Displays Restricted Parking Zone Areas maintained by the Seattle Department of Transportation. This is a group layer consisting of the following layers:Restricted Parking Zone Signed Blocks (V_RPZ)Residents Eligible for Restricted Parking Zone Permits (V_Blockface, Def Query: Parking_Category <> 'None')Restricted Parking Zone A or B Husky Game Days (RPZ_AreasAorB)Restricted Parking Zones (RPZ_AreasNotAorB)Refresh Cycle: Daily

This map displays blocks that are part of the RPP program. This map does not necessarily display areas with available RPP parking. Some blocks where residents are eligible for an RPP Permit may have meters or other signage that restrict the usage of RPP Permits.

RPP Permits and Visitor Parking Permits (VPPs) allow parking at green and red RPP zone signs. Please review and follow on-street signage and parking regulations to avoid receiving a citation.

The Residential Permit Parking (RPP) Blocks data support the RPP program which limits residential parking to residents living on designated blocks. This dataset is derived from the District Department of Transportation (DDOT) roadway block (centerline) dataset. Residents can learn more about the RPP program at https://www.parkdc.com, including guidelines for eligibility to receive a permit and instructions on how to petition for an RPP block.This map displays blocks that are part of the RPP program. This map does not necessarily display areas with available RPP parking.RPP Permits and Visitor Parking Permits (VPPs) allow parking at green and red RPP zone signs. Please review and follow on-street signage and parking regulations to avoid receiving a citation.

Residential Neighborhood Permit Parking zone areas exported from the CIty's GIS. See summary description (txt) file for information about intended use, projection, currency, attributes, etc.

This map layer represents the enforcement areas (zones) for the City of Bloomington residential neighborhood permit parking program. The City's Municipal Code defines qualifications and regulations for the zones.

Related Links
Neighborhood Permit Parking Zone Maps

Polygon Feature class for Paid Parking Zones in the City of Alexandria. Paid Parking Zones contain a zone number that can be used in various parking applications to pay for on-street parking

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.

On Street parking zones and areas in the City of Kingston. Part of the on-street permit program to regulate street parking in the downtown region.

Gallery of parking related maps in Bloomington, including neighborhood permit zone series maps.

Public Facing Residential Parking Permit Area Map

Locations of parking meters with corresponding parking zones and areas.

Displays Restricted Parking Zone Areas maintained by the Seattle Department of Transportation. This is a group layer consisting of the following layers:Restricted Parking Zone Signed Blocks (V_RPZ)Residents Eligible for Restricted Parking Zone Permits (V_Blockface, Def Query: Parking_Category <> 'None')Restricted Parking Zone A or B Husky Game Days (RPZ_AreasAorB)Restricted Parking Zones (RPZ_AreasNotAorB)Refresh Cycle: Daily

City of Eugene Parking, Residential Parking Permit Zones 2021. Available on Mapping HUB and in Find your Parking Zone Map.

This map layer represents the enforcement areas (zones) for the City of Charlottesville's permit parking program. The City's Municipal Code defines qualifications and regulations for the zones.

GIS data file for city of Eugene Parking services regulated curbs. Available on Mapping HUB and in Find your Parking Zone Map.

Our Garage Resale Program requires any property owner selling a residential building within the designated parking impacted area to sign off on a report regarding the availability of legally required off-street parking spaces. If the property is not included in the parking impacted area, this program does not apply.

Map of Permitted Valet Staging Zones in the District. This map is a guide only. Users should review and follow on-street parking regulations to avoid receiving a citation. DDOT issues permits for valet parking in metered parking spaces or commercial loading zones. Reserved valet parking zones have specified hours and days. If a valet space is established in a commercial loading zones areas, the valet zone will be active when the loading zones is inactive. Valet permits can be annual or event-based. Annual valet parking permits are typically issued to restaurants or entertainment venues and are valid year-round. Event valet permits are temporary permits for specific events at a venue or an individual’s home. ParkDC.com

Displays the locations and attributes of SDOT Restricted Parking Zones (RPZs) and local zone sub-areas. Residents with RPZ permits have special parking privileges in their zone. RPZ residents may acquire the application form for a permit by clicking the link in a popup on an RPZ.

Available on Mapping HUB and in Find your Parking Zone Map.

NYCDOT sets parking meter rates across the five boroughs to be reflective of individual neighborhood parking conditions such as land use, density, and parking demand. Individual rate zones are represented in this dataset by large geographic areas. NYCDOT provides this map for informational purpose only. Confirm the rate at the parking meter or on the ParkNYC app before paying and observe all posted signs.

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.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930