Max Speed limit values in miles per hour. This data is an extract from the Geospatial Roadway Inventory Databse (GRID), which is TxDOT's system for managing roadway assets in Texas.Note: Extracts from GRID are made on a regular basis and reflect the state of the data at that moment. Assets on routes that are in the process of being edited may be affected.Update Frequency: 1 MonthsSource: Geospatial Roadway Inventory Database (GRID)Security Level: PublicOwned by TxDOT: TrueRelated LinksData Dictionary PDF [Generated 2025/04/24]

Speed limit information for most of Queensland's roads. Includes state and locally controlled roads. Point-in-time data as per date of collection in dataset.

Esri ArcGIS Online (AGOL) Hosted, View Feature Layer which provides access to the MDOT SHA Roadway Posted Speed Limit Signs data product.MDOT SHA Roadway Posted Speed Limit Signs data consists of point geometric features which represent the geographic locations of posted speed limit signs along MDOT SHA-maintained roadways throughout the State of Maryland. This layer is a hosted, view layer showing only Posted Speed Limit signage from the comprehensive MDOT SHA Roadway Sign Inventory. Roadway signs that share a sign support structure will be represented as stacked geometry.MDOT SHA Roadway Sign Inventory data is owned by the MDOT SHA Office of Traffic & Safety (OOTS). This data is currently updated on an annual basis. This is the latest version of the data, which was last updated in November 2019 (11/04/2019).MDOT SHA Roadway Sign Inventory data is published on ArcGIS Online for Maryland as a publicly available Hosted Feature Layer with Non-Restricted Access. Download / Export of the data is available in a variety of formats.For additional information, contact MDOT SHA OIT Enterprise Information Services:GIS@mdot.maryland.gov

Speed zones are set to enable drivers travelling at a speed limit to safely respond to potential risks in the road environment. This dataset contains data for NSW speed zones that are categorised as: Ordinary Permanent Shared High Pedestrian School Variable Local Traffic Truck & bus Wet Weather School Bus Toll Plaza

Speed Zones (State and Local Roads)This layer shows the location of Speed Zones on all public access roads found in the Integrated Road Information System (IRIS) and includes temporal speed zones such as school zones. A school zone refers to an area on a street near a school or near a pedestrian crossing leading to a school that has a likely presence of younger pedestrians. School zones generally have a reduced speed limit during certain hours.Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

This feature class identifies the legal speed limit for Portland streets. Not every street segment in the City is included. The dataset includes the speed limits for surface streets where the limit was implemented by an ODOT speed zone order or where a speed limit was authorized by statutes governing speeds in business districts, around schools, along roadways with bicycle or pedestrian improvements, or on non-arterial, residential streets.-- Additional Information: Category: Transportation - Streets Purpose: To identify designated speed limits on Portland streets. Update Frequency: As Needed-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?=DisplayLayer=54340

Geospatial data about City of Dallas, Texas Speed Limits. Export to CAD, GIS, PDF, CSV and access via API.

Speed Zones (State and Local Roads)This layer shows the location of Speed Zones on all public access roads found in the Integrated Road Information System (IRIS) and includes temporal speed zones such as school zones. A school zone refers to an area on a street near a school or near a pedestrian crossing leading to a school that has a likely presence of younger pedestrians. School zones generally have a reduced speed limit during certain hours.Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

The linear centerline of the temporary speed limit zones around schools

This dataset contains information relating to ACT Speed Zones.

DATASET DISCLAIMER: This dataset shall be used for general reference only. Because of the complexity in processing to generate this dataset, we cannot be liable for omissions and inaccuracies. Users of this dataset are encouraged to check with related agencies if you have any concerns about the data displayed. Please be aware that new data are added and changed periodically, and data may become out-of-date quickly due to change in business processes and data processing time. TCCS disclaims liability to any person/entity who acts in reliance on the information provided on this dataset. This dataset does not have any legal status, and it may not correspond with the actual speed limit since the date of publication.

Displays Speed Limit Ordinances for the City of Tucson. Created by copying features from stnetall.PurposeLine Layer that shows the speed limits of streets in Tucson.Dataset ClassificationLevel 0 - OpenKnown UsesUsed in Various Web MapsKnown ErrorsUrban streets from 12000 scale & rural streets from 100000 scale; 1/3 streets rectified to parcel base. 10/2013: While this layer is maintained as a Shapefile, the coverage format is still required for certain nightly processing. Data ContactDepartment of Transportation and MobilityUpdate FrequencyUpdated as needed

City of Riverside Open Data for use in the city.

Areas near schools that are designated as school speed zones.Refresh: DailyContact: DOT_IT_GIS@seattle.gov

Automated Speed Enforcement (ASE), an automated system that uses a camera and a speed measurement device to enforce speed limits, is a speed enforcement tool that uses technology to help make roads safer for all users. Last updated in August 2024.

ASE is designed to work in tandem with other road safety measures, such as traffic calming, community safety zones, speed display boards, education initiatives, and police enforcement, to help improve safety for people of all ages by: ​ increasing speed compliancealtering driver behaviourincreasing public awareness about the critical need to slow down

An ASE system captures and records images only of vehicles travelling in excess of the posted speed limit in school zones and community safety zones, with tickets issued to the registered plate holder regardless of who was driving. This will result in a monetary fine, but no demerit points will be applied. The cameras are designed to follow traditional speed enforcement however the ASE threshold speed to trigger a fine is not being made public. This is standard practice across the province.

The fines for speeding in a Community Safety Zone are as follows: 1 to 19 km over the limit is $5.00 per kilometre plus costs 20 to 29 km over the limit is $7.50 per kilometre plus costs 30 to 49 km over the limit is $12.00 per kilometre plus costs ​

The City of Brampton was one of the first municipalities in Ontario to implement ASE, introducing its first notification sign on December 19, 2019.ASE will be deployed in Brampton in September 2020. You won’t see ASE everywhere – just in community safety zones​, and on roadways posted less than 80 km per hour, where it will be used to protect our most vulnerable populations and help them feel comfortable while walking, running, playing, cycling, and generally enjoying their communities. In an effort to build awareness, signage will also be installed at entry points in to the city to advise motorists that ASE is in effect and that speeding is not tolerated in the community.Council Approval of ASE Implementation (July 2020)ASE Camera Certificates of AccuracyASE Ontario

Roadway Posted Speed Limit Signs data consists of point feature geometry which represents the geographic location of posted speed limit signs along public roadways in the State of Maryland. PLEASE NOTE: This layer is now deprecated as of September, 2020 and will be removed in September 2021.Data has been replaced with the following https://maryland.maps.arcgis.com/home/item.html?id=7549d74e31df427a82a64ab5a19d74e3#overviewRoadway Posted Speed Limit Signs data is developed as part of the Highway Performance Monitoring System (HPMS) which maintains and reports transportation related information to the Federal Highway Administration (FHWA) on an annual basis. HPMS is maintained by the Maryland Department of Transportation State Highway Administration (MDOT SHA), under the Office of Planning and Preliminary Engineering (OPPE) Data Services Division (DSD). This data is used by various business units throughout MDOT, as well as many other Federal, State and local government agencies. Roadway Posted Speed Limit Signs data is key to understanding the location of posted speed limit signs throughout the State of Maryland. Roadway Posted Speed Limit Signs data is updated and published on an annual basis for the prior year. This data is for the year 2017.For additional information, contact the MDOT SHA Geospatial Technologies Email: GIS@mdot.state.md.us For additional information related to the Maryland Department of Transportation (MDOT) Website: https://www.mdot.maryland.gov/ For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA) Website: https://roads.maryland.gov/Home.aspx Data Legal Disclaimer: The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Link:https://geodata.md.gov/imap/rest/services/Transportation/MD_RoadwayPostedSpeedLimits/FeatureServer/0

1. Better understanding human-wildlife interactions and their links with management can help improve the design of wildlife protection zones. One important example is the problem of wildlife collisions with vehicles or human-built structures (e.g. power lines, wind farms). In fact, collisions between marine wildlife and watercraft are among the major threats faced by several endangered species of marine mammals. Natural resource managers are therefore interested in finding cost-effective solutions to mitigate these threats. 2. We combined abundance estimators with encounter rate theory to estimate relative lethal collision risk of the Florida manatee (Trichechus manatus latirostris) from watercraft. We first modeled seasonal abundance of watercraft and manatees using a Bayesian analysis of aerial survey count data. We then modeled relative lethal collision risk in space and across seasons. Finally, we applied decision analysis and Linear Integer Programming to determine the optimal design of speed zones in terms of relative risk to manatees and costs to waterway users. We used a Pareto efficient frontier approach to evaluate the performance of alternative zones, which included additional practical considerations (e.g. spatial aggregation of speed zones) in relation to the optimal zone configurations. 3. Under the various relationships for probability of death given strike speed that we considered, the current speed zones reduced the relative lethal collision risk by an average of 51.5% to 70% compared to the scenario in which all speed regulations were removed (i.e. the no-protection scenario). We identified optimal zones and near-optimal zones with additional management considerations that improved upon the current zones in terms of cost or relative risk. 4. Policy Implications: Our analytical framework combines encounter rate theory and decision analysis to quantify the effectiveness of speed zones protecting manatees while accounting for uncertainty. Our approach can be used to optimize the design of protection zones intended to reduce conflicts between human waterborne activity and marine mammals. This framework could be extended to address many other problems of human-wildlife interactions, such as the optimal placement of wind farms to minimize collisions with wildlife or the optimal allocation of ranger effort to mitigate poaching threats.

This data set represents the FWC Boating Restricted Areas as described in Florida Administrative Code Chapter 68D-24.

This layer contains the traffic regulatory signs, such as speed limit, no left turns, weight restrictions and other signs for motorists on the streets of Los Angeles.

The travel time map was generated using the Pedestrian Evacuation Analyst model (version 1.0.1 for ArcGIS 10.5) from the USGS (https://geography.wr.usgs.gov/science/vulnerability/tools.html). The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface output from the model is grouped into 1-minute increments for easier visualization. The times in the attribute table represent the estimated time to travel on foot to the nearest safe zone at the speed designated in the map title. The file name indicates whether the map is of the recreated tsunami inundation zone for the 2009 Mw 8.1 Samoa earthquake or a probable maximum tsunami (PMT) inundation zone for American Samoa and which travel speed was used in the modelling (slow walk, fast walk, slow run, or fast run). These data, in polygon shapefile format, are intended for use in GIS software applications. These data support the following publication: Wood, N.J., Jones, J.M., Yamazaki, Y., Cheung, K-F., Brown, J., Jones, J.L., and Abdollahian, N., 2018, Population vulnerability to tsunami hazards informed by previous and projected disasters—a case study of American Samoa: Natural Hazards, 24 p., https://doi.org/10.1007/s11069-018-3493-7.

Posted speed limits in miles per hour (mph).