In England, women walked more than men for almost all age groups, apart from 17-20 year-olds. In 2019, women aged 30-39 years were the most active out of all age groups, walking *** miles per year. By comparison, men of the same age group walked ** miles less per year. Despite this, there was only a small difference between the distance walked between age groups. The greatest prevalence of people who walked was recorded in the South West region.

Walking duration

Those who took daily walks tended to do so for 30 to ** minutes. However, **** percent of the English population reported they spent over *** minutes a day walking for leisure or recreational purposes.

The sporty population

28 million adults in England were physically active for more than *** minutes a week. Out of those participating in any sport, the majority were engaged for longer periods of time.

Surface rail is the public transport mode with which people in England cover the largest distances. The average distance covered by rail in a year had been rising in the years leading up to the COVID-19 pandemic, peaking at *** miles per person in 2019. While rail travel started to recover in 2021, the average distance covered still stood at *** miles per person in 2022.

Helsinki Region Travel Time Matrix contains travel time and distance information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by walking, cycling, public transportation and car. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (yhdyskuntarakenteen seurantajärjestelmä) data set. The Helsinki Region Travel Time Matrix is available for three different years:

2018

2015

2013

The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.

The data have been calculated for two different times of the day: 1) midday and 2) rush hour.

The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.

Organization of data

The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).

In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field ‘from_id’ in the text files and the field ‘YKR_ID’ in MetropAccess-YKR-grid shapefile as a common key.

Data structure

The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.

NODATA values have been stored as value -1.

Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t

The fields are separated by semicolon in the text files.

Attributes

from_id: ID number of the origin grid cell

to_id: ID number of the destination grid cell

walk_t: Travel time in minutes from origin to destination by walking

walk_d: Distance in meters of the walking route

bike_f_t: Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike

bike_s_t: Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike

bike_d:Distance in meters of the cycling route

pt_r_tt: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home

pt_r_t: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home

pt_r_d: Distance in meters of the public transportation route in rush hour traffic

pt_m_tt: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home

pt_m_t: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home

pt_m_d: Distance in meters of the public transportation route in midday traffic

car_r_t: Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account

car_r_d: Distance in meters of the private car route in rush hour traffic

car_m_t: Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account

car_m_d: Distance in meters of the private car route in midday traffic

car_sl_t: Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account

METHODS

For detailed documentation and how to reproduce the data, see HelsinkiRegionTravelTimeMatrix2018 GitHub repository.

THE ROUTE BY CAR have been calculated with a dedicated open source tool called DORA (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.

The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).

The whole travel chain (“door-to-door approach”) is taken into account in the calculations: 1) walking time from the real origin to the nearest network location (based on Euclidean distance), 2) average walking time from the origin to the parking lot, 3) travel time from parking lot to destination, 4) average time for searching a parking lot, 5) walking time from parking lot to nearest network location of the destination and 6) walking time from network location to the real destination (based on Euclidean distance).

THE ROUTES BY PUBLIC TRANSPORTATION have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination: 1) possible waiting at home before leaving, 2) walking from home to the transit stop, 3) waiting at the transit stop, 4) travel time to next transit stop, 5) transport mode change, 6) travel time to next transit stop and 7) walking to the destination.

Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.

THE ROUTES BY CYCLING are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.

For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.

The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.

More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.

THE ROUTES BY WALKING were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.

The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).

All calculations were done using the computing resources of CSC-IT Center for Science (https://www.csc.fi/home).

This table contains data on the annual miles traveled by place of occurrence and by mode of transportation (vehicle, pedestrian, bicycle), for California, its regions, counties, and cities/towns. The ratio uses data from the California Department of Transportation, the U.S. Department of Transportation, and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Miles traveled by individuals and their choice of mode – car, truck, public transit, walking or bicycling – have a major impact on mobility and population health. Miles traveled by automobile offers extraordinary personal mobility and independence, but it is also associated with air pollution, greenhouse gas emissions linked to global warming, road traffic injuries, and sedentary lifestyles. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which has many documented health benefits. More information about the data table and a data dictionary can be found in the About/Attachments section.

This statistic shows the average annual journey of a heavy vehicle in France between 2004 and 2018, according to the type of vehicle and in kilometers. In 2009, a heavy vehicle traveled on average more than 33,900 kilometers per year, while in 2018 this distance was approximately 32,250 kilometers.

This statistic shows the average annual distance traveled by a passenger cars in France from 2004 to 2018, by fuel type, in kilometers. It turns out that in 2018, a gasoline passenger car traveled an average of more than 8,900 kilometers, while for a diesel passenger car this distance was 15,895 kilometers.

This map grades each county based on Unacast's Social Distancing Metric. Using the change in average distance traveled from pre-COVID-19 days and visitation to non-essential venues, we determined a daily county-level "Social Distancing" score. Now updated daily.Methodology: driven by data science for actionabilityReal World Graph® — Unacast's core asset that provides a range of perspectives and critical context for how people relate to physical locations.The COVID-19 toolkit empowers organizations in unprecedented times, to make quality decisions quickly. The toolkit, powered by Unacast's robust team of data scientists and PhDs, focuses on data accuracy to ensure that our partners and clients get high-quality insights that reflect real-world events.Using the change in average distance traveled from pre-COVID-19 days and visitation to non-essential venues, we determined a "Social Distancing" score for each county:Read more on the scoring here.Because our data comes in at varying latencies, we wait for three days after an event day for all the data for that given event date. Processing takes another day and therefore, the existing and default delay is four days from a given event date.Since we know time is of the essence in the fight against COVID19, our team is figuring out if we can reduce the lag, use less data, and still provide strong signals. At the same time, we want to further enhance our score to make it more robust and explain different facets of social distancing.A Note About PrivacyThe Social Distancing Scoreboard and other tools being developed for the Covid-19 Toolkit do not identify any individual person, device, or household. However, to calculate the actual underlying social indexing score we combine tens of millions of anonymous mobile phones and their interactions with each other each day - and then extrapolate the results to the population level. As a company that originated in Norway we put privacy front and center and believe it to be an individual right, and since we operate on both sides of the Atlantic, we have been operating within the GDPR guidelines since May 2018 and this year also adopted the CCPA guidelines for the whole of the US (not only California).Read more on the methodology here...Contact Unacast for more information on this and other data products: dataforgood@unacast.comView Terms of Use

Data from the Transport and Travel related questions asked in the Scottish Household Survey 2018. Data is of average distance traveled (km) nationally. Data is classified by Local Authority, Regional Transport Partnership, and Urban/Rural Classification. Reproduced via Open Government Licence. https://www.transport.gov.scot/publication/transport-and-travel-in-scotland-results-from-the-scottish-household-survey-1/

China Transport: Average Distance: Passenger data was reported at 189.000 km in 2023. This records a decrease from the previous number of 231.261 km for 2022. China Transport: Average Distance: Passenger data is updated yearly, averaging 80.942 km from Dec 1950 (Median) to 2023, with 74 observations. The data reached an all-time high of 237.976 km in 2021 and a record low of 64.000 km in 1982. China Transport: Average Distance: Passenger data remains active status in CEIC and is reported by Ministry of Transport. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TA: Transport: Passenger and Freight Average Distance.

This bar chart presents the average distance traveled by trains in France, between 2015 and 2016, according to the type of train, in kilometers. The average distance traveled by Regional express trains (TER) remained constant between 2015 and 2016, and was equal to ** kilometers in the last year.

Data from the Transport and Travel related questions asked in the Scottish Household Survey 2016. Data is of average distance traveled (km) nationally. Data is classified by Local Authority, Regional Transport Partnership, and Urban/Rural Classification. Reproduced via Open Government Licence. https://www.transport.gov.scot/publication/26-september-2017-transport-and-travel-in-scotland-2016/

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13132) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.

The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.

Data formats

The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.

• Helsinki_Travel_Time_Matrix_2023.csv.zst: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.
• Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• Helsinki_Travel_Matrix_2023_travel_times.csv.zip: a set of 13132 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv where 5787545 is replaced by the to_id by which the rows in the file are grouped. Use the from_id column to join with the geometries from one of the files below.

Geometry, only:

• Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• Helsinki_Travel_Time_Matrix_2023_grid.shp.zip: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files.

Table structure

from_id: ID number of the origin grid cell
to_id: ID number of the destination grid cell
walk_avg: Travel time in minutes from origin to destination by walking at an average speed
walk_slo: Travel time in minutes from origin to destination by walking slowly
bike_avg: Travel time in minutes from origin to destination by cycling at an average speed
bike_fst: Travel time in minutes from origin to destination by cycling fast
bike_slo: Travel time in minutes from origin to destination by cycling slowly
pt_r_avg: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed
pt_r_slo: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed
pt_m_avg: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed
pt_m_slo: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed
pt_n_avg: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed
pt_n_slo: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed
car_r: Travel time in minutes from origin to destination by private car in rush hour traffic
car_m: Travel time in minutes from origin to destination by private car in midday traffic
car_n: Travel time in minutes from origin to destination by private car in nighttime traffic
walk_d: Distance from origin to destination, in meters, on foot

Data for 2013, 2015, and 2018

At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations' results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.

For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.

Methodology

Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. 'Rush hour' refers to an 1-hour window between 8 and 9 am, 'midday' to 12 noon to 1 pm, and 'nighttime' to 2-3 am.

All routes have been calculated using r5py, a Python library making use of the R5 engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:

Walking

Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for walk_avg (as well as the respective pt_*_walk_avg), and the slowest quintile of all measured walker across all conditions for walk_slo (and the respective pt_*_walk_slo).

Cycling

Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.

Public Transport

We used public transport schedules in General Transit Feed Specification (GTFS) format published by the Helsinki Regional Transport Authority, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for walking.

Private motorcar

To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see Perola (2023) for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.

Our modifications were carried out in two ways: some changes can

The graph displays the average miles driven per person in the United States from 1980 to 2022. The x-axis represents the years, while the y-axis shows the average miles driven annually by one person. The data shows that the lowest average was 10,511 miles in 1980, and the highest was 14,906 miles in 2004. A notable drop occurred in 2020, with the average falling to 12,724 miles, likely reflecting reduced travel during the COVID-19 pandemic. Overall, the data highlights a long-term increase in driving over the decades, with fluctuations in recent years.

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 travel motives, travel characteristics (e.g. departure time and day on which the travel 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 10 February 2022, the figures for the year 2018 have changed as a result of a revision of the ODiN files, but the total of the motives in 2018 has remained the same. In the year 2019, the revision has sometimes led to minor changes in travel times. 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.

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Apr 2025 about miles, travel, vehicles, and USA.