Traffic-related data collected by the Boston Transportation Department, as well as other City departments and State agencies. Various types of counts: Turning Movement Counts, Automated Traffic Recordings, Pedestrian Counts, Delay Studies, and Gap Studies.

~_Turning Movement Counts (TMC)_ present the number of motor vehicles, pedestrians, and cyclists passing through the particular intersection. Specific movements and crossings are recorded for all street approaches involved with the intersection. This data is used in traffic signal retiming programs and for signal requests. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Automated Traffic Recordings (ATR)_ record the volume of motor vehicles traveling along a particular road, measures of travel speeds, and approximations of the class of the vehicles (motorcycle, 2-axle, large box truck, bus, etc). This type of count is conducted only along a street link/corridor, to gather data between two intersections or points of interest. This data is used in travel studies, as well as to review concerns about street use, speeding, and capacity. Counts are typically conducted for 12- & 24-Hr periods.

~_Pedestrian Counts (PED)_ record the volume of individual persons crossing a given street, whether at an existing intersection or a mid-block crossing. This data is used to review concerns about crossing safety, as well as for access analysis for points of interest. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Delay Studies (DEL)_ measure the delay experienced by motor vehicles due to the effects of congestion. Counts are typically conducted for a 1-Hr period at a given intersection or point of intersecting vehicular traffic.

~_Gap Studies (GAP)_ record the number of gaps which are typically present between groups of vehicles traveling through an intersection or past a point on a street. This data is used to assess opportunities for pedestrians to cross the street and for analyses on vehicular “platooning”. Counts are typically conducted for a specific 1-Hr period at a single point of crossing.

This dataset contains hourly pedestrian counts since 2009 from pedestrian sensor devices located across the city. The data is updated on a monthly basis and can be used to determine variations in pedestrian activity throughout the day.The sensor_id column can be used to merge the data with the Pedestrian Counting System - Sensor Locations dataset which details the location, status and directional readings of sensors. Any changes to sensor locations are important to consider when analysing and interpreting pedestrian counts over time.Importants notes about this dataset:• Where no pedestrians have passed underneath a sensor during an hour, a count of zero will be shown for the sensor for that hour.• Directional readings are not included, though we hope to make this available later in the year. Directional readings are provided in the Pedestrian Counting System – Past Hour (counts per minute) dataset.The Pedestrian Counting System helps to understand how people use different city locations at different times of day to better inform decision-making and plan for the future. A representation of pedestrian volume which compares each location on any given day and time can be found in our Online Visualisation.Related datasets:Pedestrian Counting System – Past Hour (counts per minute)Pedestrian Counting System - Sensor Locations

DVRPC counts bicyclists and pedestrians because count data helps us understand and plan for the role bicyclists and pedestrians play in our transportation network. Similar to how planners use vehicular traffic counts to analyze roadway facilities, our bicycle and pedestrian counting program allows planners to measure existing levels of bicycling and walking, monitor travel trends, plan for new or improved facilities and measure outcomes of bicycle and pedestrian related projects. Our counting program consists of four types of counts: Project Counts, Cyclical Counts, Screenline Counts and Permanent Counts.

Results of the measurements of the permanent counting points and short-term counting points for bicycle and pedestrian traffic. The count data for pedestrian traffic are adjusted monthly by applying a correction function and subsequently published. The exception here is the counting point 817 Elisabethenanlage. Due to the current COVID-19 crisis, this is processed daily and observed For cost reasons, only the values of the current and last year as a table/visualisation are visible or can be accessed via API. The complete data from the year 2000 can be downloaded here: Easily processed data set: https://data-bs.ch/mobilitaet/converted_Velo_Fuss_Count.csv Raw data: https://data-bs.ch/mobilitaet/Velo_Fuss_Count.csvDie Data of individual years starting from the year 2000 can be downloaded individually from the URL with the pattern Ω, i.e. for the year 2020 here: https://data-bs.ch/mobilitaet/2020_Velo_Fuss_Count.csv.Die Counting points are set to MET (columns TimeFrom and Timeto), i.e. the time change is carried out as in Central Europe. When switching from winter to daylight saving time, the hour of change is missing, this day then has 23 hours. When switching from summer to winter, one hour too much is included (the day has 25 hours). In this case, the counts of the two hours are counted together.

Counts of vehicles, cyclists and pedestrians at the majority of intersections equipped with traffic lights, as well as some intersections where the installation of lights was under consideration. For each of these intersections, all located in the 19 boroughs, the number of vehicles and pedestrians was recorded at different times of a typical day. Counts may sometimes not take into account certain users (pedestrians, cyclists, trucks for example). NOTE: since 2009, the surveys also include the passage of cyclists. The counts were carried out as part of the harmonization of fires. These are therefore counts from the central services and they do not include the counts made by the boroughs.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset is historical. For recent data, we recommend using https://chicagotraffictracker.com. -- Average Daily Traffic (ADT) counts are analogous to a census count of vehicles on city streets. These counts provide a close approximation to the actual number of vehicles passing through a given location on an average weekday. Since it is not possible to count every vehicle on every city street, sample counts are taken along larger streets to get an estimate of traffic on half-mile or one-mile street segments. ADT counts are used by city planners, transportation engineers, real-estate developers, marketers and many others for myriad planning and operational purposes. Data Owner: Transportation. Time Period: 2006. Frequency: A citywide count is taken approximately every 10 years. A limited number of traffic counts will be taken and added to the list periodically. Related Applications: Traffic Information Interactive Map (http://webapps.cityofchicago.org/traffic/).

The Transportation Planning Division is responsible for the planning, design, operation and maintenance necessary to provide an effective and safe transportation network for vehicular, bicycle and pedestrian traffic. This dataset contains selected attributes and spatial locations of traffic count sites in the city of Gainesville owned and operated by the city, county and state.

This data logs bicycles and pedestrians continuously at the location noted in the title. Counts are displayed in real-time on eco-totem displays and are grouped together in 15-minute intervals for reporting. Adjustment factors for data have been applied to reflect field validation of equipment. Please note that this data comes from a third-party vendor, Eco-Counter, and the City of Boulder's data reporting process relies on the data extracted from their system.

The City of Toronto's Transportation Services Division collects short-term traffic count data across the City on an ad-hoc basis to support a variety of safety initiatives and projects. The data available in this repository are a full collection of Turning Movement Counts (TMC) conducted across the City since 1984. The two most common types of short-term traffic counts are Turning Movement Counts and Speed / Volume / Classification Counts. Speed / Volume / Classification Count data, comprised of vehicle speeds and volumes broken down by vehicle type, can be found here. Turning Movement Counts include the movements of motor vehicles, bicycles, and pedestrians through intersections. Counts are captured using video technology. Older counts were conducted manually by field staff. The City of Toronto uses this data to inform signal timing and infrastructure design. Each Turning Movement Count is comprised of data collected over 8 non-continuous hours (before September 2023) or over a continuous 14-hour period (September 2023 and after), at a single location. Some key notes about these counts include: Motor vehicle volumes are available for movements through the intersection (left-turn, right-turn and through-movement for each leg of the intersection). Motor vehicle volumes are further broken down by vehicle type (car, truck, bus). Total bicycle volumes approaching the intersection from each direction are available. Total pedestrian volumes crossing each leg of the intersection are available. Raw data are recorded and aggregated into 15-minute intervals. The following files showing different views of the data are available: Data Dictionary (tmc_data_dictionary.xlsx): Provides a detailed definition of every data field in all files. Summary Data (tmc_summary_data): Provides metadata about every TMC available, including information about the count location and count date, as well as summary data about each count (total 8- or 14-hour pedestrian volumes, total 8- or 14-hour vehicle and bicycle volumes for each approach to the intersection, percent of total that are heavy vehicles and a.m. and p.m. peak hour vehicle and bicycle volumes). Most Recent Count Data (tmc_most_recent_summary_data): Provides metadata about the most recent TMC available at each location for which a TMC exists, including information about the count location and count date, as well as the summary data provided in the “Summary Data” file (see above). Raw Data (tmc_raw_data_yyyy_yyyy): These files—grouped by 5-10 year interval—provide count volumes for cars, trucks, buses, cyclists and pedestrians in 15-minute intervals, for movements through the intersection, for every TMC available. Vehicle volumes are broken down by movement through the intersection (left-turn, right-turn and through-movement, for each approach), cyclist volumes are broken down by leg they enter the intersection and pedestrian volumes are broken down by the leg of the intersection they are counted crossing. This dataset references the City of Toronto's Street Centreline dataset, Intersection File dataset and Street Traffic Signal dataset.

This downloadable dataset contains daily foot traffic at intersections on 4th Street in downtown San Rafael. Foot traffic count are obtained from Miovision Cameras. Daily Foot Traffic is the number of times an intersection was crossed each day. Foot traffic is not a count of individual pedestrians – Miovision cameras cannot differentiate between individuals.Miovision traffic cameras used by the City of San Rafael capture only information that is pertinent to tracking the movement and classification of vehicles, bicycles, and pedestrians over short period (12-72 hours). Cameras capture at a video resolution low enough to ensure faces, license plates, and other features are not recognizable. There is no recording of sound and cameras are angled to capture only approach lanes and pedestrian crossings.Once videos are captured, they are uploaded to Miovision’s website where they are stored and processed using automated detection zones similar to video detection technology used for traffic signals. The City of San Rafael data team pulls data from Miovision once daily, aggregates the pedestrian counts from 15 minute intervals to daily intervals, and uploads the information to the open data portal. Data from Miovision does not contain identifying information.The latitude and longitude of each intersection can viewed here.

This data set contains counting data from mobile counting stations of the MIV as well as bicycle and pedestrian traffic in the city of Konstanz. The field of action mobility is a central topic on the way to climate neutrality. A turnaround in mobility aims to reduce energy consumption without restricting mobility. It is not only about clarifying how and with which means of transport the citizens will move in the future. It is also about how the scarce public space is used. For a better overview of the number of cyclists, cars and pedestrians at individual points, the city of Konstanz has procured two new, mobile counting devices. Below this record are various resources related to mobile traffic counts. (Source, City of Constance, Office for Urban Planning and Environment) ### Data source: Open Data Constance at CC BY 4.0

Traffic Counts: Cordon Count, Quays Count DCC. Published by Dublin City Council. Available under the license cc-by (CC-BY-4.0).Every November Dublin City Council (DCC) conducts traffic counts at 33 locations on entry points into the city centre around a 'cordon' formed by the Royal and Grand Canals. As the name suggests, the cordon has been chosen to ensure (as far as possible) that any person entering the City Centre from outside must pass through one of the 33 locations where the surveys are undertaken. In addition, every May there is a wider traffic count survey carried out at approximately 60 locations where in addition to the canal cordon locations, further counts are carried out at bridges along the River Liffey and points such as Parnell Street and St. Stephens Green.

These traffic counts provide a reliable measurement of the modal distribution of persons travelling into, and out of, Dublin City on a year on year comparable basis. The data collected is divided into the various transport modes allowing us to better understand the changing usage trends in cycling, pedestrian and various vehicle types.

Resources include a map with the 33 locations on the Cordon where data is annually collected. All 33 cordon points are on routes for general traffic into the City Centre, while 22 of the cordon points are on bus routes into the City. The numbers of people using Bus, Luas, DART and suburban rail services to enter the City Centre are collated from each of the various service providers and an Annual Monitoring Report is prepared by the National Transport Authority. ...

DVRPC collects traffic volume, bicycle, and pedestrian counts at over 5,000 locations each year. The data is collected by the pneumatic tubes you see laying across the road. DVRPC also obtains traffic data collected by other entities and includes that data in its database as a public service. Traffic data is used by transportation engineers and planners, developers, market analysts, and may be of interest to the general public. This file is updated nightly.

Use RECORDNUM in the location feature and DVRPCNUM in the hourly table to join the two.

Current issue 23/09/2020
Please note: Sensors 67, 68 and 69 are showing duplicate records. We are currently working on a fix to resolve this.

This dataset contains minute by minute directional pedestrian counts for the last hour from pedestrian sensor devices located across the city. The data is updated every 15 minutes and can be used to determine variations in pedestrian activity throughout the day.

The sensor_id column can be used to merge the data with the Sensor Locations dataset which details the location, status and directional readings of sensors. Any changes to sensor locations are important to consider when analysing and interpreting historical pedestrian counting data.

Note this dataset may not contain a reading for every sensor for every minute as sensor devices only create a record when one or more pedestrians have passed underneath the sensor.

The Pedestrian Counting System helps us to understand how people use different city locations at different times of day to better inform decision-making and plan for the future. A representation of pedestrian volume which compares each location on any given day and time can be found in our Online Visualisation.

Related datasets:
Pedestrian Counting System – 2009 to Present (counts per hour).
Pedestrian Counting System - Sensor Locations

The global traffic counter market is experiencing robust growth, driven by increasing urbanization, the need for efficient transportation management, and the rising adoption of smart city initiatives. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the expanding deployment of intelligent transportation systems (ITS) to optimize traffic flow and reduce congestion in major cities worldwide. Furthermore, the increasing demand for accurate pedestrian and vehicle count data for improved urban planning, retail analytics, and enhanced security measures contributes significantly to market expansion. Technological advancements, such as the development of more sophisticated sensor technologies (IR beam, thermal imaging, video-based) offering higher accuracy and reliability, further propel market growth. Different applications, from tourism management to optimizing retail store layouts based on foot traffic, demonstrate the diverse applicability and potential of traffic counters. However, the market faces certain restraints. High initial investment costs for advanced traffic counting systems can be a barrier for smaller municipalities or businesses. Additionally, data privacy concerns related to the collection and use of pedestrian movement data require careful consideration and robust data protection measures. Despite these challenges, the long-term outlook for the traffic counter market remains positive, driven by the increasing need for data-driven decision-making in urban planning and transportation management, coupled with continuous technological innovation to enhance accuracy, affordability, and user-friendliness. The market segmentation by technology (IR beam, thermal imaging, video-based) and application (tourism, transportation, retail) showcases the diverse opportunities within the sector. Key players like Axis, V-Count, and others are actively contributing to market development through continuous innovation and product expansion.

Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017

Current issue 23/09/2020 Please note: Sensors 67, 68 and 69 are showing duplicate records. We are currently working on a fix to resolve this.

This dataset contains minute by minute directional pedestrian counts for the last hour from pedestrian sensor devices located across the city. The data is updated every 15 minutes and can be used to determine variations in pedestrian activity throughout the day. The sensor_id column can be used to merge the data with the Sensor Locations dataset which details the location, status and directional readings of sensors. Any changes to sensor locations are important to consider when analysing and interpreting historical pedestrian counting data. Note this dataset may not contain a reading for every sensor for every minute as sensor devices only create a record when one or more pedestrians have passed underneath the sensor. The Pedestrian Counting System helps us to understand how people use different city locations at different times of day to better inform decision-making and plan for the future. A representation of pedestrian volume which compares each location on any given day and time can be found in our Online Visualisation. Related datasets: Pedestrian Counting System – 2009 to Present (counts per hour).Pedestrian Counting System - Sensor Locations

The City of Toronto's Transportation Services Division collects short-term traffic count data across the City on an ad-hoc basis to support a variety of safety initiatives and projects. The data available in this repository are a full collection of Speed, Volume and Classification Counts conducted across the City since 1993. The two most common types of short-term traffic counts are Turning Movement Counts and Speed / Volume / Classification Counts. Turning Movement Count data, comprised of motor vehicle, bicycle and pedestrian movements through intersections, can be found here. Speed / Volume / Classification Counts are collected using pneumatic rubber tubes installed across the roadway. This dataset is a critical input into transportation safety initiatives, infrastructure design and program design such as speed limit changes, signal coordination studies, traffic calming and complete street designs. Each Speed / Volume / Classification Count is comprised of motor vehicle count data collected over a continuous 24-hour to 168-hour period (1-7 days), at a single location. A handful of non-standard 2-week counts are also included. Some key notes about these counts include: Not all counts have complete speed and classification data. These data are provided for locations and dates only where they exist. Raw data are recorded in 15-minute intervals. Raw data are recorded separately for each direction of traffic movement. Some data are only available for one direction, even if the street is two-way. Within each 15 minute interval, speed data are aggregated into approximately 5 km/h increments. Within each 15 minute interval, classification data are aggregated into vehicle type bins by the number of axles, according to the FWHA classification system attached below. The following files showing different views of the data are available: Data Dictionary (svc_data_dictionary.xlsx): Provides a detailed definition of every data field in all files. Summary Data (svc_summary_data): Provides metadata about every Speed / Volume / Classification Count available, including information about the count location and count date, as well as summary data about each count (total vehicle volumes, average daily volumes, a.m. and p.m. peak hour volumes, average / 85 percentile / 95 percentile speeds, where available, and heavy vehicle percentage, where available). Most Recent Count Data (svc_most_recent_summary_data): Provides metadata about the most recent Speed / Volume / Classification Count data available at each location for which a count exists, including information about the count location and count date, as well as the summary data provided in the “Summary Data” file (see above). Raw Data: Raw data is available in 15-minute intervals, and is distributed into one of three different file types based on the count type: volume-only, speed and volume, or classification and volume. If you’re looking for 15-minute data for a specific count, identify the count type and count date, then download the raw data file associated with the count type and period. If you’re looking for volume data for all count types, you will need to download and aggregate all three file types for a given period. Volume Raw Data (svc_raw_data_volume_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data in 15-minute intervals, for each direction separately. You will find the raw data for volume-only counts (ATR_VOLUME) here. Speed and Volume Raw Data (svc_raw_data_speed_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data aggregated into speed bins in approximately 5 km/h increments. Speed data are not available for all counts. You will find the raw data for speed and volume counts (ATR_SPEED_VOLUME) here. Classification and Volume Raw Data (svc_raw_data_classification_yyyy_yyyy): These files—grouped by 5-10 year interval—provide volume data aggregated into vehicle type bins by the number of axles, according to the FWHA classification system. Classification data are not available for all counts. You will find the raw data for classification and volume counts (VEHICLE_CLASS) here. FWHA Classification Reference (fwha_classification.png): Provides a reference for the FWHA classification system. This dataset references the City of Toronto's Street Centreline dataset, Intersection File dataset and Street Traffic Signal dataset.

The dataset indicates where traffic, pedestrian, running and residence counts have been made;

The City of Perth traffic count provides information about the number of vehicles, speed of travel and peak travel numbers on particular roads within the Perth LGA (Local Government Area). Show full description