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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Railroad Investigation Report: New York City Transit Train Collision, January 4, 2024</title>
      <link>https://trid.trb.org/View/2625875</link>
      <description><![CDATA[​On January 4, 2024, about 2:59 p.m., northbound New York City Transit (NYCT) non-revenue train 1345 (striking train) collided with northbound passenger train 1427 (struck train) during a shoving movement on the underground Number 1 Line north of 96th Street Station in Manhattan, New York. The struck train was crossing over from express track 3 to local track 4 when the striking train overran a red signal at the north end of the station platform, entered the crossover, and collided with the fifth railcar of the struck train. Two railcars on the striking train derailed and three railcars on the struck train derailed. The collision resulted in minor injuries to 18 passengers and 6 NYCT employees. NYCT estimated the damages to be about $12,975,187. The National Transportation Safety Board (NTSB)  determined that the probable cause of the collision between New York City Transit (NYCT) train 1345 and NYCT train 1427 was NYCT’s inadequate procedures for operating sectionalized trains from positions other than the head end, leading to the crew’s failure to stop non-revenue train 1345 at a red signal at 96th Street station and collide with revenue train 1427. Contributing factors included the mechanically cut-out brakes on the first five railcars, which disabled the automatic emergency braking system, and NYCT’s outdated communications network, which hindered reliable transmission of critical train movement information.​​]]></description>
      <pubDate>Mon, 08 Dec 2025 11:39:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625875</guid>
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      <title>Positioning, the Next Generation</title>
      <link>https://trid.trb.org/View/1736619</link>
      <description><![CDATA[Ultra-Wide Band (UWB) is a wireless technology innovation that offers faster and less expensive installation of modern communications-based train control (CBTC) by eliminating much of the onboard and wayside equipment traditionally needed for advanced technology signaling. UWB was demonstrated by Pete Tomlin, MTA New York City Transit (NYCT) Vice President Network and Resignaling, just before his resignation from the agency. This article profiles the design, development and deployment of NYCT's UWB on the Flushing (7) Line. It discusses the key advantages and features of UWB as well as the NYCT's plans for its broader implementation.]]></description>
      <pubDate>Tue, 29 Sep 2020 11:05:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1736619</guid>
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    <item>
      <title>Automated Train Identification and Train Position Monitoring at New York City Transit</title>
      <link>https://trid.trb.org/View/1721137</link>
      <description><![CDATA[Like any legacy subway system that first opened in the early 1900s, the New York City subway system operates using technology that dates from many different eras. Although some of this technology may be outdated, efforts to modernize are often hindered by budgetary limits, competing priorities, and managing the tradeoff between short-term service disruptions and long-term service improvements. At New York City Transit (NYCT), the locations of all trains on all lines are not visible to any one person in any one place and, for much of the system, train locations can only be seen at field towers for the handful of interlockings in its operational jurisdiction as result of the legacy signal system, which may come as a surprise to many daily commuters or personnel at newer metros. In 2019, developers at NYCT gained full access to the legacy signal system’s underlying track circuit occupancy data and developed an algorithm to automatically track trains and match these data with schedules and manual dispatchers’ logs in real time. This data-driven solution enables real-time train identification and tracking long before a full system modernization could be completed. This information is being provided to select personnel as part of a pilot program via several different tools with the aim of improving service management and reporting.]]></description>
      <pubDate>Thu, 16 Jul 2020 11:02:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1721137</guid>
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    <item>
      <title>Algorithm for Tracing Train Delays to Incident Causes</title>
      <link>https://trid.trb.org/View/1716395</link>
      <description><![CDATA[Knowledge of the root cause(s) of delays in transit networks has obvious value; it can be used to direct resources toward mitigation efforts and measure the effectiveness of those efforts. However, delays with indirect causes can be difficult to attribute, and may be assigned to broad categories that indicate “overcrowding,” incorrectly naming heavy ridership, train congestion, or both, as the cause. This paper describes a methodology to improve such incident assignments using historical train movement and incident data to determine if there is a root-cause incident responsible for the delay. It is intended as first step toward improved, data-driven delay recording to help time-strapped dispatchers investigate incident impacts. This methodology considers a train’s previous trip and when it arrived at the terminal to begin its next trip, as well as en route running times and dwell times. If the largest source of delay can be traced to a specific incident, that incident is suggested as the cause. For New York City Transit (NYCT), this methodology reassigns about 7% of trains originally without a root cause identified by dispatchers. Its results are provided to NYCT’s Rail Control Center staff via automated daily reports which, along with other improvements to delay recording procedures, has reduced these “overcrowding” categories from making up 38% of all delays in early 2018 to only 28% in 2019. The results confirm both that it is possible to improve delay cause diagnoses with algorithms and that there are delays for which both humans and algorithms find it difficult to determine a cause.]]></description>
      <pubDate>Mon, 29 Jun 2020 16:55:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1716395</guid>
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    <item>
      <title>Analysis of Wheel Wear and Forecasting of Wheel Life for Transit Rail Operations</title>
      <link>https://trid.trb.org/View/1689751</link>
      <description><![CDATA[As transit vehicle wheels accrue mileage, they experience flange and tread wear based on the contact between the railhead and wheel-running surface. When wheels wear excessively, the likelihood of accidents and derailments increases. Thus, regular maintenance is performed on the wheels, until they require replacement. One common maintenance practice is truing; using a specially designed cutting machine to bring a wheel back to an acceptable profile. This process removes metal from the wheel and is often based on wheel flange thickness standards (and sometimes wheel flange angle). Wheel replacement is usually driven by rim thickness, which is continuously reduced by wear, as well as metal removal during truing. This research study used wheel wear data provided by the New York City Transit Authority (NYCTA) to analyze wheel wear trends and forecast wheel maintenance (truing based on flange thickness) and wheel life (replacement based on rim thickness). Using automatic wheel-scanning technology, NYCTA was able to collect wheel profile measurements for nearly 4,000 wheels in its fleet over a two year period, measured weekly. The resulting wheel measurement data was analyzed using advanced stochastic techniques to determine relationships for the changes in flange thickness over time for each wheel in the fleet. Flange thickness wear rate relationships for each wheel were then used to forecast the time it would take for a wheel to reach the flange thickness maintenance threshold as defined by NYCTA standards. Furthermore, a subpopulation of wheels that exhibited very high rates of wear were classified as “bad actors”, and identified for further investigation to understand the cause of accelerated wear. This allows for identification and addressing of causal factors that relate to accelerated wear, such as angle of attack and L/V ratio. NYCTA has recently started capturing such data that relates truck performance, which in turn, can be related to rate of wear.]]></description>
      <pubDate>Wed, 11 Mar 2020 17:15:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1689751</guid>
    </item>
    <item>
      <title>Railroad Accident Brief: New York City Transit Train Strikes Two Flagmen, Brooklyn, New York, November 3, 2016</title>
      <link>https://trid.trb.org/View/1635913</link>
      <description><![CDATA[​On November 3, 2016, at 12:05 a.m. eastern daylight time, New York City Transit (NYCT) subway train 2328G, operating underground in a tunnel between the Fort Hamilton Parkway and Church Avenue stations, struck two NYCT employees on the F Line in Brooklyn, New York. The employees were setting up flagging protection for a contractor who needed to cross the track to access an instrument control room in the tunnel. One employee was killed, and one was seriously injured. After the accident, 23 passengers were evacuated while the crew remained with the train. The transit equipment and the track structure did not sustain any damage. ​The National Transportation Safety Board determines that the probable cause of the accident was the failure of the Rail Control Center to communicate to the train dispatcher and tower operator that flaggers were on the track. Also contributing to the accident is New York City Transit’s absence of a risk assessment when planning its flagging operations and permitting train movements into unprotected work zones.]]></description>
      <pubDate>Wed, 24 Jul 2019 22:35:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1635913</guid>
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    <item>
      <title>Correlation of Continuous Vehicle Based and Wayside Inspection Data to Develop Non-Traditional Maintenance Intervention Strategies </title>
      <link>https://trid.trb.org/View/1601564</link>
      <description><![CDATA[The vehicle/track interface continues to be of great concern in all rail operations to include high speed and conventional passenger, freight and transit operations. Much research has been performed in this area and in the associated area of the wheel/rail interface, and have resulted in the development of sophisticated inspection technologies aimed at evaluating various track and train component performance and conditions. Until now, the data from these varied inspection technologies have been evaluated at a basic data analysis level, e.g. threshold analysis based.  This activity extends improved data Analytic techniques to the analysis of data collect on a complete New York City Transit Authority (NYCT) transit line. 
The focus of this study was analysis of wayside measurement data with a specific emphasis Wheel Profile Data using wheel profile data for every passing wheel on every vehicle in the fleet that operates on the study NYCT line. This data was supplemented by the following additional data: (1) Applied Rail Force Data: measured lateral and vertical forces on the rail from every passing axle and (2) Angle of Attack Data: measured angle of attack of the bogie for every passing vehicle.
This research study used this wheel wear data, as provided by the NYCT, to analyze wheel wear trends and forecast wheel maintenance (truing based on flange thickness) and wheel life (replacement based on rim thickness). Using automatic wheel-scanning technology, NYCT was able to collect wheel profile measurements for nearly 4,000 wheels in their fleet over a six-month period, measured weekly. The resulting wheel measurement data was analyzed using advanced stochastic techniques to determine relationships for the changes in flange thickness over time for each wheel in the fleet. 
The expected research results will be a set of wheel wear models that can be used to forecast the time it would take for a wheel to reach the flange thickness maintenance threshold as defined by NYCT standards. This is to include both wheel truing maintenance and wheel replacement.
]]></description>
      <pubDate>Mon, 22 Apr 2019 23:42:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1601564</guid>
    </item>
    <item>
      <title>Hitting Fast Forward</title>
      <link>https://trid.trb.org/View/1581937</link>
      <description><![CDATA[Metropolitan Transit Authority's New York City Transit (NYCT) has launched Fast Forward, a $40 billion plan to modernize the New York City Subway. The plan seeks to bring the transit network's signals and communications up to modern standards within 10 years. The article highlights the challenges that the MTA faces in implementing the plan, and the role that advocacy campaigns and supplier collaboration will play in the project's success.]]></description>
      <pubDate>Mon, 01 Apr 2019 10:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1581937</guid>
    </item>
    <item>
      <title>Passenger-Centric Performance Metrics for the New York City Subway</title>
      <link>https://trid.trb.org/View/1576997</link>
      <description><![CDATA[Like many transit agencies, New York City Transit (NYCT) has long relied on operations-focused metrics to measure its performance. Although these metrics, such as capacity provided and terminal on-time performance, are useful internally to indicate the actions needed to improve service, they typically do not represent the customer experience. To improve its transparency and public communications, NYCT launched a new online Subway Dashboard in September 2017. Two new passenger-centric metrics were developed for the dashboard: additional platform time (APT), the extra time passengers spend waiting for a train over the scheduled time, and additional train time (ATT), the extra time they spend riding a train over the scheduled time. Unlike similar existing metrics, NYCT’s new methodology is easily transferable to other agencies, even those without exit data from an automated fare collection system. Using a representative origin–destination matrix and daily scheduled and actual train movement data, a simplified train assignment model assigns each passenger trip to a train based on scheduled and actual service. APT and ATT are calculated as the difference in travel times between these two assignments for each individual trip and can then be aggregated based on line or time period. These new customer-centric metrics received praise from transit advocates, academics, other agencies, and the press, and are now used within NYCT for communicating with customers, as well as to understand the customer impacts of operational initiatives.]]></description>
      <pubDate>Thu, 10 Jan 2019 10:38:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1576997</guid>
    </item>
    <item>
      <title>New York City Transit: Why Envision?</title>
      <link>https://trid.trb.org/View/1559050</link>
      <description><![CDATA[Many public entities responsible for the design and construction of infrastructure projects have established ways to perform a thorough environmental review to ensure compliance. However, sustainability considerations have little regulatory representation and as a result, alternate ways of assessing the quality of organizational performance are often sought. Some available frameworks seek to completely change the way Metropolitan Transportation Authority (MTA) New York City Transit (NYCT) do business; others can complement and seamlessly augment existing processes to ensure greater success of integration. This paper discusses how MTA NYCT Capital Program Management (CPM) has been approaching environmental and sustainability considerations historically and how the Envision rating system for sustainable infrastructure is being incorporated into CPM’s regular procedures.]]></description>
      <pubDate>Tue, 11 Dec 2018 09:21:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1559050</guid>
    </item>
    <item>
      <title>Laboratory and Field Testing of NYCTA Power Frequency Track Circuits</title>
      <link>https://trid.trb.org/View/1501947</link>
      <description><![CDATA[This report addresses the possible electromagnetic interference (EMI) between the electronic AC propulsion control systems and the signaling and train control systems. The potential exists for AC-drive propulsion systems to cause EMI that can adversely affect the operation of New York City Transit Authority's (NYCTA's) power-frequency track circuits. Harmonic components of the variable-frequency propulsion currents generated by the inverters conceivably could leak onto the third rail and then be magnetically coupled to the track circuits. General operating characteristics of the track circuits, test methods, and results are presented in this report. A good qualitative picture of overall NYCTA track circuit susceptibility to EMI was obtained. Each of the thirteen track circuits tested in the lab gave precise and repeatable results. Results obtained from the field tests of three track circuits were more variable, due to variability of conditions; however, results from the field were very similar to those obtained in the lab. The data from these measurements have been provided to the equipment manufacturers involved in the AC propulsion program and to the NYCTA.]]></description>
      <pubDate>Tue, 10 Apr 2018 17:03:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1501947</guid>
    </item>
    <item>
      <title>A Data-Driven Approach to Prioritizing Bus Schedule Revisions at New York City Transit</title>
      <link>https://trid.trb.org/View/1496081</link>
      <description><![CDATA[Over two million trips are taken every weekday across the New York City Transit (NYCT) bus network. Revising the schedules for each of these routes is a labor-intensive process, and because of limited resources, fewer than half of all routes are examined each year. Traditionally, schedules have been revised on a first-in, first-out basis, with most schedules rewritten once every 2 years. This approach leaves no room for reviewing routes that need more frequent changes, meaning service may not catch up to changes in demand or traffic patterns for several years. It also requires staff to spend valuable staff time analyzing routes that may be inactive. To better address rapidly evolving bus corridors, NYCT developed a methodology to pinpoint the routes most in need of schedule revisions. This data-driven approach uses automatic vehicle location data and a ridership algorithm that combines automated fare collection data with other sources to infer stop-by-stop boardings and alightings for individual trips. Each route’s schedule is evaluated on service capacity versus actual ridership, and scheduled versus actual running times. Routes that show the greatest discrepancies are designated for later in-depth review. This methodology was applied to develop the 2018 list of schedule revisions. As this process identifies routes with too much capacity or running time, as well as those with too little, resource-costly schedule adjustments can be offset with resource-saving ones. Using this approach allows scheduling staff to react more quickly to changes in customer demand and new development, thereby providing better service to passengers.]]></description>
      <pubDate>Thu, 22 Mar 2018 11:57:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1496081</guid>
    </item>
    <item>
      <title>A Real-Time Service Management Decision Support System for Train Dispatching at New York City Transit</title>
      <link>https://trid.trb.org/View/1495679</link>
      <description><![CDATA[With ridership near modern highs, New York City Transit’s (NYCT) subway network frequently operates at or near capacity. This makes maintaining a high-quality service both challenging, due to the lack of “slack,” and exceptionally important, due to the large number of riders affected by disruptions. To this end, train dispatchers constantly monitor the network and adjust service to respond to delays. This paper presents a decision support system developed by NYCT which uses real-time train movements and historical ridership information to provide dispatchers with recommendations for holds and station skips in real time. The system uses heuristic headway criteria to determine hold or skip candidate trains, and then estimates the net passenger time savings of each potential hold or skip using estimated origin–destination flows and basic assumptions about passenger behavior. Potential actions that meet a passenger benefit threshold are recommended, and communicated to dispatchers with a simple dashboard. A pilot implementation of the system has been in use at NYCT’s Rail Control Center (RCC) for several months, though many details of the system are still in development. Initial observations indicate the system is helping dispatchers manage train service more effectively, producing large passenger time savings.]]></description>
      <pubDate>Thu, 22 Mar 2018 11:57:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1495679</guid>
    </item>
    <item>
      <title>Recovery and Resiliency</title>
      <link>https://trid.trb.org/View/1485526</link>
      <description><![CDATA[In 2012, Hurricane Sandy left a major impact on New York City Transit. Subway tunnels were flooded and walls, tracks, and equipment were damaged. Recovery plans were estimated to take several years, but would include improvements to protect the infrastructure from another similar storm. Extensive restoration and resilience efforts have been underway for four years, with an estimated $2.5 billion spent on resiliency.]]></description>
      <pubDate>Mon, 23 Oct 2017 13:39:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1485526</guid>
    </item>
    <item>
      <title>Workforce Challenges Confronting New York City Transit</title>
      <link>https://trid.trb.org/View/1468649</link>
      <description><![CDATA[The purpose of this research was to identify the pressing workforce issues confronted by transit authorities nationwide and promising ways in which they are being addressed. The study also included a closer examination of New York City Transit (NYCT), the nation’s largest transit authority, to consider its challenges and which solutions could be brought to bear to address them. The research questions guiding the study were: (1) How do the labor and management of the nation’s transit authorities define their workforce challenges? (2) How have they addressed these challenges? (3) What if any workforce issues uniquely apply to NYCT’s workforce? (4) How is NYCT addressing the identified issues? and (5) What other strategies can be adopted by NYCT to address its challenges? The results are expected to be instructive not only to NYCT, but also to transit systems and their labor partners throughout the nation. The findings are especially important in light of the transit industry’s unique economic and public safety importance, and the career opportunities the industry can provide to current and future workers who have been negatively affected by the confluence of globalization and technology.]]></description>
      <pubDate>Tue, 20 Jun 2017 13:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1468649</guid>
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