Service Delivered measures the percentage of scheduled trains that are actually provided during peak hours on weekdays (7 AM to 10 AM and 4 PM to 7 PM) and weekends (10 AM to 6 PM) for a given month. This dataset covers data from 2020 and on. For data between 2015 and 2019, use dataset https://data.ny.gov/Transportation/MTA-Subway-Service-Delivered-2015-2019/32ch-sei3.

This dataset is of major incidents on Metro-North, which are defined as events that lead to ten or more trains that are delayed by more than 5 minutes 59 seconds, canceled, or terminated.

Max Ride Time Performance presents the percentage of trips performed with Access-A-Ride’s established max ride time standards between 2018 and 2023. This dataset is now retired as this metric can be calculated from MTA NYCT Paratransit Ride Time (https://data.ny.gov/Transportation/MTA-NYCT-Paratransit-Ride-Time-Beginning-2017/wtm4-vjm7/).

Bus Speeds measure how quickly buses travel along their routes. This dataset covers data from 2020 and on. For data between 2015 and 2019, use dataset https://data.ny.gov/Transportation/MTA-Bus-Speeds-2015-2019/cudb-vcni.

The percent of the time that elevators and escalators are operational systemwide.

On-Time Performance (OTP) determines how frequently a commuter train arrives at its final destination within 5 minutes and 59 seconds of its scheduled time.

Service Reliability metrics evaluate MNR’s ability to deliver timely and effective service to its customers. Included in these metrics are: Service Type, Major Incidents, 4m to 6m Train Delays, Average Delay per Late Train, Number of Trains Over 15 Minutes Late, Number of Short Trains.

Service Delivered measures the percentage of scheduled trains that are actually provided during peak hours on weekdays (7 AM to 10 AM and 4 PM to 7 PM) and weekends (10 AM to 6 PM) for a given month. This dataset covers data between 2015 and 2019. For data from 2020 on, use dataset https://data.ny.gov/Transportation/MTA-Subway-Service-Delivered-Beginning-2020/bg59-42xi.

Drop-off On-Time Performance (OTP) measures the quality of provider service, comparing actual to customer-requested drop-off time for trips scheduled with an appointment time. Trips are considered on-time if they are no more than 30 minutes early, and no later than the appointment time.

Ride Time measures customer trip duration. Actual vs. Scheduled presents travel time variance, Average Travel Time presents the average actual trip duration, and Max Ride Time by trip distance category.

Pickup On-Time Performance (OTP) measures the quality of provider service, comparing actual to promised pickup time. Trips are considered on-time if they arrive no more than 20 minutes after the promised time. This dataset also includes trips that arrive within 15 and 30 minutes of the promised time.

Terminal On-Time Performance measures the percentage of trains arriving at their destination terminals as scheduled. This dataset covers data from 2020 and on. For data between 2015 and 2019, use dataset https://data.ny.gov/Transportation/MTA-Subway-Terminal-On-Time-Performance-2015-2019/f6rf-2a3t.

Mean Distance Between Failures represents the number of miles a rail car travels before a mechanical failure makes the train arrive at its final destination more than 5 minutes and 59 seconds later than scheduled. This measures the reliability of equipment in revenue service.

This indicator shows the number of subway and bus journeys made each month by passengers using Reduced-Fare MetroCards between 2018 and 2023. This dataset is now retired, with data on reduced-fare usage of the transit system available in the hourly bus (https://data.ny.gov/Transportation/MTA-Bus-Hourly-Ridership-Beginning-February-2022/kv7t-n8in/) and subway (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s/) ridership datasets.

Wait Assessment measures how regularly trains are spaced during peak hours at selected timepoints on each line.

This metric tracks the monthly dollar amount of chargebacks and the percentage of transactions that have been disputed with the customer receiving a refund.

Mean Distance Between Failures reports how frequently bus mechanical problems cause delays.

Terminal On-Time Performance measures the percentage of trains arriving at their destination terminals as scheduled. This dataset covers data between 2015 and 2019. For data from 2020 on, use dataset https://data.ny.gov/Transportation/MTA-Subway-Terminal-On-Time-Performance-Beginning-/vtvh-gimj.

Mean Distance Between Failures represents the number of miles a rail car travels before a mechanical failure makes the train arrive at its final destination more than 5 minutes and 59 seconds later than scheduled. This measures the reliability of equipment in revenue service.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.

This dataset includes electricity and natural gas usage data reported at the county level level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-County-Energy-Use-/46pe-aat9.

Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.

This dataset includes electricity and natural gas usage data reported at the city, town, and village level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the county and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-Community-Energy-U/4txm-py4p.

Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.

This dataset includes electricity and natural gas usage data reported at the ZIP Code level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the city, town, village, and county level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-ZIP-Code-Energy-Us/g2x3-izm4.

Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The number of reportable job-related incidents that result in the inability of an employee to perform full job duties for at least one working day beyond the day of the incident, as well as a breakdown of the number of Employees and Lost Time Accident Rates.