How Rapidly Deployed Analytics Can Improve Rail Maintenance and Control Costs

Narinder Manku, Solution Marketing Manager for Road and Rail Asset Performance
  • June 09, 2021

Rail and transit agencies face one of their biggest threats in decades. The pandemic has resulted in a loss of revenue and ridership for many Agencies like Bay Area Rapid Transit (BART) who estimate below normal levels with an ongoing losses of million's of dollars a month in passenger revenue. This is just one example of many transit agencies struggling during these unfortunate times. With inevitable budget cuts, how can you do more with less?  How will you ensure you are doing the right work and the right maintenance to maintain safe and reliable service? Can digital transformation be the fast track to deliver the knowledge and information you need to not only improve maintenance strategies but significantly reduce costs in these trying times?

The gap between the fixed costs of running a railway and passenger-generated revenue creates significant pressure for improving track maintenance, which constitutes a substantial proportion of the whole life cost of a network. While governments worldwide recognize this and have funded support and recovery packages for the industry, the situation forces agencies to make tough decisions relating to how they prioritize rail maintenance activities, which are often done manually. Agencies have no choice but to digitally transform their processes and systems to do more work for the same or less money. Industry leaders are leveraging advanced linear analytics to gain insights and to increase the visibility of data, facilitating informed decisions to ensure networks remain safe and reliable, and in turn, deliver the required return on investment.

Confidently Do More for Less

For example, NYC MTA has proven how to take advantage of digital solutions to control costs. In 2019, the organization introduced its transformation plan to change how they work and provide a better system for safe and reliable service by focusing on critical maintenance and repairs. First, they analyzed how data was exchanged throughout the organization and how it could be digitalized for more efficient and better operations. “When carrying out data collection or condition assessment, NYC MTA wanted to understand how this data is being interpreted and solve the data-rich to information-poor problem,” said David Kraft, senior director of enterprise asset management, Program Management Office, NYC MTA.

Today’s digital twin framework solves this problem. Information is data that is viewed and analyzed in context, including both the surrounding context and historical context. A digital twin provides a contextual framework that transforms raw data and makes it easy to understand current and potential conditions and make fast and accurate decisions. “By gathering necessary information, you can visualize the status or condition of assets and present that same information to field workers and executives who, within seconds, easily understand what is required enabling you to make efficient decisions and reducing costs,” Kraft said. NYC MTA’s approach saw record improvements in efficiency and on-time performance to provide safe and reliable service for millions of New York commuters. 

“Advancing technology offers rail owners and operators the opportunity to improve rail network asset performance by consolidating data for accurate condition assessment and visibility,” said Andrew Smith, senior product manager, transportation asset performance at Bentley Systems. “Rail and transit agencies, like NYC MTA, are putting their trust in digital transformation by leveraging digitalization to confidently provide them with timely data visibility to better understand past, present, and future rail asset conditions.” 
“With intelligent linear analytics powered by a digital twin, agencies can be more productive and make smarter decisions to improve track maintenance strategies and control costs by enabling the right work to be performed in the right place at the right time, creating operational efficiency,” Smith said. 

Given these capabilities, rail agencies can make better use of existing information through digital transformation to deliver increased value. By harnessing the vast amount of existing rail corridor data in a prioritized plan, many rail agencies realize they can do much more even with budget reductions. This not only results in steady-state asset performance under the pressure of constrained resources, but can also provide a demonstrable positive return on investment. 

Data Science at Work with Linear Data Visualization and Analysis

In the past, advanced linear analytic tools have tended to be a scientific solution configured to each individual users' requirements. Because of this, advanced linear analytics were typically implemented on larger-scale networks or by agencies with existing remits and strategies for using track measurement data to improve maintenance efficiency and asset reliability.
However, in most scenarios, a common set of capabilities is needed with only small individual variations from one rail operator to another. With this in mind, coupled with the urgent need to quickly offer rail agencies a cost-effective yet powerful solution in the time of a pandemic, Bentley took action to package the common core functionality of AssetWise in a cloud-based and off-the-shelf solution to ensure it can be rolled out to an organization quickly and efficiently for maximum benefit in the shortest time possible.    

Once implemented, additional bespoke requirements can be added during a second deployment phase if needed – but typically, the solution is already live and adding value for the organization and its users. AssetWise, Bentley’s rapidly deployed analytics solution includes the following capabilities:

  1. Data Alignment: The process of identifying issues with the reported location of measurement data. Many recording vehicles are good at measuring key geometric parameters such as rail wear, but not so good at placing exactly where they are on the network. Reporting a track defect at the wrong location risks work being performed at the wrong location, leaving the issue on the network and requiring repeat work and expense to rectify the problem.
  2. Threshold Calculations: Identifying where imported and validated measurement data passed through user-defined thresholds such as excessive twist or broad gauge. Multiple thresholds can be defined within multiple channels of data.
  3. Network Segmentation: Unlimited segmentations to divide the network into manageable chunks, including dynamic segmentation. The two main segmentations generated are an equal spaced segment (e.g., 500ft for US networks or 100 / 200m for European) and a geometric segmentation based on straights and transition curves.
  4. Track Quality Index: These calculations include design geometry derivation by reverse-engineering the designed locations of straights, transitions, and curves from geometry data.  These indexes are used to assess the condition of ballast and track and support maintenance and renewal activities. These quality indexes can be used to report on the overall condition of the asset and can be normalized to predefined quality bands, such as the FTA SGR 5-1 scale.
  5. Trending and prediction: Once historic Track Quality Indexes have been derived, AssetWise can predict the future deterioration rate of the rail network. This can provide advanced warning of asset failure, justify planned maintenance, and assess the effectiveness of historic maintenance in tackling the root causes associated with poor asset conditions. 

Maximize transit operations with a rapid ROI

The primary focus is to ensure agencies receive sufficient funding to maximize transit operations. This presents an excellent opportunity for rail systems to review and adapt existing processes and systems, and where relevant, look to invest in new and more adaptable systems. With the flexible linear analytical capabilities provided by AssetWise, users can create and implement new forms of analysis without changing their primary operations and maintenance software, enabling them to improve processes and efficiency – saving time and reducing costs without sacrificing safety. By leveraging existing processes and data with new linear analytics tools, organizations can work smarter, minimize risk, increase productivity, improve operational efficiency, and create a safer network for tomorrow.