This dataset captures detailed information about urban parking activity, traffic conditions, and vehicle types over time. With over 18,400 entries spread across 11 columns, it offers a sizable and rich set of observations—ideal for anyone looking to explore parking trends, analyze traffic flow, or build models to predict parking availability.

What’s Inside:

Timestamps: Each entry is time-stamped (from October 4 to December 19, 2016), making this a time-series dataset. That means you can track how parking behavior changes over days, weeks, or months—like identifying peak hours or weekend patterns.

Unique Events: Every row comes with a unique ID, so each record represents a single moment or observation. That ensures clean data without duplicates.

Parking Locations: The SystemCodeNumber column identifies where each record came from—there are 14 different locations or systems in total. Codes like BHMBONCSTB1, Broad Street, and Others-OCCSP119A show that data comes from multiple spots, which helps in comparing how parking demand varies by area.

Capacity vs. Occupancy: Two of the most important columns show how many parking spaces were available (Capacity) and how many were filled (Occupancy) at any given time. Together, they tell us how full a lot was and help track usage levels. Some locations had space for thousands of cars, while others were much smaller.

Geolocation: Latitude and longitude are included, meaning you can map every observation. This is especially helpful if you're working with GIS tools or want to visualize parking availability across a city.

Vehicle Types: Most vehicles in the data are cars (81%), followed by bikes (20%) and a small number of other types (about 13%, or 3,578 entries). This breakdown can help in designing parking facilities or allocating space differently based on need.

Traffic Conditions: The TrafficCondition column categorizes how busy the surrounding roads were: low (42%), average (35%), and high (23%). These conditions can be correlated with parking occupancy—like whether traffic is worse when lots are full.

Queue Length: This column tracks how many vehicles were waiting for a spot (from 0 to 15), giving insight into where and when demand exceeded supply.

Special Days: There’s also a flag (IsSpecialDay) indicating whether a day was out of the ordinary—perhaps due to an event, holiday, or other factor affecting usual patterns.

Entrances and exits to surface and garage parking lots in the downtown area of Austin, TX. The data is also available as an Esri File Geodatabase, here: http://www.arcgis.com/home/item.html?id=c84e634d0d074d70b3cdb2da4e065dda This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been produced by Austin Transportation Department for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness.

Comprehensive dataset of 223 Free parking lots in Norway as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Parking lots may be a significant source of pollution. Oil, sediments, and heavy metals may accumulate on their surface, then be flushed into rivers, streams, and lakes via rainfall. At present no dataset provides a mapping or estimation of parking lot area or locations nationwide. This product consists of a time series of five national 60-meter raster datasets which estimate the proportion of each pixel represented by parking lots, based on land-use coefficients. The rasters span the conterminous United States, for the years 1974, 1982, 1992, 2002, and 2012. The dataset was derived by calculating coefficients for 18 land-use types (Commercial, Industrial, Residential, Recreation, and so on) from the 2012 U.S. Geological Survey NAWQA Wall-to-wall Anthropogenic land-use Trends (NWALT) product. The coefficients were calculated by comparing NWALT land-uses to 1-meter rasters representing detailed paved surface parking lot polygons available from six cities: Bloomington, IN; Chattanooga, TN; Denver, CO; Hartford, CT; Raleigh, NC; and St. Paul, MN. The land-use classification overlying the largest amount of parking lot land areas was Commercial land (20.1% of land area), followed by Industrial land (19.6%), and Major Transportation (7.4%). The results were cross-validated against ground-truth data withheld from the calculations. The coefficients derived from the year 2012 data were then applied to the prior four years to create a time series. The rasters provide a way to estimate percent parking lot area by watershed or other area of interest, over the last four decades.

How many parking spaces are there in the Netherlands? According to a 2018 report, no exact data exists on this. It is estimated to be between ** to ** million spaces. The data this is based on comes from two different research methods, both of which have been included in this statistic. For 2016, the number is based on a calculation on the total number of passenger cars which was multiplied with a number of *** parking spaces per car (based on one space on the place of departure, one space on the destination and "other" capacity). All 2014 numbers were extrapolated from 2002 parking space data with the help of car ownership and population growth. The source therefore states quite clearly that both 2014 and 2016 are not based on actual censuses and should therefore be approached with caution.

Private Parking Lots (not City owned or maintained). These are surface lots; there are additional City and Private Parkade datasets available as well. Data are manually updated as needed.The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through scripts which does not trigger the "last updated" date to change.Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

Work in progress: data might be changed The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies. Vehicle Detections Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock. The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides. Figure 1: Overview map of detected vehicles Parking Areas

Comprehensive dataset containing 84 verified Parking lot for bicycles businesses in Norway with complete contact information, ratings, reviews, and location data.

The dataset consists of images of parking spaces along with corresponding bounding box masks. In order to facilitate object detection and localization, every parking space in the images is annotated with a bounding box mask. The bounding box mask outlines the boundary of the parking space, marking its position and shape within the image. This allows for accurate identification and extraction of individual parking spaces. Each parking spot is also labeled in accordance to its occupancy: free, not free or partially free. This dataset can be leveraged for a range of applications such as parking lot management, autonomous vehicle navigation, smart city implementations, and traffic analysis.

A. SUMMARY This dataset was first created as part of the SFMTA On Street Car Share Pilot Program (approved by the MTA Board in July 2013) to illustrate the _location of implemented and planned (various stages) spaces throughout the city. B. METHODOLOGY The locations were originally provided to the MTA as requests by the three car share organizations (CSOs). These were given as a .kml file, which was converted to a .shp. Additional fields were created using spatial joins (zipcode, supervisor district, CNN, etc). Use definition query tool to display those locations with a certain attribute. For example, query Existing = 1 to display those locations that are on street operating. 500 submissions were given by CSOs to the MTA, but only a portion of those were brought to the MTA Board for approval, and even fewer were implemented as operational on street spaces. With no definition query, you can see all spaces as features, with varying levels of data completion. C. UPDATE FREQUENCY During periods of implementation/construction, updates were as frequent as daily or weekly. However, as the frequency of newly implemented spaces slowed over the course of the pilot, updates occurred less frequently--weekly or monthly. Updates will be needed as new spaces are implemented--many of the spaces not taken past MTA Board approval have incomplete data. D. OTHER CRITICAL INFO Each feature (or each row, or point) represents a single car share parking space. Some parking spaces belong to a "pod" where there are two adjacent car share parking spaces, indicated by the "PodType" field. To summarize or analyze by pod, use the "POD" field.

Park Parking Lots data includes parking areas and angled street parking. Parking lots provide a stable surface for vehicles and cyclists. Made of asphalt. Lines are marked and maintained.Data are updated by city staff as needed, and automatically copied to the Open Data Portal. The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through automated scripts which does not trigger the "last updated" date to change.Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

The Transport Department is installing new parking meters in phases to replace the existing ones. The occupancy data of parking spaces installed with new parking meters will be provided in below data resources. The distribution of all metered parking spaces (no matter installed with existing or new parking meters) at different districts in Hong Kong are also included.

Metered and unmetered parking spaces in the City of Victoria. Includes information on rates and time limits. Parking space information is updated manually as needed.Parking space types may include: metered, accessible, church, commercial, general, electric vehicle, hotel, hotel taxi, motorcycle, no parking, passenger, police, school, small vehicle, taxi, tour bus.The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through scripts which does not trigger the "last updated" date to change.Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

Parking surfaces within the City of Chattanooga. The data is buffered 1/4 past the existing city boundary so parcels not within the city boundary may also be included in this dataset.

Point feature class showing locations of parking lots, mostly public lots, used for quick reference. Not comprehensive; no effort was made to include private parking lots, though some are shown, such as Cayuga Garage and Cornell Parking Garage on Hoy Rd. Data is infrequently updated.

This game provides parking data on the territory of the City of Liège. It is provided for information purposes only and does not release the user from compliance with the Highway Code and vertical parking signage, which is the only authentic data. The game provides the different categories of parking as well as all the no-parking zones and special cases. The data relate to the entire territory of the City of Liège. It should be noted, however, that data on roads along the tram route are incomplete or non-existent. They will be added at the latest by the end of 2024. Note that in streets where parking is alternated by fortnight, the 2 sides of the roadway are divided into parking spaces but only one of the 2 sides is allowed parking by fortnight according to the vertical signs on the ground. The types of parking data are as follows: Free of charge: parking space without any special status and on which parking is free,Entry: entrances to garages or private driveways and where parking is prohibited except for the owner,Prohibited: parts of carriageways prohibited from parking because of an E1 or other type of traffic sign or in accordance with the rules of the Highway Code (e.g. prohibition of parking in the 5m preceding a pedestrian crossing),Rivers: parking place where the vertical signage shows the indication ‘Riverains’ and where parking is reserved for residents of the area in question who have taken steps to park there in accordance with the associated regulations of the City of LiègePayant: parking place where the vertical signage shows the indication ‘Ticket’ or ‘Paying’ and where parking is payable in accordance with the procedures defined in the associated regulations of the City of Liège,Hachure: parts of road where ground marking of the ‘hatch’ type is present on the ground and where parking is therefore not authorised,Handicapped: parking space dedicated to people with disabilities and having taken steps to park there in accordance with the associated regulations of the City of Liège,Yellow Line: parts of carriageways where ‘yellow line’ markings are present on curbs and where parking is therefore not permitted,Bus TEC: bus stop on the road where parking is not allowed,Shop&Drive: parking space limited in time to 30 minutes with vertical signage, specific ground markings and ground detection sensors; any overrun of the authorised time is punishable by an administrative penalty,Delivery: parking space dedicated to delivery with any additional indications of duration (time and day of the week), Free with disc: parking space where the vertical signs display the logo of a parking disc limiting parking in time to a maximum of 2 hours and requiring the use of that disc by the user,Police: parking space reserved for police vehicles,Other: parking space reserved for specific categories of users,Kiss&Ride: parking space limited in time to 15 minutes with vertical signage, specific ground markings and ground detection sensors; any overrun of the authorised time is punishable by an administrative penalty, parking space reserved for coaches and/or school buses, Zigzag: parts of carriageways where ‘zigzag’ ground markings are present on the ground and parking is therefore not permitted,Taxis: parking space reserved for taxi vehicles, Shared cars: parking space reserved for vehicles provided by a shared car operator approved by the City of Liège,Terrasse: parking space occupied by a commercial terrace having obtained an authorisation from the City of Liège in accordance with the associated by-law,Bornes électriques: on-street parking space equipped with charging stations for electric vehicles and where parking is only allowed for the time of charging such a vehicle.For more information on the parking plan, see https://www.liege.be/parking Do you see a degradation on an infrastructure? You can report it via the online form "To report on the street" on the e-desk or on the Liège app in your pocket or contact the Home Maintenance Cell during opening hours. The information is regularly checked by our services in the field. However, there may be a time lag between the time the data is collected, its changes, and the publication of the database update on the platform. Do you have a question or comment about this dataset? Contact us.

Building plan “In the tiles part 1 1.Adjustment Parking lot Bahnhofstrasse No.41_1” of the city of Saarlouis

Graph and download economic data for Expenses for Parking Lots and Garages, All Establishments, Employer Firms (PLAGEAEEF381293) from 2004 to 2022 about parking, employer firms, establishments, expenditures, and USA.

To explain how the study was set, in our group of four, we were randomly assigned to one of four parking areas, two of which were parking lots and the other two parking garages. As individuals, we compiled 4 datasets of 20 samples for each parking lot. The 20 samples have a split of 10 with and without transponders, ensuring a balanced datasets between parking areas. Data was collected at 1-4pm on two days a week for two weeks.Experimental designGiven the assigned parking area, we randomly selected 20 cars from the parking lot. We recorded the make and model of the car and whether or not they had a transponder, then took a picture of the back to later record the year. If the either set with or without transponder was not completed, we randomly selected more cars until the requirements of 10 cars with and without transponders were fulfilled. In order to reduce the chances of pseudoreplication in our design we framed the experiment by these key points. We measured in an implemented time range (1-4pm), randomized selection of vehicles in parking lots and implemented a balanced design in the form of same number of frequencies across datasetsData Input and Statistical AnalysisUsing the year, make and model we determined the price of the car through a constant source (KBB, 2019). Two 2-tailed t-tests were done that measured for the prices of garage parking vs lots, and the other test for with and without transponders in all parking areas.

This map data layer represents the parking lot areas for the City of Bloomington. It includes paved parking areas, gravel parking areas, and non-parking island features created from surrounding parking areas, roadway pavement, or building features.