrete hardware components, or combinations thereof configured to provide logic for performing the functions described herein. Additionally, or alternatively, when implemented in software, one or more of the functional blocks, or some portion thereof, may comprise code segments operable upon a processor to provide logic for performing the functions described herein.

[0035] It is also noted that various components of system 100 are illustrated as single and separate components. However, it will be appreciated that each of the various illustrated components may be implemented as a single component (e.g., a single application, server module, etc.), may be functional components of a single component, or the functionality of these various components may be distributed over multiple devices/components. In such embodiments, the functionality of each respective component may be aggregated from the functionality of multiple modules residing in a single, or in multiple devices.

[0036] It is further noted that functionalities described with reference to each of the different functional blocks of system 100 described herein is provided for purposes of illustration, rather than by way of limitation and that functionalities described as being provided by different functional blocks may be combined into a single component or may be provided via computing resources disposed in a cloud-based environment accessible over a network, such as one of network 145.

[0037] Classification yard 140 may represent a train yard, such as a hump yard, in which train cars are routed or marshalled to a destination track to be coupled to a destination train. In a typical operation of classification yard 140, such as a hump yard, a stock train that includes train cars to be marshalled to their assigned train may be pushed by a hump push engine at a push speed along the approach section of the hump to the crest of the hump. As the train cars roll past the hump crest, gravity may begin pulling the train cars towards the bottom of the hump. In embodiments, the train cars are cut from the stock train and the cut is allowed to roll down the hump and is marshalled to the destination train along a route through the marshalling tracks of the hump yard. In embodiments, classification yard 140 may include functionality to plan, track, control, and report the movement of the train cars through the marshalling tracks, including the hump approach section, the hump crest, the hump release area, and multiple marshalling tracks.

[0038] As noted above, ensuring that the cut reaches the assigned destination train at the appropriate coupling speed is very important. As such, in embodiments, a cut may be tracked and controlled as the cut moves along the marshalling tracks of classification yard 140. In particular, the route and the speed of the cut from the hump to its destination track or train may be controlled using various components of classification yard 140. For example, classification yard 140 may include various components enabling classification yard 140 to track and/or control the movement of a cut through the marshalling tracks.

[0039] In embodiments, the various components enabling classification yard 140 to track and/or control the movement and/or speed of a cut through the marshalling tracks may include devices 142 and segments 144, which may include hardware devices such as switches, retarders, radars, wheel detectors, distance units, identification devices, etc. In embodiments, the cooperative operation of the various components of classification yard 140 may enable classification yard 140 to ensure that various cuts traverse the marshalling tracks and arrive at the destination coupling point at the appropriate coupling speed.

[0040] In embodiments, a switch device may be configured to route a cut from a source track to a target track. A switch may be thrown from a first position (e.g., corresponding to a first track) to a second position (e.g., corresponding to a second track) in order to route a cut passing through the switch from a source track to the second track, the second track being the target track of the cut. In this manner, switches may be used to control the route of a cut as it travels through the marshalling tracks.

[0041] A wheel detector may be configured to detect a speed of a cut by detecting the presence of a first wheel of the cut at a first time, detecting the presence of a second wheel of the cut at a second time, and determining a speed from the difference between the first and second times over the known distance between the first wheel and the second wheel. In embodiments, a radar device may be configured to detect the presence of a cut and/or to measure the speed of the cut traveling through the detection area of the radar devices. In this manner, wheel detectors and/or radars may be used to measure the speed and/or presence of cuts as the cuts move through the hump yard. In some embodiments, a device may include a combination of devices, such as a switch wheel detector, which may be configured to perform operations related to switch operations and wheel detector operations. In this manner, a switch wheel detector may be configured to route a cut to a target track and to detect the wheels of the cut (e.g., for speed measurement operations).

[0042] A retarder may be configured to slow down a cut as the cut travels through the. A retarder may be configured to apply a pressure against one or more wheels of the cut (e.g., using a braking element, such as a brake pad, etc.), which may cause the cut to slow down. In this manner, retarders may be used to further control the speed of a cut as it travels through the marshalling tracks of the hump yard.

[0043] In embodiments, distance units that may be configured to detect how far a current location of a cut within the hump yard may be from other cuts, from other devices, and/or from other points along the cut's route. In some embodiments, distance units may be used to measure a distance. In embodiments, identification devices may be configured to detect an identification of a cut, such as via radio frequency identification (RFID) devices. Identification devices may be used to identify cuts as the cuts travel through the marshalling tracks of the hump yard.

[0044] In embodiments, occupancy devices may include track circuits, light detectors, presence detectors, etc., and may be configured to detect occupancy of a track and/or track segment, such as to detect a presence of a vehicle within the track and/or track segment. In some embodiments, occupancy devices may be configured to detect when a track or track segment has been filled to a safe capacity, which may enable a system to prevent overfilling of the track or track segment. In embodiments, occupancy devices may be used in long sections of track that may not include a wheel detector, a retarder, etc. In embodiments, an occupancy device may be configured with predicted on and off times. If the on and off times are exceeded, a segment protected by the occupancy device may be temporarily protected and if the condition persists, the protection may remain. In embodiments, protecting a track or track segment may include routing away from the protected track or track segment, such as in response to a determination that sufficient time exists to route the vehicle away from the protected track or track segment. However, the railroad vehicle may be routed into the protected track segment (e.g., to prevent a side-swipe) in response to a determination that there is not sufficient time to route the vehicle away from the protected track or track segment.

[0045] In embodiments, the various devices in devices 142 may be positioned at different locations or points within the layout of the classification yard. In embodiments, the position of the various devices in devices 142 may be determined based on the layout of the track segments (e.g., segments 144) making up the marshalling tracks of classification yard 140. For example, a first retarder may be positioned along the release section of the hump, whereas a second retarder may be positioned along a different segment. In embodiments, wheel detectors and/or switches may be positioned at the entry point of each track segment within classification yard 140.

[0046] For example, the marshalling tracks of classification yard 140 may be divided into a plurality of segments (e.g., segments 144). In embodiments, each of segments 144 may be defined by an entry point and an exit. In some embodiments, the entry point and/or the exit point of a segment may correspond to a device (e.g., a switch, a retarder, a detector, etc.) of classification yard 140. In this manner, devices may be used to divide the marshalling tracks of classification yard 140 into segments 144. In embodiments, a route followed by a cut may be defined by a connection of various segments of segments 144 that may route the cut from the hump section to the assigned destination track. In some embodiments, a segment may include one or more devices within the segment. For example, a segment may include one or more retarders, one or more switches, one or more detectors, within the boundaries of the segment (e.g., in addition to the entry and/or exit point devices of the segment). In some embodiments, segments may be defined by geographical features.

[0047] Mapper 150 may be configured to map classification yard 140. In embodiments, the functionality of mapper 150 to perform mapping operations may include the use of mapping devices, such as light detection and ranging (LIDAR) devices, radio detection and ranging (RADAR), optical sensors (e.g., cameras, infrared (IR) sensors, etc.).

[0048] In embodiments, mapper 150 may include functionality to determine and/or identify a geographical location of each component (e.g., track, segment, and/or hardware device) of and/or within classification yard 140. The geographical location for each component of classification yard 140 may be obtained as a geographical information system (GIS) coordinate and may be stored in a database (e.g., database 155). In embodiments, the GIS coordinate of the components (e.g., tracks, segments, and/or hardware devices) of classification yard 140 may be used by other components of system 100 to perform operations in accordance with embodiments of the present disclosure. For example, TPC 160 and/or backoffice 165 may use the GIS coordinate of the components (e.g., tracks, segments, and/or hardware devices) of classification yard 140 to make determinations regarding hump list creation operations, cut routing and speed control operations (e.g., including cut speed predictions, cut energy removal), cut protection operations, etc.

[0049] In embodiments, the functionality of mapper 150 to perform mapping operations of the components of classification yard 140 may provide functionality to facilitate the integration or addition of new components (e.g., new tracks, new segments, new hardware devices, etc.) to classification yard 140. For example, adding new hardware devices, (e.g., adding new devices due to expansion of classification yard 140), or relocating an existing hardware device (e.g., relocating an existing hardware devices due geographical changes in classification yard 140) may include adding or changing the GIS coordinate of the hardware device, which may eliminate having to make extensive changes to the underlying code of the system (e.g., as is done in current systems).

[0050] Database 155 may be configured to store a mapping of classification yard 140. In embodiments, the mapping of classification yard 140 may include GIS coordinates for each component (e.g., each track, each segment, and/or each hardware device) of classification yard 140. In embodiments, the GIS coordinate of the components (e.g., tracks, segments, and/or hardware devices) of classification yard 140 may be used by other components of system 100 to perform operations in accordance with embodiments of the present disclosure.

[0051] User terminal 130 may include a mobile device, a smartphone, a tablet computing device, a personal computing device, a laptop computing device, a desktop computing device, a computer system of a vehicle, a personal digital assistant (PDA), a smart watch, another type of wired and/or wireless computing device, or any part thereof. In embodiments, user terminal 130 may provide a user interface that may be configured to provide an interface (e.g., a graphical user interface (GUI)) structured to facilitate an operator interacting with system 100, e.g., via network 145, to execute and leverage the features provided by server 110a or 110b. In embodiments, the operator may be enabled, e.g., through the functionality of user terminal 130, to provide configuration parameters that may be used by system 100 to provide functionality for managing operations of classification yard 140. In embodiments, user terminal 130 may be configured to communicate with other components of system 100.

[0052] In embodiments, user terminal 130 may be configured to present the GUI via a web-based scheme, which may include a web-based system that may allow system 100 to plan, control, track, and report the movement of cuts (e.g., train cars) from the hump rolling stock to the destination train.

[0053] In embodiments, network 145 may facilitate communications between the various components of system 100 (e.g., classification yard 140, mapper 150, database 155, TPC 160, backoffice 165, and/or user terminal 130. Network 145 may include a wired network, a wireless communication network, a cellular network, a cable transmission system, a Local Area Network (LAN), a Wireless LAN (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), the Internet, the Public Switched Telephone Network (PSTN), etc.

[0054] TPC 160 may be configured to provide main functionality of system 100 to manage operations of classification yard 140. In embodiments, TPC system may be configured to facilitate operations to plan, control, track, and report the movement of cuts (e.g., train cars) from the hump rolling stock to the destination train. In embodiments, TPC 160 may represent an integrated, standardized, configurable, reliable, and efficient control system that may utilize a sustainable and maintainable software/hardware design to support operations of classification yard 140.

[0055] In embodiments, TPC 160 may provide functionality to create a hump list, and to classify each train car in the hump list in accordance with the configuration of the hump list. The hump list may represent a listing of the train cars that are to be classified by classification yard 140 (e.g., the train cars to be routed to their assigned destination train) and may include an indication of the assigned destination track/train of each train car. In embodiments, TPC 160 may create the hump list by communicating with external systems (e.g., a transportation support system (TSS), etc.) and obtaining train car data associated with the train cars in the rolling stock train to be classified. The hump list may include an indication or identification of each cut (e.g., each group of one or more cars to be included in each cut) that is to be cut from the rolling stock train at the hump.

[0056] In embodiments, as each cut enters the hump, each cut is separated from the rolling stock (e.g., using gravity) and allowed to coast downhill through the hump yard, and each cut may be routed to its assigned destination track/train to be coupled at the appropriate coupling speed. In embodiments, the release speed, the coupling speed, and/or the speed of each cut through the marshalling tracks of classification yard 140 may be determined by the operations of TPC 160 to control the push engine speed, to control operations of retarders to remove energy from the cuts as the cuts move through the marshalling tracks of classification yard 140, to predict arrival times and/or speeds of the cuts at various devices and/or segments of classification yard 140, etc. Operations of TPC 160 are described in more detail below (e.g., with reference to FIG. 2).

[0057] Backoffice 165 may be configured to provide a development environment that may be used to perform operations to support on-field operations of TPC 160. For example, the functionality of TPC 160 to control the push engine speed, to control operations of retarders to remove energy from the cuts as the cuts move through the marshalling tracks of classification yard 140, to predict arrival times and/or speeds of the cuts at various devices and/or segments of classification yard 140, etc., may include the use of control parameters (e.g., tuning coefficients) that have been deployed to a production environment. The production environment may include operations that are real-world operations performed on the field and/or in an operation environment. The production environment may be contrasted to a backoffice environment in which operations may not include on-field operations and may instead include development and/or testing operations. In this manner, the operations of backoffice 165, as described herein, may include operations that may be performed using data, parameter values, and/or coefficient values that have not yet been deployed to the production environment and thus, without deployment to the production environment, may not affect on-field operations but instead may affect development operations. For example, backoffice 165 may be configured to perform analysis on measurements obtain from classification operations to determine control parameters candidates that may be used to iteratively improve the prediction functionality of TPC 160. In embodiments, the control parameters candidates generated by backoffice 165 may not be deployed to TPC 160 until the control parameters candidates have been validated. Until such point, the control parameters candidates may not affect on-field operations of TPC 160. Operations of backoffice 165 are described in more detail below (e.g., with reference to FIG. 2).

[0058] FIG. 2 is a block diagram illustrating an example of TPC 160 and backoffice 165 configured with capabilities and functionality for managing operations of a classification yard in accordance with embodiments of the present disclosure. As shown in FIG. 2, TPC 160 and backoffice 165 may be implemented in a server. For example, TPC 160 may be implemented in server 110a and backoffice 165 may be implemented in server 110b. In embodiments, functionality of server 110a (e.g., to facilitate operations of TPC 160) and functionality of server 110b (e.g., to facilitate operations of backoffice 165) may be provided by the cooperative operation of the various components of each of servers 110a and 110b, as will be described in more detail below.

[0059] Although FIG. 2 shows each of servers 110a and 110b as separate components, it will be appreciated that servers 110a and 110b (and their individual functional blocks) may be implemented as a single device or may be distributed over multiple devices having their own processing resources, whose aggregate functionality may be configured to perform operations in accordance with the present disclosure. Furthermore, those of skill in the art would recognize that although FIG. 2 illustrates components of each of servers 110a and 110b as single and separate blocks, each of the various components of servers 110a and/or 110b may be a single component (e.g., a single application, server module, etc.), may be functional components of a same component, or the functionality may be distributed over multiple devices/components. In such embodiments, the functionality of each respective component may be aggregated from the functionality of multiple modules residing in a single, or in multiple devices. In addition, particular functionality described for a particular component of servers 110a and/or 110b may actually be part of a different component of servers 110a and/or 110b, and as such, the description of the particular functionality described for the particular component of servers 110a and/or 110b is for illustrative purposes and not limiting in any way.

[0060] As shown in FIG. 2, server 110a includes processor 111a, memory 112a, speed control manager 120, autotuning manager 121, device monitor 122, and event replay manager 123, and server 110b includes processor 111b, memory 112b, backoffice regression manager 125, and backoffice prediction manager 126.

[0061] Processor 111a and/or processor 111b may comprise a processor, a microprocessor, a controller, a microcontroller, a plurality of microprocessors, an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), or any combination thereof, and may be configured to execute instructions to perform operations in accordance with the disclosure herein. In some embodiments, implementations of processor 111a and/or processor 111b may comprise code segments (e.g., software, firmware, and/or hardware logic) executable in hardware, such as a processor, to perform the tasks and functions described herein. In yet other embodiments, processor 111a and/or processor 111b may be implemented as a combination of hardware and software. Processor 111a may be communicatively coupled to memory 112a, and processor 111b may be communicatively coupled to memory 112b.

[0062] Memory 112a and/or memory 112b may comprise one or more semiconductor memory devices, read only memory (ROM) devices, random access memory (RAM) devices, one or more hard disk drives (HDDs), flash memory devices, solid state drives (SSDs), erasable ROM (EROM), compact disk ROM (CD-ROM), optical disks, other devices configured to store data in a persistent or non-persistent state, network memory, cloud memory, local memory, or a combination of different memory devices. Memory 112a and/or memory 112b may comprise a processor readable medium configured to store one or more instruction sets (e.g., software, firmware, etc.) which, when executed by a processor (e.g., one or more processors of processor 111a and/or processor 111b), perform tasks and functions as described herein.

[0063] In embodiments, TPC 160 may be configured to facilitate operations to plan, control, track, and report the movement of cuts (e.g., train cars) from the hump rolling stock to the destination train. In particular embodiments, TPC 160 may be configured to leverage the functionality of speed control manager 120 to determine a release speed of a cut, to determine a target coupling speed of the cut at the coupling point, and/or to control the speed of the cut through the marshalling tracks of classification yard 140 (e.g., by determining an amount of energy to be removed from the cut as the cut travels through classification yard 140) in order to arrive at the coupling point at the target coupling speed.

[0064] In embodiments, classification yard 140 may operate various components (e.g., switches, detectors, retarders, etc.) to route cuts through the marshalling tracks of classification yard 140 to their designated destination tracks/trains, while ensuring that the coupling speed of each cut at the respective coupling point is as close to a target coupling speed as possible. In embodiments, classification yard may operate to control the speed of the cuts through a route along the marshalling tracks based on various parameters (e.g., coefficients) that may affect or describe the rollability of the cuts through various segments of the route of the cuts. For example, predictions related to the speed of a cut through one or more segments or at a particular device may be obtained based on rollability parameters (e.g., coefficients) associated with the one or more segments, which may include environmental, track, and/or cut characteristics, as well as determinations of energy to be removed by retarders along the route.

[0065] Speed control manager 120 may be configured with functionality to predict arrival times and/or speeds of one or more cuts at various points along a route that the one or more cuts may travel along the marshalling tracks of classification yard 140 to reach their assigned destination track/train. In embodiments, the functionality of speed control manager 120 to predict arrival times and/or speeds of the one or more cuts at various points along the route may be leveraged by TPC 160 to determine a release speed of a cut during humping (e.g., classification), to determine a target coupling speed of the cut at the coupling point, and/or to control the speed of the cut through the marshalling tracks of classification yard 140 (e.g., by determining an amount of energy to be removed from the cut as the cut travels through classification yard 140) in order to arrive at the coupling point at the target coupling speed.

[0066] In embodiments, the various points for which speed control manager 120 may predict arrival times and/or speeds of a cut may include track segments or hardware devices (e.g., switches, retarders, wheel detectors, etc.) of classification yard 140. In this manner, speed control manager 120 may TPC 160 with functionality to determine an expected arrival time and/or speed of a cut at which the cut (e.g., a cut having particular characteristics) may arrive at the particular track segment or hardware device.

[0067] In some embodiments, speed control manager 120 may include functionality to predict an amount of energy, or speed, that a retarder may be expected to remove. For example, a cut may travel, or may be assigned to travel, along a route through the marshalling tracks of classification yard 140 to reach an assigned destination track/train that may include one or more retarders. In embodiments, speed control manager 120 may include functionality to predict the amount of energy, or speed, that the one or more retarders are expected to remove from the cut as the cut travels through the one or more retarders. In some embodiments, predicting the amount of energy, or speed, that the one or more retarders are expected to remove from the cut as the cut travels through the one or more retarders may include predicting an amount of energy, or speed, that the cut is expected to have at the entry point of the one or more retarders and an amount of energy, or speed, that the cut is expected to have at the exit point of the one or more retarders. In these cases, the difference between the entry point energy, or speed, and the exit point energy, or speed, may indicate the amount of energy, or speed, removed by the one or more retarders.

[0068] In embodiments, the various points for which speed control manager 120 may predict arrival times and/or speeds of a cut may include and entry and/or exit point of a route segment. In these cases, speed control manager 120 may be configured to predict arrival times and/or speeds of one or more cuts at the entry point and/or exit point of a segment.

[0069] In embodiments, the functionality of speed control manager 120 to predict arrival times and/or speeds of one or more cuts at various points along a route that the one or more cuts may travel along the marshalling tracks to reach their assigned destination track/train may include application of analysis using parameter values and/or coefficient values that have been deployed to the production environment of TPC 160.

[0070] In embodiments, the functionality of speed control manager 120 to predict arrival times and/or speeds of one or more cuts at various points (e.g., hardware devices and/or segments) along a route that the one or more cuts may travel along the marshalling tracks to reach their assigned destination train may include various operations. FIG. 3 is a block diagram illustrating exemplary operations of a speed control manager to predict arrival times and/or speeds of one or more cuts at various points along a route that the one or more cuts may travel along the marshalling tracks of a classification yard to reach their assigned destination train in accordance with embodiments of the present disclosure. In embodiments, the operations illustrated in FIG. 3 may be performed by a speed control manager (e.g., speed control manager 120) configured in accordance with aspects of the present disclosure.

[0071] At block 302, speed control manager 120 may determine a target coupling speed for a cut at the coupling point on the destination track/train to which the cut may be assigned. In embodiments, speed control manager 120 may determine the target coupling speed by determining an amount of energy needed by the cut at the coupling point (e.g., the rear portion of the last train car in the destination car assigned to the cut) in order to properly couple to the destination car. In embodiments, speed control manager 120 may determine the target coupling speed for the cut based, at least in part, on characteristics of the cut and/or of the hump yard track. For example, the target coupling speed for a cut may be based on the weight of the cut, the type of train cars in the cut, the type of freight being carried by the cut, whether the cut is a special cut (e.g., a cut having a flag indicating that the cut is to be coupled slowly), whether the destination track is overfilled (e.g., past a clearance point), the type of track, the layout of the destination track, characteristics of the previous cut routed through the marshalling tracks (e.g., the weight of the previous cut, the type of freight being carried by the previous cut, a status of the previous cut, special handling instructions for the previous cut (e.g., the previous cut having a flag indicating that the previous cut is to be given a large clearance between cuts), etc.), etc.

[0072] At block 304, speed control manager 120 may calculate an energy change (e.g., amount of energy gained or lost) at each segment of the route along which the cut may travel to reach the coupling point and, at block 306, speed control manager 120 may determine a maximum amount of energy removeable by retarders at each of the plurality of segments of the route of the cut. In embodiments, calculations at block 304 and/or at block 306 may be performed uphill. For example, speed control manager 120 may calculate an amount of energy gained or lost at each segment of the route and/or may determine a maximum amount of energy removeable by retarders at each of the plurality of segments of the route starting at the segment immediately next to the point at which the rear of the cut will stop when coupled to the destination train (e.g., the coupling point) and continuing with subsequent segments along the route in the direction of the hump until the hump crest is reached. In this manner, these calculations of speed control manager 120 may be considered to be uphill.

[0073] In embodiments, speed control manager 120 may calculate an amount of energy gained or lost by a cut at each segment of the route based, at least in part, on track characteristics and/or cut characteristics, among other factors. In embodiments, the energy gained or lost by a cut at a segment may include the energy needed by the cut at the entry point of the segment to reach the exit point of the segment at a desired speed. In some cases, the energy needed by the cut to traverse the segment may be a negative value, which may indicate that the cut may have excess energy in the segment that may need to be removed (e.g., by a retarder) in order to ensure that the cut may reach the exit point of the segment at the desired speed. In some cases, the energy needed by the cut to traverse the segment may be a positive value, which may indicate that the cut may lose energy while traversing the segment.

[0074] In embodiments, cut characteristics may include characteristics that affect the rollability of a cut through a segment (e.g., how well the cut is expected to roll through a segment), and may include cut weight (e.g., total and/or average), modified gravity (e.g., acceleration of the cut due to gravity specific to the cut based on differences in train cars included in the cut, or differences in train cars generally), wind dynamics (e.g., the aerodynamic profile of the cut and/or the train cars included in the cut), rollability (e.g., rolling resistance of the cut), etc., of the cut.

[0075] In embodiments, track characteristics may include characteristics of the tracks within the segment that may affect the energy change (e.g., loss or gain) of a cut as it travels through the segment. For example, characteristics such as a grade of the segment (e.g., a higher grade may cause a lesser loss of energy or a greater gain of energy than a lower grade), distance or length of the segment (e.g., a longer segment may cause a greater loss of energy or a greater gain of energy than a shorter segment), temperature (e.g., higher temperature may remove more energy), wind (e.g., a headwind or stronger wind may cause greater loss of energy than a lighter wind), curves, track conditions, etc., may affect how well a cut may travel through a segment and thus, may affect the amount of energy lost or gained by the cut while traveling through the segment.

[0076] In embodiments, the track characteristics that may be used by speed control manager 120 to determine the energy change of a cut through a particular segment may include tuning coefficients that may be defined for the particular segment. The tuning coefficients may be used by speed control manager 120 (e.g., along with others of the track and/or cut characteristics) to calculate an amount of energy gained or lost by the cut at the particular segment of the route. In embodiments, the calculation of the amount of energy gained or lost by the cut at the particular segment of the route may include applying an equation that includes one or more of the tuning coefficients. In embodiments, the tuning coefficients may include coefficients related to rolling resistance, such as X.sub.0 (e.g., rolling resistance loss coefficient), X.sub.1 (e.g., rolling resistance loss first order coefficient, which may be related to energy loss due to rolling resistance for all cuts traveling through the segment), X.sub.2 (e.g., rolling resistance loss second order coefficient, which may be related to energy loss due to rolling resistance for the particular cut as it travels through the segment), X.sub.3 (e.g., rolling resistance loss third order coefficient), etc. In some embodiments, the tuning coefficients may include coefficients related to the effects on rolling resistance due to temperature, such as T.sub.0, T.sub.1, T.sub.2, and T.sub.3.

[0077] In embodiments, the tuning coefficients for a particular segments may be defined based on various factors. In embodiments, the various factors may include environmental conditions (e.g., weather, etc.), train car type, and/or bearing type. For example, a set of tuning coefficients for a particular segment may be specific to particular factors. In this case, for different factors, the set of tuning coefficients for the segment may be different. For example, a first set of tuning coefficients for a particular segment may be associated with a first set of factors (e.g., a first set of environmental conditions, train car type, and/or bearing type) for a segment. However, a second set of tuning coefficients (e.g., different from the first Set of coefficients) may be associated with a second set of factors (e.g., a second set of environmental conditions, train car type, and/or bearing type) for the segment. In this manner, the tuning coefficients may be applicable to particular situations and/or conditions. For example, a set of tuning coefficients may be used for dry conditions, whereas a second set of tuning coefficients may be used for wet conditions (e.g., rain or snow, and in some embodiments, the tuning coefficients used for rain conditions may be different from the tuning coefficients used for snow conditions), a third set of tuning coefficients may be used for cold weather conditions, and still a fourth set of tuning coefficients may be used for hot weather conditions. In some embodiments, a set of tuning coefficients may be associated with a combination of conditions, such that a set of tuning coefficients may be used for hot and dry conditions and a different set of tuning coefficients may be used for hot and wet conditions. It should be appreciated that using different sets of tuning coefficients provides a mechanism to further tune the classification yard by allowing speed control manager 120 to calculate energy losses or gains by a cut in a segment based on weather conditions and other factors providing a more accurate prediction.

[0078] In embodiments, speed control manager 120 may determine a maximum amount of energy removeable by retarders at each of the plurality of segments of the route based, at least in part, on retarder characteristics and/or cut characteristics, among other factors. Cut characteristics may include the weight of the cut (e.g., total car weight and/or average car weight), a number of axles in the cut, etc. Retarder characteristics may include a length of the retarder, where a longer retarder may be able to remove more energy from a cut than a shorter retarder. In some implementations, a retarder may include multiple sections, each section being configured to remove some energy from a cut. In these implementations, the retarder may be able to remove an amount of energy from a cut equivalent to the sum of the amounts of energy removable by the individual sections of the retarder. In some cases, some of the sections may be out of service and/or may be defective. In embodiments, retarder characteristics that speed control manager 120 may use to calculate the maximum energy that a retarder may be able to remove may include a type of the retarder (e.g., hydraulic, pneumatic, mechanic, magnetic, etc.), where different types of retarders may be able to remove different maximum amounts of energy from a cut.

[0079] Based on the retarder characteristics of retarders within a segment and/or cut characteristics, speed control manager 120 may determine the maximum amount of energy removable at the segment by the retarders. As this calculations are performed uphill, speed control manager 120 may determine the maximum amount of energy removable at each of the segments by the retarders within each of the segments. Speed control manager 120 may then calculate the total amount of energy removable by the retarders from the total route of the cut based, at least in part, on the maximum energy removable at each of the segments of the route.

[0080] At block 308, speed control manager 120 may determine a release speed. In embodiments, the release speed may be set to a current or a preset hump speed (e.g., the speed at which the push engine is set to push the cut over the hump). In some embodiments, speed control manager 120 may calculate the release speed based on the coupling speed and the calculated energy losses or gains at each of the segment of the route. The release speed of the cut may be used to calculate a hump push engine speed (e.g., a speed at which the hump push engine should push the cut through the hump to ensure the target release speed). In some embodiments, TPC 160 may send a request (e.g., to a hump push engine speed controller) for the target release speed and/or the requested hump push engine speed.

[0081] At block 310, speed control manager 120 may determine a total energy needed to be removed by the retarders throughout the route. In embodiments, speed control manager 120 may calculate, based on the requested release speed of the cut (e.g., at the release point on the hump) and the target coupling speed, the total energy that needs to be removed from the cut throughout the route in order to reach the coupling speed at the coupling point. Once the total energy needed to be removed by the retarders throughout the route is calculated by speed control manager 120, speed control manager 120 may allocate a respective amount of energy to be removed by each individual retarder within the route of the cut. In some embodiments, speed control manager 120 may distribute the total energy needed to be removed from the cut through the route to the retarders available in the route based on the maximum amount of energy removable by each individual retarder to load balance.

[0082] In some embodiments, speed control manager 120 may distribute the total energy to be removed from the cut through the route the individual segments of the route. For example, speed control manager 120 may determine and/or allocate a respective amount of energy to be removed by each segment of the route, where the total amount of energy allocated to the segments equals the total energy to be removed from the route. In these embodiments, prediction manager 121 may allocate a respective amount of energy to be removed by each retarder within each segment such that the sum of energy removed by all retarders within a segment equal the amount of energy to be removed by the segment. This functionality of speed control manager 120 may enable TPC 160 to allocate an amount of energy to be removed from a cut by each segment of a route to ensure that the cut reaches the target coupling speed at the coupling point at the end of the route.

[0083] At block 312, speed control manager 120 may predict arrival times and/or speeds of the cut at each of a plurality of points (e.g., segments and/or hardware devices) throughout the route of cut. For example, speed control manager 120 may predict arrival times and/or speeds of the cut at each segment (e.g., arrival time and/or speed at the entry point and/or exit point of one or more segments of the route) and/or arrival times and/or speeds of the cut at each of one or more hardware devices in the route. In embodiments, speed control manager 120 may predict an amount of energy to be removed at one or more segments of the route.

[0084] In embodiments, speed control manager 120 may predict arrival times and/or speeds of the cut at each of a plurality of points throughout the route of cut by calculating the arrival time and/or speed of the cut at each of the plurality of points based on the energy calculations and energy removal calculations at blocks 302-312. In some embodiments, the calculations of speed control manager 120 may yield energy calculations (e.g., the energy of the cut at each of the plurality of points. In these embodiments, the energy predictions may be converted to speed predictions associated with the front and/or rear of the cut. In embodiments, the calculations of speed control manager 120 to predict the arrival times and/or speeds of the cut at each of a plurality of points may be performed in a downhill manner. For example, speed control manager 120 may first calculate arrival times and/or speeds of the cut starting at the hump crest, continuing with a segment, or device, next to the hump crest in the direction of the coupling point, and continuing with a segment and/or device along the route in the direction of the coupling point. In this manner, the calculations to predict arrival times and/or speeds is performed downhill by speed control manager 120.

[0085] In embodiments, speed control manager 120 may calculate the arrival times and/or speeds of the cut at each of the segment by determining, for each segment, the energy change by the cut through the segment, based on the cut characteristics, track characteristics, and/or retarder energy removal associated with the segment, as described above. For example, speed control manager 120 may calculate, for a particular segment, the energy change in the segment due to cut characteristics (e.g., weight, type, etc.), the energy change due to track characteristics (e.g., due to segment grade, segment distance, weather conditions, etc.), energy change due to frictional resistance and/or temperature, and energy to be removed from the segments by the retarders. In addition, speed control manager 120 may calculate the arrival times and/or speeds of the cut at the segment based on the tuning parameters associated with the segment, which may further refine a prediction of the energy change due to friction in the segment. These calculations may enable speed control manager 120 to determine a speed of the cut at the entry point and/or the exit point of each segment of the route of the cut (and or of each device within the route).

[0086] As will be appreciated, the results of the operations in FIG. 3 may include a prediction of arrival times and/or speeds of one or more cuts at various points (e.g., devices and/or segments) along the route that a cut may travel along the marshalling tracks of classification yard 140 to reach their assigned destination train at the target coupling speed. In embodiments, the predictions associated with each segment may be stored in a database (e.g., database 155). In this manner, the functionality of speed control manager 120 to predict arrival times and/or speeds of the one or more cuts at various points along the route may be leveraged by TPC 160 to determine a release speed of a cut during humping (e.g., classification), to determine a target coupling speed of the cut at the coupling point, and/or to control the speed of the cut through the marshalling tracks of classification yard 140 (e.g., by determining an amount of energy to be removed from the cut as the cut travels through classification yard 140) in order to arrive at the coupling point at the target coupling speed.

[0087] With reference back to FIG. 2, in embodiments, TPC 160 may be configured to facilitate operations for automatic tuning of parameters for controlling operations of classification yard 140. In particular embodiments, TPC 160 may be configured to leverage the functionality of autotuning manger 121. Autotuning manager 121 may be configured to automatically tune coefficients (e.g., tuning coefficients) that may be used to calculate or predict the movement (e.g., speed and/or arrival time) of a cut through segments or devices of classification yard 140.

[0088] For example, in embodiments, autotuning manger 121 may be configured to automatically tune the tuning coefficients of a segment or device by obtaining a set of car events (where each car event represents a cut passing through the segment or device, and where each car event includes information related to the actual speed or energy of the cut while passing through the segment or device), where each car event is associated with a prediction (e.g., a prediction determined by speed control manager regarding the speed at which the associated cut would pass the segment or device based on the current production tuning coefficients), generating a candidate set of tuning coefficients associated with the segment or device based on the actual speed measurements included in the car events associated with the segment or device, using the candidate set of tuning coefficients to generate a set of backoffice predictions associated with the car events at the segment or device, and comparing the predictions generated by speed control manager 120 with the set of backoffice predictions to determine whether the predictions generated by speed control manager 120 or the candidate set of tuning coefficients for the segment or device yields more accurate predictions for car events at the segment or device. Autotuning manager may be configured to determine to replace the current production tuning coefficients for the segment or device with the candidate set of tuning coefficients in response to a determination that the set of tuning coefficients yields more accurate predictions for car events at the segment or device than the current production tuning coefficients. It is noted that the functionality of autotuning manager 121 may leverage functionality of backoffice 165, as will be described in more detail below.

[0089] In embodiments, obtaining the set of car events may include obtaining the car events from a database (e.g., database 155). In embodiments, the predictions generated by speed control manager 120 may include predictions of the speed at which a cut may arrive at a particular segment or device based on tuning coefficients associated with the segment or device. A prediction may be generated for each cut passing through the segment or device. Each cut passing through the segment or device may represent a car event, and the predicted speed for each of the cuts may be associated in the car event for the segment or device. In this manner, each car event in the set of car events may represent a cut passing through the segment or device, and each car event may be associated with a prediction. In addition, as cuts travel through the segments or devices of classification yard 140, measurements of the actual speed of the cut are taken (e.g., using hardware devices, such as wheel detectors, retarder radars, etc.) The real-world data of the actual speed of a cut passing through a segment or device may be included with the car event associated with the cut passing through the segment or device. In this manner, each car event in the set of car events includes information related to the actual speed or energy of the cut while passing through the segment or device.

[0090] In embodiments, generating a candidate set of tuning coefficients associated with the segment or device based on the actual speed measurements included in the set of car events associated with the segment or device may include applying regression analysis to the set of car events to obtain one or more candidate tuning coefficients. In embodiments, the generation of the candidate set of tuning coefficients may be performed using the functionality of backoffice regression manager 125. Backoffice regression manager 125 may be configured to perform analysis of the set of car events to obtain one or more candidate tuning coefficients, for one or more segments and/or devices within classification yard 140.

[0091] In embodiment, the analysis performed by backoffice regression manager 125 may include regression analysis (e.g., linear regression analysis), machine learning analysis (e.g., machine learning algorithms and/or models), etc., and/or any other analytical framework or scheme that may be configured to provide a predictive model associated with the set of car events. For example, the analysis performed by backoffice regression manager 125 may generate one or more suggestions for one or more tuning coefficients associated with the set of car events.

[0092] In embodiments, each car event of the set of car events may include information associated with the segment and/or device at which the car event occurred, as well as an indication of the speed, energy, and or arrival time of the cut for which the car event was generated at the device or segment. Backoffice regression manager 125 may use this information in the set of car events to obtain candidate tuning coefficients. For example, one or more segments or devices of classification yard 140, for which car events may be included in the set of car events, may be associated with a set of tuning coefficients (e.g., rolling resistance coefficients, temperature coefficients, angle coefficients, switch coefficients, retarder coefficients, detector coefficients, etc.) that may be used to define the amount of energy or speed that may be expected, gained, or lost by a cut traveling through the one or more segments or devices. In one example, the rolling resistance tuning coefficients of a segment may be determined by applying a best fit line to a set of car events at a segment and setting the rolling resistance tuning coefficients based on the values of the best fit line.

[0093] In embodiments, backoffice regression manager 125 may be configured to apply regression to the set of car events associated with the segment or device to obtain the candidate set of tuning coefficients for the segment or device. For example, backoffice prediction manager 126 may apply a best fit line analysis to the set of car events associated with the segment or device and may set the candidate tuning coefficients of the segment based on the values (e.g., slope and y-intercept) of the best fit line of the set of car events for the segment or device. The same may be performed for all segments and/or devices for which car event data is present in the set of car events.

[0094] In a particular example, a first segment may have a set of tuning coefficients (e.g., rolling resistance coefficients, temperature coefficients, angle coefficients, etc.) assigned to the segment. In this example, the production set of tuning coefficients may be used (e.g., by speed control manager 120) to make predictions regarding the speed and/or arrival time of a cut passing through the first segment. Further in this example, a set of car events may be obtained that may include a plurality of car events associated with the first segment (e.g., car events representing various cuts passing through the first segment). The plurality of car events may include information regarding the amount of energy lost or gained for each car event at the first segment, which may include real-world measurements of actual speed and/or energy as well as real-world timestamps. In this example, backoffice regression manager 125 may generate a candidate set of tuning coefficients for the first segment based on analysis (e.g., regression and/or machine learning analysis) of the plurality of car events associated with the first segment. In some embodiments, the candidate set of tuning coefficients for the first segment my include rolling resistance coefficients (X.sub.0, X.sub.1, X.sub.2, X.sub.3, etc.). In some embodiments, backoffice regression manager 125 may generate, for each car event associated with a segment, a set of tuning coefficients based on the data in the respective car event to generate a plurality of sets of tuning coefficients, each set of tuning coefficients in the plurality of sets of tuning coefficients associated with a car event for the segment. Backoffice regression manager 125 may then aggregate or average the plurality of sets of tuning coefficients into a single candidate set of tuning coefficients for the segment.

[0095] In embodiments, using the candidate set of tuning coefficients generated by backoffice regression manager 125 to generate a set of backoffice predictions associated with the car events at the segment or device may include leveraging the functionality of backoffice prediction manager 126. Backoffice prediction manager 126 may be configured to generate, based, at least in part, on the candidate set of tuning coefficients (e.g., the set of tuning coefficients generated by backoffice regression manager 125), for each segment represented in the set of car events, a backoffice prediction of arrival times and/or speeds for each cut represented in each of the car events in the set of car events at each segment. For example, for a first segment represented in the set of car events, backoffice prediction manager 126 may generate a backoffice prediction of arrival times and/or speeds for each car event associated with the first segment (e.g., for each cut that passed through the first segment generating a car event for the segment) based on the candidate set of tuning coefficients generated for the first segment. In another example, for a second segment represented in the set of car events, backoffice prediction manager 126 may generate a backoffice prediction of arrival times and/or speeds for each car event associated with the second segment (e.g., for each cut that passed through the second segment generating a car event for the segment) based on the candidate set of tuning coefficients generated for the second segment.

[0096] In embodiments, a backoffice prediction generated by backoffice prediction manager 126 may represent an after-the-fact prediction, in that the arrival times and/or speeds of a cut at a particular device or segment may be made by backoffice prediction manager 126 using tuning coefficients generated based on real-world data measured during car events that occurred at the segment or device. In this manner, the backoffice prediction associated with a car event is made after the car event has already happened.

[0097] In embodiment, autotuning manager 121 may be configured to compare the predictions generated by speed control manager 120 (e.g., using the current set of production tuning coefficients) with the backoffice predictions generated by backoffice prediction manager 126 (e.g., using the candidate set of tuning coefficients) for a segment or device to determine whether the predictions generated by speed control manager 120 or the backoffice predictions generated by backoffice prediction manager 126 yields more accurate predictions for car events at the segment or device. Because the predictions generated by speed control manager 120 are generated using the current set of production tuning coefficients and the backoffice predictions generated by backoffice prediction manager 126 are generated using the candidate set of tuning coefficients, autotuning manager 121 determines that the more accurate predictions may be associated with better tuned tuning coefficients. In this manner,

[0098] In embodiments, functionality of autotuning manager 121 to compare the production predictions generated by speed control manager 120 and the backoffice predictions generated by backoffice prediction manager 126 may include comparing the production predictions and the backoffice predictions related to a car event (e.g., speed and/or arrival times of a cut at a particular segment or device) with real-world measurements related to the car event, to determine whether the production predictions or the backoffice predictions more accurately reflect the real-world measurements.

[0099] In embodiments, autotuning manager 121 may employ one or more approaches when comparing the production predictions and the backoffice predictions related to a car event with the real-world measurements related to the car event. For example, autotuning manager 121 may compare the production predictions and the backoffice predictions related to a car event with the real-world measurements related to the car event using an absolute deviation analysis, variance analysis, r-squared deviation analysis, etc. In particular, in the absolute deviation analysis, autotuning manager 121 may, for each car event associated with a segment or device, measure an absolute value deviation between a predicted value and the real-world measured value for each car event, and may then average the absolute value deviations over all the car events associated with the segment or device to obtain an absolute value average difference for the segment or device, which may represent an absolute value average difference of the predictions for all car events associated with the segment or device.

[0100] For example, autotuning manager 121 may, for each production prediction associated with a car event (e.g., for each production prediction associated with a car event at a segment or device in the set of car events), obtain an absolute value deviation between the predicted values (e.g., production predicted speed and/or arrival time of the corresponding cut at the corresponding device or segment associated with each car event) and the real-world measurements related to the car event (e.g., actual speed and/or arrival time of the corresponding cut at the corresponding device or segment). In embodiments, the absolute value deviation between a predicted value and the real-world measurement may include an absolute difference between the predicted value and the real-world measurement. The result may include an absolute value deviation value for each car event associated with a segment or device, representing the deviation between the production predicted value and the actual value measured. In embodiments, autotuning manager 121 may average, for each segment or device, the absolute value deviations for all car events associated with the segment or device into a production absolute value average difference for the segment or device. The result may include a production absolute value average difference associated with the segment or device.

[0101] In embodiments, autotuning manager 121 may, for each backoffice prediction associated with a car event (e.g., for each backoffice prediction associated with a car event at a segment or device in the set of car events), obtain an absolute value deviation between the predicted values (e.g., backoffice predicted speed and/or arrival time of the corresponding cut at the corresponding device or segment associated with each car event) and the real-world measurements related to the car event (e.g., actual speed and/or arrival time of the corresponding cut at the corresponding device or segment). The result may include an absolute value deviation value for each car event associated with a segment or device, representing the deviation between the backoffice predicted value and the actual value measured. In embodiments, autotuning manager 121 may average, for each segment or device, the absolute value deviations for all car events associated with the segment or device into a backoffice absolute value average difference for the segment or device. The result may include a backoffice absolute value average difference associated with the segment or device.

[0102] Autotuning manager 121 may be configured to determine to replace the current production set of tuning coefficients for the segment or device with the candidate set of tuning coefficients in response to a determination that the candidate set of tuning coefficients yields more accurate predictions for car events at the segment or device than the current production set of tuning coefficients. For example, autotuning manager 121 may determine, based on the respective absolute value average difference, which of the current production set of coefficients and the candidate set of coefficients provides a more accurate prediction with respect to real-world measurements. For example, in embodiments, autotuning manager 121 may compare the production absolute value average difference to the backoffice absolute value average difference for a first segment or device, and may determine to either replace the current production set of tuning coefficients with the candidate set of tuning coefficients or to reject the candidate set of tuning coefficients and continue using the current production set of tuning coefficients based on which of the current production set of tuning coefficients and the candidate set of tuning coefficients provides a more accurate prediction with respect to real-world measurements. For example, autotuning manager 121 may determine to continue using the current production set of tuning coefficients for a segment or device in operations of classification yard 140 (e.g., operations to calculate and/or predict speed, energy, and/or arrival times at the first segment or device) and to reject the candidate set of tuning coefficients in response to a determination that the current production set of tuning coefficients provides a more accurate prediction of car events at the segment or device than the candidate set of tuning coefficients (e.g., in response to a determination that the production absolute value average difference is not larger than the backoffice absolute value average difference). On the other hand, autotuning manager 121 may determine to replace the current production set of tuning coefficients for the segment or device with the candidate set of tuning coefficients in response to a determination that the candidate set of tuning coefficients provides a more accurate prediction of car events at the segment or device than the current production set of tuning coefficients (e.g., in response to a determination that the backoffice absolute value average difference is larger than the production absolute value average difference).

[0103] In embodiments, a similar process may be performed for the other segments and/or devices represented in the set of car events to determine whether the current production set of tuning coefficients may continue to be used or whether the candidate set of tuning coefficients may be deployed to replace the current production set of tuning coefficients.

[0104] In embodiments, continuing to use the current production set of tuning coefficients or deploying the candidate set of tuning coefficients to replace the current production set of control parameters for a segment or device may be performed automatically by autotuning manger 121. In these embodiments, autotuning manger 121 may either replace the current production set of tuning coefficients for a segment or device in a database (e.g., database 155) with the candidate set of tuning coefficients (e.g., when deploying the candidate set of tuning coefficients), or may leave the current production set of tuning coefficients for the segment or device in the database.

[0105] In embodiments, TPC 160 may be configured to facilitate operations for determining a status of hardware devices of classification yard 140. In particular embodiments, TPC 160 may be configured to leverage the functionality of device monitor 122. Device monitor 122 may be configured to determine, based on a set of event data associated with a hardware device, the performance of the hardware device during operations of classification yard 140.

[0106] In embodiments, device event data may be generated during operations hardware devices of classification yard 140. For example, the hardware devices (e.g., devices 142) of classification yard 140 may perform operations (e.g., switching operations, speed detection operations, energy removal operations), and each hardware device operation may generate a device event. For example, an event may include a detector detecting the speed and/or arrival time of a cut passing through the detector, and this may generate a device event (e.g., a detector event). In another example, an event may include a cut passing through a retarder and the retarder operating to reduce the speed of the cut as the cut passes through the retarder, and this may generate a device event (e.g., a retarder event). In still another example, an event may include a switch being thrown from a first position (e.g., right or left) to a second position (e.g., left or right), and this may generate a device event (e.g., a switch event). In embodiments, the device event data may be collected and stored in a database (e.g., database 155).

[0107] In embodiments, the device event data may include real-world actual measurements of the various device events. For example, a device event may include a measured speed of a cut at a device, an amount of pressure applied by a retarder device against the wheels of the cut to remove an amount of energy necessary to reach the requested exit speed, a distribution of the amount of pressure applied by the retarder device at each section of the retarder, a speed of the cut at each of the sections of the retarder device, an actual switch throw time (e.g., the actual throw time of a switch event), etc.

[0108] In embodiments, the device event data may include expected measurements for each device event. For example, during operations, predictions of the speed and/or arrival times of a cut at a hardware device (e.g., a retarder device, detector device, a switch device, etc.) may be made during classification operations of a classification yards. For example, a production prediction manager may generate production predictions (e.g., using control parameters including tuning coefficients, characteristics of the cut, characteristics of the track or device, etc.) of a speed and/or arrival times of a cut at a particular hardware device. In embodiments, the production predictions may include production predictions for a plurality of cuts scheduled to be classified through the classification yard and for many devices (e.g., switches, detectors) and/or segments of the classification yard. In some embodiments, expected results of hardware device operations (e.g., device events) may include an expected measurement related to the device event, such as a throw switch time for a switch event, an expected exit speed at a retarder, a predicted speed at a detector, etc. For example, a speed detection event at a wheel detector may be associated with a predicted speed. For example, as described above, speed control manager 120 may generate predictions of a cut at various points along the route of the cut. At a particular wheel detector, a car event may be associated with a predicted speed. In this manner, when the car event occurs (e.g., when the cut is detected at the wheel detector) an expected speed measurement may be the predicted speed. It is noted that the wheel detection event may also be associated with a real-world, actual speed measurement, as described above. In this manner, a device event may have at least an expected measurement and an actual measurement associated therewith.

[0109] In embodiments, device monitor 122 may be configured to analyze a set of device events associated with a hardware device to determine a status of the hardware device. In embodiments, the analysis applied by device monitor 122 may include a thresholding analysis. The thresholding analysis may include comparing the differences between the expected and the actual measurements in the device events associated with the hardware device with one or more thresholds. The results of the comparison may be used to determine whether the hardware device is operating with an expected performance, or whether the device is degraded or bad.

[0110] For example, a set of differential rules may be applied by device monitor 122 to the set of device events associated with a hardware device to determine a status of the hardware device. Applying the set of differential rules may include first obtaining a set of differences associated with the set of device events. In this case, for each device event in the set of device events, the difference between the expected measurement and the actual measurement for the device event is obtained. The result is the set of difference associated with the hardware device. The set of differential rules is then applied to the set of differences.

[0111] In embodiments, the set of differential rules may include a first rule that determines the status of a hardware device based on whether a threshold percentage of the set of differences are outside the range defined between [(+) expected measurement and () expected measurement]. When the percentage of the set of differences that are outside the range defined between [(+) expected measurement and () expected measurement-exceeds the threshold percentage, the hardware device is determined to be degraded or bad. Otherwise, the hardware device is determined to be good.

[0112] In embodiments, a second differential rule may include a rule that determines the status of a hardware device based on whether a median and/or average of the set of differences associated with the hardware device is outside of a range defined by plus or minus an average threshold. When the median and/or average of the set of differences associated with the hardware device is outside of a range defined by plus or minus an average threshold, the hardware device is determined to be degraded or bad. Otherwise, the hardware device is determined to be good.

[0113] In embodiments, a third differential rule may include a rule that determines the status of a hardware device based on whether a defined middle percentage of the measurement differences associated with the hardware device is not spread over a spread range greater than a spread threshold. In embodiments, the defined middle percentage may be defined as a threshold and may include a range of measurement differences values defined by a top percentile threshold and a bottom percentile threshold. For example, a defined middle percentage may include a range of 50% of the measurement differences associated with the hardware device, and may be defined as the range between the bottom 25th percentile and the top 75th percentile. When the defined middle percentage of the measurement differences associated with the hardware device is determined to be spread over a spread range greater than the spread threshold, the hardware device is determined to be degraded or bad. Otherwise, the hardware device is determined to be good.

[0114] In embodiments, a fourth differential rule may include a weighted combination of the first, second, and third differential rules. In embodiments, device monitor 122 may apply the fourth differential rule by combining the results of the first, second, and third differential rules in a weighted combination. The weighted combination may include applying a respective multiplier to the results of each of the first, second, and third differential rules. For example, weighted factors of 3, 2, and 2.25 may be configured for the results of the first, second, and third differential rules, respectively. In this example, device monitor 122 may apply the respective weighted factor to each of the first, second, and third differential rules and may then sum the results to obtain a single result for the combination rule. In embodiments, applying the respective weighted factor to each of the first, second, and third differential rules may include raising each of the of the first, second, and third differential rules results by a power equal to the respective weighted factors. For example, in this example, the results of the first rule may be raised to a power of 3, the results of the second rule may be raised to a power of 2, and the results of the third rule may be raised to a power of 2.5. In this example, the results of each exponential operation may be summed to obtain an overall result for the fourth rule. In some embodiments, as the results of the first, second, and third differential rules may include a percentage value, the result of the combination rule (e.g., fourth differential rule) may also be a percentage value.

[0115] In embodiments, other sets of rules may be applied in the thresholding analysis. For example, utilization rules that may apply thresholding rules to the set of utilization metrics may be applied to determine the status of a hardware device. These rules may determine how close the utilization metrics are to expected values, whether the utilization metrics are below expected values, and/or the spread of the utilization metric values, in order to determine whether the utilization metric data indicates that a hardware device is degraded or not.

[0116] In embodiments, TPC 160 may be configured to facilitate operations for visualizing event data associated with operations of classification yard 140. In particular embodiments, TPC 160 may be configured to leverage the functionality of event replay manager 123. Event replay manager 123 may be configured to visualize event messages associated with operational events in an event log of operations of classification yard 140 and to replay the visualized event messages in a graphical user interface (GUI) provided to an operator.

[0117] Event replay manager 123 may be configured to obtain a set of event messages (e.g., event messages associated with car events and/or device events stored in database 155), where each event message may be associated with an event that occurred during operations of classification yard 140. For example, during operations of classification yard 140, various events may occur at the various components (e.g., devices, segments, software modules, etc.) of classification yard 140. The various events may include operational events occurring at the various components of classification yard 140, such as switch activations, wheel detections, retarder operations, cut speed measurements, notifications, a segment being occupied, a segment being cleared, a protection fault, a route calculation for a cut, and/or many other types of events. In embodiments, an event may include any operational event, action, operation, etc., that may be performed or may occur at a component (e.g., a device, segment, software module, etc.) of classification yard.

[0118] In embodiments, the event messages may be stored (e.g., in database 155) as an event log, in which each event message may be an entry into the event log. In embodiments, an event message may include a timestamp at which the event associated with the event message occurred, or a date/time at which the event message was generated and stored in the event log. An event message may also include a description and/or information related to the nature of the event associated with the event message (e.g., the event that caused the generation of the event message), as well as an identification of the source of the event message (e.g., device and/or segment at which the event occurred).

[0119] In embodiments, event replay manager 123 may be configured to obtain the set of event messages based on configuration parameters that may be specified by an operator. For example, in some embodiments, a GUI may be provided (e.g., via user terminal 130) to an operator, via which the operator may specify various of the configuration parameters that event message compiler 120 may use to compile the set of event messages.

[0120] FIG. 4 show an example of a graphical user interface (GUI) 400 configured to visualize and replay event messages in accordance with embodiments of the present disclosure. In particular, FIG. 4 shows GUI 400 as including graphical control elements that provide functionality for an operator to specify various of the configuration parameters that event message compiler 120 may use to compile the set of event messages to be visualized. For example, GUI 400 may include control elements 410 to enable an operator to specify a start date/time threshold and/or a stop date/time threshold for event messages to be compiled in the set of event messages. In this case, event messages with a timestamp earlier than the start date/time or later than the stop date/time may not be included in the set of event messages.

[0121] With additional reference to FIG. 2, in embodiments, event replay manager 123 may be configured to analyze the event messages in the set of event messages to determine a component (e.g., a device or segment) of classification yard 140 with which each event message may be associated. For example, a first event message in the compiled set of event messages may be analyzed to determine which device or which segment the event message is associated with. Event replay manager 123 may determine that the first event message may be associated with a particular switch device, a particular retarder, a particular wheel detector, a particular segment, etc. In each case, and for each event message in the set of event messages, event replay manager 123 may determine a component (e.g., device and/or segment) of classification yard 140 with which the event message may be associated.

[0122] Event replay manager 123 may be configured to generate a visualization of each event message that is to be replayed in the replay of event messages. In embodiments, visualizing an event message may include generating a visual representation of the event associated with the event message (e.g., the event that occurred at a component of the classification yard that caused the event message to be generated) and overlaying the visual representation of the event onto a graphical representation of the component found to be associated with the event message associated with the event. To that end, in embodiments, event replay manager 123 may generate a graphical schematic diagram of the classification yard. In embodiments, the graphical schematic diagram of the classification yard may include a graphical representation of one or more components (e.g., devices and/or track segments) of the classification yard. In embodiments, the graphical representation of the classification yard may be presented (e.g., displayed) to an operator, such as using GUI 400.

[0123] Graphical schematic diagram 460 of classification yard 140 may be generated by event replay manager 123 and presented to an operator (e.g., via GUI 400). In embodiments, graphical schematic diagram 460 may include a graphical representation of the various marshalling tracks of classification yard, where the various marshalling tracks are represented by line segments. In particular, graphical schematic diagram 460 may include a graphical representation of the hump section 450, including the hump approach section 452, the hump crest 454, and the release section 456. In embodiments, hump section 450 may graphically represent the physical hump section of classification yard 140. In embodiments, the dimensions in the graphical representation may not be to scale, but the location of each section in the graphical representation may be a representation relative to the physical layout of the hump section. In this manner, hump section 450 may be used to visualize events occurring at components or sections of the hump section and the line segments representing the marshalling tracks may be used to visualize events occurring at components or sections on the marshalling tracks.

[0124] In embodiments, the marshalling tracks of classification yard 140 may be divided into segments. In embodiments, the line segments representing the marshalling tracks in the graphical representation may be divided into segments as well. In embodiments, the track segments may be defined by switch/wheel detector devices. For example, at switch/wheel detector 485, a segment may be divided into segment 486 and segment 487. In this manner, a cut traveling through the hump track may be routed (e.g., by switch/wheel detector 485) to one of either segment 486 or segment 487, depending on the position of switch/wheel detector 485. In this example, each of the segments may be defined as the segment between two switch devices.

[0125] In embodiments, graphical schematic diagram 460 may include a graphical representation of the various devices of classification yard 140. For example, a switch device, a detector device, and/or a switch/wheel detector device may be represented in graphical schematic diagram 460 by the identification of the device. For example, switch/wheel detector 485 may be represented by a numerical value indicating the identification of switch/wheel detector 485 (e.g., S2 in this example). In this manner, a device may be represented in the graphical schematic diagram 460 by its identification. In embodiments, a device may additionally or alternatively, by a special graphical features. For example, in graphical schematic diagram 460, retarder devices may be represented by rectangles. For example, retarder 458 may be represented by a rectangle that is slightly larger than the line segment in which it may be located. In embodiments, destination coupling points (e.g., destination trains to which cuts may be routed) may be represented by a number associated with the destination train.

[0126] In embodiments, the graphical representation in graphical schematic diagram 460 of the various devices of classification yard 140 may be positioned within graphical schematic diagram 460 in positions relative to each other that represents the relative position of the physical devices in the physical layout of classification yard 140. In this manner, even if the distances between the graphical representation of the devices is not to scale, the relative position reflects the real-world relative position of the various devices.

[0127] In embodiments, event replay manager 123 may be configured to generate, for each event message in the set of event messages, a visual representation of the event associated with each of the event messages in the set of event messages and to overlay the visual representation generated for each event onto the graphical representation of the component found to be associated with each event message associated with the event to visualize each event. In this manner, the visual representation for an event may be shown in relation to the component in which the event occurred.

[0128] In embodiments, the visual representation of an event may include a color-based or highlight-based indication to represent the event. For example, a route schedule event may include determining a series of connected segments (e.g., a route) along which a cut being marshalled through the hump is to travel to reach the assigned destination track and train, and may be represented with a highlighting the scheduled route (e.g., the track segments making up the route) in a particular color (e.g., green) to indicate a scheduled route. In embodiments, a visual representation of an occupancy event (e.g., an event in which a track segment is found to be occupied by a cut, such as based on a detection of a cut at a wheel detector positioned at the entry point of the segment) may include highlighting the occupied track segment in a particular color (e.g., red) to indicate that the track segment is occupied. For example, segment 486 has been highlighted in a particular color (e.g., red) to indicate that segment 486 is occupied. In some embodiments, events related to a track segment being closed (e.g., an event in which the result is that a track segment cannot be traversed by a cut, such as by a defective component within the segment, a closed retarder, etc.) may also be represented visually using a color-coded indication (e.g., red or yellow).

[0129] In embodiments, a visual representation of an event may include a representation of an identification of a component. For example, in embodiments, an event may include an identification of a cut being detected at an identification detector. In these embodiments, a visual representation of the identification event may include displaying a message indicating the identification of the detected cut.

[0130] In embodiments, a visual representation of an event may include a numerical representation associated with the event. In embodiments, the numerical representation may include a detected speed of a cut. For example, in embodiments, an event may include a speed calculation event for a detected cut. In these embodiments, a visual representation of the identification event may include displaying a numerical representation of the speed of the detected cut based on the calculated speed in the event message associated with the event. For example, speed indication 489 may be a representation of a speed calculation event at wheel detector 485 (e.g., detecting a speed of 8.1 MPH of the cut passing through wheel detector 485). In embodiments, the numerical representation may include an indication of a specific wheel of a cut detected. For example, in embodiments, an event message associated with a wheel detection event may include an indication of the specific wheel of the cut detected (e.g., may include an indication that the fourth wheel of eight total wheels of the cut was detected). In this case, a visual representation of the wheel detection event may include displaying a numerical representation of the specific wheel of the cut detected based on the indication in the event message associated with the event. For example, wheel indication 488 may be a representation of a wheel detection event at wheel detector 485 (e.g., detecting wheel number 5 of the cut passing through wheel detector 485).

[0131] It is noted that the description herein with respect to visual representations that may be generated for various events is not intended to be exhaustive and/or limiting in any way. Indeed, many other types of visual representations for many other types of events may be generated, and the present description is intended to be illustrative and not limiting.

[0132] In embodiments, event replay manager 123 may be configured to display the overlays generated for each of the event messages in the compiled set of event messages sequentially, based on the timestamps associated with each event message, to replay the visualized events associated with the event messages in the graphical user interface. In this manner, an operator may view the visual representations of the events associated with the event messages in the compiled set of event messages in the order that the events occur at the various components of the classification yard. In embodiments, replay controls 490 may be provide a control mechanism that may be provided to the operator for controlling the replay speed, the number of events that are played each time the replay is played, for pausing the replay, for rewinding the replay, etc.

[0133] FIG. 5 shows a high-level flow diagram 500 of operation of a system configured for providing management of a classification yard in accordance with embodiments of the present disclosure. For example, the functions illustrated in the example blocks shown in FIG. 5 may be performed by system 100 of FIG. 1 according to embodiments herein. In embodiments, the operations of the method 500 may be stored as instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of the method 500.

[0134] At block 502, one or more of a release speed, coupling speed, and a predicted speed of the cut at one or more points of a route along which the cut is traveling to reach the destination train is determined for a cut being routed to a destination train. In embodiments, each in a set of event messages represents an event that occurred during the traveling of the cut along the route to reach the destination train. In embodiments, functionality of a speed control manager (e.g., speed control manager 120) may be used to determine, for a cut being routed to a destination train, one or more of a release speed, coupling speed, and a predicted speed of the cut at one or more points of a route along which the cut is traveling to reach the destination train. In embodiments, the speed control manager may perform operations to determine, for a cut being routed to a destination train, one or more of a release speed, coupling speed, and a predicted speed of the cut at one or more points of a route along which the cut is traveling to reach the destination train according to operations and functionality as described above with reference to speed control manager 120 and as illustrated in FIGS. 1-4.

[0135] At block 504, real-world measurements associated with an actual speed of the cut at the one or more points of the route are obtained. In embodiments, functionality of a TPC (e.g., TPC 160) may be used to obtain real-world measurements associated with an actual speed of the cut at the one or more points of the route. In embodiments, the TPC may perform operations to obtain real-world measurements associated with an actual speed of the cut at the one or more points of the route according to operations and functionality as described above with reference to TPC 160 and as illustrated in FIGS. 1-4.

[0136] At block 506, one or more coefficients associated with the predicted speed of the cut at the one or more points of the route are autotuned based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route. In embodiments, functionality of an autotuning manager (e.g., autotuning manager 121) may be used to autotune one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route. In embodiments, the autotuning manager may perform operations to autotune one or more coefficients associated with the predicted speed of the cut at the one or more points of the route based on the real-world measurements associated with the actual speed of the cut at the one or more points of the route according to operations and functionality as described above with reference to autotuning manager 121 and as illustrated in FIGS. 1-4.

[0137] At block 508, a status of one or more devices used to route the cut to the destination train is determined based, at least in part, on thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route. In embodiments, functionality of a device monitor (e.g., device monitor 122) may be used to determine a status of one or more devices used to route the cut to the destination train based, at least in part, on thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route. In embodiments, the device monitor may perform operations to determine a status of one or more devices used to route the cut to the destination train based, at least in part, on thresholding analysis applied to the real-world measurements associated with the actual speed of the cut at the one or more points of the route according to operations and functionality as described above with reference to device monitor 122 and as illustrated in FIGS. 1-4.

[0138] At block 510, the set of event messages are stored in an event log for subsequent replaying in a GUI. In embodiments, functionality of a event replay manager (e.g., event replay manager 123) may be used to store the set of event messages in an event log for subsequent replaying in a GUI. In embodiments, the event replay manager may perform operations to store the set of event messages in an event log for subsequent replaying in a GUI according to operations and functionality as described above with reference to event replay manager 123 and as illustrated in FIGS. 1-4.

[0139] Persons skilled in the art will readily understand that advantages and objectives described above would not be possible without the particular combination of computer hardware and other structural components and mechanisms assembled in this inventive system and described herein. Additionally, the algorithms, methods, and processes disclosed herein improve and transform any general-purpose computer or processor disclosed in this specification and drawings into a special purpose computer programmed to perform the disclosed algorithms, methods, and processes to achieve the aforementioned functionality, advantages, and objectives. It will be further understood that a variety of programming tools, known to persons skilled in the art, are available for generating and implementing the features and operations described in the foregoing. Moreover, the particular choice of programming tool(s) may be governed by the specific objectives and constraints placed on the implementation selected for realizing the concepts set forth herein and in the appended claims.

[0140] The description in this patent document should not be read as implying that any particular element, step, or function can be an essential or critical element that must be included in the claim scope. Also, none of the claims can be intended to invoke 35 U.S.C. 112 (f) with respect to any of the appended claims or claim elements unless the exact words means for or step for are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) mechanism, module, device, unit, component, element, member, apparatus, machine, system, processor, processing device, or controller within a claim can be understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and can be not intended to invoke 35 U.S.C. 112 (f). Even under the broadest reasonable interpretation, in light of this paragraph of this specification, the claims are not intended to invoke 35 U.S.C. 112 (f) absent the specific language described above.

[0141] The disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. For example, each of the new structures described herein, may be modified to suit particular local variations or requirements while retaining their basic configurations or structural relationships with each other or while performing the same or similar functions described herein. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive. Accordingly, the scope of the inventions can be established by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Further, the individual elements of the claims are not well-understood, routine, or conventional. Instead, the claims are directed to the unconventional inventive concept described in the specification

[0142] Those of skill in the art would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. Skilled artisans will also readily recognize that the order or combination of components, methods, or interactions that are described herein are merely examples and that the components, methods, or interactions of the various embodiments of the present disclosure may be combined or performed in ways other than those illustrated and described herein.

[0143] Functional blocks and modules in FIGS. 1-5 may comprise processors, electronics devices, hardware devices, electronics components, logical circuits, memories, software codes, firmware codes, etc., or any combination thereof. Consistent with the foregoing, various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0144] The steps of a method or algorithm described in connection with the disclosure herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal, base station, a sensor, or any other communication device. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0145] In one or more exemplary designs, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. Computer-readable storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, a connection may be properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL), then the coaxial cable, fiber optic cable, twisted pair, or DSL, are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0146] Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods, and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.