This dataset reflects the number and percentage of subway trains delayed per weekday based on scheduled service, broken down into specific delay categories.

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

Mean Distance Between Failures reports how frequently train mechanical problems cause delays by month and subway car class. It is the main indicator for subway car reliability used at NYCT and is used to compare performance over time and across different subway car types. Only a small proportion of subway delays are due to malfunctioning trains.

This metric measures the frequency with which primary providers do not arrive at the pick-up location within 30 minutes of the promised time and the trip is not provided.

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

This dataset is of major incidents on the LIRR, 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.

Customer Experience is the percentage of valid trips and no-show trips that meet all four separate measurable established standards for quality of provider service (Pick-up OTP, Drop-off OTP, Max Ride Time, Provider No-Show).

On-Time Performance (OTP) measures the percentage of trains arriving at their destination terminals as scheduled. While the main measure for OTP excludes delays caused by trains held for connections with the Staten Island Ferry, OTP including boat delays is also included. The dataset also includes the number of on-time trips and delayed trips per month (both including and excluding boat delays), the number of scheduled trips, incomplete trains per month, and the percent of trains that are completed per month.

Major Incidents are unplanned incidents that delay 50 or more trains, which cause the most disruption to customers. This dataset covers data between 2015 and 2019. For data from 2020 on, use dataset https://data.ny.gov/Transportation/MTA-Subway-Major-Incidents-Beginning-2020/j6d2-s8m2.

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.

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.

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.

Bus Speeds measure how quickly buses travel along their routes. This dataset covers data between 2015 and 2019. For data from 2020 on, use https://data.ny.gov/Transportation/MTA-Bus-Speeds-Beginning-2020/6ksi-7cxr.

This deprecated dataset contains information on LIRR Systemwide Delays using reporting categories that were active from 2015 to 2022. More up-to-date and granular information on LIRR Delays can be found at data.ny.gov/d/e32g-kbe9.

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.

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

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/).

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.

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.

Major Incidents are incidents that delay 50 or more trains, which cause the most disruption to customers. This dataset covers data from 2020 and on. For data between 2015 and 2019, use dataset https://data.ny.gov/Transportation/MTA-Subway-Major-Incidents-2015-2019/ereg-mcvp.

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.

Service Delivered measures the percentage of scheduled buses that are actually provided during peak hours. This dataset covers data from 2020 and on. For data between 2015 and 2019, use dataset https://data.ny.gov/Transportation/MTA-Bus-Service-Delivered-2015-2019/tw28-zvtk.

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

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.

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.