This paper provides an introduction to a Rail Break Detection and Train Location Detection using fiber optic technology called Smart Rail. The technology concept detailed in this paper not only detects the location of a broken rail, it also provides advanced warning of rail damage that may evolve into a dangerous condition. The same technology allows for the detection of trains and vehicles in the detection zone and provides location, speed and direction information. The Smart Rail continuously monitors rails for changes in their ability support the train in a safe manner. Smart Rail technology is based on direct observations of the structural quality of the rail by means of rail strain detection. Smart Rail technology detects the location, speed and direction of vehicles on the rails without the need for onboard equipment. It reports the location of vehicles whether moving or not. Smart Rail technology is also capable of detecting and providing a warning of: rail gauge separation, lose or missing track hardware, missing tie plates and flat wheels. Taken together this technology can assist in the prevention of derailments. The technology can be applied to existing rails with no need to modify any existing equipment or track circuits. The train detection range can be up to 50 miles per direction from one wayside location.

The North American Freight Railroad industry has been exploring ways in which on-board real time or near real-time monitoring of important railcar components and cargo can be accomplished. This approach alleviates the danger from fast occurring catastrophic events like bearing failure, which is not always possible using the traditional wayside monitoring techniques. The use of Wireless Sensor Networks is a viable candidate technology that is being explored for this application. However, popular communication protocols based on IEEE 802.15.4 have been evaluated by the railroad industry and our lab, and were found to perform unacceptably for this application domain, among other reasons, as a consequence of the long linear chain-like network topology of a sensor network deployment on a train. Hybrid Technology Networking (HTN) protocol has been designed to address these issues. HTN structures the network as a communication hierarchy using multiple different network technologies. It allows small clusters to communicate internally using IEEE 802.15.4 and utilizes IEEE 802.11 as the inter-cluster transport method for data delivery over multiple hops to the locomotive. It aims to maximize the benefits afforded by each technology. Energy is a scarce resource in such networks and hence modeling it for energy analysis and optimization is vital. A model that accurately predicts the energy consumption of a particular network deployment is therefore of utmost necessity. However, most modelling efforts concentrate on network deployments utilizing only a single type of communication protocol and the structure of such deployments are often mesh-like. Also the existing modelling approaches tend to model the entire network as a single phenomenon, which is often not the case in network deployments such as those in the freight railroad scenario. It is also expensive to commission large network deployments to evaluate energy consumption profiles. The problem is compounded when this process has to be repeated for several different communication protocols and channel conditions. The task will be made economically viable and massively scalable with the use of a modular energy model. The philosophy behind our approach is to model the important and contributing constituents of the protocol within each node and also external to the node and then utilize inter-dependencies to connect the individual models. This work is the first step towards a modular energy model for the Hybrid Technology Network, and is also applicable to many other networking approaches. In this work we propose a model design that is capable of predicting the network behavior of nodes in a linear chain-like topology utilizing the ContikiMAC duty cycling protocol for multi-hop communication with the sink node. We have used channel emulation to test hardware nodes in a chain-like topology to validate the model predictions, and present our findings in this paper.

Freight rail carriers have been continuously challenged to reduce costs and comply with increasingly stringent environmental standards, into a continuously competing and environmentally driven industry. In this context, current availability and relative abundance of clean and low cost non conventional gas reserves have aroused a comprehensive reevaluation of rail industry into fuel option, especially where freight rail are strongly diesel based. Countries in which rail sector is required to play an important role in transport matrix, where fuel expenditures currently accounts for a significant share of operational costs, like Australia, Brazil, United States and other continental countries, can be seen as strong candidates to adopt fuel alternatives to diesel fueled freight railways. Moreover, from an environmental perspective, the use of alternative fuels (like natural gas) for locomotive traction may allow rail freight carriers to comply with emission standards into a less technologically complex and costly way. In this context, liquefied natural gas (LNG) fueled freight locomotives are seen as a strong potential near-term driver for natural gas use in rail sector, with its intrinsic cost and environmental benefits and with the potential to revolutionize rail industry much like the transition from steam to diesel experienced into the fifties, as well as the more recent advent of use of alternating current diesel-electric locomotives. LNG rail fueled approach has been focused on both retrofitting existing locomotive diesel engines, as well as on original manufactured engines. Given the lower polluting potential of natural gas heavy engines, when compared to diesel counterparts, LNG locomotives can be used to comply with increasingly restrictive Particulate Matter (PM) and Nitrogen Oxides (NO x ) emission standards with less technological complexity (engine design and aftertreatment hardware) and their intrinsic lower associated costs. Prior to commercial operation of LNG locomotives, there are some technical, operational and economic hurdles that need to be addressed, i.e. : i) locomotive engine and fuel tender car technological maturity and reliability improvement; ii) regulation improvement, basically focused on operational safety and interchange operations; iii) current and long term diesel - gas price differential, a decisive driver, and, finally, iv) LNG infrastructure requirements (fueling facilities, locomotives and tender car specifications). This work involved an extensive research into already published works to present an overview of LNG use in freight rail industry into a technical, operational and economical perspective, followed by a critical evaluation of its potential into some relevant freight rail markets, such as United States, Brazil and Australia, as well as some European non electrified rail freight lines.

Train car wheels are subjected to different types of damages due to their interactions with the brake shoes and track. If not detected early, these defects can worsen, possibly causing damage to the bogie and rail. In the worst-case scenario, this rail damage can possibly lead to later derailments, a serious concern for the rail industry. Therefore, automatic inspection and detection of wheel defects are high priority research areas. An automatic detection system not only can prevent train and rail damage, but also can reduce operating costs as an alternative for tedious and expensive manned inspection. The main contribution of this paper is to develop a computer vision method for automatically detecting the defects of rail car wheels using a wayside thermal camera. We concentrate on identification of flat-spotted/sliding wheels, which is an important issue for both wheel and suspension hardware and also rail and track structure. Flat spots occur when a wheel locks up and slides while the vehicle is still moving. As a consequence, this process heats up local areas on the metal wheel, which can be observed and potentially detected in thermal imagery. Excessive heat buildup at the flat spot will eventually lead to additional wheel and possibly rail damage, reducing the life of other train wheels and suspension components, such as bearings. Furthermore, as a byproduct of our algorithm, we propose a method for detecting hot bearings. A major part of our proposed hot bearing detection algorithm is common with our sliding wheel detection algorithm. In this paper, we first propose an automatic detection and segmentation method that identifies the wheel and bearing portion of the image. We then develop a computer vision method, using Histogram of Oriented Gradients to extract features of these regions. These feature descriptors are input to a Support Vector Machine classifier, a fast classifier with a good detection rate, which can detect abnormalities in the wheel. We demonstrate our methods on several real data sets taken on a Union Pacific rail line, identifying sliding wheels and hot bearings in these images.

The New York City Transit (NYCT) Signal Modernization Program has been ongoing since the mid-1990s. The current phase of modernization involves the procurement of Solid State Interlocking (SSI) systems that are designed to replace relay-based interlockings. SSI procurement has necessitated significant adjustments to NYCT’s system deployment processes, most notably in the areas of design, implementation, test, maintenance, and safety certification. NYCT has successfully met the challenge of applying the updated deployment processes to multiple, concurrent system procurements. The most fundamental change to the NYCT procurement approach required a shift from the traditional design-build model of acquisition for relay-based systems to a software-based development lifecycle for SSIs. The relay-based Interlocking systems’ design-build model has traditionally involved the realization of complex relay logic with well-known hardware components such as relays, trip-stops, signals and switch machines. The SSI systems’ software model however requires additional consideration of software and hardware development phases, such as designated in the V-lifecycle. V-model phases include requirement, design, implementation, and test. For SSI systems, NYCT adopted a “double” V-Life cycle approach, one V for the supplier’s SSI hardware and software (executive) platform, and one V for the SSI application (site-specific field) logic. At NYCT, the first V is dedicated to the suppliers’ executive platform. Hardware and software comprising the supplier platform are verified to meet safety and performance requirements. Safety analyses such as Fault Tree Analysis, Failure Modes and Effects Analysis, Timing Analysis, and Hazard Analysis are generated by SSI suppliers. System Safety Concepts, e.g., Numerical Assurance, Checked Redundancy, Intrinsic Fail-Safety are also assessed. NYCT’s second V is dedicated to the application software, i.e., the site-specific relay-based logic, which is implemented as Boolean logic within the SSI. For the Booleans, the process of traditional circuit checking is supplemented by Model Checking, wherein NYCT General Safety Properties are used to verify the site-specific logic. Model Checking provides assurance that safety properties are met throughout the entire interlocking design, for every system state, and does not rely on a manual review process. This paper will focus on the benefits NYCT has realized as a result of adopting Model Checking as a requirement for safety certification, along with an overview of the NYCT SSI safety certification process.

Hardware-in-the-Loop (HIL) testing allows for scaled to full-scale evaluation of components without constructing full-scale vehicle prototypes. HIL is also a rapid means of performance evaluation of difficult to model components, in real-time simulation with up to full-scale components providing their own response in place of a model. HIL also provides a working tool for control algorithm refinements prior to full system integration. In hybrid and electric locomotive applications this is particularly important while choosing the battery chemistry and sizing the energy storage system. Energy storage HIL testing permits optimal sizing of battery power and energy density along with management algorithm tuning. In general, HIL provides insight into optimal locomotive powertrain design. After the development of full EV and HEV locomotive models, battery component models were replaced with real-time interfaces to lead carbon and lithium cobalt batteries, which validated the battery models and helped tune vehicle controllers for direct application onto the vehicle platform.

Wireless Sensor Networks have been a focus of research in the North American freight railroad industry to enable on-board real-time sensing of critical railcar parameters. Important railcar aspects like wheel bearing temperature, air pressure, brake failure, and the integrity of transported goods can then be monitored closely and reliably. This enables immediate preventive actions in case of impending failures and also enables trend analysis that can be used to fine-tune maintenance efforts on railcars. These measures increase the safety, efficiency, and dependability of freight railroad operations. In our previous work [1–3] we have presented our Hybrid Technology Networking (HTN) protocol. This protocol provides optimal network performance for railcar monitoring applications. We have also presented HTNMote, a hardware prototyping platform that implements HTN. A deployment of HTNMotes was conducted and evaluated at the TTCI facility in Pueblo, Colorado in the US. The results from our field tests confirm that this approach is an order of magnitude better in performance compared to solutions based on ZigBee alone. In such an application, energy considerations represent a key challenge. These sensors have no readily available continuous energy source, but are expected to operate for years in harsh conditions. Energy harvesting — from vibrations, temperature differences, or solar radiation — may provide a potential solution to the energy scarcity. This also mandates that the HTNMote hardware and HTN protocol both be as energy efficient as possible. In this paper we present detailed measurements of the energy consumed by the HTNMote in various operational situations that are encountered during their operation onboard freight railcars. We introduce an energy consumption model based on our analysis of the measurements. This model demonstrates the energy-efficiency of the HTNMote implementation.

Railroads are increasingly using Communication-Based Train Control (CBTC) technology to improve service capacity and operating efficiency. CBTC is a mission-critical system under which train monitoring and train control are integrated into a single unified system through data links between vehicles, central processors, and wayside equipment. Radio over fiber technology provides a flexible and efficient solution for the Data Communication System (DCS) which needs to ensure integrity and reliability of message delivery in a transparent manner for the train control functions. A Security Device (SD) is defined as a network entity located between the railroad administration’s (the customer) trusted wired network and the non-trusted portion of the DCS network including the radio-based segment, which runs on a customized piece of hardware with a secure operating system and provides secure gateway functionality. This paper puts forward a network architecture and SD software platform design which meets the requirements of a typical CBTC system. The IPSEC protocol used by the SD for data protection renders authentication service through X.509 certificates. A network setup is put together as the proof-of-concept for the presented design proposal and performance assessment is conducted through experimental studies.

Real-time monitoring of various components of a railcar such as wheel bearing temperature, brake line status, integrity of transported goods and many more has become a key focus area of research for the North American freight railroad industry. The ability for timely detection of abnormalities and impending failures prevents costly accidents, the potential loss of life and damage to the environment. Monitoring also increases overall operational efficiency, reliability and safety of freight railroads. Wireless Sensor Networks (WSN) are an obvious choice for implementing such a monitoring scheme. The accumulated data from various sensors distributed throughout each railcar along the length of the train is transmitted wirelessly using multi-hop transmissions to the locomotive for alerting and monitoring. From there, this information is also transmitted to dispatch centers for further analysis and recording. ZigBee technology based on the IEEE 802.15.4 standard is a popular choice among WSN communication protocols, owing to its low cost and low power requirements. ZigBee performance degrades severely in the long chain-like topology characteristic of the railroad application environment. This effectively disqualifies ZigBee as a candidate technology for such railcar monitoring deployments. To overcome these issues with ZigBee deployments for freight train monitoring we developed our Hybrid Technology Networking (HTN) approach [5–7]. HTN leverages both ZigBee and Wi-Fi communication to achieve reliable communication along an entire freight train. Railcar monitoring nodes are grouped into smaller clusters, within which we utilize ZigBee for its low-power operation and report to each cluster’s gateway node. The gateway nodes of all the clusters on a train communicate using Wi-Fi, to benefit from the high throughput and long communication range. This tiered architecture also results in a drastic reduction in overall hop count for end-to-end communication. In this paper we present HTNMote, a hardware platform that we are developing and employing for real-world evaluation of the HTN protocol. We also present results from our field tests of the HTNMotes at the Transportation Technology Center (TTCI) facility in Pueblo, Colorado, operated by the US Association of American Railroads (AAR). The results show that the use of HTN improves performance of the network by at least an order of magnitude compared to a ZigBee-only network. This paper details the design of our HTNMote platform, present the test setup and results, as well as conduct an in-depth analysis of the obtained results as they relate to railroad operations.

Traditional Wireless Sensor Network (WSN) solutions have been deemed insufficient to address the requirements of freight railroad companies to implement real-time monitoring and control of their trains, tracks and wayside equipment. With only ZigBee-based elements, the transmission capabilities of WSN devices are limited in terms of coverage range and throughput. This leads to severe delay and congestion in the network, particularly in railroad scenarios that usually require the nodes to be arranged in linear chain-like topology. In such a multi-hop topology to communicate from one end of a train to the locomotive — and due to ZigBee’s limited communication range — data needs to be transmitted using a very high number of hops and thus generates long delays and congestion problems. To overcome this drawback, we have proposed a heterogeneous multi-hop networking approach called “Hybrid Technology Networking” (HTN). In HTN we combined Wireless Local Area Network (WLAN) technologies like WiFi, which provide improved communication range and higher data rates, with low-power communication technologies like ZigBee. This significantly reduces the number of hops required to deliver data across the network and hence solves the issues of delay and congestion, while also achieving superior enery efficiency and network lifetime. The sensor nodes are logically divided into clusters and each cluster has a WiFi “gateway”. All intra-cluster communication is achieved via IEEE 802.15.4 and ZigBee protocols, while all inter-cluster communication utilizes WiFi protocol standards. To implement our proposed technology in railroad networks, we are designing hardware prototypes and simulation models to evaluate the functionality and performance of our HTN solution, which is designed around a dual network stack design governed by the HTN protocol. This ensures full compliance with IEEE and industry communication protocols for interoperability. Since no simulation tools that seamlessly combine both WSN and WLAN technologies in a single module exist, we wrote our own simulation environment using OPNET. In this paper, we have provided information of implementing the HTN protocol in OPNET and the simulation results for different scenarios relevant to railroad operations. These results will demonstrate the efficacy of our proposed system as well as provide the baseline data for testing the hardware devices in live networks. Under simulated traffic and channel conditions and device configurations, we observed a decrease of 77.27% in end-to-end delay and an increase of 69.70% in received data volume when using HTN compared to ZigBee-only multi-hop networks, simulated over 14 railcars in railroad-relevant scenarios.

Positive Train Control (PTC) was mandated by Congress in 2008 to be installed on an extensive network of rail carriers within the United States before the end of 2015. As such, the short time frame for implementation has dictated that several key features that could be adopted within the train control area not be considered until the base development is finished. One of the areas that has been pushed off until later is the interface to Highway crossings. This is being explored in part by Caltrain in their Communications Based Overlay Signal System (CBOSS) project that adapts Incremental Train Control System (ITCS) protocols; however this approach does not undertake the full replacement of existing technology. This paper investigates the concept of operations for PTC controlled highway crossings with the premise that the existing train detection technology will be fully replaced by PTC and its train detection system. This will allow for much reduced hardware in the field while potentially adding additional functionality. Detailed analysis of the safety case will be introduced and interface logic will be included. Additional functions such as prediction, preemption, near side control and switching scenarios will be included in the research for both freight and passenger operations and how these situations can be addressed with available PTC technology.

Modern trains must provide a certain level of control, diagnostic and information services. To satisfy it, rail vehicles have to be equipped with devices communicating over special networks. Nowadays, commonly used networks are described in the IEC 61375 and the UIC 556 standards. The National Train Bus (NTB) is a train network standard created in parallel with IEC 61375. The main difference is that it can operate on lower speed than it is common in rail environment, while still keeping the time-critical character of communication in mind. Therefore, it is suitable for rail vehicles, including refurbished ones, to avoid high requirements on installed hardware and software. It was successfully tested and it is being used in many different DMUs operated by Czech and Finnish Railways. This article describes the main communication and diagnostic principles of NTB and shows the differences between the IEC 61375 solutions and NTB.

United States (US) Freight and Passenger railroads and transit entities are preparing for the deployment of Positive Train Control (PTC) systems as soon as the end of 2012, and by Federal mandate, by the end of 2015. PTC depends on a reliable and robust wireless data link between train and wayside equipment, train and network access points, and waysides and network access points. While, for the wireless component, some spectrum has been acquired and initial functionality defined, there will be lessons learned and new expectations as systems are rolled out over this period and beyond. Since the average service life of radio equipment in rail and transit applications is in excess of 10–15 years, and a system design may remain in place for 20–25 years, the challenge is to ensure that wireless systems are flexible enough to adapt to technology changes that may occur during the radio system’s long service life. Software-Defined Radio (SDR) is the appropriate approach to deal with this future uncertainty. The worldwide cellular communications standards are a great example: 3GPP established its first 3G specification, Release-99, in the year 2000; there have been 7 full specification releases since then, demonstrating the challenges of predicting future use based on current expectation. Deployment and long-term maintenance account for the majority of system cost: when system deployment is expensive and time-consuming, the needs for flexible design are crucial. The maintenance cost should be added and weighted higher in the calculation for total cost of ownership. One can argue that fixed design is cheaper; however, the total cost of ownership may end up much higher when changes are needed before the end of the product’s expected service life. In Information Technology, the system performance doubles approximately every 18–24 months. Thoughtful radio design can leverage this generational improvement. To address the issue of broad interoperability and a forward-looking specification, radio experts, including transit systems integrators, Federal agencies, end device and chipset manufacturers, and researchers, from around the world have teamed up under the aegis of the Institute of Electrical and Electronic Engineers (IEEE) to establish a Positive Train Control group for the RF/PHY/MAC based upon the well adopted IEEE 802.15.4 standard. The outcome of this effort will ensure the maturity of a PTC specification and long term maintenance of this mission-critical PTC wireless interface protocol. With any new specification development, continuous vetting, modification and upgrade will occur over coming years to improve the functionality and stability. SDR architectures allow the radio system to follow the standard. SDR allows railroads to start system validation and hardware qualification now, in order to meet the required Federal deadline. Since SDR provides a continuous path for interoperability as radios can reprogram and reconfigure on the fly, the hardware can remain unchanged while interoperability and advances in standards can be met through software upgrade.

In recent years, image recognition, along with improvements in image processing technology, has become a mainstream method of object inspection. The quality of images directly affects the image-recognition rate. Track monitoring systems play an important role in ensuring safety and preventing derailments of trains, on the basis of the prevailing track conditions. Therefore, the conditions of track systems are very important. In this research, a high-speed frame grabber system is designed. The triggering rate of its line scan camera can be adjusted to match the vehicle speed of the train, so that the pixels per inch of images captured by this camera are fixed. The hardware equipment of the designed system includes a line scan camera, an image acquisition card, artificial lightings, a contrast sensor, and a system on a programmable chip (SOPC) development board. An exposure time control system is designed on the basis of a field-programmable gate array (FPGA) core. The designed system can acquire clear high-speed images while the vehicle speed changes dramatically. The experimental results confirm the feasibility of using line scan cameras for railway vehicles.

Robust, reliable, and interoperable wireless communications play a vital role in the success of railroad operations. This paper describes an effort towards developing a railroad-specific “cognitive radio” (Rail-CR) that can meet the needs of future wireless communication systems for railways by making positive train control (PTC) communication more interoperable, robust, reliable, and spectrally efficient, and less costly to deploy and maintain. Cognitive radios are a cutting edge research area that combines artificial intelligence (AI) with Software Defined Radios (SDRs) with the goal of improving upon existing radio performance. SDRs are radios in which capabilities are flexible due to realizing some functionality in software as opposed to a purely hardware platform. By utilizing situational awareness from the radio in the form of observable parameters, often known as ‘meters’, a cognitive engine (CE) utilizes software-based decision-making algorithms to determine if a change in the radio parameters, commonly referred to as ‘knobs’, is required based on sets of predefined goals. Additionally, learning algorithms dovetail with the decision making to enable the system to track and utilize past decisions and observations.

With the latest initiative of the government to develop a high speed passenger rail system in the United States the first and most important strategic transportation goal is to “Ensure safe and efficient transportation choices. A key element of safe railroad operation is to address the issue of fatigue among railroad operating employees and how to fight it. In this paper, we are presenting a novel approach to estimating fatigue levels of train conductors by analyzing the speech signal in the communication between the conductor and dispatch. We extract vocal indicators of fatigue from the speech signal and use Fuzzy Logic to generate an estimate of the mental state of the train conductor. Previous research has shown that sleeping disorders, reduced hours of rest and disrupted circadian rhythms lead to significantly increased fatigue levels which manifest themselves in alterations of speech patterns as compared to alert states of mind. To make a decision about the level of fatigue, we are proposing a Fuzzy Logic algorithm which combines inputs such as word production rate and speech intensity to generate a Fatigue Quotient at any moment in time when speech is present. The computation of the Fatigue Quotient relies on a rule base which draws from existing knowledge about fatigue indicators and their relation to the level of fatigue of the subject. For this project, the rule base and the membership functions associated with it were derived from real time testing and the subsequent tuning of parameters to refine the detection of changes in patterns. It was successfully shown that Fuzzy Logic can be implemented to estimate alertness levels from speech metrics in real-time and that the membership functions for this purpose can be found empirically through iterative testing. Furthermore, this study has proven that the framework to run such an analysis continuously as a monitoring function in locomotive cabins is feasible and can be realized with relatively inexpensive hardware.

In 2007–08, a study of passenger side entrance door types and integration with overall car design was conducted by Parsons Brinckerhoff (PB) for a client in the State of California. As found on many other intercity and commuter operations in North America, the client had for years endured a variety of technical challenges with the side entrance doors that had compromised passenger service and safety. Recent advances in side entrance door designs and the subsequent changes to new car specifications have opened the door to solutions to many of these challenges; solutions include hardware changes to the door panels and assemblies, and intelligent systems included in software and sensors of the door control for monitoring and diagnosing door settings, operations and faults in real time. Through field inspections and equipment evaluations, this study determined that the majority of the faults were caused by contamination. A principle solution to eliminating this contamination was determined to be in the door seals and subsequent door panel type. Among the recommendations made to the client were to change from a bi-parting pocket sliding door system to a bi-parting sliding plug door system. This paper presents two portions of the PB study: 1) an overview of the technical challenges of operating intercity and commuter rail passenger service in California, and 2) an industry study that identifies the latest in appropriate technologies for service in California. Modern rail passenger cars with similar service and bi-parting pocket sliding doors are evaluated. This paper also discusses how the findings in California can be applied to other commuter, heavy and light rail transit operations in North America.

The main objective of the proposed work is to introduce a new concept of advanced power quality assessment. The introduced system is implemented using applications of a set of powerful software algorithms and a digital signal processor based hardware data acquisition system. The suggested scheme is mainly to construct a system for real time detection and identification of different types of power quality disturbances that produce a sudden change in the power quality levels. Moreover, a new mitigation technique through generating feedback correction signals for disturbance compensation is addressed. The performance of the suggested system is tested and verified through real test examples. The obtained results reveal that, the introduced system detects fast and accurately most of the power quality disturbance events and introduces new indicative factors estimating the performance of any supply system subjected to a set number of disturbance events.

This paper presents a new generation Gage Restraint Measurement System (GRMS) recently developed by ENSCO, Inc. and Plasser American Corporation for delivery to the Federal Railroad Administration (FRA), Office of Research and Development. The original GRMS technology was developed in the early 1980’s as a performance-based method to evaluate ties and fasteners. The technology has been adopted by many railroads, and commercial versions of the GRMS are being utilized daily throughout the U.S. A shortcoming of the current commercial system is that the unique split-axle system can derail when operating through switches and other track hardware. Since the split-axle is a main running axle, a derailment can have significant consequences. The new GRMS is designed as a fifth axle that is deployed from the frame of a track geometry vehicle instead of using one of the vehicle’s running axles. This results in a faster, more flexible, and safer GRMS design. This paper discusses the details of the new design and its advantages.