SYSTEMS AND METHODS FOR DETERMINING RAILROAD TRACK LOCATIONS AT RISK FOR BUCKLING
In some embodiments, 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.
Latest BNSF Railway Company Patents:
- SYSTEMS AND METHODS FOR INDEPENDENT CHECKING AND HEALING OF POSITIVE TRAIN CONTROL SYSTEMS
- INTELLIGENT SYSTEMS AND METHODS FOR ALGORITHMICALLY MANAGING DISTRIBUTION OF CERTIFICATES OF TRANSPORTATION
- SYSTEM, METHOD AND APPARATUS FOR LADING MEASUREMENT IN A RAIL CAR
- System and method for optimizing utilization of hostler resources of a hub based on a dual-stream resource optimization
- System and method for intermodal facility management
The present disclosure relates generally to railroad tracks, and more particularly to systems and methods for determining railroad track locations at risk for buckling.
BACKGROUNDRail 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.
SUMMARYThe 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.
For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
It should be understood that the drawings are not necessarily to scale and that the disclosed embodiments are sometimes illustrated diagrammatically and in partial views. In certain instances, details which are not necessary for an understanding of the disclosed methods and apparatuses or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the particular embodiments illustrated herein.
DETAILED DESCRIPTIONThe 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.
The disclosed embodiments will not be described in reference to
It is noted that the functional blocks, and components thereof, of railroad track buckling risk prediction system 100 of 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.
It is also noted that various components of railroad track buckling risk prediction system 100 are illustrated as single and separate components. However, it will be appreciated that each of the various illustrated components may be implemented as a single component (e.g., a single application, server module, etc.), may be functional components of a single component, or the functionality of these various components may be distributed over multiple devices/components. In such embodiments, the functionality of each respective component may be aggregated from the functionality of multiple modules residing in a single, or in multiple devices.
It is further noted that functionalities described with reference to each of the different functional blocks of railroad track buckling risk prediction system 100 described herein is provided for purposes of illustration, rather than by way of limitation and that functionalities described as being provided by different functional blocks may be combined into a single component or may be provided via computing resources disposed in a cloud-based environment accessible over a network, such as one of network 140.
In general, railroad track buckling risk prediction system 100 generates and displays a priority level 125 for each railroad track segment 182 of a railroad track 180. Each priority level 125 indicates a risk severity for a track buckle 181 for the particular railroad track segment 182 (i.e., the likelihood of track buckle 181 occurring at some point in the future on railroad track 180). To determine priority levels 125, railroad track buckling risk prediction system 100 segments railroad track 180 into multiple railroad track segments 182 and then determines track features (e.g., railroad track features 150) for each of the railroad track segments 182. 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 system 100 may then determine a priority level 125 for each of the railroad track segments 182 based on the track features of each railroad track segment 182 and a rule weight matrix 160. The priority level 125 for each railroad track segment 182 indicates a risk of a future track buckle 181 for railroad track 180 of the railroad track segment 182. The priority levels 125 for railroad track segments 182 (e.g., 182A-182B) may then be displayed to a user such as a track inspection crew member 190 in order to inform the user of the most important track locations to inspect and/or address for risk of track buckle 181. For example, the priority levels 125 may be displayed to track inspection crew member 190 via an interactive track map 1260 and/or via a notification 1270 (e.g., an email or text message) on client system 130. By utilizing railroad track buckling risk prediction system 100 to determine railroad track locations at risk for track buckle 181, 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.
Computing system 110 may be any appropriate computing system in any suitable physical form. As example and not by way of limitation, computing system 110 may 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 system 110 may 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 system 110 may 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 system 110 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. Computing system 110 may 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 system 110 is described in reference to
Computing system 110 includes one or more memory units/devices 115 (collectively herein, “memory 115”) that may store railroad track buckling risk prediction module 120. Railroad track buckling risk prediction module 120 may be a software module/application utilized by computing system 110 to generate and display priority levels 125 for railroad track 180, as described herein. Railroad track buckling risk prediction module 120 represents any suitable set of instructions, logic, or code embodied in a computer-readable storage medium. For example, railroad track buckling risk prediction module 120 may be embodied in memory 115, a disk, a CD, or a flash drive. In particular embodiments, railroad track buckling risk prediction module 120 may 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 module 120 is discussed below in reference to
In some embodiments, railroad track buckling risk prediction module 120 includes a track classification module 210, a track segmentation module 220, a track feature identification module 230, a feature allocation to segments module 240, a priority assignment module 250, a trigger events module 260, a clustering module 270, an insights module 280, and a notification module 290. Each of these individual modules will be described in more detail below. It is noted that although
In some embodiments, railroad track buckling risk prediction module 120 includes track classification module 210. In general, track classification module 210 determines whether railroad track 180 is CWR or non-CWR. In some embodiments, for example, track classification module 210 may analyze railroad track features 150 (e.g., track structures 310 as described below) to determine whether railroad track 180 is CWR or non-CWR. In some embodiments, if track classification module 210 determines that railroad track 180 is CWR, railroad track buckling risk prediction module 120 continues processing data to calculate priority level 125 since CWR rail is more prone to develop track buckles 181. However, if railroad track buckling risk prediction module 120 determines that railroad track 180 is non-CWR, some embodiments of railroad track 180 may discontinue processing data and decline to calculate priority level 125 since non-CWR rail is much less prone to develop track buckles 181.
In some embodiments, railroad track buckling risk prediction module 120 includes track segmentation module 220. In general, track segmentation module 220 divides railroad track 180 into smaller segments (i.e., railroad track segments 182A, 182B, etc.) and then calculates a priority level 125 for each specific railroad track segment 182. In some embodiments, each railroad track segment 182 is a predetermined length of track and includes a starting milepost and an ending milepost. For example, railroad track segments 182 may be between 200 and 300 feet of railroad track 180. As a specific example, each railroad track segment 182 may be 1/20 of a mile (approximately 264 feet). In other embodiments, railroad track segments 182 may be any other appropriate length according to the specifications of railroad track buckling risk prediction system 100 or user input.
In some embodiments, railroad track buckling risk prediction module 120 includes track feature identification module 230. In general, track feature identification module 230 collects or otherwise accesses all possible railroad track features 150 to analyze. Railroad track features 150 are discussed in more detail below. In some embodiments, track feature identification module 230 accesses railroad track features 150 from memory 115. In other embodiments, track feature identification module 230 accesses all or a portion of railroad track features 150 from another computer system (e.g., via network 140).
In some embodiments, railroad track buckling risk prediction module 120 includes feature allocation to segments module 240. In general, feature allocation to segments module 240 determines a subset of railroad track features 150 that are applicable to each particular railroad track segment 182. In some embodiments, for example, feature allocation to segments module 240 analyzes a geographic location (e.g., milepost range, GPS coordinates, etc.) associated with each particular railroad track segment 182 and then filters railroad track features 150 to determine those railroad track features 150 having locations that match the geographic location for the particular railroad track segment 182. As a specific example, feature allocation to segments module 240 may determine that railroad track segment 182A has a beginning milepost of 216.443 and an ending milepost of 216.481. Feature allocation to segments module 240 may then analyze all of railroad track features 150 to determine a subset of railroad track features 150 that each have an associated location that falls within the milepost range of railroad track segment 182A. The subset of railroad track features 150 that are determined by feature allocation to segments module 240 to correspond to railroad track segment 182A may then be passed to priority assignment module 250 for processing.
In some embodiments, railroad track buckling risk prediction module 120 includes priority assignment module 250. In general, priority assignment module 250 utilizes the railroad track features 150 for a particular railroad track segment 182 (i.e., as determined by feature allocation to segments module 240) along with rule weight matrix 160 to determine or otherwise assign a priority level 125 for the particular railroad track segment 182. A particular embodiment of rule weight matrix 160 is described in more detail below. In general, rule weight matrix 160 includes railroad track features 150 and an associated preassigned risk weight (e.g., risk weight 161) for each track feature 150. In some embodiments, feature allocation to segments module 240 cross-references the railroad track features 150 for a particular railroad track segment 182 with rule weight matrix 160 in order to determine a risk weight for each railroad track feature 150 for the particular railroad track segment 182. In some embodiments, feature allocation to segments module 240 may then sum all of the risk weights for the particular railroad track segment 182 in order to calculate a cumulative feature risk weight for the particular railroad track segment 182. The calculated cumulative feature risk weight may then be used to determine which priority level 125 to assign to the particular railroad track segment 182. For example, if the possible priority levels 125 are P1, P2, P3, and P4 (with P1 being the highest priority and P4 being the lowest priority), the priority level 125 for the particular railroad track segment 182 may be assigned according to the following table:
In some embodiments, railroad track buckling risk prediction module 120 includes trigger events module 260. In general, trigger events module 260 determines if any trigger events 170 are applicable to each particular railroad track segment 182. As described in more detail below, trigger events 170 are a list of rules/events that cause priority levels 125 to be automatically set to a specific level. For example, certain trigger events 170 cause the priority level 125 for a particular railroad track segment 182 to be set to the highest prioritization level (e.g., P1) regardless of the cumulative feature risk weight for the particular railroad track segment 182. If trigger events module 260 determines that any trigger events 170 are applicable to a particular railroad track segment 182, trigger events module 260 may immediately adjust or otherwise assign the priority level 125 for the particular railroad track segment 182 to the indicated priority level 125 (e.g., typically the highest priority level 125 such as P1).
In some embodiments, railroad track buckling risk prediction module 120 includes clustering module 270. In general, clustering module 270 attempts to locate two priority levels 125 that are within a certain distance of each other. For example, if a priority level 125 of P1 is within a certain distance of a priority level 125 of P2, clustering module 270 may combine the two priority levels 125 into a single P1. In some embodiments, clustering module 270 may utilize any appropriate algorithm to cluster priority level 125. For example, some embodiments of clustering module 270 may utilize Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
In some embodiments, railroad track buckling risk prediction module 120 includes insights module 280. In general, insights module 280 generates insights such as interactive track map 126 for display to users such as track inspection crew member 190. In some embodiments, for example, insights module 280 generates and electronically transmits interactive track map 126 for display on client system 130. Interactive track map 126 is discussed in more detail below in reference to
In some embodiments, railroad track buckling risk prediction module 120 includes notification module 290. In general, notification module 290 generates notifications 127 for display to users such as track inspection crew member 190. In some embodiments, for example, notification module 290 generates and electronically transmits notifications 127 for display on client system 130. In some embodiments, notifications 127 include priority levels 125. Notifications 127 are discussed in more detail below in reference to
Returning to
Client system 130 is any appropriate user device for communicating with components of railroad track buckling risk prediction system 100 over network 140 (e.g., the internet). In particular embodiments, client system 130 may 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 130. As an example, and not by way of limitation, a client system 130 may include a computer system (e.g., computer system 800) 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 130. A client system 130 may enable a network user (e.g., track inspection crew member 190) at client system 130 to access network 140. A client system 130 may enable a user to communicate with other users at other client systems 130. Client system 130 may include an electronic display that displays graphical user interface 132, a processor such processor 802, and memory such as memory 804.
Switching equipment 135 includes equipment or devices that direct trains to specific railroad tracks 180. In some embodiments, switching equipment 135 includes automatic track switches and retarders that operate to switch traing or railcars onto specific railroad tracks 180. In some embodiments, computing system 110 is electronically coupled to switching equipment 135 using any wired or wireless technology via network 140. In general, computing system 110 sends switching signals 136 to switching equipment 135 in order to automatically direct trains away from areas along railroad track 180 at risk for track buckle 181 as determined by railroad track buckling risk prediction system 100.
Switching signals 136 are any electronic signals that are sent (e.g., wirelessly or wired) to switching equipment 135 in order to automatically control switching operations to automatically direct trains away from areas along railroad track 180 at risk for track buckle 181 as determined by railroad track buckling risk prediction system 100. For example, if railroad track buckling risk prediction system 100 determines a priority level 125 for railroad track segment 182A that indicates a high risk for track buckle 181, computing system 110 may send switching signals 180 to switching equipment 135 in order to automatically direct trains away from railroad track segment 182A.
Network 140 allows communication between and amongst the various components of railroad track buckling risk prediction system 100. This disclosure contemplates network 140 being any suitable network operable to facilitate communication between the components of railroad track buckling risk prediction system 100. Network 140 may include any interconnecting system capable of transmitting audio, video, signals, data, messages, or any combination of the preceding. Network 140 may 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.
Railroad track features 150 include any data about railroad track 180 that is gathered, stored, or otherwise accessed. For example, a portion of railroad track features 150 may be gathered by a vehicle as the vehicle travels along railroad track 180 (e.g., using sensors such as LiDAR). As another example, railroad track features 150 may include weather data for the location of railroad track 180. In general, railroad track features 150 are utilized along with rule weight matrix 160 to determine priority levels 125, as described in more detail herein. A particular embodiment of railroad track features 150 will now be described in reference to
In some embodiments, railroad track features 150 includes track structures 310. In general, each track structure 310 is a physical aspect of railroad track 180. In some embodiments, track structures 310 include whether or not railroad track 180 includes a curve (and/or an amount of curve of railroad track 180). In some embodiments, track structures 310 include whether or not railroad track 180 includes a tangent or a spiral. In some embodiments, track structures 310 include a grade of railroad track 180 (e.g., a specific grade of railroad track 180 and/or whether railroad track 180 includes a grade that is greater than a predetermine amount such as +/−1%). In some embodiments, track structures 310 include whether or not railroad track 180 is CWR, a CWR zone, or an open rail removal zone.
In some embodiments, railroad track features 150 includes fixed assets 320. In general, each fixed asset 320 is specific physical structure or element of railroad track 180. For example, in some embodiments, fixed assets 320 include bridges and culverts of railroad track 180. As another example, fixed assets 320 may include grade crossings and railroad crossings (e.g., diamonds) of railroad track 180. As yet another example, fixed assets 320 may include control points (e.g., signals) and switches of railroad track 180.
In some embodiments, railroad track features 150 includes tie-rail interactions 330. In general, tie-rail interactions 330 indicate the quality of the interface between the railroad ties and the rails of railroad track 180. In some embodiments, tie-rail interactions 330 include an anchor and fastener condition. In some embodiments, tie-rail interactions 330 include a railroad tie type (e.g., concrete or wood). In some embodiments, tie-rail interactions 330 include a railroad tie density. In some embodiments, tie-rail interactions 330 include a railroad tie condition.
In some embodiments, railroad track features 150 includes tie-ballast interactions 340. In general, tie-ballast interactions 340 indicate the tie quality or the quality of the interface between the railroad ties and the ballast of railroad track 180. In some embodiments, tie-ballast interactions 340 include measurements from one or more sensors (e.g., a LiDAR device) attached to vehicle travelling along railroad track 180. In some embodiments, tie-ballast interactions 340 include a ballast fouling index (BFI) (e.g., a measurement of the structural condition of the ballast of railroad track 180) for one or more of a left side of railroad track 180, a right side of railroad track 180, and a center of railroad track 180. In some embodiments, tie-ballast interactions 340 include a ballast condition or volume. In some embodiments, tie-ballast interactions 340 include a crib ballast condition or volume (i.e., the ballast that is packed between sleepers of railroad track 180). In some embodiments, tie-ballast interactions 340 include a shoulder size of railroad track 180. In some embodiments, tie-ballast interactions 340 include a ballast lateral strength of railroad track 180. In some embodiments, tie-ballast interactions 340 include a ballast consolidation.
In some embodiments, railroad track features 150 includes axial/lateral dynamic forces 350. In general, dynamic forces 350 indicate events or features of railroad track 180 that may cause excessive or abnormal axial/lateral forces on railroad track 180. In some embodiments, dynamic forces 350 include the speed limit of railroad track 180 (e.g., the track class). In some embodiments, dynamic forces 350 include a track slope (e.g., grade) or grade changes of railroad track 180. In some embodiments, dynamic forces 350 include lateral movements (e.g., alignment values) for railroad track 180 that indicate vertical and lateral movement of the rails of railroad track 180. In some embodiments, dynamic forces 350 include braking and track side forces of railroad track 180. In some embodiments, dynamic forces 350 include MGT (i.e., the total weight of freight transported) on railroad track 180. In some embodiments, dynamic forces 350 include any slow orders for railroad track 180 (i.e., temporary speed restrictions placed on railroad track 180 when it is unsafe for trains to operate at the normal speed).
In embodiments where dynamic forces 350 include 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 100. On the contrary, slow orders that have been predefined as high-risk slow orders may be further analyzed by railroad track buckling risk prediction system 100 and may result in a higher priority level 125. 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:
In some embodiments, railroad track features 150 includes work orders 360. In general, work orders 360 include any work orders for railroad track 180 that have been performed within a certain prior amount of time (e.g., work orders performed on a specific railroad track segment 182 or milepost location within the last 30 days). In some embodiments, work orders 360 include rail work orders. In some embodiments, work orders 360 include tie work orders. In some embodiments, work orders 360 include curve staking. In some embodiments, each work order 360 includes a location of the work performed on railroad track 180 (e.g., a milepost or GPS coordinates).
In some embodiments, work orders 360 are 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 100. On the contrary, high-risk work orders 360 may be further analyzed by railroad track buckling risk prediction system 100 and may result in a higher priority level 125. As an example for illustrative purposes only, work orders 360 may be analyzed and filtered according to the table below in order to identify high-risk work orders 360:
In some embodiments, railroad track features 150 includes track conditions 370. In general, track conditions 370 each identify a specific physical state of railroad track 180. In some embodiments, track conditions 370 include measurements from one or more sensors (e.g., a LiDAR device) attached to vehicle travelling along railroad track 180. In some embodiments, track conditions 370 include rail alignment defects or conditions of railroad track 180. In some embodiments, track conditions 370 include rail surface defects or conditions of railroad track 180. In some embodiments, track conditions 370 include manual defects or conditions of railroad track 180. In some embodiments, track conditions 370 include joint defects or conditions of railroad track 180. In some embodiments, track conditions 370 include past track buckle data for railroad track 180 (e.g., dates and locations of past track buckles 181). In some embodiments, track conditions 370 include destress locations of railroad track 180. In some embodiments, track conditions 370 include track neutral temperature (TNT) of railroad track 180.
In some embodiments, track conditions 370 are filtered based on a predetermined assigned risk. For example, lower-risk track conditions 370 may be discarded, discounted, or otherwise ignored by railroad track buckling risk prediction system 100. On the contrary, high-risk track conditions 370 may be further analyzed by railroad track buckling risk prediction system 100 and may result in a higher priority level 125. As an example for illustrative purposes only, track conditions 370 may be analyzed and filtered according to the table below in order to identify high-risk track conditions 370:
In some embodiments, railroad track features 150 includes weather data 380. In general, weather data 380 includes forecasted or historical weather conditions at a specific location (e.g., milepost, GPS coordinates, or railroad track segment 182) along railroad track 180. In some embodiments, weather data 380 includes ambient temperatures. In some embodiments, weather data 380 includes rail temperatures. In some embodiments, weather data 380 includes ambient or rail temperature swings. In some embodiments, weather data 380 is accessed from an online source such as AccuWeather.
Rule weight matrix 160 is data (e.g., a database table stored in memory 115) that includes a set of track features (e.g., railroad track features 150) and an associated preassigned risk weight 161 for each track feature. For example,
In some embodiments, railroad track buckling risk prediction system 100 utilizes a single rule weight matrix 160 for all railroad track segments 182 irrespective of the actual geographic location of each railroad track segment 182. In other embodiments, however, railroad track buckling risk prediction system 100 includes multiple rule weight matrices 160. In these embodiments, each rule weight matrix 160 may 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 system 100 selects a particular rule weight matrix 160 to use to determine a priority level 125 for each particular railroad track segment 182 based on the particular geographical area in which the particular railroad track segment 182 is physically located. For example, if railroad track segment 182 is physically located between a specific milepost range, railroad track buckling risk prediction system 100 may select the particular rule weight matrix 160 associated with the specific milepost range in order to calculate priority level 125 for the railroad track segment 182.
Trigger events 170 are a list of rules/events that cause priority levels 125 to be automatically set to a specific level. For example, certain trigger events 170 cause the priority level 125 for a particular railroad track segment 182 to be set to the highest prioritization level (e.g., P1) regardless of any other railroad track features 150 for the particular railroad track segment 182. As a first example, trigger events 170 may include a surface undercut trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when there has been a surface undercut. As a second example, trigger events 170 may include a tie work trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when there has been railroad tie work performed on railroad track segment 182. As a third example, trigger events 170 may include a rail work trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when there has been rail work performed on railroad track segment 182 within a previous amount of time (e.g., any rail work performed within the last week). As a fourth example, trigger events 170 may include a geo defects trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when certain defects have been detected on railroad track segment 182 (e.g., alignment defects or gage defects). As a fifth example, trigger events 170 may include a destress trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when certain destress work orders have been detected for railroad track segment 182 (e.g., any current open destress work orders or any closed/completed destress work orders within the last week). As a sixth example, trigger events 170 may include a slow order trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when certain slow orders have been detected for railroad track segment 182. As a seventh example, trigger events 170 may include a compaction slow order trigger that causes priority level 125 to be set to the highest prioritization level (e.g., P1) when an open compaction slow order has been detected for railroad track segment 182. While certain trigger events 170 have been described, other embodiments may utilize any other appropriate trigger events 170 to automatically set priority level 125 to a specific level.
In some embodiments, railroad track buckling risk prediction system 100 may display an interactive track map 126 on user interface 132 of client system 130 (e.g., a smartphone, a computer, a tablet, etc.) to notify the user of priority levels 125 for railroad track 180. For example, computing system 110 may display priority levels 125 for a specific railroad track segment 182 within a certain geographical distance from client system 130. A user (e.g., track inspection crew member 190) may view interactive track map 126 and take any appropriate action (e.g., inspect locations of railroad track 180 at risk for track buckles 181 according to priority levels 125). As a result, the safety of railroad operations may be improved. A specific example of interactive track map 126 is discussed below in reference to
In some embodiments, railroad track buckling risk prediction system 100 may send one or more notifications 127 (e.g., a text message, an email message, and the like) to client system 130 (e.g., a smartphone, a computer, a tablet, etc.) to notify the user of priority levels 125. For example, computing system 110 may send a notification 127 for display on client system 130 to notify a user of the priority level 125 for a specific railroad track segment 182 within a certain geographical distance from client system 130. A user (e.g., track inspection crew member 190) may view notification 127 and take any appropriate action (e.g., inspect locations of railroad track 180 at risk for track buckle 181 according to priority level 125). As a result, the safety of railroad operations may be improved. A specific example of ballast profile generation module 127 is discussed below in reference to
In operation, railroad track buckling risk prediction system 100 generates and displays a priority level 125 for each railroad track segment 182 of a railroad track 180. Each priority level 125 indicates a risk severity for a track buckle 181 for the particular railroad track segment 182 (i.e., the likelihood of track buckle 181 occurring at some point in the future on railroad track 180). To determine priority levels 125 for railroad track segments 182, some embodiments of railroad track buckling risk prediction system 100 may first determine the classification of railroad track 180 using track classification module 210. If track classification module 210 determines that railroad track 180 is CWR, railroad track buckling risk prediction system 100 may continue processing data in order to determine priority level 125. If track classification module 210 determines that railroad track 180 is non-CWR, railroad track buckling risk prediction system 100 may not determine priority levels 125.
Next, railroad track buckling risk prediction system 100 segments railroad track 180 into multiple railroad track segments 182. In some embodiments, railroad track buckling risk prediction system 100 utilizes track segmentation module 220 to segment railroad track 180 into railroad track segments 182. After segmenting railroad track 180 into railroad track segments 182, railroad track buckling risk prediction system 100 may then determine railroad track features 150 for each of the railroad track segments 182. 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 features 150 for each railroad track segment 182, some embodiments of railroad track buckling risk prediction system 100 may utilize track feature identification module 230 and feature allocation to segments module 240 as described above.
After determining railroad track features 150 for each railroad track segment 182, railroad track buckling risk prediction system 100 may then determine a priority level 125 for each of the railroad track segments 182 based on the track features 150 of each railroad track segment 182 and a rule weight matrix 160. In some embodiments, railroad track buckling risk prediction system 100 may utilize priority assignment module 250 as described above to determine priority levels 125. The priority level 125 for each railroad track segment 182 indicates a risk of a future track buckle 181 for railroad track 180 of the railroad track segment 182.
In some embodiments, railroad track buckling risk prediction system 100 may utilize trigger events module 260 as described above after determining priority levels 125 in order to determine and apply any trigger events 170 to the determined priority levels 125. After applying any trigger events 170, some embodiments of railroad track buckling risk prediction system 100 may utilize clustering module 270 as described above to cluster the determined priority level 125 based on proximity. The priority levels 125 for railroad track segments 182 (e.g., 182A-182B) may then be displayed to a user such as a track inspection crew member 190 in order to inform the user of the most important track locations to inspect and/or address for risk of track buckle 181. For example, the priority levels 125 may be displayed to track inspection crew member 190 via an interactive track map 126 generated by insights module 280. As another example, the priority levels 125 may be displayed to track inspection crew member 190 via a notification 127 (e.g., an email or text message) generated by notification module 290. By utilizing railroad track buckling risk prediction system 100 to determine railroad track locations at risk for track buckle 181, 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.
At step 720, method 700 segments a railroad track into a plurality of railroad track segments. In some embodiments, step 720 is performed by track segmentation module 220. In some embodiments, the track segments are railroad track segment 182 and are a predetermined length such as 200 feet.
In some embodiments, steps 730 and 740 are performed for each segment of the railroad track as determined in step 720. At step 730, method 700 determines, from the railroad track data of step 710, a plurality of track features for the particular railroad track segment. In some embodiments, the track features are railroad track features 150 associated with the particular railroad track segment (e.g., by geographical location). In some embodiments, track segmentation module 220 and track feature identification module 230 are utilized in step 730.
At step 740, method 700 determines a priority level for the particular railroad track segment. In some embodiments, the priority level is priority level 125. 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 step 730 and 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 160.
In some embodiments, step 740 includes 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 161. Step 740 may 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. Step 740 may also include choosing the priority level for the particular railroad track segment based on the cumulative features risk weight.
At step 750, method 700 displays the determined priority levels for the plurality of track segments on an electronic display. In some embodiments, step 750 includes 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 510) 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 750, method 700 may end.
In some embodiments, method 700 may additionally include sending a notification to a user. The notification may be notification 127. In some embodiments, the notification indicates the determined priority levels for the plurality of track segments of step 740.
In some embodiments, method 700 may additionally include determining a plurality of trigger events for the particular railroad track segment. In some embodiments, the trigger events are trigger events 170. In some embodiments, the priority level for the particular railroad track segment is further determined in step 740 using the determined plurality of trigger events for the particular railroad track segment.
In some embodiments, method 700 may 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.
Particular embodiments may repeat one or more steps of the method of
This disclosure contemplates any suitable number of computer systems 800. This disclosure contemplates computer system 800 taking any suitable physical form. As example and not by way of limitation, computer system 800 may 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 system 800 may include one or more computer systems 800; 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 systems 800 may 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 systems 800 may 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 systems 800 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.
In particular embodiments, computer system 800 includes a processor 802, memory 804, storage 806, an input/output (I/O) interface 808, a communication interface 810, and a bus 812. 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.
In particular embodiments, processor 802 includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor 802 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 804, or storage 806; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 804, or storage 806. In particular embodiments, processor 802 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 802 including any suitable number of any suitable internal caches, where appropriate. As an example, and not by way of limitation, processor 802 may 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 memory 804 or storage 806, and the instruction caches may speed up retrieval of those instructions by processor 802. Data in the data caches may be copies of data in memory 804 or storage 806 for instructions executing at processor 802 to operate on; the results of previous instructions executed at processor 802 for access by subsequent instructions executing at processor 802 or for writing to memory 804 or storage 806; or other suitable data. The data caches may speed up read or write operations by processor 802. The TLBs may speed up virtual-address translation for processor 802. In particular embodiments, processor 802 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 802 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 802 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 802. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.
In particular embodiments, memory 804 includes main memory for storing instructions for processor 802 to execute or data for processor 802 to operate on. As an example, and not by way of limitation, computer system 800 may load instructions from storage 806 or another source (such as, for example, another computer system 800) to memory 804. Processor 802 may then load the instructions from memory 804 to an internal register or internal cache. To execute the instructions, processor 802 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 802 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 802 may then write one or more of those results to memory 804. In particular embodiments, processor 802 executes only instructions in one or more internal registers or internal caches or in memory 804 (as opposed to storage 806 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 804 (as opposed to storage 806 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 802 to memory 804. Bus 812 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 802 and memory 804 and facilitate accesses to memory 804 requested by processor 802. In particular embodiments, memory 804 includes 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. Memory 804 may include one or more memories 804, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.
In particular embodiments, storage 806 includes mass storage for data or instructions. As an example, and not by way of limitation, storage 806 may 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. Storage 806 may include removable or non-removable (or fixed) media, where appropriate. Storage 806 may be internal or external to computer system 800, where appropriate. In particular embodiments, storage 806 is non-volatile, solid-state memory. In particular embodiments, storage 806 includes 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 storage 806 taking any suitable physical form. Storage 806 may include one or more storage control units facilitating communication between processor 802 and storage 806, where appropriate. Where appropriate, storage 806 may include one or more storages 806. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.
In particular embodiments, I/O interface 808 includes hardware, software, or both, providing one or more interfaces for communication between computer system 800 and one or more I/O devices. Computer system 800 may 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 800. 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 interfaces 808 for them. Where appropriate, I/O interface 808 may include one or more device or software drivers enabling processor 802 to drive one or more of these I/O devices. I/O interface 808 may include one or more I/O interfaces 808, where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface.
In particular embodiments, communication interface 810 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 800 and one or more other computer systems 800 or one or more networks. As an example, and not by way of limitation, communication interface 810 may 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 interface 810 for it. As an example, and not by way of limitation, computer system 800 may 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 system 800 may 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 system 800 may include any suitable communication interface 810 for any of these networks, where appropriate. Communication interface 810 may include one or more communication interfaces 810, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.
In particular embodiments, bus 812 includes hardware, software, or both coupling components of computer system 800 to each other. As an example and not by way of limitation, bus 812 may 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. Bus 812 may include one or more buses 812, 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.
Claims
1. A system for determining railroad track locations at risk for buckling, the system comprising:
- one or more memory units; and
- one or more computer processors communicatively coupled to the one or more memory units and configured to perform operations comprising: accessing railroad track data; segmenting a railroad track into a plurality of railroad track segments; for each particular railroad track segment of the plurality of railroad track segments: determining, from the railroad track data, a plurality of track features for the particular railroad track segment; and determining a priority level for the particular railroad track segment, the priority level indicating a track buckling risk severity for the particular railroad track segment, the priority level determined using: the determined plurality of track features for the particular railroad track segment; and a particular rule weight matrix comprising a set of track features and an associated risk weight for each track feature of the set of track features; and displaying the determined priority levels for the plurality of track segments on an electronic display.
2. The system of claim 1, wherein determining the priority level for the particular railroad track segment comprises:
- determining, using the rule weight matrix, a risk weight for each of the determined plurality of track features for the particular railroad track segment;
- 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; and
- choosing the priority level for the particular railroad track segment based on the cumulative features risk weight.
3. The system of claim 1, the operations further comprising determining a plurality of trigger events for the particular railroad track segment, wherein the priority level for the particular railroad track segment is further determined using the determined plurality of trigger events for the particular railroad track segment.
4. The system of claim 1, the operations further comprising 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.
5. The system of claim 1, further comprising a plurality of rule weight matrices that are each associated with a respective geographical area, wherein the particular rule weight matrix used to determine the priority level for the particular railroad track segment is associated with a particular geographical area in which the particular railroad track segment is physically located.
6. The system of claim 1, wherein the determined priority levels for the plurality of track segments are displayed in an interactive track map comprising:
- a plurality of user-selectable elements for selecting which priority levels to display;
- 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, wherein the determined priority levels are displayed in a plurality of different colors.
7. The system of claim 1, the operations further comprising sending a notification to a user, the notification indicating the determined priority levels for the plurality of track segments.
8. A method by a computing system for determining railroad track locations at risk for buckling, the method comprising:
- accessing railroad track data;
- segmenting a railroad track into a plurality of railroad track segments;
- for each particular railroad track segment of the plurality of railroad track segments: determining, from the railroad track data, a plurality of track features for the particular railroad track segment; and determining a priority level for the particular railroad track segment, the priority level indicating a track buckling risk severity for the particular railroad track segment, the priority level determined using: the determined plurality of track features for the particular railroad track segment; and a particular rule weight matrix comprising a set of track features and an associated risk weight for each track feature of the set of track features; and
- displaying the determined priority levels for the plurality of track segments on an electronic display.
9. The method of claim 8, wherein determining the priority level for the particular railroad track segment comprises:
- determining, using the rule weight matrix, a risk weight for each of the determined plurality of track features for the particular railroad track segment;
- 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; and
- choosing the priority level for the particular railroad track segment based on the cumulative features risk weight.
10. The method of claim 8, further comprising determining a plurality of trigger events for the particular railroad track segment, wherein the priority level for the particular railroad track segment is further determined using the determined plurality of trigger events for the particular railroad track segment.
11. The method of claim 8, further comprising 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.
12. The method of claim 8, wherein the particular rule weight matrix used to determine the priority level for the particular railroad track segment is associated with a particular geographical area in which the particular railroad track segment is physically located.
13. The method of claim 8, wherein the determined priority levels for the plurality of track segments are displayed in an interactive track map comprising:
- a plurality of user-selectable elements for selecting which priority levels to display;
- 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, wherein the determined priority levels are displayed in a plurality of different colors.
14. The method of claim 8, further comprising sending a notification to a user, the notification indicating the determined priority levels for the plurality of track segments.
15. One or more computer-readable non-transitory storage media embodying instructions that, when executed by a processor, cause the processor to perform operations comprising:
- accessing railroad track data;
- segmenting a railroad track into a plurality of railroad track segments;
- for each particular railroad track segment of the plurality of railroad track segments: determining, from the railroad track data, a plurality of track features for the particular railroad track segment; and determining a priority level for the particular railroad track segment, the priority level indicating a track buckling risk severity for the particular railroad track segment, the priority level determined using: the determined plurality of track features for the particular railroad track segment; and a particular rule weight matrix comprising a set of track features and an associated risk weight for each track feature of the set of track features; and
- displaying the determined priority levels for the plurality of track segments on an electronic display.
16. The one or more computer-readable non-transitory storage media of claim 15, wherein determining the priority level for the particular railroad track segment comprises:
- determining, using the rule weight matrix, a risk weight for each of the determined plurality of track features for the particular railroad track segment;
- 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; and
- choosing the priority level for the particular railroad track segment based on the cumulative features risk weight.
17. The one or more computer-readable non-transitory storage media of claim 15, the operations further comprising determining a plurality of trigger events for the particular railroad track segment, wherein the priority level for the particular railroad track segment is further determined using the determined plurality of trigger events for the particular railroad track segment.
18. The one or more computer-readable non-transitory storage media of claim 15, the operations further comprising 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.
19. The one or more computer-readable non-transitory storage media of claim 15, wherein the particular rule weight matrix used to determine the priority level for the particular railroad track segment is associated with a particular geographical area in which the particular railroad track segment is physically located.
20. The one or more computer-readable non-transitory storage media of claim 15, wherein the determined priority levels for the plurality of track segments are displayed in an interactive track map comprising:
- a plurality of user-selectable elements for selecting which priority levels to display;
- 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, wherein the determined priority levels are displayed in a plurality of different colors.
Type: Application
Filed: Jan 16, 2025
Publication Date: Jul 16, 2026
Applicant: BNSF Railway Company (Fort Worth, TX)
Inventors: Ryan Medlin (Azle, TX), Ranjan Dash (Flower Mound, TX), Srilakshmi Tayi (Flower Mound, TX), Keshav Subramaniam (Irving, TX), Charity Marie Duran (Fort Worth, TX)
Application Number: 19/023,774
