Internet of Things (IoT)-Based Apparatus and Method for Rail Crossing Alerting of Static or Dynamic Rail Track Intrusions

2017 ◽  
Author(s):  
Daniel Minoli ◽  
Benedict Occhiogrosso
Keyword(s):  
Internet Of Things ◽  
Rail Track ◽  
Train Control ◽  
Positive Train Control ◽  
Grade Crossings ◽  
The U.S

This paper deals with Physical Safety and Security at rail crossings. There are about 150,000 public railroad grade crossings in the U.S. Unfortunately, approximately 2,000 accidents occur every year in the U.S., resulting not only in many injuries, but also in over 200 deaths annually. The predicament is that for various reasons, people, cars, and trucks find themselves on the rail tracks of an oncoming train at a railroad crossing. The system discussed in this paper provides a relatively inexpensive Internet of Things (IoT)-based capability that can be used to alert a rail operator that there is an obstruction on the tracks, and/or possibly to interwork with (but not replace) a Positive Train Control (PTC) system thus attempting to automatically stop an incoming train. In fact, IoT is now being deployed in railroads for a variety of applications. A brief description of cybersecurity issues related to IoT deployment is also included.

Regulatory Risk Evaluation of Positive Train Control Systems

2007 ◽  
Author(s):  
Mark W. Hartong ◽  
Olga K. Cataldi
Keyword(s):  
Control Systems ◽  
Risk Evaluation ◽  
Risk Assessments ◽  
Performance Standard ◽  
Risk Modeling ◽  
Department Of Transportation ◽  
Train Control ◽  
Modeling Techniques ◽  
Positive Train Control ◽  
The U.S

In February of 2005, the Federal Railroad Administration of the U.S. Department of Transportation issued a set of new performance-based regulations governing the development and use of microprocessor-based signal and train control systems. The new standard, effective March 2005, requires that replacement systems be at least as safe as the existing condition. Among the key elements used in evaluating the compliance of products to the new performance standard are quantitative and qualitative risk assessments. This paper explains the performance standard that must be followed, the regulatory background behind it, various quantitative and qualitative risk modeling techniques that can be used to support claims of compliance, and issues associated with their implementation.

Case Studies of PTC-Preventable Accidents

2016 ◽  
Author(s):  
Dave Schlesinger
Keyword(s):  
Control System ◽  
Case Studies ◽  
Human Factor ◽  
Transportation Safety ◽  
Train Control ◽  
Detailed Case ◽  
Train Control System ◽  
Safety Improvement ◽  
Positive Train Control

A 1969 collision of two Penn Central train resulted in four fatalities and forty-five injuries. This accident could have been prevented, had some type of train control system been in place. After this accident, the National Transportation Safety Board (NTSB) asked the Federal Railroad Administration (FRA) to study the feasibility of requiring railroads to install some type of automatic train control system that would prevent human-factor caused accidents. Over the next almost four decades, a number of additional accidents occurred, culminating in the January, 2005 Graniteville Norfolk-Southern accident and the September, 2008 Metrolink Chatsworth accident. A little more than one month after the Metrolink accident, Congress passed the Rail Safety Improvement Act, which requires Positive Train Control (PTC). To better explain the positive train control requirements, this paper traces each to a detailed case study. Four different accidents are studied, each being an example of one of the four, core positive train control requirements. Included in the case study is a discussion about how positive train control would have prevented the accident, had it been present. This provides positive train control implementers and other railroad professionals with a better understanding of the factors that have caused or contributed to the cause of the positive train control preventable accidents studied.

Risk Assessment of Positive Train Control by Using Simulation of Rare Events

2012 ◽  
Vol 2289 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Timothy Meyers ◽  
Amine Stambouli ◽  
Karen McClure ◽  
Daniel Brod
Keyword(s):  
Risk Assessment ◽  
Rare Events ◽  
Train Control ◽  
Positive Train Control

scholarly journals Positive Train Control (PTC): Calculating Benefits and Costs of a New Railroad Control Technology

2010 ◽  
Author(s):  
Randolph R. Resor ◽  
Michael E. Smith ◽  
Pradeep K. Patel
Keyword(s):  
Real Time ◽  
Point Of View ◽  
Class I ◽  
Location Information ◽  
Service Reliability ◽  
Economic Point ◽  
Track Maintenance ◽  
The Road ◽  
Train Control ◽  
Positive Train Control

The purpose of this analysis was to quantify the business benefits of Positive Train Control (PTC) for the Class I freight railroad industry. This report does not address the safety benefits of PTC. These were previously quantified by the Rail Safety Advisory Committee (RSAC), which identified nearly a thousand "PPAs" (PTC-preventable accidents) on U.S. railroads over a 12-year period, and determined the savings to be realized from each avoided accident. The RSAC finding was that avoidance of these PPAs was not, by itself, sufficient (from a strictly economic point of view) to justify an investment in PTC. Examples of potential business benefits include: * Line capacity enhancement * Improved service reliability * Faster over-the-road running times * More efficient use of cars and locomotives (made possible by real-time location information) * Reduction in locomotive failures (due to availability of real-time diagnostics) * Larger "windows" (periods during which no trains operate and maintenance workers can safely occupy the track) for track maintenance (made possible by real-time location information) * Fuel savings This paper presents the results of the analysis. It is important to recognize, however, that the state of the art in making these estimates is not sufficiently mature to make exact answers feasible. Presented here are the best estimates now possible, with observations as to how better information may be developed. Benefits were estimated in the above areas and the cost of deploying PTC on the Class I network (99,000 route miles and 20,000 locomotives) were calculated. The conclusions of the analysis were as follows: * Deployment of PTC on the Class I railroad network (99,000 route miles, 20,000 locomotives) would cost between $2.3 billion and $4.4 billion over five years * Annual benefits, once the system was fully implemented, were estimated at $2.2 billion to $3.8 billion * Internal rate of return was estimated (depending on timing and cost) to be between 44% and 160%

Techniques for Evaluating and Improving the Operational Efficiency of Positive Train Control Braking Enforcement Algorithms

2014 ◽  
Author(s):  
Joe Brosseau
Keyword(s):  
Monte Carlo ◽  
Field Testing ◽  
Operational Efficiency ◽  
Dynamics Simulation ◽  
Emergency Braking ◽  
Stopping Distance ◽  
Train Control ◽  
Positive Train Control ◽  
Prediction Techniques ◽  
Distance Prediction

Software algorithms are used in Positive Train Control (PTC) systems to predict train stopping distance and to enforce a penalty brake application. These algorithms have been shown to be overly conservative, leading to operational inefficiencies by interfering with normal train operations. A braking enforcement algorithm that can safely stop trains to prevent authority and speed limit violations without impacting existing railroad operations is critical to successful widespread implementation of PTC. Due to operational issues observed with early PTC braking enforcement algorithms, a number of techniques are proposed and evaluated to improve the operational efficiency of these algorithms, with emphasis on applicability to PTC systems currently being implemented. Transportation Technology Center, Inc. (TTCI) is employing a new methodology for evaluation of braking algorithms that uses Monte Carlo simulation techniques to statistically evaluate the performance of the algorithm, with limited need for field testing to verify the simulation results. In the Monte Carlo process, computer simulations are run repeatedly using randomly selected input values to predict the resulting probability distribution of stopping locations. The method provides a higher level of confidence in algorithm performance with reduced time and cost compared to traditional methods, which rely heavily on field testing. For freight trains, the method utilizes a detailed train dynamics simulation model previously developed and validated by the Association of American Railroads (AAR). For passenger trains, TTCI is developing and validating a new model capable of simulating brake systems and components specific to passenger and commuter equipment. New methods for addressing operational efficiency of braking algorithms focus on improving the accuracy of stopping distance prediction and reducing the potential variation from the prediction. Techniques investigated by TTCI include adaptive functions, which measure train braking performance en route and adapt the algorithm to these characteristics; emergency brake backup, which uses feedback following a penalty application to determine if additional emergency braking is required to stop the train short of the target; an improved target offset function, which relies on statistical multi-variable regression of thousands of stopping distance simulations; and including information about dynamic braking effort in the stopping distance prediction. Results from TTCI’s investigations show potential to reduce the operational impact, by demonstrating the probability of stopping excessively short of the target is significantly less than that of previous algorithms. The techniques are already being adopted by PTC onboard suppliers for the largest North American railroads, and many are applicable to railways worldwide.

Avoiding Increased Trip Times and Other Operational Impacts When Implementing Positive Train Control

2010 ◽  
Author(s):  
Davis Dure
Keyword(s):  
Speed Limits ◽  
Transit Systems ◽  
Speed Reduction ◽  
Train Control ◽  
Trip Time ◽  
Positive Train Control ◽  
Cab Signal ◽  
Operational Impacts ◽  
Speed Enforcement ◽  
Speed Restrictions

Implementing safety systems on railroads and transit systems to prevent collisions and the risks of excess speeds often come at the price of lengthened trip time, reduced capacity, or both. This paper will recommend a method for designing Positive Train Control (PTC) systems to avoid the degradation of operating speeds, trip times and line capacities which is a frequent by product of train-control systems. One of the more significant operational impacts of PTC is expected to be similar to the impacts of enforcing civil speed restrictions by cab signaling, which is that the safe-braking rate used for signal-system design and which is expected to be used for PTC is significantly more conservative than the service brake rate of the train equipment and the deceleration rate used by train operators. This means that the enforced braking and speed reduction for any given curve speed restriction is initiated sooner than it otherwise would be by a human train operator, resulting in trains beginning to slow and/or reaching the target speed well in advance of where they would absent enforcement. This results in increased trip time, which can decrease track capacity. Another impact of speed enforcement is that it often results in “underspeeding.” In a cab-signal (and manual-train-operation) environment, it has been well documented that train operators typically operate two or three mph below the nominal enforced speed to avoid the risk of penalty brake applications. Target and location speed enforcement under PTC is likely to foster the same behaviors unless the design is prepared to mitigate this phenomenon. While the trip-time and capacity impacts of earlier braking and train-operator underspeeding are generally marginal, that margin can be very significant in terms of incremental capacity and/or resource for recovery from minor perturbations (aka system reliability). The operational and design methodology that is discussed in this paper involves the use of a higher unbalance (cant deficiency) for calculating the safety speed of each curve that is to be enforced by PTC, while retaining the existing maximum unbalance standard and existing speed limits as “timetable speed restrictions”. Train operators will continue to be held responsible for observing the timetable speed limits, while the PTC system would stand ready to enforce the higher safety speeds and unbalance should the train operator fail to properly control his or her train. The paper will present a potential methodology for calculating safety speeds that are in excess of the normal operating speeds. The paper will also calculate using TPC software the trip-time tradeoffs for using or not using this potential concept, for which there are some significant precedents. Other operational impacts, and proposed remedies, will be discussed as well. These will include the issues of total speed enforcement versus safety-speed enforcement, the ability of a railroad’s management to perform the speed checks required by the FRA regulations under normal conditions, and the operation of trains under occasional but expected PTC failures.

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