The American motor vehicle fleet traveled about ***** billion vehicle-miles in February 2025. Compared with January 2025, traffic decreased by about **** billion vehicle-miles. Between January and December 2024, traffic volume came to around *** trillion vehicle-miles of travel.

This dataset contains historic Link Definitions and Performance Statistics with Geometry of traffic flow. The detailed documentation is included at https://www.data.act.gov.au/dataset/realtime-traffic/cjkg-rvmu. Disclaimer : Even though the real-time API updates the info every 30 seconds, we only sample at every 5 minutes for historical archiving

Brazil Highways Statistics: Traffic Accidents: Accidents: São Paulo data was reported at 36,588.000 Unit in 2017. This records an increase from the previous number of 36,068.000 Unit for 2016. Brazil Highways Statistics: Traffic Accidents: Accidents: São Paulo data is updated yearly, averaging 28,034.500 Unit from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 41,810.000 Unit in 2011 and a record low of 9,737.000 Unit in 1998. Brazil Highways Statistics: Traffic Accidents: Accidents: São Paulo data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW008: Highways Statistics: Traffic Accidents. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

Brazil Highways Statistics: Traffic Tolled: Total Traffic data was reported at 1,834.949 Unit in 2017. This records an increase from the previous number of 1,782.727 Unit for 2016. Brazil Highways Statistics: Traffic Tolled: Total Traffic data is updated yearly, averaging 681.920 Unit from Dec 1996 (Median) to 2017, with 22 observations. The data reached an all-time high of 1,834.949 Unit in 2017 and a record low of 19.564 Unit in 1996. Brazil Highways Statistics: Traffic Tolled: Total Traffic data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW003: Highways Statistics: Traffic Tolled. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

In 2024, smartphones across the globe used an average of 20.29 gigabytes of mobile data per month, up from 17.27 gigabytes the previous year. This figure is expected to reach 23.78 gigabytes in 2025, and 42.38 gigabytes by 2029.

The streets and highways of Metro Manila in the Philippines saw an average of 3.82 million vehicles traveling each day in 2024. This was about 20,000 vehicles more than the year before. Metro Manila is notorious for its traffic congestion, and motorists in Manila City alone can lose about 100 hours annually when driving during rush hour. Which vehicles congest Metro Manila?  In 2024, motorcycles were the vehicles with the highest average daily traffic volume in Metro Manila. Aside from being affordable, motorcycles are the best option for navigating the traffic-congested streets of the region. Most Filipinos own a scooter, followed by mopeds. Next to motorcycles, cars, either private or rental, were also a major source of traffic in Metro Manila. Commuting in Metro Manila As the volume of vehicles traveling within Metro Manila continues to increase, commuters may have to spend hours each day getting to their destination. While public transportation options, such as commuter trains, jeepneys, and buses, are available, travelers may face difficulties in reaching their destination with less stress and travel time. According to a nationwide Statista survey in June 2024, most Filipinos spend 15 minutes to about an hour each day commuting to work, school, or university. However, factors such as proximity to the destination and whether the respondents live in rural or urban areas should be considered.

The traffic accident and casualties statistics tabulated in this dataset are compiled and retrieved from the Transport Information System developed and enhanced by the Road Safety and Standards Division. The accidents included are those personal injury accidents reported to the Police and do not include damage-only accidents.

Linear network representing the estimated traffic flows for roads and highways managed by the Ministry of Transport and Sustainable Mobility (MTMD). These flows are obtained using a statistical estimation method applied to data from more than 4,500 collection sites spread over the main roads of Quebec. It includes DJMA (annual average daily flow), DJME (summer average daily flow), DJME (summer average daily flow (June, July, August, September) and DJMH (average daily winter flow (December, January, February, March) as well as other traffic data. It is important to note that these values are calculated for total traffic directions. Interactive map: Some files are accessible by querying a section of traffic à la carte with a click (the file links are displayed in the descriptive table that is displayed when clicking): • Historical aggregated data (PDF) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

A. SUMMARY San Francisco International Airport Report on Monthly Passenger Traffic Statistics by Airline.

B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level

C. UPDATE PROCESS Data updated quarterly

D. HOW TO USE THIS DATASET Airport data is seasonal in nature, therefore any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Passenger Counts belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Passenger Counts as desired.

The statistics on road traffic accidents with personal injury (persons injured or killed) records all accidents occurring on roads with public traffic in Austria. The accident data is recorded electronically by the police authorities and an extract relevant for the statistic is transmitted daily to Statistics Austria by the Federal Ministry of the Interior. The objective is to continuously monitor accidents on Austrian roads in order to provide reliable data for research and accident prevention.

The results are published quarterly (preliminary data) and annually (final data).

Daily data showing weekday adjusted busyness indices using traffic camera data to monitor flows of cars, pedestrians, cyclists, buses and commercial vehicles for selected cities and regions of the UK. These are official statistics in development. Source: Transport for London, Transport for Greater Manchester

This data is the monthly statistical data on "road traffic accidents" in the "Chiayi City statistics database" of the Directorate General of Budget, Accounting and Statistics. It has been compiled monthly since 2014.

Data on road traffic by road and vehicle type, produced by Department for Transport.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) air traffic cargo dataset contains data about cargo volume into and out of SFO, in both metric tons and pounds, with monthly totals by airline, region and aircraft type.

B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level.

C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly.

D. HOW TO USE THIS DATASET Airport data is seasonal in nature; therefore, any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired.

E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at

https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

The dataset contains the quarterly international traffic statistics in the regions in the form of city pairs in both categories - freight and passengers.