Talking about mobility, the highest commuters in selected countries worldwide can be found in Vietnam, where ** percent of consumers are part of this category. The second highest ranking country is United Arab Emirates with ** percent of respondents falling into this category. The last place is taken by the United States.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

This dataset supports commuter guidebook by providing estimated travel times between selected TAZs. Data is modeled or calculated and reflects average conditions, not real-world travel. Intended for visualization and informational use.Travel times are estimates only and not based on observed trips. Values are calculated or modeled using assumptions and average speeds. Not suitable for routing, operational planning, or emergency response.Metadata

When asked about "Duration of daily commute", ** percent of U.S. respondents answer "15 to ** minutes". This online survey was conducted in 2025, among ****** consumers.

According to a recent report released by TomTom, drivers during the rush hour commute can expect congestion levels to significantly increase their commute time. On average, drivers will spend double the time in the car for most large metropolitan areas across the world during the evening commute alone. The average commuter spent an extra 100 hours a year traveling during the evening rush hour.In Los Angeles (Ranked 1st in the United States (U.S.) and 10th Worldwide), a 30 minute commute in the evening will take 54 minutes due to congestion, an extra 92 hours annually. Commuters in the San Francisco Bay Region are only slightly better off than their counter parts in Los Angeles. The measured congestion in San Francisco (Ranked 2nd in the U.S. and 26th Worldwide) is at 34%, while San Jose (Ranked 6th in the U.S. and 51st Worldwide) is at 30%.TomTom roadway congestion is measured as an increase in overall travel times when compared to the posted speed limits on roadways. For example, a Congestion Level of 12% corresponds to 12% longer travel times. The latest results and press release can be viewed here: https://www.tomtom.com/traffic-index/

When asked about "Duration of daily commute", ** percent of Indonesian respondents answer "15 to ** minutes". This online survey was conducted in 2024, among ***** consumers.As an element of Statista Consumer Insights, our Consumer Insights Global survey offers you up-to-date market research data from over ** countries and territories worldwide.

Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.

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 speedbike_fst: Travel time in minutes from origin to destination by cycling fastbike_slo: Travel time in minutes from origin to destination by cycling slowlypt_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 be controlled by preparing input data sets in a certain way, or by setting model parameters outside of R5 or r5py. Other modifications required more profound changes to the source code of the R5 engine.

You can find a fully patched fork of the R5 engine in the Digital Geography Lab's GitHub repositories at github.com/DigitalGeographyLab/r5. The code that handles input data mangling and model parameter estimations is kept together with the logic to read input parameters and to collate output data, in the repository at github.com/DigitalGeographyLab/Helsinki-Travel-Time-Matrices.

Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least.

About This Data

The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.

How to use this dataset

• Analyze State Name in relation to Baseline Jan Vmt
• Study the influence of Date At Low on Mean7 County Vmt At Low
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Angeliki Kastanis

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Public Transport Market Outlook

The global public transport market size was estimated at USD 200 billion in 2023 and is projected to reach USD 350 billion by 2032, growing at a CAGR of 6.5% from 2024 to 2032. The increasing urbanization and the growing demand for efficient and sustainable transportation solutions are primary growth factors driving the market. As more people move towards urban centers, the need for reliable and extensive public transport systems becomes vital, promoting investments and developments in this sector.

One of the primary growth drivers of the public transport market is the increasing awareness and governmental policies emphasizing environmental sustainability. As emissions from personal vehicles continue to contribute significantly to urban pollution, public transport emerges as a green alternative. Governments worldwide are adopting policies and providing subsidies to promote the use of public transportation, thereby reducing the carbon footprint. Furthermore, advancements in technology, such as the integration of IoT, AI, and big data analytics, are enhancing operational efficiencies and passenger experiences, further encouraging public transport adoption.

The rising fuel prices and the economic feasibility of public transport over private vehicles also play a crucial role in the market's expansion. As fuel costs soar, maintaining private automobiles becomes financially burdensome for the average consumer. Public transport offers a cost-effective alternative, minimizing travel expenditure. Moreover, many urban areas are experiencing increased congestion, and public transport systems present a more viable solution for reducing traffic, thus saving time and improving quality of life for commuters. This economic advantage is further supported by the rising trend of shared mobility services, which integrate seamlessly with existing public transport modes.

Demographic shifts, particularly the aging population and the increasing number of working professionals, also fuel market demand. Older individuals often rely on public transport for accessibility reasons, while the working populace seeks convenient and time-saving travel options. Urban planners are increasingly considering these demographic factors in transport planning, leading to enhanced and expanded transit networks. Additionally, the shift towards smart cities is pushing the development of integrated public transport systems that are crucial for the seamless flow of urban life.

Regionally, the Asia Pacific is expected to dominate the public transport market due to rapid urbanization and economic growth in countries such as China and India. These nations are heavily investing in public transport infrastructure to support their burgeoning urban populations. Meanwhile, Europe and North America are witnessing significant technological advancements and policy initiatives aimed at modernizing existing public transport systems to make them more environment-friendly and efficient. Latin America and the Middle East & Africa are also expected to see substantial growth, driven by infrastructure development and increased investments in public transport.

Mode of Transport Analysis

The mode of transport segment in the public transport market is crucial as it encompasses the various means through which passengers are transported, including buses, trains, trams, metros, and others. Buses, being the most accessible and prevalent form, dominate the market due to their flexibility and coverage. Buses are extensively used in both urban and rural areas, offering extensive routes and frequency. The affordability and government initiatives promoting bus usage for reducing urban congestion contribute significantly to this segment's growth. Moreover, technological advancements in bus systems, such as electric and hybrid models, further enhance their appeal.

Trains and metros are central to the public transport market, especially in densely populated urban areas. These modes provide fast, reliable, and high-capacity transit solutions, making them indispensable for daily commuters in metropolitan regions. Governments are investing heavily in expanding rail networks and modernizing existing infrastructure with state-of-the-art technologies to boost efficiency and safety. The development of high-speed rail networks, particularly in Asia and Europe, highlights the segment's importance in reducing travel times and enhancing regional connectivity.

Trams are gaining popularity in urban areas due to their environmental benefits and ability to integrate w

Real travel times in Dublin are ** percent longer than travel under free-flow conditions, making it the most congested urban sprawl in the world as of 2023. This figure refers to additional average travel time throughout the week. Tackling trust in transport With trust in public transport globally taking a knock following the outbreak of Covid-19, switching to public transport modes for commuting trips to save time, emissions, and traffic may prove difficult. Activities at transit stations declined in many cities around the world as a result of ebbing demand amid the coronavirus pandemic. Consequently, transport services in highly populated cities have suffered devastating financial losses. While public transport transit had started to pick up in the beginning of 2021, it could not offset the drop recorded as a result of the pandemic. India: a climate for new policies? Among the twelve cities displayed, India is represented by *****. To tackle high levels of congestion, a congestion pricing policy was recently proposed in India, which would serve to introduce parking fees and thus push commuters to take public transport rather than drive their cars to work. Surveys collecting public opinion on this proposal have indicated that this would be a popular policy, should it be implemented. The motive behind curbing congestion in the nation’s largest cities is more than just to reduce pollution levels and time spent in traffic; India has some of the highest levels of traffic-related fatalities globally: some ******* people died in traffic accidents in 2019 – this is the highest number on record since 2005.

When asked about "Duration of daily commute", ** percent of Norwegian respondents answer "15 to ** minutes". This online survey was conducted in 2024, among ***** consumers.As an element of Statista Consumer Insights, our Consumer Insights Global survey offers you up-to-date market research data from over ** countries and territories worldwide.

Problem Statement

👉 Download the case studies here

Urban areas worldwide face increasing traffic congestion due to rapid urbanization and rising vehicle density. A city’s transportation department struggled with inefficient traffic flow, leading to longer travel times, increased fuel consumption, and higher emissions. Traditional traffic management systems were reactive rather than predictive, requiring a smarter, data-driven solution to address these issues.

Challenge

Developing an intelligent traffic management system involved tackling several challenges:

Collecting and processing real-time traffic data from multiple sources, including sensors, cameras, and GPS devices.

Predicting traffic patterns and optimizing signal timings to reduce congestion.

Ensuring scalability to handle the growing urban population and vehicle density.

Solution Provided

An AI-powered traffic management system was developed using advanced algorithms, real-time data analytics, and IoT sensors. The solution was designed to:

Monitor and analyze traffic flow in real time using data from IoT-enabled sensors and connected vehicles.

Optimize traffic signal timings dynamically to minimize congestion at key intersections.

Provide actionable insights to city planners for long-term infrastructure improvements.

Development Steps

Data Collection

Installed IoT sensors at intersections and leveraged data from traffic cameras and connected vehicles to gather real-time traffic data.

Preprocessing

Cleaned and processed the collected data to identify patterns, peak congestion times, and traffic bottlenecks.

AI Model Development

Developed machine learning models to predict traffic flow and congestion based on historical and real-time data. Implemented optimization algorithms to adjust traffic signal timings dynamically.

Simulation & Validation

Tested the system in simulated environments to evaluate its effectiveness in reducing congestion and improving traffic flow.

Deployment

Deployed the system across key urban areas, integrating it with existing traffic control systems for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world performance and new traffic data.

Results

Decreased Traffic Congestion

The system reduced congestion by 25%, resulting in smoother traffic flow across the city.

Improved Travel Times

Optimized traffic management led to significant reductions in average travel times for commuters.

Enhanced Urban Mobility

Efficient traffic flow improved access to key areas, benefiting both residents and businesses.

Reduced Environmental Impact

Lower congestion levels minimized fuel consumption and greenhouse gas emissions, contributing to sustainability goals.

Scalable and Future-Ready

The system’s modular design allowed easy expansion to new areas and integration with emerging transportation technologies.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

When asked about "Duration of daily commute", ** percent of Danish respondents answer "15 to ** minutes". This online survey was conducted in 2024, among ***** consumers.As an element of Statista Consumer Insights, our Consumer Insights Global survey offers you up-to-date market research data from over ** countries and territories worldwide.

The statistic depicts the amount of time necessary to commute from downtown to selected airports in the world in 2017, by type of transportation. According to the source, traveling to Beijing International Airport from downtown by car would take ** minutes.

When asked about "Duration of daily commute", ** percent of Nigerian respondents answer "15 to ** minutes". This online survey was conducted in 2023, among ***** consumers.As an element of Statista Consumer Insights, our Consumer Insights Global survey offers you up-to-date market research data from over ** countries and territories worldwide.