Systems and Methods for Determining Railroad Track Locations at Risk for Buckling

The present disclosure relates generally to railroad tracks, and more particularly to systems and methods for determining railroad track locations at risk for buckling.

Rail transport systems traverse entire continents to enable the transport and delivery of passengers and goods throughout the world. A quintessential component of railroad infrastructure is the track. Laid over a myriad of geographies and terrains, railroad tracks are designed to withstand the worst of the elements and facilitate disbursement of locomotives throughout the railroad system. Because of this constant exposure of the tracks to hazardous conditions, railroad companies must be vigilant in maintaining track integrity. If a section of railroad track is compromised and the damage or obstruction is not quickly addressed, the consequences can be catastrophic.

The general structure of a track can include several components. Generally, a foundation referred to as the ballast, often composed of crushed stone, gravel, or other aggregate, provides a compacted pathway on which the track can be laid. The rails and ties of the railroad track are laid on top of the ballast. Rails afford an actual surface on which rail vehicle wheels can roll. The rails run parallel with one another for thousands of miles, and the wheel-span of rail vehicles are specially designed and sized to match the track footprint. If rails were to separate laterally, the results would be disastrous. As such, to maintain a consistent and uniform distance between the rails, lateral slat-like components called ties are disposed between and coupled to the rails. The ties can be wood, concrete, or any other suitable material, and the ties can be secured to or within the ballast to facilitate track stability. The ties serve the very important purpose of maintaining lateral tension between the rails, such that the extreme weight of rail traffic does not lead to rail separation.

Considering the dire consequences of disjunctive rails in a railroad track, an especially problematic type of track damage is what is known as rail buckling. A track buckle is when the rails of a track bend or shift out of place, such as due to longitudinal strain and/or pressure on the rails due to hot weather. Continuous welded rail (“CWR”), e.g., rail that is essentially a singular metal track stretching over several miles (which can be accomplished by welding rail segments together) is especially prone to rail buckling. For example, because of the sheer length of a given CWR section, normally-insignificant changes in rail span can be drastically compounded. For example, when a rail increases in temperature (e.g., such as due to weather conditions), linear expansion can occur. Such expansion, while negligible for smaller pieces of rail, can be feet or yards long for CWR sections that traverse several miles. These increases in length can cause significant longitudinal strain on the rail, and such strain, if unaddressed, can potentially overcome the strength of the lateral compression enabled by the ties, thereby resulting in a buckle. A rail buckle is a drastic angular alteration in track dimensions that can certainly lead to vehicle derailment if left unaddressed.

The present disclosure achieves technical advantages as systems, methods, and computer-readable storage media for determining railroad track locations at risk for buckling. The functionality for determining railroad track locations at risk for buckling is based on segmenting a railroad track into multiple segments and then calculating a priority level for each segment. The priority level indicates a track buckling risk severity for the particular railroad track segment.

In embodiments, the present disclosure provides for a system integrated into a practical application with meaningful limitations as a railroad track buckling risk prediction system with functionality for determining railroad track locations at risk for buckling. In embodiments, the railroad track buckling risk prediction system may be configured to access railroad track data and segment a railroad track into a plurality of railroad track segments. The railroad track buckling risk prediction system may be further configured to determine, from the railroad track data, a plurality of track features for the particular railroad track segment, determine a priority level for each particular railroad track segment, and display the determined priority levels for the plurality of track segments on an electronic display.

A technical improvement of the features provided herein includes automatically determining railroad track locations at risk for buckling. The disclosed embodiments contribute to the overall safety of railroad operations by preemptively preventing derailments due to track buckling. In addition, the system of embodiments can generate control signals for automatically switching trains away from tracks at high risk for buckling, thereby increasing the overall safety and efficiency of railroad operations.

Collectively, these technical improvements provided by the railroad track buckling risk prediction functionality of the present disclosure contribute to a more efficient, reliable, and safe railroad, capable of handling the complexities of modern freight transportation.

Thus, it will be appreciated that the technological solutions provided herein, and missing from conventional systems, are more than a mere application of a manual process to a computerized environment, but rather include functionality to implement a technical process to replace or supplement current manual solutions or non-existing solutions for determining railroad track locations at risk for buckling. In doing so, the present disclosure goes well beyond a mere application the manual process to a computer. Accordingly, the disclosure and/or claims herein necessarily provide a technological solution that overcomes a technological problem.

Furthermore, the functionality for determining railroad track locations at risk for buckling provided by the present disclosure represents a specific and particular implementation that results in an improvement in the utilization of a computing system for resource optimization. Thus, rather than a mere improvement that comes about from using a computing system, the present disclosure, in enabling a system to determine railroad track locations at risk for buckling, represents features that result in a computing system device that can be used more efficiently and is improved over current systems that do not implement the functionality described herein. As such, the present disclosure and/or claims are directed to patent eligible subject matter.

In embodiments, the present disclosure includes techniques for training models (e.g., machine-learning models, artificial intelligence models, algorithmic constructs, etc.) for performing or executing a designated task or a series of tasks (e.g., one or more features for determining railroad track locations at risk for buckling in accordance with embodiments of the present disclosure). The disclosed techniques provide a systematic approach for the training of such models to enhance performance, accuracy, and efficiency in their respective applications. In embodiments, the techniques for training the models may include collecting a set of data from a database, conditioning the set of data to generate a set of conditioned data, and/or generating a set of training data including the collected set of data and/or the conditioned set of data. In embodiments, that model may undergo a training phase wherein the model may be exposed to the set of training data, such as through an iterative processes of learning in which the model adjusts and optimizes its parameters and algorithms to improve its performance on the designated task or series of tasks. This training phase may configure the model to develop the capability to perform its intended function with a high degree of accuracy and efficiency. In embodiments, the conditioning of the set of data may include modification, transformation, and/or the application of targeted algorithms to prepare the data for training. The conditioning step may be configured to ensure that the set of data is in an optimal state for training the model, resulting in an enhancement of the effectiveness of the model's learning process. These features and techniques not only qualify as patent-eligible features but also introduce substantial improvements to the field of computational modeling. These features are not merely theoretical but represent an integration of a concepts into a practical applications that significantly enhance the functionality, reliability, and efficiency of the models developed through these processes.

In embodiments, the present disclosure includes techniques for generating a notification or an alert that includes information specifying the location of a source of data associated with an event, formatting the alert into data structured according to an information format, and transmitting the formatted alert over a network to a device associated with a receiver based upon a destination address and a transmission schedule. In embodiments, receiving the alert enables a connection from the device associated with the receiver to the data source over the network when the device is connected to the source to retrieve the data associated with the event and causes a viewer application (e.g., a graphical user interface (GUI)) to be activated to display the data associated with the event. These features represent patent eligible features, as these features amount to significantly more than an abstract idea. These features, when considered as an ordered combination, amount to significantly more than simply organizing and comparing data. The features address the Internet-centric challenge of alerting a receiver with time sensitive information. This is addressed by transmitting the alert over a network to activate the viewer application, which enables the connection of the device of the receiver to the source over the network to retrieve the data associated with the event. These are meaningful limitations that add more than generally linking the use of an abstract idea (e.g., the general concept of organizing and comparing data) to the Internet, because they solve an Internet-centric problem with a solution that is necessarily rooted in computer technology. These features, when taken as an ordered combination, provide unconventional steps that confine the abstract idea to a particular useful application. Therefore, these features represent patent eligible subject matter.

In various embodiments, the system comprises one or more processors interconnected with a memory module, capable of executing machine-readable instructions. These instructions include, but are not limited to, the steps outlined in any flow diagram, system diagram, block diagram, and/or process diagram disclosed herein, as well as steps corresponding to any functionality detailed herein. In embodiments, the execution of these machine-readable instructions may involve initiating multiple concurrent computer processes. Each process of the concurrent computer process may be configured to handle or process a designated subset or portion of the of the machine-readable instructions. This division of tasks enables parallel processing, multi-processing, and/or multi-threading, enabling multiple operations to be conducted or executed concurrently rather than sequentially. This functionality for spawning a plurality of concurrent processes to manage separate portions of the machine-readable instructions markedly increases the overall speed of execution of the machine-readable instructions. By leveraging parallel or concurrent processing, the time required to complete a set or subset of program steps is substantially reduced (e.g., when compared to execution without concurrent or parallel processing). This efficiency gain not only accelerates the processing speed but also optimizes the use of processor resources, leading to an improved performance of the computing system. This enhancement in computational efficiency constitutes a significant technological improvement, as it enhances the functional capabilities of the processors and the system as a whole, representing a practical and tangible technological advancement. The result of this concurrent processing functionality results in an improvement in the functioning of the one or more processor and/or the computing system, and thus, represents a practical application.

In embodiments, one or more operations and/or functionality of components described herein can be distributed across a plurality of computing systems (e.g., personal computers (PCs), user devices, servers, processors, etc.), such as by implementing the operations over a plurality of computing systems. This distribution can be configured to facilitate the optimal load balancing of traffic (e.g., requests, responses, notifications, etc.), which can encompass a wide spectrum of network traffic or data transactions. By leveraging a distributed operational framework, a system implemented in accordance with embodiments of the present disclosure can effectively manage and mitigate potential bottlenecks, ensuring equitable processing distribution and preventing any single device from shouldering an excessive burden. This load balancing approach significantly enhances the overall responsiveness and efficiency of the network, markedly reducing the risk of system overload and ensuring continuous operational uptime. The technical advantages of this distributed load balancing can extend beyond mere efficiency improvements. It introduces a higher degree of fault tolerance within the network, where the failure of a single component does not precipitate a systemic collapse, markedly enhancing system reliability. Additionally, this distributed configuration promotes a dynamic scalability feature, enabling the system to adapt to varying levels of demand without necessitating substantial infrastructural modifications. The integration of advanced algorithmic strategies for traffic distribution and resource allocation can further refine the load balancing process, ensuring that computational resources are utilized with optimal efficiency and that data flow is maintained at an optimal pace, regardless of the volume or complexity of the requests being processed. Moreover, the practical application of these disclosed features represents a significant technical improvement over traditional centralized systems. Through the integration of the disclosed technology into existing networks, entities can achieve a superior level of service quality, with minimized latency, increased throughput, and enhanced data integrity. The distributed approach of embodiments can not only bolster the operational capacity of computing networks but can also offer a robust framework for the development of future technologies, underscoring its value as a foundational advancement in the field of network computing.

To aid in the load balancing, the computing system of embodiments of the present disclosure can spawn multiple processes and threads to process data traffic concurrently. The speed and efficiency of the computing system can be greatly improved by instantiating more than one process or thread to implement the claimed functionality. However, one skilled in the art of programming will appreciate that use of a single process or thread can also be utilized and is within the scope of the present disclosure.

It is an object of the disclosure to provide a method for determining railroad track locations at risk for buckling. It is a further object of the disclosure to provide a system for determining railroad track locations at risk for buckling, and a computer-based tool for determining railroad track locations at risk for buckling. These and other objects are provided by the present disclosure, including at least the following embodiments.

In one particular embodiment, a method for determining railroad track locations at risk for buckling includes accessing railroad track data and segmenting a railroad track into a plurality of railroad track segments. The method further includes determining, from the railroad track data, a plurality of track features for each particular railroad track segment. The method further includes determining a priority level for each particular railroad track segment. The priority level indicates a track buckling risk severity for the particular railroad track segment. The priority level is determined using the determined plurality of track features for the particular railroad track segment and a particular rule weight matrix that includes a set of track features and an associated risk weight for each track feature of the set of track features. The method further includes displaying the determined priority levels for the plurality of track segments on an electronic display.

The foregoing has outlined rather broadly the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. Additional features and advantages of the disclosure will be described hereinafter which form the subject of the claims of the disclosure. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the disclosure as set forth in the appended claims. The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

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.

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.

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.

Rail transport systems traverse entire continents to enable the transport and delivery of passengers and goods throughout the world. A quintessential component of railroad infrastructure is the track. Laid over a myriad of geographies and terrains, railroad tracks are designed to withstand the worst of the elements and facilitate disbursement of locomotives throughout the railroad system. Because of this constant exposure of the tracks to hazardous conditions, railroad companies must be vigilant in maintaining track integrity. If a section of railroad track is compromised and the damage or obstruction is not quickly addressed, the consequences can be catastrophic.

The general structure of a track can include several components. Generally, a foundation referred to as the ballast, often composed of crushed stone, gravel, or other aggregate, provides a compacted pathway on which the track can be laid. The rails and ties of the railroad track are laid on top of the ballast. Rails afford an actual surface on which rail vehicle wheels can roll. The rails run parallel with one another for thousands of miles, and the wheel-span of rail vehicles are specially designed and sized to match the track footprint. If rails were to separate laterally, the results would be disastrous. As such, to maintain a consistent and uniform distance between the rails, lateral slat-like components called ties are disposed between and coupled to the rails. The ties can be wood, concrete, or any other suitable material, and the ties can be secured to or within the ballast to facilitate track stability. The ties serve the very important purpose of maintaining lateral tension between the rails, such that the extreme weight of rail traffic does not lead to rail separation.

Considering the dire consequences of disjunctive rails in a railroad track, an especially problematic type of track damage is what is known as rail buckling. A track buckle is when the rails of a track bend or shift out of place, such as due to longitudinal strain and/or pressure on the rails due to hot weather. Continuous welded rail (“CWR”), e.g., rail that is essentially a singular metal track stretching over several miles (which can be accomplished by welding rail segments together) is especially prone to rail buckling. For example, because of the sheer length of a given CWR section, normally-insignificant changes in rail span can be drastically compounded. For example, when a rail increases in temperature (e.g., such as due to weather conditions), linear expansion can occur. Such expansion, while negligible for smaller pieces of rail, can be feet or yards long for CWR sections that traverse several miles. These increases in length can cause significant longitudinal strain on the rail, and such strain, if unaddressed, can potentially overcome the strength of the lateral compression enabled by the ties, thereby resulting in a buckle. A rail buckle is a drastic angular alteration in track dimensions that can certainly lead to vehicle derailment if left unaddressed.

To address these and other problems with buckling of railroad tracks such as CWR, embodiments of the disclosure provide systems and methods for determining railroad track locations at risk for buckling. In some embodiments, the disclosed embodiments segment a railroad track into multiple railroad track segments and then determine track features for each of the railroad track segments. The track features may include, for example, weather data for the railroad track segment, track conditions for the railroad track segment, track structures of the railroad track segment, and the like. The disclosed embodiments then determine a priority level for each of the railroad track segments based on the track features of each railroad track segment and a rule weight matrix. The priority level for each railroad track segment indicates a track buckling risk for the railroad track segment. The priority levels for the railroad track segments may then be displayed to a user such as track inspection crew member in order to inform the user of the most important track locations to inspect for buckling. For example, the priority levels may be displayed to the user via an interactive track map and/or via a notification (e.g., an email or text message). By utilizing the systems and methods of the disclosed embodiments to determine railroad track locations at risk for buckling, railroads are more likely to locate and address high-risk track locations prior to buckling. This may reduce or eliminate train derailment events, thereby increasing the public safety and increasing the efficiency of the railroad operations.

1 8 FIGS.- 1 FIG. 2 FIG. 1 FIG. 3 FIG. 1 FIG. 4 FIG. 1 FIG. 5 FIG. 1 FIG. 6 FIG. 1 FIG. 7 FIG. 8 FIG. The disclosed embodiments will not be described in reference to.is a block diagram of an exemplary system configured with capabilities and functionality for determining railroad track locations at risk for buckling, according to particular embodiments.illustrates a railroad track buckling risk prediction module that may be utilized by the system of, according to particular embodiments.illustrates track features that may be utilized by the system of, according to particular embodiments.illustrates a rule weight matrix that may be utilized by the system of, according to particular embodiments.illustrates an interactive track map that may display priority levels generated by the system of, according to particular embodiments.illustrates a notification that may display priority levels generated by the system of, according to particular embodiments.is a chart illustrating a method for determining railroad track locations at risk for buckling, according to particular embodiments.is an example computer system that can be utilized to implement aspects of the various technologies presented herein, according to particular embodiments.

1 FIG. 1 FIG. 100 100 110 130 140 110 130 140 110 802 115 120 130 132 is a block diagram of an exemplary railroad track buckling risk prediction systemconfigured with capabilities and functionality for determining railroad track locations at risk for buckling, according to certain embodiments of the present disclosure. As shown in, railroad track buckling risk prediction systemmay include a computing system, a client system, and a network. Computing systemand client systemare communicatively coupled with each other using any appropriate wired or wireless communication system or network (e.g., network). Computing systemincludes a computer processor (e.g., processor) and memorythat stores railroad track buckling risk prediction module. Client systemincludes an electronic display for displaying a user interface. These components, and their individual components, may cooperatively operate to provide functionality in accordance with the discussion herein.

100 It is noted that the functional blocks, and components thereof, of railroad track buckling risk prediction systemof embodiments of the present disclosure 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.

100 It is also noted that various components of railroad track buckling risk prediction systemare 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.

100 140 It is further noted that functionalities described with reference to each of the different functional blocks of railroad track buckling risk prediction systemdescribed 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.

100 125 182 180 125 181 182 181 180 125 100 180 182 150 182 100 125 182 182 160 125 182 181 180 182 125 182 182 182 190 181 125 190 1260 1270 130 100 181 In general, railroad track buckling risk prediction systemgenerates and displays a priority levelfor each railroad track segmentof a railroad track. Each priority levelindicates a risk severity for a track bucklefor the particular railroad track segment(i.e., the likelihood of track buckleoccurring at some point in the future on railroad track). To determine priority levels, railroad track buckling risk prediction systemsegments railroad trackinto multiple railroad track segmentsand then determines track features (e.g., railroad track features) for each of the railroad track segments. The track features may include, for example, weather data for the railroad track segment, track conditions for the railroad track segment, track structures of the railroad track segment, and the like. Railroad track buckling risk prediction systemmay then determine a priority levelfor each of the railroad track segmentsbased on the track features of each railroad track segmentand a rule weight matrix. The priority levelfor each railroad track segmentindicates a risk of a future track bucklefor railroad trackof the railroad track segment. The priority levelsfor railroad track segments(e.g.,A-B) may then be displayed to a user such as a track inspection crew memberin order to inform the user of the most important track locations to inspect and/or address for risk of track buckle. For example, the priority levelsmay be displayed to track inspection crew membervia an interactive track mapand/or via a notification(e.g., an email or text message) on client system. By utilizing railroad track buckling risk prediction systemto determine railroad track locations at risk for track buckle, railroads are more likely to locate and address high-risk track locations prior to buckling. This may reduce or eliminate train derailment events, thereby increasing the public safety and increasing the efficiency of the railroad operations.

110 110 110 110 110 110 110 8 FIG. Computing systemmay be any appropriate computing system in any suitable physical form. As example and not by way of limitation, computing systemmay be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these. Where appropriate, computing systemmay include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, computing systemmay perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, computing systemmay perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. Computing systemmay perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate. A particular example of a computing systemis described in reference to.

110 115 115 120 120 110 125 180 120 120 115 120 120 2 FIG. Computing systemincludes one or more memory units/devices(collectively herein, “memory”) that may store railroad track buckling risk prediction module. Railroad track buckling risk prediction modulemay be a software module/application utilized by computing systemto generate and display priority levelsfor railroad track, as described herein. Railroad track buckling risk prediction modulerepresents any suitable set of instructions, logic, or code embodied in a computer-readable storage medium. For example, railroad track buckling risk prediction modulemay be embodied in memory, a disk, a CD, or a flash drive. In particular embodiments, railroad track buckling risk prediction modulemay include instructions (e.g., a software application) executable by a computer processor to perform some or all of the functions described herein. A specific example of railroad track buckling risk prediction moduleis discussed below in reference to.

2 FIG. 120 100 120 150 160 170 125 125 181 180 125 120 126 127 130 illustrates a railroad track buckling risk prediction modulethat may be utilized by railroad track buckling risk prediction system, according to particular embodiments. In general, railroad track buckling risk prediction module, which may be a machine learning model in some embodiments, ingests data such as railroad track features, rule weight matrix, and trigger eventsin order to generate priority levels. Priority levels, as explained in more detail below, indicate the severity of risk for a track bucklealong railroad track. The priority levelsmay be presented by railroad track buckling risk prediction modulein interactive track map, in notification, or any other appropriate manner on client system.

120 210 220 230 240 250 260 270 280 290 120 110 120 120 120 120 120 120 120 1 FIG. 2 FIG. In some embodiments, railroad track buckling risk prediction moduleincludes a track classification module, a track segmentation module, a track feature identification module, a feature allocation to segments module, a priority assignment module, a trigger events module, a clustering module, an insights module, and a notification module. Each of these individual modules will be described in more detail below. It is noted that althoughshows railroad track buckling risk prediction modulerunning on a single computing system(e.g., a single server), it will be appreciated that railroad track buckling risk prediction module(and the individual functional blocks of railroad track buckling risk prediction module) may be implemented as separate devices and/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 althoughillustrates components of railroad track buckling risk prediction moduleas single and separate blocks, each of the various components/modules of railroad track buckling risk prediction modulemay 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 railroad track buckling risk prediction modulemay actually be part of a different component of railroad track buckling risk prediction module, and as such, the description of the particular functionality described for the particular component of railroad track buckling risk prediction moduleis for illustrative purposes and not limiting in any way.

120 210 210 180 210 150 310 180 210 180 120 125 181 120 180 180 125 181 In some embodiments, railroad track buckling risk prediction moduleincludes track classification module. In general, track classification moduledetermines whether railroad trackis CWR or non-CWR. In some embodiments, for example, track classification modulemay analyze railroad track features(e.g., track structuresas described below) to determine whether railroad trackis CWR or non-CWR. In some embodiments, if track classification moduledetermines that railroad trackis CWR, railroad track buckling risk prediction modulecontinues processing data to calculate priority levelsince CWR rail is more prone to develop track buckles. However, if railroad track buckling risk prediction moduledetermines that railroad trackis non-CWR, some embodiments of railroad trackmay discontinue processing data and decline to calculate priority levelsince non-CWR rail is much less prone to develop track buckles.

120 220 220 180 182 182 125 182 182 182 180 182 182 100 In some embodiments, railroad track buckling risk prediction moduleincludes track segmentation module. In general, track segmentation moduledivides railroad trackinto smaller segments (i.e., railroad track segmentsA,B, etc.) and then calculates a priority levelfor each specific railroad track segment. In some embodiments, each railroad track segmentis a predetermined length of track and includes a starting milepost and an ending milepost. For example, railroad track segmentsmay be between 200 and 300 feet of railroad track. As a specific example, each railroad track segmentmay be 1/20 of a mile (approximately 264 feet). In other embodiments, railroad track segmentsmay be any other appropriate length according to the specifications of railroad track buckling risk prediction systemor user input.

120 230 230 150 150 230 150 115 230 150 140 In some embodiments, railroad track buckling risk prediction moduleincludes track feature identification module. In general, track feature identification modulecollects or otherwise accesses all possible railroad track featuresto analyze. Railroad track featuresare discussed in more detail below. In some embodiments, track feature identification moduleaccesses railroad track featuresfrom memory. In other embodiments, track feature identification moduleaccesses all or a portion of railroad track featuresfrom another computer system (e.g., via network).

120 240 240 150 182 240 182 150 150 182 240 182 240 150 150 182 150 240 182 250 In some embodiments, railroad track buckling risk prediction moduleincludes feature allocation to segments module. In general, feature allocation to segments moduledetermines a subset of railroad track featuresthat are applicable to each particular railroad track segment. In some embodiments, for example, feature allocation to segments moduleanalyzes a geographic location (e.g., milepost range, GPS coordinates, etc.) associated with each particular railroad track segmentand then filters railroad track featuresto determine those railroad track featureshaving locations that match the geographic location for the particular railroad track segment. As a specific example, feature allocation to segments modulemay determine that railroad track segmentA has a beginning milepost of 216.443 and an ending milepost of 216.481. Feature allocation to segments modulemay then analyze all of railroad track featuresto determine a subset of railroad track featuresthat each have an associated location that falls within the milepost range of railroad track segmentA. The subset of railroad track featuresthat are determined by feature allocation to segments moduleto correspond to railroad track segmentA may then be passed to priority assignment modulefor processing.

120 250 250 150 182 240 160 125 182 160 160 150 161 150 240 150 182 160 150 182 240 182 182 125 182 125 125 182 In some embodiments, railroad track buckling risk prediction moduleincludes priority assignment module. In general, priority assignment moduleutilizes the railroad track featuresfor a particular railroad track segment(i.e., as determined by feature allocation to segments module) along with rule weight matrixto determine or otherwise assign a priority levelfor the particular railroad track segment. A particular embodiment of rule weight matrixis described in more detail below. In general, rule weight matrixincludes railroad track featuresand an associated preassigned risk weight (e.g., risk weight) for each track feature. In some embodiments, feature allocation to segments modulecross-references the railroad track featuresfor a particular railroad track segmentwith rule weight matrixin order to determine a risk weight for each railroad track featurefor the particular railroad track segment. In some embodiments, feature allocation to segments modulemay then sum all of the risk weights for the particular railroad track segmentin order to calculate a cumulative feature risk weight for the particular railroad track segment. The calculated cumulative feature risk weight may then be used to determine which priority levelto assign to the particular railroad track segment. For example, if the possible priority levelsare P1, P2, P3, and P4 (with P1 being the highest priority and P4 being the lowest priority), the priority levelfor the particular railroad track segmentmay be assigned according to the following table:

Priority Level 125 Cumulative Feature Risk Weight Range P1  75-100 P2 50-75 P3 25-50 P4  0-25

120 260 260 170 182 170 125 170 125 182 182 260 170 182 260 125 182 125 125 In some embodiments, railroad track buckling risk prediction moduleincludes trigger events module. In general, trigger events moduledetermines if any trigger eventsare applicable to each particular railroad track segment. As described in more detail below, trigger eventsare a list of rules/events that cause priority levelsto be automatically set to a specific level. For example, certain trigger eventscause the priority levelfor a particular railroad track segmentto be set to the highest prioritization level (e.g., P1) regardless of the cumulative feature risk weight for the particular railroad track segment. If trigger events moduledetermines that any trigger eventsare applicable to a particular railroad track segment, trigger events modulemay immediately adjust or otherwise assign the priority levelfor the particular railroad track segmentto the indicated priority level(e.g., typically the highest priority levelsuch as P1).

120 270 270 125 125 125 270 125 270 125 270 In some embodiments, railroad track buckling risk prediction moduleincludes clustering module. In general, clustering moduleattempts to locate two priority levelsthat are within a certain distance of each other. For example, if a priority levelof P1 is within a certain distance of a priority levelof P2, clustering modulemay combine the two priority levelsinto a single P1. In some embodiments, clustering modulemay utilize any appropriate algorithm to cluster priority level. For example, some embodiments of clustering modulemay utilize Density-Based Spatial Clustering of Applications with Noise (DBSCAN).

120 280 280 126 190 280 126 130 126 5 FIG. In some embodiments, railroad track buckling risk prediction moduleincludes insights module. In general, insights modulegenerates insights such as interactive track mapfor display to users such as track inspection crew member. In some embodiments, for example, insights modulegenerates and electronically transmits interactive track mapfor display on client system. Interactive track mapis discussed in more detail below in reference to.

120 290 290 127 190 290 127 130 127 125 127 6 FIG. In some embodiments, railroad track buckling risk prediction moduleincludes notification module. In general, notification modulegenerates notificationsfor display to users such as track inspection crew member. In some embodiments, for example, notification modulegenerates and electronically transmits notificationsfor display on client system. In some embodiments, notificationsinclude priority levels. Notificationsare discussed in more detail below in reference to.

1 FIG. 125 100 181 182 125 190 180 181 125 181 125 181 181 125 181 181 125 181 182 181 181 Returning to, priority levelis an indicator (e.g., a number or rank) that is generated by railroad track buckling risk prediction systemin order to indicate the severity of risk for a track bucklewithin a particular railroad track segment. In this way, priority levelscan be used by track inspection crew memberto prioritize locations along railroad trackto inspect for a possible track buckle. In some embodiments, priority levelsare numbered or ordered lists according to the severity of risk for a track buckle. For example, priority levelmay be selected from a list of numbers (e.g., 1-3), where one number (e.g., 1) indicates the most severe risk of a track buckleand the number on the opposite end of the list (e.g., 3) indicates the least severe risk of a track buckle. As another example, priority levelmay be selected from a list of letters (e.g., A-D), where one letter (e.g., A) indicates the most severe risk of a track buckleand the letter on the opposite end of the list (e.g., D) indicates the least severe risk of a track buckle. In some embodiments, priority levelmay be any combination of letters and numbers in order to indicate the severity of risk for a track bucklewithin a particular railroad track segment(e.g., P1-P3, where P1 indicates the most severe risk of a track buckleand P3 indicates the least severe risk of a track buckle).

130 100 140 130 130 130 800 130 130 190 130 140 130 130 130 132 802 804 Client systemis any appropriate user device for communicating with components of railroad track buckling risk prediction systemover network(e.g., the internet). In particular embodiments, client systemmay be an electronic device including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functionalities implemented or supported by client system. As an example, and not by way of limitation, a client systemmay include a computer system (e.g., computer system) such as a desktop computer, notebook or laptop computer, netbook, a tablet computer, e-book reader, GPS device, camera, personal digital assistant (PDA), handheld electronic device, cellular telephone, smartphone, smartwatch, augmented/virtual reality device such as wearable computer glasses, other suitable electronic device, or any suitable combination thereof. This disclosure contemplates any suitable client system. A client systemmay enable a network user (e.g., track inspection crew member) at client systemto access network. A client systemmay enable a user to communicate with other users at other client systems. Client systemmay include an electronic display that displays graphical user interface, a processor such processor, and memory such as memory.

135 180 135 180 110 135 140 110 136 135 180 181 100 Switching equipmentincludes equipment or devices that direct trains to specific railroad tracks. In some embodiments, switching equipmentincludes automatic track switches and retarders that operate to switch traing or railcars onto specific railroad tracks. In some embodiments, computing systemis electronically coupled to switching equipmentusing any wired or wireless technology via network. In general, computing systemsends switching signalsto switching equipmentin order to automatically direct trains away from areas along railroad trackat risk for track buckleas determined by railroad track buckling risk prediction system.

136 135 180 181 100 100 125 182 181 110 180 135 182 Switching signalsare any electronic signals that are sent (e.g., wirelessly or wired) to switching equipmentin order to automatically control switching operations to automatically direct trains away from areas along railroad trackat risk for track buckleas determined by railroad track buckling risk prediction system. For example, if railroad track buckling risk prediction systemdetermines a priority levelfor railroad track segmentA that indicates a high risk for track buckle, computing systemmay send switching signalsto switching equipmentin order to automatically direct trains away from railroad track segmentA.

140 100 140 100 140 140 Networkallows communication between and amongst the various components of railroad track buckling risk prediction system. This disclosure contemplates networkbeing any suitable network operable to facilitate communication between the components of railroad track buckling risk prediction system. Networkmay include any interconnecting system capable of transmitting audio, video, signals, data, messages, or any combination of the preceding. Networkmay include all or a portion of a local area network (LAN), a wide area network (WAN), an overlay network, a software-defined network (SDN), a virtual private network (VPN), a packet data network (e.g., the Internet), a mobile telephone network (e.g., cellular networks, such as 4G or 5G), a Plain Old Telephone (POT) network, a wireless data network (e.g., WiFi, WiGig, WiMax, etc.), a Long Term Evolution (LTE) network, a Universal Mobile Telecommunications System (UMTS) network, a peer-to-peer (P2P) network, a Bluetooth network, a Near Field Communication network, a Zigbee network, and/or any other suitable network.

150 180 150 180 150 180 150 160 125 150 3 FIG. Railroad track featuresinclude any data about railroad trackthat is gathered, stored, or otherwise accessed. For example, a portion of railroad track featuresmay be gathered by a vehicle as the vehicle travels along railroad track(e.g., using sensors such as LiDAR). As another example, railroad track featuresmay include weather data for the location of railroad track. In general, railroad track featuresare utilized along with rule weight matrixto determine priority levels, as described in more detail herein. A particular embodiment of railroad track featureswill now be described in reference to.

3 FIG. 1 FIG. 150 100 150 310 320 330 340 350 360 370 380 150 180 182 150 illustrates railroad track featuresthat may be utilized by railroad track buckling risk prediction systemof, according to particular embodiments. In some embodiments, railroad track featuresmay include one or more of track structures, fixed assets, tie-rail interactions, tie-ballast interactions, dynamic forces, work orders, track conditions, and weather data. Each railroad track featuremay be associated with a specific location along railroad track(e.g., a milepost or a GPS coordinate) or a specific railroad track segment. Each of these example railroad track featureswill be discussed in more detail below.

150 310 310 180 310 180 180 310 180 310 180 180 180 310 180 In some embodiments, railroad track featuresincludes track structures. In general, each track structureis a physical aspect of railroad track. In some embodiments, track structuresinclude whether or not railroad trackincludes a curve (and/or an amount of curve of railroad track). In some embodiments, track structuresinclude whether or not railroad trackincludes a tangent or a spiral. In some embodiments, track structuresinclude a grade of railroad track(e.g., a specific grade of railroad trackand/or whether railroad trackincludes a grade that is greater than a predetermine amount such as +/−1%). In some embodiments, track structuresinclude whether or not railroad trackis CWR, a CWR zone, or an open rail removal zone.

150 320 320 180 320 180 320 180 320 180 In some embodiments, railroad track featuresincludes fixed assets. In general, each fixed assetis specific physical structure or element of railroad track. For example, in some embodiments, fixed assetsinclude bridges and culverts of railroad track. As another example, fixed assetsmay include grade crossings and railroad crossings (e.g., diamonds) of railroad track. As yet another example, fixed assetsmay include control points (e.g., signals) and switches of railroad track.

150 330 330 180 330 330 330 330 In some embodiments, railroad track featuresincludes tie-rail interactions. In general, tie-rail interactionsindicate the quality of the interface between the railroad ties and the rails of railroad track. In some embodiments, tie-rail interactionsinclude an anchor and fastener condition. In some embodiments, tie-rail interactionsinclude a railroad tie type (e.g., concrete or wood). In some embodiments, tie-rail interactionsinclude a railroad tie density. In some embodiments, tie-rail interactionsinclude a railroad tie condition.

150 340 340 180 340 180 340 180 180 180 180 340 340 180 340 180 340 180 340 In some embodiments, railroad track featuresincludes tie-ballast interactions. In general, tie-ballast interactionsindicate the tie quality or the quality of the interface between the railroad ties and the ballast of railroad track. In some embodiments, tie-ballast interactionsinclude measurements from one or more sensors (e.g., a LiDAR device) attached to vehicle travelling along railroad track. In some embodiments, tie-ballast interactionsinclude a ballast fouling index (BFI) (e.g., a measurement of the structural condition of the ballast of railroad track) for one or more of a left side of railroad track, a right side of railroad track, and a center of railroad track. In some embodiments, tie-ballast interactionsinclude a ballast condition or volume. In some embodiments, tie-ballast interactionsinclude a crib ballast condition or volume (i.e., the ballast that is packed between sleepers of railroad track). In some embodiments, tie-ballast interactionsinclude a shoulder size of railroad track. In some embodiments, tie-ballast interactionsinclude a ballast lateral strength of railroad track. In some embodiments, tie-ballast interactionsinclude a ballast consolidation.

150 350 350 180 180 350 180 350 180 350 180 180 350 180 350 180 350 180 180 In some embodiments, railroad track featuresincludes axial/lateral dynamic forces. In general, dynamic forcesindicate events or features of railroad trackthat may cause excessive or abnormal axial/lateral forces on railroad track. In some embodiments, dynamic forcesinclude the speed limit of railroad track(e.g., the track class). In some embodiments, dynamic forcesinclude a track slope (e.g., grade) or grade changes of railroad track. In some embodiments, dynamic forcesinclude lateral movements (e.g., alignment values) for railroad trackthat indicate vertical and lateral movement of the rails of railroad track. In some embodiments, dynamic forcesinclude braking and track side forces of railroad track. In some embodiments, dynamic forcesinclude MGT (i.e., the total weight of freight transported) on railroad track. In some embodiments, dynamic forcesinclude any slow orders for railroad track(i.e., temporary speed restrictions placed on railroad trackwhen it is unsafe for trains to operate at the normal speed).

350 100 100 125 In embodiments where dynamic forcesinclude slow orders, the slow orders may be filtered based on a predetermined assigned risk. For example, lower-risk slow orders may be discarded, discounted, or otherwise ignored by railroad track buckling risk prediction system. On the contrary, slow orders that have been predefined as high-risk slow orders may be further analyzed by railroad track buckling risk prediction systemand may result in a higher priority level. As an example for illustrative purposes only, slow orders may be analyzed and filtered according to the table below in order to identify high-risk slow orders:

SLOW ORDER REASON DESCRIPTION HIGH RISK ALIGNMENT X BRIDGE CONDITION X BRIDGE RENEWAL X CRITICAL JOINT CLEARANCES COMPACTION X CONTRACTOR SITE DETECTOR CAR TESTING DERAILMENT LOCATION DEFECTIVE FROG DEFECTIVE RAIL SWITCH POINT DISTURBED TRACK X DEFECTIVE WELD EXTREME HEAT RESTRICTION X FROST HEAVE DEFECTIVE GAUGE X JOINT CONDITION JOINTED RAIL LOW JOINT LOW RAIL NEUTRAL TEMPERATURE X NO RAIL ANCHOR X RAIL DETECTOR ROUGH SURFACE X RAIL WEAR ROAD CROSSING SNOW CONDITION SAND CONDITION SLIDE AREA STABILIZATION WORK X TIE GANG X TIE CONDITION X TIE REPLACEMENT X UNDERCUTTER X UNSTABLE SUBGRADE X VEHICLE TRACK INTERACTION WEATHER - FLASH FLOOD CRITICAL AREA WEATHER FLASH FLOOD EMERGENCY WEATHER - FLASH FLOOD WARNING 200K HIGH SPEED SURFACING SLOW ORDER [NEAR X FUTURE] WASHOUT SITE DEFECTIVE XING FROG, FOREIGN MAINTENANCE DEFECTIVE XING FROG, BNSF MAINTENANCE

150 360 360 180 182 360 360 360 360 180 In some embodiments, railroad track featuresincludes work orders. In general, work ordersinclude any work orders for railroad trackthat have been performed within a certain prior amount of time (e.g., work orders performed on a specific railroad track segmentor milepost location within the last 30 days). In some embodiments, work ordersinclude rail work orders. In some embodiments, work ordersinclude tie work orders. In some embodiments, work ordersinclude curve staking. In some embodiments, each work orderincludes a location of the work performed on railroad track(e.g., a milepost or GPS coordinates).

360 100 360 100 125 360 360 In some embodiments, work ordersare filtered based on a predetermined assigned risk. For example, lower-risk work orders may be discarded, discounted, or otherwise ignored by railroad track buckling risk prediction system. On the contrary, high-risk work ordersmay be further analyzed by railroad track buckling risk prediction systemand may result in a higher priority level. As an example for illustrative purposes only, work ordersmay be analyzed and filtered according to the table below in order to identify high-risk work orders:

WORK ORDER 360 TYPE HIGH RISK REPLACE BRIDGE TIES X FINALIZE BRIDGE PROJECT SURFACE BRIDGE X REPLACE GRADE CROSSING - TOTAL REHAB X REPLACE CULVERT X INSTALL CULVERT X INSTALL DRAINAGE SYSTEM SPECIAL INSPECTION REPLACE TRACK PANEL X SURFACE TRACK X SWITCH INSTALLED X SUPPORT GANGS - SHOULDER BALLAST CLEANER X BALLAST CLEANING X REPLACE GRADE CROSSING - SURFACE ONLY X REPAIR GRADE CROSSING GENERAL INSTALL GRADE CROSSING X REPLACE RAIL/OTM F/CROSSING RELOCATE GRADE CROSSING X DIG OUT CROSSING X SURFACE GRADE CROSSING X PULL/REPLACE PANEL - CROSSING X REPLACE CROSSING DIAMOND PULL/REPLACE - DIAMOND X INSTALL CROSSING DIAMOND X SURFACE DIAMOND X CRIB/CUT SHOULDER - DIAMOND OFF TRACK UNDERCUT - DIAMOND X MAINTAIN EQUIPMENT - CROSS TIES MAINTAIN EQUIPMENT - CONC TIES MAINTAIN EQUIPMENT - REPLACE SWITCH TIES X INSTALL INSULATED JOINT REPLACE INSULATED JOINT PLUG REMOVE INSULATED JOINT PULL/REINSTALL CROSSINGS X REPAIR LOAD BEARING DEFECTS X REMOVE/REINSTALL TURNOUT OTM INSTALL ADDITIONAL ANCHORS X GAGE RAIL X REPLACE RAIL X DESTRESS RAIL X REMOVE RAIL - LESS THAN 12 INCHES X WELDING (NO EXISTING JT ELIM ORDER) X RAIL CRITICAL JOINT ELIMINATION X REPLACE STOCK RAIL REPLACE FULL SWITCH PANEL CUT AND SLIDE SWITCH POINTS SWITCH MAINTENANCE - OTHER INSTALL TIES X REPLACE SWITCH TIES X REPLACE TURNOUT X INSTALL TURNOUT X REPLACE FROG - COMPLETE FROG SURFACE TURNOUT X REPLACE GUARD RAIL PULL/REPLACE - TURNOUT X CRIB/CUT SHOULDER - TURNOUT X OFF TRACK UNDERCUT - TURNOUT X RELOCATE TURNOUT REPLACE FROG PANEL REPLACE HEEL RAIL REPLACE SWITCH POINT TAMP UP HEAD BLOCKS/SWITCH AREA TAMP UP HEEL BLOCK WELD SWITCH POINT TRACK SHIFT UNDERCUT TRACK X REPLACE RAIL/OTM REMOVE RAIL/OTM LINE OUT CURVE X CRIB/CUT SHOULDER - SPOT X OFF TRACK UNDERCUT - SPOT X PULL/REPLACE TRACK PANEL- SPOT X HAND TAMP TRACK SHOULDER BALLAST CLEANING X BUILD BRIDGE BALLAST DECK INSTALL BRIDGE BALLAST DECK REPAIR BRIDGE PATCH GRADE CROSSING SURFACE/APPROACH REPLACE OTM REPLACE GUARD RAIL JOINT LIST AUDIT DITCHING INSTALL CURVE BLOCK - GAGING INSTALL OTM REPLACE TIE PLATES REPAIR SERVICE FAILED RAIL X REMOVE RAIL X REPAIR PREVIOUSLY FOUND RAIL DEFECT(S) REPLACE TIES X REPAIR RAIL SEAT ABRASION REPLACE PADS GAGE SW PTS - CLOSURE RAILS REAPPLY LOOSE/MISSING OTM GAGE FROG REPLACE SWITCH POINT & STOCK RAIL BUILD NEW TRACK X CUTOVER TRACK X STAKE CURVE DERAILMENT DAMAGE REPAIR

150 370 370 180 370 180 370 180 370 180 370 180 370 180 370 180 181 370 180 370 180 In some embodiments, railroad track featuresincludes track conditions. In general, track conditionseach identify a specific physical state of railroad track. In some embodiments, track conditionsinclude measurements from one or more sensors (e.g., a LiDAR device) attached to vehicle travelling along railroad track. In some embodiments, track conditionsinclude rail alignment defects or conditions of railroad track. In some embodiments, track conditionsinclude rail surface defects or conditions of railroad track. In some embodiments, track conditionsinclude manual defects or conditions of railroad track. In some embodiments, track conditionsinclude joint defects or conditions of railroad track. In some embodiments, track conditionsinclude past track buckle data for railroad track(e.g., dates and locations of past track buckles). In some embodiments, track conditionsinclude destress locations of railroad track. In some embodiments, track conditionsinclude track neutral temperature (TNT) of railroad track.

370 370 100 370 100 125 370 370 In some embodiments, track conditionsare filtered based on a predetermined assigned risk. For example, lower-risk track conditionsmay be discarded, discounted, or otherwise ignored by railroad track buckling risk prediction system. On the contrary, high-risk track conditionsmay be further analyzed by railroad track buckling risk prediction systemand may result in a higher priority level. As an example for illustrative purposes only, track conditionsmay be analyzed and filtered according to the table below in order to identify high-risk track conditions:

TRACK CONDITION 370 TYPE HIGH RISK FACILITY OBSTRUCTED BY SILTING DETERIORATED ALLOWING SUB SAT. WATER UNDERCUTTING TRACK GAGE EXCEEDS (TANG.) X GAGE LESS THAN ALLOWABLE (TANG.) X GAGE EXCEEDS (CURVE) X GAGE LESS THAN ALLOWABLE (CURVE) X GAGE EXCEEDS (EXCEPTED) X ALIGNMENT EXCEEDS (TANG 62′) X ALIGNMENT EXCEEDS (CURVE 62′) X ALIGNMENT EXCEEDS (CURVE 31′) X RUNOFF EXCEEDS X PROFILE DEVIATION X CROSSLEVEL (TANGENT) X REVERSE CROSSLEVEL (CURVE) WARP (TANGENT) X WARP (CURVE) X WARP (SPIRAL) X FOUL/INSUF FAILS TO TRANSMIT LOAD X FOUL/INSUF FAILS TO RESTRAIN TRACK X FOUL/INSUF FAILS ADEQUATE DRAINAGE X FOUL/INSUF FAILS TO MTN PROPER GEO X INSUFFICIENT TIES 39′ X TIES NOT EFFECTIVELY DISTRIBUTED X RAIL JT CLS 1&2 TRK <1 TIE IN 48″ RAIL JT CLS 3-5 TRK <1 TIE IN 36″ RAIL JT CLS 3-5 TRK <2 TIE IN 48″ FAILURE TO MAINTAIN # OF TIES 39′ X CONSTRUCTED W/OUT CROSSTIE SUPPORT TRANSVERSE FISSURE ORDINARY BREAK DAMAGED RAIL FLATTENED RAIL (CRUSHED HEAD) BOLT HOLE BREAK BROKEN OR DEFECTIVE WELD HEAD WEB SEPARATION COMPOUND FISSURE HORIZONTAL SPILTHEAD VERTICAL SPLITHEAD SPLIT WEB PIPED RAIL BROKEN BASE DETAIL FRACTURE ENGINE BURN FRACTURE RAIL END MISMATCH (TREAD) RAIL END MISMATCH CWR (TREAD) RAIL END MISMATCH (GAGE) X RAIL END MISMATCH CWR (GAGE) X NOT STRUCTURALLY SOUND (JOINTED) NOT STRUCTURALLY SOUND (CWR) CRACK/BROKEN NOT CENTER (JOINTED) CRACK/BROKEN NOT CENTER (CWR) CRACK/BROKEN NOT CENTER (INSUL) BAR ALLOWS EXCESSIVE MVMNT (JNT) BAR ALLOWS EXCESSIVE MVMNT (CWR) X CENTER CRACKED (JOINTED) CENTER CRACKED (CWR) CENTER CRACKED (INSULATED CWR) X LESS THAN 2 BOLTS PER RAIL LESS THAN 1 BOLT PER RAIL LESS THAN 2 BOLTS PER RAIL (CWR) LOOSE JOINT BARS (JOINTED) LOOSE JOINT BARS (CWR) TORCHED OR BURNT BOLT HOLE (CWR) INSUFFICIENT TIE PLATES CLASS 3-5 X CONCENTRATED LOAD CLASS 3-5 X FASTENERS FAIL TO MAINTAIN GAGE X INSUFFICIENT FASTENERS X INSUFFICIENT FASTENERS AT JOINT LONG RESTRAINT FAILURE OF ANCHORS X FASTENER JOINT OR SUPPORT FAILURE X LOOSE, WORN, MISSING SWITCH CLIPS MISSING SWITCH, FROG, GR PLATES LOOSE OR MISSING SW. PT. STOPS LOOSE, WORN, MISSING FROG BOLTS LOOSE, WORN, MISSING GR BOLTS LOOSE/WORN/MISSING GR COMPONENT FASTENINGS NOT INTACT/MAINTAINED LOOSE, WORN, MISSING BOLTS INSUFFICIENT ANCHORAGE X STOCK RAIL NOT SECURELY SEATED STOCK RAIL CANTED IMPROPER FIT BETWEEN SW PT AND STOCK EXCESSIVE MOVEMENT OF SW PT EXCESSIVE MOVEMENT OF STOCK RAIL WHEEL CONTACT W/ GAGE SIDE STOCK HEEL OF SWITCH INSECURE SWITCH STAND INSECURE CONNECTING ROD INSECURE IMPROPER SW CLOSURE (METAL FLOW) FROG PT CHIPPED, BROKEN, WORN FROG TREAD WORN EXCESSIVELY SEVERE FROG CONDITION WHEEL CONTACT SIDE OF SPRING RAIL TIES UNDER WING RAIL NOT TAMPED BOLT HOLE DEFECT IN SPRING FROG FROG POINT GUARD FACE NOT RESTORED GUARD CHECK GAGE LESS THAN REQUIRED GUARD FACE GAGE EXCEEDS CRACKED OR BROKEN GUARD RAIL OTHER DRAINAGE CONDITION OTHER VEGETATION CONDITION BALLAST CONDITION X CROSSTIE CONDITION (TANGENT) X WELD WEB SPLIT SSC RAIL EXCEPTION RAIL END MISMATCH CONDITION RAIL END BATTER CONDITION RAIL JOINT SIGNATURE NOT PRES TIE PLATE CONDITION X LONG RAIL MOVEMENT CONDITION X FLANGEWAY CONDITION CHIPPED/WORN SW PT CONDITION FROG POINT CONDITION GUARD WEAR CONDITION GUARD CHECK GAGE CONDITION GAGE CONDITION X ALIGNMENT 62 CONDITION X ALIGNMENT 31 CONDITION X LUBRICATION CONDITION PROFILE 62 CONDITION X CROSSLEVEL CONDITION X RUNOFF CONDITION HARMONIC OTHR HRMONIC XLEVL CONDITN HOPPER OTHER HOPPER CAR CONDITION JOINT MOVE W/ FOUL BALLAST JOINT MOVEMENT IN CURVE X DETERIORATED JOINT BAR RAIL END BATTER EXCEEDS WIDE RAIL GAP >1.5″ EXCESSIVE LONG RAIL MOVE X UNBALANCED ELEVATION F HOPPER CAR OTHER TANK CAR CONDITION OTHER WARP CONDITION X GAGE EXCEEDS (TANG.) GAGE EXCEEDS CONC (TANG.) X GAGE EXCEEDS (CURVE) X GAGE EXCEEDS CONC (CURVE) X ALIGNMENT EXCEEDS (TANG 62′) X ALIGNMENT EXCEEDS (CURVE 62′) X CROSSLEVEL EXCEEDS (CURVE) X OVER ELEVATION PROFILE DEVIATION LEFT X PROFILE DEVIATION RIGHT X CROSSLEVEL (TANGENT) X REVERSE CROSSLEVEL (CURVE) X TANK CAR (TANGENT) X WARP (TANGENT) X WARP (CURVE) X

150 380 380 182 180 380 380 380 380 In some embodiments, railroad track featuresincludes weather data. In general, weather dataincludes forecasted or historical weather conditions at a specific location (e.g., milepost, GPS coordinates, or railroad track segment) along railroad track. In some embodiments, weather dataincludes ambient temperatures. In some embodiments, weather dataincludes rail temperatures. In some embodiments, weather dataincludes ambient or rail temperature swings. In some embodiments, weather datais accessed from an online source such as AccuWeather.

160 115 150 161 160 100 160 150 150 161 150 160 150 161 160 150 161 4 FIG. 1 FIG. 4 FIG. 4 FIG. Rule weight matrixis data (e.g., a database table stored in memory) that includes a set of track features (e.g., railroad track features) and an associated preassigned risk weightfor each track feature. For example,illustrates a rule weight matrixthat may be utilized by railroad track buckling risk prediction systemof, according to particular embodiments. As illustrated in this example, rule weight matrixincludes railroad track features(e.g., all known or possible railroad track features) and a risk weightfor each particular railroad track features. As a specific example, the rule weight matrixillustrated inincludes “curve present” railroad track featureand an associated risk weightfor “curve present” of “4.” As another specific example, the rule weight matrixillustrated inincludes “change in grade” railroad track featureand an associated risk weightfor “change in grade” of “2.”

100 160 182 182 100 160 160 100 160 125 182 182 182 100 160 125 182 In some embodiments, railroad track buckling risk prediction systemutilizes a single rule weight matrixfor all railroad track segmentsirrespective of the actual geographic location of each railroad track segment. In other embodiments, however, railroad track buckling risk prediction systemincludes multiple rule weight matrices. In these embodiments, each rule weight matrixmay be associated with a respective geographical area (e.g., a specific milepost range, a specific geographic area bounded by specific GPS coordinates, a specific state, etc.), and railroad track buckling risk prediction systemselects a particular rule weight matrixto use to determine a priority levelfor each particular railroad track segmentbased on the particular geographical area in which the particular railroad track segmentis physically located. For example, if railroad track segmentis physically located between a specific milepost range, railroad track buckling risk prediction systemmay select the particular rule weight matrixassociated with the specific milepost range in order to calculate priority levelfor the railroad track segment.

170 125 170 125 182 150 182 170 125 170 125 182 170 125 182 170 125 182 170 125 182 170 125 182 170 125 182 170 170 125 Trigger eventsare a list of rules/events that cause priority levelsto be automatically set to a specific level. For example, certain trigger eventscause the priority levelfor a particular railroad track segmentto be set to the highest prioritization level (e.g., P1) regardless of any other railroad track featuresfor the particular railroad track segment. As a first example, trigger eventsmay include a surface undercut trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when there has been a surface undercut. As a second example, trigger eventsmay include a tie work trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when there has been railroad tie work performed on railroad track segment. As a third example, trigger eventsmay include a rail work trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when there has been rail work performed on railroad track segmentwithin a previous amount of time (e.g., any rail work performed within the last week). As a fourth example, trigger eventsmay include a geo defects trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when certain defects have been detected on railroad track segment(e.g., alignment defects or gage defects). As a fifth example, trigger eventsmay include a destress trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when certain destress work orders have been detected for railroad track segment(e.g., any current open destress work orders or any closed/completed destress work orders within the last week). As a sixth example, trigger eventsmay include a slow order trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when certain slow orders have been detected for railroad track segment. As a seventh example, trigger eventsmay include a compaction slow order trigger that causes priority levelto be set to the highest prioritization level (e.g., P1) when an open compaction slow order has been detected for railroad track segment. While certain trigger eventshave been described, other embodiments may utilize any other appropriate trigger eventsto automatically set priority levelto a specific level.

100 126 132 130 125 180 110 125 182 130 190 126 180 181 125 126 5 FIG. In some embodiments, railroad track buckling risk prediction systemmay display an interactive track mapon user interfaceof client system(e.g., a smartphone, a computer, a tablet, etc.) to notify the user of priority levelsfor railroad track. For example, computing systemmay display priority levelsfor a specific railroad track segmentwithin a certain geographical distance from client system. A user (e.g., track inspection crew member) may view interactive track mapand take any appropriate action (e.g., inspect locations of railroad trackat risk for track bucklesaccording to priority levels). As a result, the safety of railroad operations may be improved. A specific example of interactive track mapis discussed below in reference to.

5 FIG. 5 FIG. 126 125 100 126 510 510 510 180 180 180 125 125 510 125 126 125 126 510 510 135 180 125 125 125 125 illustrates an interactive track mapthat may display priority levelsgenerated by railroad track buckling risk prediction system, according to particular embodiments. In the illustrated embodiment of, interactive track mapincludes user-selectable elements(e.g.,A-C), graphical representations of railroad tracks(e.g.,A andB), and graphical representations of one or more of the determined priority levelsA-H. User-selectable elementsenable a user to select which priority levelsto display on interactive track map. In this example, the user has selected Priority 1 (e.g., P1) and Priority 2 (e.g., P2) levelsto display on interactive track mapas indicated by the checked boxes of user-selectable elementsA andB. As a result, Priority 1 and Priority 2 levelsare displayed along the graphical representation of railroad tracksat their appropriate locations as illustrated. In some embodiments, the displayed priority levelsare displayed in a plurality of different colors in order to indicate their priority (e.g., priority levelsof P1 are displayed in red, priority levelsof P2 are displayed in orange, priority levelsof P3 are displayed in yellow, etc.).

100 127 130 125 110 127 130 125 182 130 190 127 180 181 125 127 6 FIG. In some embodiments, railroad track buckling risk prediction systemmay send one or more notifications(e.g., a text message, an email message, and the like) to client system(e.g., a smartphone, a computer, a tablet, etc.) to notify the user of priority levels. For example, computing systemmay send a notificationfor display on client systemto notify a user of the priority levelfor a specific railroad track segmentwithin a certain geographical distance from client system. A user (e.g., track inspection crew member) may view notificationand take any appropriate action (e.g., inspect locations of railroad trackat risk for track buckleaccording to priority level). As a result, the safety of railroad operations may be improved. A specific example of ballast profile generation moduleis discussed below in reference to.

6 FIG. 127 125 100 127 190 180 181 127 605 605 605 127 610 620 630 640 650 660 670 605 182 180 610 180 125 620 180 125 630 180 125 640 180 125 650 182 125 660 182 125 670 150 125 illustrates a notificationthat may display priority levelsgenerated by railroad track buckling risk prediction system, according to particular embodiments. In general, notificationmay be sent to and viewed by a user such as track inspection crew memberin order to determine which locations along railroad trackto inspect for possible future track buckles. Notificationmay be any appropriate notification such as a text message or an email. In some embodiments, each entry(e.g.,A,B, etc.) of notificationincludes a division, a territory, a subdivision, a line segment, a beginning milepost, an ending milepost, and a top feature. Each entrymay be associated with a particular railroad track segmentof railroad track. Divisionindicates an identification of a division of railroad trackfor the particular priority level. Territoryindicates an identification of a territory of railroad trackfor the particular priority level. Subdivisionindicates an identification of a subdivision of railroad trackfor the particular priority level. Line segmentindicates an identification of a line segment of railroad trackfor the particular priority level. Beginning milepostindicates an identification of a milepost of the start of railroad track segmentfor the particular priority level. Ending milepostindicates an identification of a milepost of the end of railroad track segmentfor the particular priority level. Top featureindicates an identification of the particular railroad track featurethat contributed the most to the particular priority level.

100 125 182 180 125 181 182 181 180 125 182 100 180 210 210 180 100 125 210 180 100 125 In operation, railroad track buckling risk prediction systemgenerates and displays a priority levelfor each railroad track segmentof a railroad track. Each priority levelindicates a risk severity for a track bucklefor the particular railroad track segment(i.e., the likelihood of track buckleoccurring at some point in the future on railroad track). To determine priority levelsfor railroad track segments, some embodiments of railroad track buckling risk prediction systemmay first determine the classification of railroad trackusing track classification module. If track classification moduledetermines that railroad trackis CWR, railroad track buckling risk prediction systemmay continue processing data in order to determine priority level. If track classification moduledetermines that railroad trackis non-CWR, railroad track buckling risk prediction systemmay not determine priority levels.

100 180 182 100 220 180 182 180 182 100 150 182 150 182 100 230 240 Next, railroad track buckling risk prediction systemsegments railroad trackinto multiple railroad track segments. In some embodiments, railroad track buckling risk prediction systemutilizes track segmentation moduleto segment railroad trackinto railroad track segments. After segmenting railroad trackinto railroad track segments, railroad track buckling risk prediction systemmay then determine railroad track featuresfor each of the railroad track segments. The track features may include, for example, weather data for the railroad track segment, track conditions for the railroad track segment, track structures of the railroad track segment, and the like. To determine railroad track featuresfor each railroad track segment, some embodiments of railroad track buckling risk prediction systemmay utilize track feature identification moduleand feature allocation to segments moduleas described above.

150 182 100 125 182 150 182 160 100 250 125 125 182 181 180 182 After determining railroad track featuresfor each railroad track segment, railroad track buckling risk prediction systemmay then determine a priority levelfor each of the railroad track segmentsbased on the track featuresof each railroad track segmentand a rule weight matrix. In some embodiments, railroad track buckling risk prediction systemmay utilize priority assignment moduleas described above to determine priority levels. The priority levelfor each railroad track segmentindicates a risk of a future track bucklefor railroad trackof the railroad track segment.

100 260 125 170 125 170 100 270 125 125 182 182 182 190 181 125 190 126 280 125 190 127 290 100 181 In some embodiments, railroad track buckling risk prediction systemmay utilize trigger events moduleas described above after determining priority levelsin order to determine and apply any trigger eventsto the determined priority levels. After applying any trigger events, some embodiments of railroad track buckling risk prediction systemmay utilize clustering moduleas described above to cluster the determined priority levelbased on proximity. The priority levelsfor railroad track segments(e.g.,A-B) may then be displayed to a user such as a track inspection crew memberin order to inform the user of the most important track locations to inspect and/or address for risk of track buckle. For example, the priority levelsmay be displayed to track inspection crew membervia an interactive track mapgenerated by insights module. As another example, the priority levelsmay be displayed to track inspection crew membervia a notification(e.g., an email or text message) generated by notification module. By utilizing railroad track buckling risk prediction systemto determine railroad track locations at risk for track buckle, railroads are more likely to locate and address high-risk track locations prior to buckling. This may reduce or eliminate train derailment events, thereby increasing the public safety and increasing the efficiency of the railroad operations.

7 FIG. 700 700 120 100 710 700 150 is a chart illustrating a methodfor determining railroad track locations at risk for buckling, according to particular embodiments. In some embodiments, methodmay be performed by railroad track buckling risk prediction moduleof railroad track buckling risk prediction system. At step, methodaccesses railroad track data. In some embodiments, the railroad track data is railroad track features.

720 700 720 220 182 At step, methodsegments a railroad track into a plurality of railroad track segments. In some embodiments, stepis performed by track segmentation module. In some embodiments, the track segments are railroad track segmentand are a predetermined length such as 200 feet.

730 740 720 730 700 710 150 220 230 730 In some embodiments, stepsandare performed for each segment of the railroad track as determined in step. At step, methoddetermines, from the railroad track data of step, a plurality of track features for the particular railroad track segment. In some embodiments, the track features are railroad track featuresassociated with the particular railroad track segment (e.g., by geographical location). In some embodiments, track segmentation moduleand track feature identification moduleare utilized in step.

740 700 125 730 160 At step, methoddetermines a priority level for the particular railroad track segment. In some embodiments, the priority level is priority level. In some embodiments, the priority level indicates a track buckling risk severity for the particular railroad track segment. In some embodiments, the priority level is determined using the determined plurality of track features for the particular railroad track segment of stepand a particular rule weight matrix. The rule weight matrix may include a set of track features and an associated weight for each track feature of the set of track features. In some embodiments, the rule weight matrix is rule weight matrix.

740 161 740 740 In some embodiments, stepincludes determining, using the rule weight matrix, a risk weight for each of the determined plurality of track features for the particular railroad track segment. In some embodiments, the risk weight is risk weight. Stepmay also include summing all of the determined risk weights for the determined plurality of track features for the particular railroad track segment to calculate a cumulative features risk weight. Stepmay also include choosing the priority level for the particular railroad track segment based on the cumulative features risk weight.

750 700 750 510 750 700 At step, methoddisplays the determined priority levels for the plurality of track segments on an electronic display. In some embodiments, stepincludes displaying the determined priority levels for the plurality of track segments in an interactive track map. The interactive track map may include a plurality of user-selectable elements (e.g., elements) for selecting which priority levels to display. The interactive track map may also include a graphical representation of a plurality of railroad tracks and graphical representations of one or more of the determined priority levels for the plurality of track segments displayed along the graphical representation of the plurality of railroad tracks according to the user-selectable elements. In some embodiments, the determined priority levels are displayed in a plurality of different colors. After step, methodmay end.

700 127 740 In some embodiments, methodmay additionally include sending a notification to a user. The notification may be notification. In some embodiments, the notification indicates the determined priority levels for the plurality of track segments of step.

700 170 740 In some embodiments, methodmay additionally include determining a plurality of trigger events for the particular railroad track segment. In some embodiments, the trigger events are trigger events. In some embodiments, the priority level for the particular railroad track segment is further determined in stepusing the determined plurality of trigger events for the particular railroad track segment.

700 In some embodiments, methodmay additionally include clustering the determined priority levels for the plurality of track segments prior to displaying the determined priority levels for the plurality of track segments on the electronic display. In some embodiments, this step may include using DBSCAN.

7 FIG. 7 FIG. 7 FIG. 7 FIG. 7 FIG. 7 FIG. 7 FIG. Particular embodiments may repeat one or more steps of the method of, where appropriate. Although this disclosure describes and illustrates particular steps of the method ofas occurring in a particular order, this disclosure contemplates any suitable steps of the method ofoccurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method including the particular steps of the method of, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of.

8 FIG. 800 800 800 800 800 illustrates an example computer systemthat can be utilized to implement aspects of the various methods and systems presented herein, according to particular embodiments. In particular embodiments, one or more computer systemsperform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computer systemsprovide functionality described or illustrated herein. In particular embodiments, software running on one or more computer systemsperforms one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Particular embodiments include one or more portions of one or more computer systems. Herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

800 800 800 800 800 800 800 800 This disclosure contemplates any suitable number of computer systems. This disclosure contemplates computer systemtaking any suitable physical form. As example and not by way of limitation, computer systemmay be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these. Where appropriate, computer systemmay include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systemsmay perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more computer systemsmay perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systemsmay perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.

800 802 804 806 808 810 812 In particular embodiments, computer systemincludes a processor, memory, storage, an input/output (I/O) interface, a communication interface, and a bus. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.

802 802 804 806 804 806 802 802 802 804 806 802 804 806 802 802 802 804 806 802 802 802 802 802 802 In particular embodiments, processorincludes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processormay retrieve (or fetch) the instructions from an internal register, an internal cache, memory, or storage; decode and execute them; and then write one or more results to an internal register, an internal cache, memory, or storage. In particular embodiments, processormay include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processorincluding any suitable number of any suitable internal caches, where appropriate. As an example, and not by way of limitation, processormay include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memoryor storage, and the instruction caches may speed up retrieval of those instructions by processor. Data in the data caches may be copies of data in memoryor storagefor instructions executing at processorto operate on; the results of previous instructions executed at processorfor access by subsequent instructions executing at processoror for writing to memoryor storage; or other suitable data. The data caches may speed up read or write operations by processor. The TLBs may speed up virtual-address translation for processor. In particular embodiments, processormay include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processorincluding any suitable number of any suitable internal registers, where appropriate. Where appropriate, processormay include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

804 802 802 800 806 800 804 802 804 802 802 802 804 802 804 806 804 806 802 804 812 802 804 804 802 804 804 804 In particular embodiments, memoryincludes main memory for storing instructions for processorto execute or data for processorto operate on. As an example, and not by way of limitation, computer systemmay load instructions from storageor another source (such as, for example, another computer system) to memory. Processormay then load the instructions from memoryto an internal register or internal cache. To execute the instructions, processormay retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processormay write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processormay then write one or more of those results to memory. In particular embodiments, processorexecutes only instructions in one or more internal registers or internal caches or in memory(as opposed to storageor elsewhere) and operates only on data in one or more internal registers or internal caches or in memory(as opposed to storageor elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processorto memory. Busmay include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processorand memoryand facilitate accesses to memoryrequested by processor. In particular embodiments, memoryincludes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memorymay include one or more memories, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

806 806 806 806 800 806 806 806 806 802 806 806 806 In particular embodiments, storageincludes mass storage for data or instructions. As an example, and not by way of limitation, storagemay include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storagemay include removable or non-removable (or fixed) media, where appropriate. Storagemay be internal or external to computer system, where appropriate. In particular embodiments, storageis non-volatile, solid-state memory. In particular embodiments, storageincludes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storagetaking any suitable physical form. Storagemay include one or more storage control units facilitating communication between processorand storage, where appropriate. Where appropriate, storagemay include one or more storages. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

808 800 800 800 808 808 802 808 808 In particular embodiments, I/O interfaceincludes hardware, software, or both, providing one or more interfaces for communication between computer systemand one or more I/O devices. Computer systemmay include one or more of these I/O devices, where appropriate. One or more of these I/O devices may enable communication between a person and computer system. As an example, and not by way of limitation, an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these. An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfacesfor them. Where appropriate, I/O interfacemay include one or more device or software drivers enabling processorto drive one or more of these I/O devices. I/O interfacemay include one or more I/O interfaces, where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface.

810 800 800 810 810 800 800 800 810 810 810 In particular embodiments, communication interfaceincludes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer systemand one or more other computer systemsor one or more networks. As an example, and not by way of limitation, communication interfacemay include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interfacefor it. As an example, and not by way of limitation, computer systemmay communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer systemmay communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network, a Long-Term Evolution (LTE) network, or a 5G network), or other suitable wireless network or a combination of two or more of these. Computer systemmay include any suitable communication interfacefor any of these networks, where appropriate. Communication interfacemay include one or more communication interfaces, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

812 800 812 812 812 In particular embodiments, busincludes hardware, software, or both coupling components of computer systemto each other. As an example and not by way of limitation, busmay include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Busmay include one or more buses, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

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.

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.

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 disclosure can be established by the appended claims. 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.

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.

Functional blocks and modules in the included FIGURES 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.

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.

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.

Although the present disclosure 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 disclosure 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 disclosure, 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 disclosure. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.