During the 2023 fiscal year, the number of passengers transported by the Los Angeles County Metropolitan Transportation Authority (LACMTA) network amounted to some 270 million, a year-over-year increase of around 6.6 percent, after figures plummeted by 36 percent amid the COVID-19 pandemic in 2021.

Other transit modes include demand response, demand response-taxi, vanpool, and ferryboat. The Federal Highway Administration estimates monthly transit ridership, released as part of the National Transit Database. Ridership estimates have been adjusted to account for changes in data collection over time. Starting in January 2012, data for Small System Waiver agencies that do not have a mode are reported under motor bus. Data reported under hybrid rail are reported under their classifications prior to January 2012.

VITAL SIGNS INDICATOR
Transit Ridership (T11)

FULL MEASURE NAME
Daily transit boardings

LAST UPDATED
February 2023

DESCRIPTION
Transit ridership refers to the number of passenger boardings on public transportation, which includes buses, rail systems and ferries. The dataset includes metropolitan area, regional, mode and operator tables for total typical weekday boardings.

DATA SOURCE
Federal Transit Administration: National Transit Database - http://www.ntdprogram.gov/ntdprogram/data.htm
1991-2022

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1991-2022

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1991-2022

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The National Transit Database (NTD) dataset was lightly cleaned to correct for erroneous zero values - in which null values (unsubmitted data) were incorrectly marked as zeroes. Paratransit data is sparse in early years of the NTD dataset, meaning that transit ridership estimates in the early 1990s are likely underestimated. Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD.

2022 data should be considered preliminary, as it comes from the monthly data tables rather than the longer-term time series dataset. Weekday ridership is calculated by taking the total annual ridership and dividing by 300, an assumption which is consistent with MTC travel modeling procedures; it was also compared to observed weekday boarding data (which is more limited in availability) to ensure consistency on the regional level. Per-capita transit ridership is calculated for the operator's general service area or taxation district; for example, BART includes the three core counties (San Francisco, Alameda, and Contra Costa), as well as northern San Mateo County post-SFO extension, and AC Transit includes the cities located within its service area. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data.

VITAL SIGNS INDICATOR
Transit Ridership (T11)

FULL MEASURE NAME
Daily transit boardings

LAST UPDATED
February 2023

DESCRIPTION
Transit ridership refers to the number of passenger boardings on public transportation, which includes buses, rail systems and ferries. The dataset includes metropolitan area, regional, mode and operator tables for total typical weekday boardings.

DATA SOURCE
Federal Transit Administration: National Transit Database - http://www.ntdprogram.gov/ntdprogram/data.htm
1991-2022

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1991-2022

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1991-2022

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The National Transit Database (NTD) dataset was lightly cleaned to correct for erroneous zero values - in which null values (unsubmitted data) were incorrectly marked as zeroes. Paratransit data is sparse in early years of the NTD dataset, meaning that transit ridership estimates in the early 1990s are likely underestimated. Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD.

2022 data should be considered preliminary, as it comes from the monthly data tables rather than the longer-term time series dataset. Weekday ridership is calculated by taking the total annual ridership and dividing by 300, an assumption which is consistent with MTC travel modeling procedures; it was also compared to observed weekday boarding data (which is more limited in availability) to ensure consistency on the regional level. Per-capita transit ridership is calculated for the operator's general service area or taxation district; for example, BART includes the three core counties (San Francisco, Alameda, and Contra Costa), as well as northern San Mateo County post-SFO extension, and AC Transit includes the cities located within its service area. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data.

This ridership is calculated from a variety of sources depending on the route, mode, and month in which the data was collected. These datasets provide a high-level overview of SEPTA’s ridership for basic analysis. They supplement – but do not replace – data provided for official financial or legal reporting requirements.

Routes included for each mode:

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Bus – all bus routes

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CCT – SEPTA’s paratransit service

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Heavy Rail – Broad Street Line, Market Frankford Line, and Norristown High Speed Line

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Regional Rail – All commuter rail lines

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Trackless Trolleys – Routes 59, 66, and 75

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Trolley – Routes 10, 11, 13, 15, 34, 36, 101, and 102

Please note the following about methodology and data sources:

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Ridership numbers are generated using Automatic Passenger Counters (APCs), through revenue derived estimates, or they are estimated using a combination of APC data and overall revenue ridership trends.

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In August 2020, SEPTA suffered a malware attack and APC data was not available for various periods of time depending on the mode:

o
Bus mode data was unavailable from August 2020 through December 2020.

o
Trolley mode data was unavailable from August 2020 through May 2023.

o
Trackless trolley mode data was unavailable from August 2020 and remains unavailable.

o
APC data for Regional Rail, Heavy Rail and CCT was never available during this time period.

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In September 2023 the formula to estimate ridership on Heavy Rail mode was adjusted to account for the high rates of fare evasion observed in a study performed in the second quarter of 2023. The spike in ridership between September 2023 and months prior can partially be attributed to this adjustment.· December 2019 ridership numbers were updated in January 2025 to reflect updated methodologies for capturing data when the alternate schedule is in effect between Christmas and New Years. The updated numbers more accurately represent the historic ridership numbers during the month of December.

For the data prior to July 2018, please visit archive.

Fixed route bus includes commuter bus, motor bus, bus rapid transit, and trolleybus. The Federal Highway Administration estimates monthly transit ridership, released as part of the National Transit Database. Ridership estimates have been adjusted to account for changes in data collection over time. Starting in January 2012, data for Small System Waiver agencies that do not have a mode are reported under motor bus. Data reported under hybrid rail are reported under their classifications prior to January 2012.

Set of annual MDOT perfromance data including port, transit, bridge and highway condition, and MVA branch office wait time data.

VITAL SIGNS INDICATOR
Transit Ridership (T11)

FULL MEASURE NAME
Daily transit boardings

LAST UPDATED
February 2023

DESCRIPTION
Transit ridership refers to the number of passenger boardings on public transportation, which includes buses, rail systems and ferries. The dataset includes metropolitan area, regional, mode and operator tables for total typical weekday boardings.

DATA SOURCE
Federal Transit Administration: National Transit Database - http://www.ntdprogram.gov/ntdprogram/data.htm
1991-2022

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1991-2022

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1991-2022

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The National Transit Database (NTD) dataset was lightly cleaned to correct for erroneous zero values - in which null values (unsubmitted data) were incorrectly marked as zeroes. Paratransit data is sparse in early years of the NTD dataset, meaning that transit ridership estimates in the early 1990s are likely underestimated. Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD.

2022 data should be considered preliminary, as it comes from the monthly data tables rather than the longer-term time series dataset. Weekday ridership is calculated by taking the total annual ridership and dividing by 300, an assumption which is consistent with MTC travel modeling procedures; it was also compared to observed weekday boarding data (which is more limited in availability) to ensure consistency on the regional level. Per-capita transit ridership is calculated for the operator's general service area or taxation district; for example, BART includes the three core counties (San Francisco, Alameda, and Contra Costa), as well as northern San Mateo County post-SFO extension, and AC Transit includes the cities located within its service area. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data.

DVRPC tracks transit ridership in the region through unlinked passenger trips, with data provided by each of the region's four transit operators—Southeastern Pennsylvania Transportation Authority (SEPTA), New Jersey Transit (NJ Transit), Port Authority Transit Corporation (PATCO), and Pottstown Area Rapid Transit (PART)—to the National Transit Database (NTD). Unlinked passenger trips count each passenger boarding, regardless of fare paid. Thus, a trip with a transfer would count as two boardings. Transit ridership does not include Amtrak, shuttles, or private bus passengers. It also does not include services that receive partial funding from SEPTA, like SCCOOT operated by the Transportation Management Association of Chester County.

One table shows the number of unlinked trips by mode—bus, trolley bus, light rail, heavy rail, commuter or regional rail, and non-scheduled services, which includes paratransit, demand response, and vanpools. All NJ Transit services are classified as being in the NJ Counties Subregion, because they mostly do not cross over to the Pennsylvania side of the region. The other table shows the number of unlinked trips by transit agency. The region's commuter rail services are mostly in the PA Suburban Counties Subregion. SEPTA is the only agency in the region with trolleybus services. SEPTA classified the Norristown High Speed Line (NHSL) as a subway, which travels through the PA Suburban Counties Subregion. SEPTA's subway services travel within the city of Philadelphia. Because of how these subway services cover multiple subregions, subways were simply classified as being in the DVRPC Region. Also, both tables contain: Total unlinked trips, unlinked trips per capita, unlinked trips per vehicle revenue hour (VRH), and unlinked trips per vehicle revenue mile (VRM). Vehicle revenue hours and miles count only when transit vehicles are operating along their scheduled routes. These figures do not account for ‘deadhead’ miles when not in passenger service, such as going to and from the depot on their way to or coming back from their scheduled route. Heavy rail and commuter rail services that operate with multiple passenger vehicles in one train count miles and hours for each vehicle individually. For example, a train with six passenger cars traveling one mile will count as six vehicle revenue miles.

NJ Transit's ridership figures are for the DVRPC region only. These figures are based on service in the Trenton and Philadelphia urbanized areas collected by the NTD. Prior to 2013, NJ Transit ridership, vehicle revenue hours, and vehicle revenue miles data was only provided at the statewide level. To calculate the region’s share of these NJ Transit figures, statewide totals from 1997 to 2012 were multiplied by the region’s percentage based on the 2013 to 2022 averages in each category. These averages were 12% of the state's total ridership, 14% of the vehicle revenue miles, and 12% of the vehicle revenue hours. PART ridership data is only available from 2005 onwards. PART vehicle revenue hour and mile data is only available from 2011 onwards. SEPTA trolley bus ridership data is not available from 2004 to 2007.

This statistic shows the annual ridership of transit in New York City between 2013 and 2017, broken down by mode. In 2017, the New York subway reported a total ridership of just under 1.73 billion rides, down from around 1.76 billion in the previous year.

The Monthly module includes a limited set of key indicators reported by transit properties. Data is reported on a monthly basis, by mode and type of service, for a fiscal year.

The four data items included are:

1. Unlinked Passenger Trips
2. Vehicle Revenue Miles
3. Vehicle Revenue Hours
4. Vehicles Operated in Maximum Service (Peak Vehicles)

This dataset presents these values in their own column in a long format (each row of the file is an individual Agency/Mode/TOS/Date). The data source is shared with the static Excel file hosted on the FTA website here: https://www.transit.dot.gov/ntd/data-product/monthly-module-adjusted-data-release. This dataset differs from the static Excel file in its formatting as well as being updated weekly, to capture data as it is reported and validated for a given publication month.

Mode Codes: Alaska Railroad (AR) Cable Car (CC) Commuter Rail (CR) Heavy Rail (HR) Hybrid Rail (YR) Inclined Plane (IP) Light Rail (LR) Monorail/Automated Guideway (MG) Streetcar Rail (SR) Aerial Tramway (TR) Commuter Bus (CB) Bus (MB) Bus Rapid Transit (RB) Demand Response (DR) Ferryboat (FB) Jitney (JT) Público (PB) Trolleybus (TB) Vanpool (VP)

Mode and Type of Service Changes and Impacts on this Time Series:

"Monthly data are reported by mode and type of service. From 2002 through 2011, there were 16 modes in the NTD. NTD monthly ridership data is now reported according to refined modal classifications. Service previously reported as bus (MB) now may be reported as either MB, Commuter Bus (CB), or Bus Rapid Transit (RB). Additionally, service previously categorized as Light Rail (LR) now may be reported as LR or Streetcar (SR).

Similarly, Types of Service were refined in Report Year 2019. From 2002 - 2018, there were two types of service: Directly Operated (DO) and Purchased Transportation (PT). As of 2019, Purchased Transportation is now classified such that agencies report the purchased transportation based on the type of contractor: general third party (PT), taxicab operator (TX), or transportation network company (TN). FTA concurrently removed the ""Demand Response Taxi"" (DT/PT) mode in 2019. FTA now considers all such service as Demand Response (DR) with Taxi (TX) type of service and this time series has been updated to reflect this change. "

For more information on this dataset, please consult the full Read Me in the attached file.

Set of annual MDOT perfromance data including port, transit, bridge and highway condition, and MVA branch office wait time data.

This file contains the total and average daily ridership per month by day type (e.g. weekday) and mode (Bus, Rapid Transit, Commuter Rail, and Ferry) and line (for Rapid Transit and Ferry). The data is available from Jan 2016 to Jun 2018.

Urban rail includes heavy rail, commuter rail, light rail, streetcar rail, and hybrid rail. The Federal Highway Administration estimates monthly transit ridership, released as part of the National Transit Database. Ridership estimates have been adjusted to account for changes in data collection over time. Starting in January 2012, data for Small System Waiver agencies that do not have a mode are reported under motor bus. Data reported under hybrid rail are reported under their classifications prior to January 2012.

In 2019, the number of passenger trips reported by the public transportation network in Lyon, France (TCL) amounted to 496.2 million journeys. During the same year, the passenger traffic in the Lyonnais metro network almost reached 220 million trips, accounting for 44.2 percent of the total passengers transported by public transit.

Due to planned diversions related to track work throughout the Fall 2024 rating, ridership was not included on days when a diversion impacted a particular line. For example, there were planned diversions on the Red Line from 9/6/2024 to 9/29/2024, so Red Line ridership data during that period is not included.

For routes with multiple branches, for example the Red Line with Braintree and Ashmont branches, and the Green Line with the B, C, D, and E branches, ridership estimates are combined through the trunk.Data Dictionary

Name

Description

Data Type

Example

mode

Mode of transportation for which ridership should be returned.

String

Heavy Rail

season

Season and year for which ridership should be returned.

String

Fall 2024

route_id

Route for which ridership should be returned.

String

Red

route_name

Description of route.

String

Red Line

direction_id

Direction for which ridership should be returned: NB, SB, EB, or WB.

String

EB

day_type_id

Shorthand for day identifier.

String

day_type_01

day_type_name

Text description of the ID. Weekday, Saturday, or Sunday; holidays are excluded from the data.

String

Weekday

time_period_id

Shorthand for time period identifier, per our Service Delivery Policy.

String

time_period_03

time_period_name

Aggregated periods of varying length to represent different levels of service provided.

String

AM_PEAK

stop_name

GTFS-compatible stop name for which ridership should be returned.

String

Wollaston

parent-station

GTFS-compatible stop for which ridership should be returned.

String

place-wlsta

total_ons

The count of passengers boarding vehicles, summed by the aggregated fields.

Integer

1491

total_offs

The count of passengers alighting vehicles, summed by the aggregated fields.

Integer

7990

number_service_days

Number of non-holiday service days in the season based on day type.

Integer

19

average_ons

Represents the ons on a typical service day within the aggregated fields. Calculated by total_ons/number_service_days.

Integer

435

average_offs

Represents the offs on a typical service day within the aggregated fields. Calculated by total_offs/number_service_days.

Integer

65

average_flow

The total number of passengers traveling through the rail system between stations within the aggregated time period, averaged to represent the typical weekday, Saturday, or Sunday.

Integer21

MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

The annual number of public transport passengers in Vienna recorded a considerable decrease between 2019 and 2020. This decrease was registered across all means of transportation. Specifically, the ridership of the Vienna subway, also known as U-Bahn, dropped by 42 percent, stood at 265 million in 2020 due to the COVID-19 pandemic.

In Germany, the most passengers used buses and trains to get to their destinations, as well as getting on local public transport. Around **** billion passengers used the former in 2023, around *** percent more than in 2022. Local public transport accounted for the largest share, with around **** billion passengers, covering around ** percent of all scheduled services in Germany. Various public transport types are available. Since May 2023, the so called "Deutschland-Ticket", which translated to "Germany-ticket" is available and allows use of all public transport in all of Germany for ** euros per month. The low cost of public transportation in combination with lifted corona rules led to an increase in passenger numbers on public transport. All aboard Public transportation choices among commuters depend on many factors. These range from availability (some German cities do not have trams, for example), ticket prices, safety while commuting, logistics and time concerns. In terms of regular long-distance transport with trains and buses in particular, significantly more travelers used the train. This was also true during the coronavirus (COVID-19) pandemic years, 2020 and 2021. Germany is, after all, the home of Deutsche Bahn. Daily commute Based on recent surveys, commuters use a variety of transportation modes to get to work, school and university. The most used of these is their own or a household car, followed by public transportation – an understandable choice for those living in cities. Some also hop on a bike.