The research project 'Vehicle choice for long-distance personal travel' was carried out on behalf of Transportforskningsdelegationen (TFD) and the aim was to clarify which factors influence the choice of means of transport for long-distance private personal travel. The survey was conducted as a sample survey in two stages. Stage 1 consisted of a postal questionnaire, the purpose of which was to identify individuals who had undertaken a longer private trip during a certain period of time and were thus assumed to have made a choice of means of transport. Stage 2 consisted of a personal interview with a selection of these individuals. Long-distance travel refers here to trips of at least 100 kilometres one-way. Private travel means all trips, except business, work commuting, educational and military service trips. Longer private trips that connect to charter trips abroad are also included. The survey only studies trips within the Nordic countries. The interview contained questions about the purpose of the trip, number of nights spent away, type of means of transport and which company you travelled with. The respondent was also asked to indicate whether they had considered other means of transport and what importance cost, and travel time had on the choice of means of transport.

A September 2024 survey analyzed the preferred mode of transport for Europeans planning a trip over the following six months. While 53 percent of respondents preferred to travel by air, just around four percent intended to travel by bus.

The statistic shows the preferred ways to travel when taking a family vacation in the United States in 2015. The survey revealed that 63 percent of respondents prefer to travel by car.

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, so excluding residents of institutions and homes. The table contains per person per day /year an overview of the average number of trips, the average distance travelled and the average time travelled. These are regular trips on Dutch territory, including domestic holiday mobility. The distance travelled is based on stage information. Excluded in this table is mobility based on series of calls trips. The mobility behaviour is broken down by modes of travel, purposes of travel, population and region characteristics. The data used are retrieved from The Dutch National travel survey named Onderweg in Nederland (ODiN). Data available from: 2018 Status of the figures: The figures in this table are final. Changes as of 4 July 2024: The figures for year 2023 are added. When will new figures be published? Figures for the 2024 research year will be published in mid-2025

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

The principal purpose of the study was to identify and describe the factors which effect the choice of means of transportation for a longer private journey. Long journey here refers to trips of more than 100 km (one way). Private journey is defined as all travel with the exception of business, work, educational and military service trips. Information was collected by means of a postal questionnaire and home interviews. The primary purpose of the questionnaire was to select respondents for the interview who had undertaken at least one long journey during a three-month period, and who also could choose their means of travel. The interview contained questions about the purpose of the journey, number of nights away, means of transport used and company travelled with. The respondent had to indicate if other means of transport were considered and the importance of costs and travelling time. Purpose: Describe the factors which effect the choice of means of transportation for a longer privat journey

72 percent of U.S. respondents answer our survey on "Most common modes of transportation for commuting" with "Own / household car". The survey was conducted in 2024, among 7,704 consumers.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our mobility statistics program.

The "Trips by Distance" data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

These data are made available under a public domain license. Data should be attributed to the "Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland and the United States Bureau of Transportation Statistics."

Daily data for a given week will be uploaded to the BTS website within 9-10 days of the end of the week in question (e.g., data for Sunday September 17-Saturday September 23 would be updated on Tuesday, October 3). All BTS visualizations and tables that rely on these data will update at approximately 10am ET on days when new data are received, processed, and uploaded.

The methodology used to develop these data can be found at: https://rosap.ntl.bts.gov/view/dot/67520.

This table contains 24 series, with data for years 1980 - 1996 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Travel duration (3 items: Total, same day and overnight travel; Same day; Overnight), Mode of transportation (8 items: Total, all modes of transportation; Automobile; Bus; Rail; ...).

1. “MODELOGIT.csv” file includes the information of choice behavior for urban travelers who have responded from the national household travel survey.
2. The data is composed of 8 modes alternative set incorporating socio-demographic information of each respondent

Mode of travel, mode's share of trip legs of Bay of Plenty remained stable at 100 % over the last 5 years.

This data includes travel demand by public transportation (buses, metros, and taxis) in Shenzhen. The collected data was from December 1, 2018, to December 31, 2018.

Commute mode is tracked by the American Community Survey (ACS) by asking respondents to provide the means of transportation usually used to travel the longest distance to work the prior week. A follow-up question asks about vehicle occupancy when "car, truck, van" is selected. This dataset tracks the sum of all individuals not selecting "car, truck, van" with one person in it. Transportation professionals often group travel modes into "single-occupancy vehicles" (SOV) and "non-single-occupancy vehicles" (non-SOV) because SOVs are a less efficient use of roadway and environmental resources. It also shows the share of modes that are classified as non-SOV.

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, i.e. excluding institutional residents. The mobility behaviour is expressed in the average number of stages per person per day and the average distance travelled per stage. The table contains only stages on Dutch territory, excluding mobility based on series of calls trips and domestic holiday mobility.

The mobility behaviour in stages and the average distance per stage are broken down by travel purpose, mode of travel, sex and age.

Margins of the values (trend estimates) are available in the form of the lower and upper limits of the 95% confidence interval.

Over the years, the research design of the successive mobility surveys has undergone various changes. With time series modeling, these have been corrected as far as possible. The figures of the resulting trend series have therefore been made sequentially comparable between the years. The figures have been calculated with data from four successive Dutch national travel surveys: the Onderzoek Verplaatsingsgedrag (OVG) - for the trend series, the years 1999 to 2003 were used; from the Mobiliteitsonderzoek Nederland (MON)- the years 2004 to 2009 were used, next, from the Onderzoek Verplaatsingen in Nederland (OViN), the years 2010 to 2017 were used; and from the newest mobility survey, which started in 2018 and is still ongoing, Onderweg in Nederland(ODiN) the years 2018 to 2023 were added to the mobility trend.

Data available from: 1999 to 2023 inclusive.

Status of the figures: The figures in this table are provisional.

Changes as of 25 September 2024: The provisional figures from 1999-2022 were re-estimated and extended to include figures from the year 2023, in addition, data on average travel time per stage were added.

When will new figures be published? The trend series is supplemented and recalculated once every 1 to 2 years.

This table shows passenger kilometres for modes of transport including passenger cars, buses, rail, air, and other. Bus and rail passenger kilometres values are trend estimates - subject to later revision when final data becomes available. BITRE modelling uses data from a range of sources to provide a consistent time series of Australian passenger travel (PKM). Vehicles not classified to passenger cars, buses, rail or air are included in ‘other transport mode’ (Table T 3.1). The other transport mode represents primarily non–freight use of light commercial vehicles (with contributions from motorcycles, non–business use of trucks and ferries).

This table contains information about the travel behavior of the Dutch population aged 6 or older in private households, ie excluding residents of institutions, institutions and homes. The table contains the number of journeys, distance traveled and journey duration on average per person, per day and per year. This concerns regular trips on Dutch territory, including domestic holiday mobility. The distance traveled is determined on the basis of trip information according to the method for regular passenger kilometers. Series moves are not regular moves. Travel behavior is broken down into modes of transport, travel characteristics (e.g. departure time and day on which the trip took place) and regions. The figures have been calculated with the study on the road in the Netherlands (ODiN). This is the successor to the study Travel in the Netherlands (OViN; 2010-2017). Compared to the previous OViN, the ODiN has changed significantly on various points and the studies are therefore not mutually comparable in succession. As of February 10, 2022, a revision of the ODiN files has sometimes led to minor changes in travel duration in the year 2019. Data available from 2018. Status of the figures: The figures in this table are final. Change as of July 5, 2023: The annual figures for 2022 have been added. When will new numbers come out? The figures for the 2023 research year will be published in mid-2024.

Germans traveled with a car the most to get to their vacation destination in 2021. Other much-used modes of transport included passenger cars and trains. The figures are based on a survey conducted in Germany.

Data showing method of travel to work or study in the week before the Census which was held on 27th March 2011. It shows the number of people who travel to work or study in the 694 data zones contained within Glasgow. For info about the source metadata, please click here Data supplied by Scotland Census 2011 which is run by the National Records of Scotland (c) Crown copyright 2013. Licence: None

Occitania Transport Mode (OCC-TM) is a mobility dataset collected using a smartphone application developed as part of Vilagil research project. This application passively collects GPS positions and accelerometer signals from the smartphone. This study focuses on data collected by a 25-year-old male user. This user then added a label corresponding to the mode of transportation (walk, still, car, bus, bike, train or metro) of each observation point. The smartphone used for collection is a Samsung Galaxy A32 with the Android 11 operating system.

The dataset data was collected by a single user in a discontinuous manner from July 26, 2022 to August 10, 2022. This user moved around the Occitania region in the south of France (between Toulouse and Montpellier), noting for each trip the start time, end time, and mode of transportation used.

The .zip file contains two folders:

• raw_data: raw accelerometer, location and label data in .csv format

• processed_data: feature dataset in .csv format

Please cite the paper below in your publications if it helps your research:

TODO