Additional Bus Stop Time is the average time that customers spend waiting at a stop beyond their scheduled wait time.

Additional Travel Time is the average time customers spend onboard a bus beyond their scheduled travel time.

Customer Journey Time Performance is the percentage of customers whose journeys are completed within 5 minutes of their scheduled time.

This dataset covers data between 2017 and 2019. For data from 2020 on, use https://data.ny.gov/Transportation/MTA-Bus-Customer-Journey-Focused-Metrics-Beginning/wrt8-4b59/about_data.

Additional Bus Stop Time is the average time that customers spend waiting at a stop beyond their scheduled wait time.

Additional Travel Time is the average time customers spend onboard a bus beyond their scheduled travel time.

Customer Journey Time Performance is the percentage of customers whose journeys are completed within 5 minutes of their scheduled time.

This dataset covers data from 2020 and on. For data between 2017 and 2019, use dataset https://data.ny.gov/Transportation/MTA-Bus-Customer-Journey-Focused-Metrics-2017-2019/8mkn-d32t.

This dataset provides insights into safety-related incidents and indicators within New York City Transit (NYCT). NYCT is responsible for overseeing the safety of subway and bus operations, aiming to ensure the well-being of both employees and passengers. This dataset offers information about various safety aspects, including incidents, collisions, and accident prevention indicators within the NYCT system.

This dataset provides information on the actual end-to-end running times for subway trips between their origin and destination stations, including the average, and 25th, 50th (median), and 75th percentile running times in minutes, along with the scheduled end-to-end running time in minutes for these trips.

Data is available for a combination of each month, day type (Weekday, Saturday, Sunday), time period (AM Peak, Midday, PM Peak, Evening, Overnight), subway line, and stop path id (each distinct set of subway stops made by trains). The dataset also includes the direction, number of stops, origin station, and destination station for each stop path, the distance and average actual speed in miles per hour along the stop path, and the actual and scheduled number of trains running along the stop path.

This dataset reflects the monthly number of employee-reported incidents of workplace violence, as defined by New York State Labor Law Section 27-B, against on-duty MTA employees. This dataset divides workplace violence incidents into groupings according to New York State Penal Law Related Offenses. The same data is available in the MTA Workplace Violence Labor Law Incidents dataset, which divides the data as reported pursuant to New York State Labor Law Section 27-B.

This dataset provides an estimate of subway travel patterns based on scaled-up OMNY and MetroCard return swipe data. It provides estimated passenger volumes for all populated origin-destination (O-D) pairs aggregated by month, day of the week, and hour of day. It also provides the name, ID, and approximate latitude and longitude of the origin and destination subway complexes.

The number of summonses and arrests made by NYPD or MTAPD for fare evasion and other violations of the rules of conduct of the transit system.

This dataset provides an estimate of subway travel patterns based on scaled-up OMNY and MetroCard return swipe data. It provides estimated passenger volumes for all populated origin-destination (O-D) pairs aggregated by month, day of the week, and hour of day. It also provides the name, ID, and approximate latitude and longitude of the origin and destination subway complexes.

This dataset reflects the monthly number of employee-reported incidents of workplace violence, as defined by New York State Labor Law Section 27-B, against on-duty MTA employees. This dataset divides workplace violence incidents into groupings as reported pursuant to New York State Labor Law Section 27-B. The same data is available in the MTA Workplace Violence Penal Law Incidents dataset, which divides the data according to New York State Penal Law Related Offenses.