In early May of 2020, ** percent of respondents reported that they used public transportation at least weekly with the arrival of the COVID-19 pandemic. The number of respondents with the same opinion has since increased, with ** percent of respondents using public transportation as reported from the fifth wave survey findings.

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

Daily domestic transport use by mode. Daily usage of selected domestic transport by mode for Great Britain.

In a 2020 survey, ** percent of male respondents in the U.S. stated that they would much less likely use public transport, if COVID-19 were to spread in their community. In that same survey, ** percent of people surveyed in the U.S. revealed that they would much less likely use public transport, if their community were to affected by COVID-19.

Dataset from Land Transport Authority. For more information, visit https://data.gov.sg/datasets/d_75248cf2fbf340de6a746dc91ec9223c/view

This dataset provides information surrounding bus patronage in the city of Leicester. The data runs from 2009/2010 and is sourced from the Department for Transport.This data is also part of a dashboard that has been produced displaying various transport related datasets. The dashboard can be viewed here.

Transport represents a crucial sector of the economy. This publication provides an overview of the most recent and most pertinent annual transport-related statistics in Europe. It covers the European Union and its 27 Member States (EU-27) and, as far as possible, the current EU candidate countries, the EFTA countries and UK. As in the reference period (1990-2018), UK was part of the European Union, aggregates for EU-28 are presented whenever possible.
The content of this pocketbook is based on a range of sources including Eurostat, international organisations and associations national statistics and, where no data were available, own estimates. Own estimates have mainly been produced to get an idea of the EU total. At the level of individual countries, they are merely indicative and should by no means be (mis-)interpreted as "official" data.
The publication consists of three parts:
(1) a general part with general economic and other relevant data,
(2) a transport part covering both passenger and freight transport as well as other transport-related data, and, finally,
(3) an energy and environmental part with data on the impact which the transport sector has on energy use and the environment.

Most of the tables have data up to 2018; where available, more recent data have been provided.
The tables of this pocketbook may also be found on the Europa site at
http://ec.europa.eu/transport/facts-fundings/statistics/index_en.htm
Many tables on the internet contain more data than could be presented in this pocketbook. The sources referencing is more detailed in the excel tables presented on the internet. Some tables may be updated on the web before the publication of the next paper version.
Eurostat, the main data provider, may be accessed directly on the internet at http://epp.eurostat.ec.europa.eu/
EEA, the data provider for the environmental part may be accessed directly on the internet at: https://www.eea.europa.eu/
Comments on this publication and suggestions for improving it are appreciated. They should be sent to: move-transport-data@ec.europa.eu

The number of public transport passengers has been on a slow upward trend and is projected to reach *** billion users by 2029. This upward trend was interrupted between 2020 and 2022, due to the COVID-19 pandemic, when user numbers dropped significantly and then began recovering to the pre-pandemic levels.

The impacts of ICT-based mobility services vary in different cities, depending on socioeconomic, urban form, and cultural parameters. The impacts of car-sharing and ridesourcing on public transport have not been investigated appropriately in post-Soviet Union cities. This study presents exploratory evidence on how ridesourcing and car-sharing affect public transport usage in Moscow. Additionally, it studies how demographics, spatial parameters, attitudes, and travel preferences influence the frequency of use of ridesourcing and car-sharing in Moscow. An online mobility survey was conducted at the beginning of 2020 among respondents (sample size is 777) in the Moscow agglomeration. Overall, 66% of ridesourcing users shifted from public transport to these mobility services, which shows the substitutional impact of ridesourcing on public transport. Additionally, the logit model indicates that the regular use of ridesourcing negatively correlates with the regular use of buses/trams/trolleybuses in Moscow. The impact of car-sharing on public transport seems less substitutional and more complementary than the impact of ridesourcing. Overall, 40% of car-sharing users would replace their last car-sharing trip with public transport if car-sharing was unavailable. Moreover, the logit model indicates a positive association between the regular use of car-sharing and the use of buses/trams/trolleybuses. Moreover, the modal split analysis shows a bigger share of public transport use and walking than car use among citizens’ urban journeys in Moscow.

Madrid Public Transportation Data 2018

This dataset contains detailed information about public transportation usage in Madrid, derived from a comprehensive survey conducted by the Madrid Transport Consortium (CRTM) from February 8, 2018, to June 11, 2018. The survey was carried out from Monday to Thursday over approximately four months, using various data collection methods to ensure accuracy and completeness.

Data Collection Methods

The primary data collection method involved individual travel diaries, where participants were contacted primarily through phone calls to answer structured surveys and questionnaires. Additionally, to enhance data accuracy, some information was collected via direct, in-person interviews. For participants who preferred or were more accessible through electronic means, online forms and digital devices were utilized. Follow-up calls or visits were made as needed to verify the information provided, fill in any missing data, or clarify responses.

Dataset Features

The CRTM dataset includes both trip-specific data and socio-economic data about the participants, as well as household information. Specifically, this dataset comprises a subset of the original data, focusing on:

Trip-Specific Data (4 features out of 15):

-Travel mode

-Distance

-Weekday

-Trip purpose

Socio-Economic Data (7 features out of 22):

-Gender

-Age

-Education level

-Employment status

-License

-Cars

To maintain data quality, records of trips that were not completed, those with a distance of zero, and those with a distance over 500 km were excluded. These outliers represented only 0.0236% of the data.

Meteorological Data

The dataset is further enriched with meteorological data from the State Meteorology Agency (AEMET), providing information about the average temperature, wind, and precipitation on the day of each trip. This additional data can help in analyzing the impact of weather conditions on public transportation usage.

Usage and Applications

This dataset is valuable for researchers, urban planners, and policymakers interested in understanding public transportation patterns in Madrid. It can be used to analyze travel behavior, evaluate the effectiveness of public transport systems, and inform decisions related to urban mobility and transportation planning.

This dataset is a collection of the public transport in Malaysia with their name, address and latitude, longitude coordinate.

Latitude Longitude is generated using mapbox

This is a work in progress

All LRT/KTM/MRT address will be updated.

Public transportation passenger counts on stop level for different modalities (tram, bus) and respective schedule information.

operating_day: Scheduled day of the stop event.
line_id: Transport line identifier.
stop_id: Stop identifier.
block_departure: Scheduled block* departure (in secs.onds since midnight).
block_arrival: Scheduled block* arrival (in secs.onds since midnight).
trip_departure: Scheduled trip departure (in secs. since midnight).
trip_arrival: Scheduled trip arrival (in secs. since midnight).
trip_stop_sum: Sum of scheduled stops within a trip.
pattern_index: Index of scheduled pattern**.
pattern_departure_index: First index of a scheduled pattern** (usually 0).
pattern_departure_index: Last index of a scheduled pattern** (usually the number of stops within a pattern).
arrival: Arrival on stop level (in secs. since midnight).
departure: Departure on stop level (in secs. since midnight).
stop_id_departure: Stop identifier of the first stop of a scheduled trip.
stop_id_end: Stop identifier of the last stop of a scheduled trip.
stop_position: Position of arrival at a stop (usually two directions).
trip_direction: Trip direction (two directions).
vehicle_seats: Number of seats within a scheduled vehicle (proxy for vehicle type).
passengers: Target variable. Number of passengers within the scheduled vehicle at a given stop.

Blocking in public transportation describes the practice of dividing the parts of a scheduled route among vehicles and drivers. It follows the process of dividing the route into trips*

A pattern describes the listing a sequence of scheduled stop points**

This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.

The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap.

Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.

Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.

Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.

Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 When using this dataset, please cite also the above-mentioned paper.

Graph and download economic data for Public Transit Ridership (TRANSIT) from Jan 2000 to Aug 2025 about public, transportation, and USA.

Buses were the most common mode of public land transport in Ukraine, accounting for over ** percent of total passengers in 2020. Furthermore, more than ** percent of the country's public land transport passengers used trolleybus services, and nearly ** percent rode trams.