LONG ISLAND RAILROAD 2018-25
The Long Island Railroad, often abbreviated as the LIRR, is a commuter rail system in the south eastern part of the U.S. state of New York, stretching from Manhattan. In 2023, the system had a ridership of 75,186,900.
Optimizing Laser Cleaning Impact 2023
Our continuous improvement strategy for the 2023 slip-slide season was to shrink respite and enhance efficiencies. To achieve this, we focused on improving machine reliability through two key targeted enhancements:
Refined GIS Target Area with Software Updates: We implemented software updates to refine our Geographical Information Set (GIS) Target Area. This optimization slashed the laser on/off time from 9 seconds to a mere 2 seconds, significantly improving the time it takes to start cleaning after crossing interlocks, switches, and grade crossings.
Expanded Sensor Types for Improved Reliability: In addition to software upgrades, we added new sensor types to enhance overall reliability. This proactive measure ensured smoother operations and minimized downtime due to sensor malfunctions.
Our targeted enhancements for the 2023 slip-slide season delivered tangible results including:
Increased Cleaning Efficiency: Despite a 11% drop in LaserTrain miles driven, miles cleaned surged by an impressive 28% in 2023! We achieved a remarkable 85.4% cleaning rate per mile driven marking significant progress from 2022.
Reduced Maintenance Incidents: Our enhancements resulted in a 25% reduction in low adhesion faults, a 29% decrease in slip-slide events, and a 17% decrease in cars out of service, providing safe, streamlined operations.
Enhanced Operational Efficiency: With a 30% cut in slip time, we optimized operational efficiency, minimizing rail transport disruptions and maximizing productivity. This reduction translates directly to cost savings and improved service reliability for our customers.
Expanding Operations 2020
In 2020, the LIRR added a second 25 mph LaserTrain to their fleet and increased the running speed to cover more of the network daily. Both LaserTrain's operated up to 18 hours per day, cleaning 2/3rds of the network. As a rail slip prevention measure, all vital parts were cleaned every 24 hours. Rail cleaning was effective, with 88% less slippage on railway tracks after 12 hours, and instances that did occur were mainly on sections that were not cleaned by the LaserTrain.
Results
• 66% reduction in low-adhesion delays
• 27% less cars taken out of service due to wheel flat spots
• 38% reduction in wheel truing labour costs (or $102,000) in October-November alone
• 95.2% OTP in November – an improvement of 4.6 percentage points
A reduction in wheel truing preserves rail wheel integrity and results in longer wheel life. Additional wheel maintenance benefits include the calculation that, according to LIRR $500,000 in shop labour and materials costs will be saved for each year of extended life!
In 2021, The LIRR announced their best annual OTP since modern record-keeping began in the 1970s, with 96.3% of trains operating to schedule over the course of the year. 2022 saw an annual OTP of 95.8%, above the 94.0% Target, proving the laser cleaning effectiveness.
Launching the LaserTrain
Like many rail companies, the Long Island Rail Road’s aim was to improve the way they deal with Autumn rail challenges that affect rail track safety and railway reliability. Wheel slip and emergency braking was forcing them to take much needed equipment out of service for repairs. It was vital to address the poor adhesion conditions that were causing issues with acceleration, braking and stopping distance, and rail wheel maintenance.
In 2018, we demonstrated a successful pilot that convinced LIRR to commission an operational prototype, capable of working from a non-passenger train at 25mph. Deployed for 12 hours a day in Autumn 2019 it achieved remarkable results, including a 65% slip reduction on the lines where LaserTrain operated, 48% fewer cancellations and 32% fewer short trains (due to fewer wheel flat spot repairs) and 65% fewer late trains due to “weather”.
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