Call Center Performance is measured as the percent of calls that are answered and the average speed with which those calls are answered.

Reflects the total number of registrants for paratransit service by borough/county per month.

NYCT Paratransit ridership measures both total trips, the count of trips provided to registered NYCT Paratransit clients in a given month, and total ridership, the count of personal care attendants (PCAs) and guests who join clients on the trips.

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).

These metrics reflect the quality of fare collection by LIRR conductors, as observed by MTA Auditors.

This dataset provides bus ridership estimates on an hourly basis by bus route and class of fare payment.

This retired dataset contains information on entry/exit values for individual control areas and is posted purely for archival purposes. More detailed subway ridership data is now supported through the Subway hourly dataset (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s).

This metric measures customer complaints about Access-a-Ride, including about Transportation and non-Transportation service quality. This data is used along with Trips/Ridership data to create the Complaint rate metric which is complaints per 1000 completed trips.

This dataset provides the load percentage for each express bus route at its maximum load point (the bus stop where the highest number of passengers are on the bus) by direction, hour, day type (weekday, weekends and holidays), aggregated by week.

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

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/).

This dataset provided statistics and performance metrics about the volume and responsiveness in engaging with customers via several customer engagement channels. Data was provided for New York City Transit Subway and Bus customer engagement and customer service teams between May 2017 and May 2022.

This dataset provides subway ridership estimates on an hourly basis by subway station complex and class of fare payment.

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.

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.

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.

This retired dataset contains information on entry/exit values for individual control areas and is posted purely for archival purposes. More detailed subway ridership data is now supported through the Subway hourly dataset (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s).

The bus fare evasion (BFE) rate is the estimated percentage of riders who illegally board buses. It is calculated systemwide quarterly using Automated Passenger Counters. Rates are calculated and reported separately for Local/Limited, Express and Select Bus Service. Prior to Q1 2020, fare evasion was estimated using surveys conducted by traffic checkers.

The daily ridership dataset provides systemwide ridership and traffic estimates for subways (including the Staten Island Railway), buses, Long Island Rail Road, Metro-North Railroad, Access-A-Ride, and Bridges and Tunnels, beginning 3/1/2020, and provides a percentage comparison against a comparable pre-pandemic date.

This retired dataset contains information on entry/exit values for individual control areas and is posted purely for archival purposes. More detailed subway ridership data is now supported through the Subway hourly dataset (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s).

Quarterly estimated Fare Evasion rate for Subways with margin of error. Rate estimated using surveys conducted by traffic checkers and applying stratified sampling.

This retired dataset contains information on entry/exit values for individual control areas and is posted purely for archival purposes. More detailed subway ridership data is now supported through the Subway hourly dataset (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s).

This retired dataset contains information on entry/exit values for individual control areas and is posted purely for archival purposes. More detailed subway ridership data is now supported through the Subway hourly dataset (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s).

This retired dataset contains information on entry/exit values for individual control areas and is posted purely for archival purposes. More detailed subway ridership data is now supported through the Subway hourly dataset (https://data.ny.gov/Transportation/MTA-Subway-Hourly-Ridership-Beginning-February-202/wujg-7c2s).

This dataset includes the following safety metrics: customer accident rate, grade crossing incidents, train derailments, train collisions, lost time accident rate, lost time injury rate, employee lost time days.