Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

This traffic volumes feature class containing Traffic Volumes (also known as Traffic Counts) on California state highway network created from AADT excel spreadsheet file maintained by Caltrans, Division of Traffic Operations.Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates, planning and designing highways and other purposes.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Annual average daily traffic is the total volume for the year divided by 365 days. The truck count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Truck Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.

2020-2024 Traffic Count DataThe Sacramento County Department of Transportation's Traffic Count Program collects data regarding the number of vehicles that travel various county roads. Traffic counts are collected utilizing pneumatic hose counters, traffic signal detector loops or by staff field observations. DOT Traffic Count Program

This dataset provides annual traffic counts from Caltrans, updated in real time via the California Open Data API.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

VITAL SIGNS INDICATOR Traffic Volumes at Regional Gateways (T6)

FULL MEASURE NAME Daily vehicles along entry/exit points to the Bay Area

LAST UPDATED April 2020

DESCRIPTION Traffic volumes at regional gateways refers to the number of vehicles crossing county boundaries on a typical day to enter or exit the nine-county San Francisco Bay Area.

DATA SOURCE California Department of Transportation: Annual Traffic Volume Reports http://traffic-counts.dot.ca.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Traffic counts reflect average annual daily traffic (AADT) counts at all state highway gateway points - entry/exit points to the nine-county San Francisco Bay Area. When the county line data was not available in the traffic volume reports, the closest intersection or interchange was used as a proxy for traffic volumes at the county line.

This data shows traffic counts approaching and occurring at signalized intersections within the City of Salinas, Monterey County, California primarily prior to 2018. It can also be found on the Map Gallery within the City of Salinas website.

Annual average daily truck traffic is the total truck traffic volume divided by 365 days. Truck counting is done throughout the state in a program of continuous truck count sampling. The sampling includes a partial day, 24-hour, 7-day and continuous vehicle classification counts. The partial day and 24-hour counts are usually made on high volume, urban highways. The 7-day counts are made on low volume, rural highways. The counts are usually taken only once in the year. About one-sixth of the locations are counted annually. The resulting counts are adjusted to an estimate of annual average daily truck traffic by compensating for seasonal influence, weekly variation, and other variables that may be present. Annual average daily truck traffic is necessary for presenting a statewide picture of truck flow, evaluating truck trends, planning and designing highways and for other purposes. Truck traffic is classified by number of axles. The two-axle class includes 11/2-ton trucks with dual rear tires and excludes pickups and vans with only four tires. Total vehicle AADT for the same year is taken from the Traffic Volumes on California State Highways booklet also published by the California Department of Transportation.Reference Link: https://www.dot.ca.gov/hq/traffops/saferesr/trafdata/index.htm

"Truck Volumes AADT" stands for "Truck Volumes Average Annual Daily Traffic." It refers to the average number of trucks that pass a specific point on a roadway or transportation route over the course of a year. Annual average daily truck traffic is the total truck traffic volume divided by 365 days. Truck counting is done throughout the state in a program of continuous truck count sampling. The sampling includes a partial day, 24-hour, 7-day and continuous vehicle classification counts.

The partial day and 24-hour counts are usually made on high volume, urban highways. The 7-day counts are made on low volume, rural highways. The counts are usually taken only once in the year. About one-sixth of the locations are counted annually. The resulting counts are adjusted to an estimate of annual average daily truck traffic by compensating for seasonal influence, weekly variation, and other variables that may be present.

Annual average daily truck traffic is necessary for presenting a statewide picture of truck flow, evaluating truck trends, planning and designing highways and for other purposes.

Truck traffic is classified by number of axles. The two-axle class includes 11/2-ton trucks with dual rear tires and excludes pickups and vans with only four tires. Total vehicle AADT for the same year is taken from the Traffic Volumes on California State Highways booklet also published by the California Department of Transportation

The PeMS traffic datasets have been collected by the California Transportation (Caltrans) agency for 30-second granularity, and the raw and aggregated data are publicly available on their website (https://pems.dot.ca.gov/?dnode=Clearinghouse&type=meta&district_id=7&submit=Submit). We have gathered 5-minute aggregated vehicular traffic state (i.e traffic speed) dataset for district four and seven of California for 2022.

We have used Bing Distance Matrix API to compute a driving distance between each sensor. The API can be used to compute a driving distance between a single source or multiple sources and source or multiple destinations at once.

In addition, the weather datasets have been collected from https://www.visualcrossing.com/weather/weather-data-services and the datasets have one-hour granularity, and we have only removed some of the unnecessary columns.

This dataset supports the research detailed in "A Hybrid Deep Learning Approach with GEH-based Loss Function and Evaluation Metric for Multi-location Traffic Flow Forecasting" containing traffic flow measurements from Stockton and Oakland, California. The dataset is organized into four sub-folders: 'Oakland-raw', 'Oakland-processed', 'Stockton-raw', and 'Stockton-processed'. The raw data folders ('Oakland-raw' and 'Stockton-raw') include the initial traffic measurements as collected directly from traffic sensors, providing a granular view of traffic patterns without any modifications. The processed data folders ('Oakland-processed' and 'Stockton-processed') contain data that has been cleaned and structured, making it more suitable for analytical tasks and modeling purposes.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Two traffic volume datasets collected from 150 sensors in Los Angeles, California, at the time resolutions one hour and 15 minutes, respectively.

This data comes from the California Highway Patrol and covers collisions from January 1st, 2001 until mid-October, 2020. I have requested full database dumps from the CHP four times, once in 2016, 2017, 2018, 2020, and 2021. I have combined these datasets into the one presented here. For additional details, see my post: Introducing the SWITRS SQLite Hosted Dataset

There are three main tables:

• collisions: Contains information about the collision, where it happened, what vehicles were involved.
• parties: Contains information about the groups people involved in the collision including age, sex, and sobriety.
• victims: Contains information about the injuries of specific people involved in the collision.

There is also a table called case_ids which I used to build the other tables. It tells you which of the four original datasets each row came from.

This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, 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.

This repository is a traffic speed data set collected by California Freeway Performance Measurement System (PeMS). In this data set, it has 11160 time series corresponding to 11160 sensors/locations. In three data files (i.e., pems-4w.csv, pems-8w.csv, and pems-12w.csv), each time series is indeed a time series with 288 (5-minute) time points per day during the first 4 weeks (PeMS-4W), first 8 weeks (PeMS-8W), and first 12 weeks (PeMS-12W) of 2018, respectively. Note that the original data was downloaded from PeMS system with 30-second traffic speed records. Our data is a subset of file speed.h5 created by article Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting and its original link is available at here.

Our research work on this data set is:

Xinyu Chen, Yixian Chen, Nicolas Saunier, Lijun Sun (2021). Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 129: 103226. [DOI] [preprint] [data] [Python code]

This dataset was collected for a research paper titled "Twitter-informed Prediction for Urban Traffic Flow Using Machine Learning," which is available online at https://ieeexplore.ieee.org/document/10185516. If you intend to use this dataset, we kindly request that you consider acknowledging our paper by including a citation. Your support in referencing our work would be greatly appreciated.

The traffic dataset was obtained through the California Performance Measurement System (PeMS) in the United States. It encompasses traffic data, including speed and flow information, for the eastbound lanes of the Ventura Highway in Los Angeles, covering the period from February 1 to May 31, 2020.

Calendar features in this dataset consist of weekdays, represented as numbers from 1 to 7, and a binary variable indicating whether a specific day is a holiday. Weather data was sourced from the Wunderground website (accessible at https://www.wunderground.com/history/daily/KLAX) throughout the study period. Weather data includes hourly observations of various meteorological factors. For consistency, we assume that weather conditions remain constant during each 5-minute time interval within an hour.

Weather conditions in the dataset include categories such as fair, blowing dust, cloudy, cloudy/windy, fair/windy, fog, haze, heavy rain, light rain, mostly cloudy, mostly cloudy/windy, partly cloudy/windy, rain, and thunder in the vicinity. Temperature is measured in Fahrenheit.

Missing data in this context refers to temporary disruptions in the availability of traffic information within specific areas of the transportation network due to sensor failures or noisy data. To address these missing values, we employed the mean imputation method.