It contains the full detail layer of the extents for certified speed limit records from 26 May 2022 onwards, and their associated attribute data.Previously, 68 different road controlling authorities (RCAs) published this information in multiple formats.You can use the data for:time-based analysisanalysis against other datasets, for example addressesbuilding additional datasets.The data is extracted from the NSLR on a nightly basis.New emergency speed limits are updated in this dataset shortly after being created in the NSLR.Note: speed limit record geometries (shapefiles) will overlap. This will be in addition to overpasses, underpasses, intersections, bus lanes, opposing speed differences and multiple speeds in the same direction. For example, if you have an overpass one speed limit will be given for the top level (bridge) and the second overlapping geometry for the bottom level (I.e. road under bridge).Permanent speed limit: A speed limit that’s in force except when a seasonal, variable, temporary - emergency or other temporary speed limit is in force.Variable speed limit: A speed limit that changes under certain conditions (excluding seasonal), for example due to the presence of a school where the speed limit is different at certain times of the day depending on the school activity.Seasonal speed limit: A speed limit that applies on a seasonal basis, for example during a holiday period. Seasonal speed limits can be one-off or recurring.Emergency speed limit: A speed limit put in place due to an emergency, for example an earthquake, tsunami or epidemic.The principles for how the speed limits interact with each other are as follows.Permanent and variable speed limits cannot overlap one another.A seasonal speed limit can overlap permanent and variable speed limits.A seasonal speed limit can overlap another seasonal speed limit, as long as the speed limit is not active at the same time as the record it is overlapping (i.e.one seasonal speed limit cannot overlap another seasonal speed limit).An emergency record can overlap a seasonal record but cannot overlap another emergency record.Temporary speed limits, other than emergency, are not included in this dataset.Members of the public can search for speed limits on New Zealand roads, obtain details of specific speed limits and obtain certified copies of speed limits through the NSLR web application. NSLR web browser applicationData reuse caveatsAs per license.Data quality statementRCAs signed off that the data in the NSLR is a true and accurate reflection of their bylaw. There is a high level of confidence in data quality, and we welcome user feedback.Data quality caveatsThe data has been migrated as provided by respective RCAs according to their current bylaw. There are errors as a result of having to migrate the legal speed limits as per the bylaw rather than what is signed on the ground. Many (but not all) of these were identified and RCAs can apply for Director’s approval to fix them.There is two known issues with the display of this data in Open Data, these are display issues within the Open Data application and do not impact the data when downloaded or used via API:Where there is no date 'December 31, 1969' is shown.Where there is no text 'null' is shown

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.

This map provides information on speed limits that are posted on state-maintained roadways in Virginia. Cities and towns set their own speed limits and these are not available to show on the map. Zoom in on the map to display the speed limits. Speed limits exist for all roads however; where this information is not available for mapping, they are not displayed. Most roads where speed limits are not shown are either rural, secondary roads (routes numbered 600 or greater) where a statutory 55 mph speed limit typically applies, or subdivision streets where a statutory 25 mph speed limit usually applies. These statutory speed limits are often are not posted on these roads. Click on any roadway to display the speed limit information.

This dynamic map service provodes a a linear representation of speed limit changes based on signs in the field or speed zones established by an Official Order.

Statewide Road Network including sealed and unsealed roads. The dataset represents navigable roads, including public and private access roads and tracks. A separate data layer stores 'unformed' DCDB centrelines which do not represent navigable roads. A limited number of associated features are stored separately as point features. Automatically updated when changes occur.

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

This webmap shows average traffic speed of major roads in Hong Kong. It is made available by the Transport Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XML web service and been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.

Speed Signs is a spatial dataset (point) that shows the location of every speed limit sign across Victoria including advisory speed signs. The data includes attributes such as Road Name, Sign Size, Type, Speed Value, Bearing and Direction. Variable speed signs display the alternative speed limit during the times that limit is active. This is captured in the variable time and day fields in the dataset. A standard ('static') speed sign with the prevailing speed limit is provided at the end of the zone which serves to end the variable zone, or act as a reminder sign for times when the variable speed limit isn't active. Data Quality Whilst every effort has been made to ensure this information is up-to-date, there may be instances where signs are not yet recorded in this system. Disclaimer No claim is made as to the accuracy or currency of the content on this site at any time. This data is provided on the basis that users undertake responsibility for assessing the relevance and accuracy of its content. The Victorian Government and Department of Transport and Planning accept no liability to any person or group for the data or advice (or the use of such data or advice) which is provided or incorporated into it by reference.

The geometries represent the maximum permitted speeds in the Berlin road network as exceptions to the general 50 km/h (50 km/h is only shown on the motorways). Via the factual data query, further features can be queried for the respectively ordered maximum speed, e.g. the reason for the order or a possible time restriction. The so-called "Berlin detail network" was selected as the reference network.

The t-Test results of speed series under different grades.

The ORN is a provincewide geographic database of over 250,000 kilometres of municipal roads, provincial highways, and resource and recreational roads. The ORN is the authoritative source of roads data for the Government of Ontario. Road names in the ORN are the official names provided by the authoritative jurisdiction for each road segment, such as a municipality or the Ontario Government. The list of authoritative sources used for the ORN is in the “Ontario Road Network - List of Partners” document in the Supporting Files section below. You can also find the authoritative jurisdiction for a specific road feature in the Jurisdiction table in ORN Road Net Element. ORN Road Net Element requires an advanced knowledge of GIS including LRS and complex table relationships. This dataset contains the following related tables: * official street name * alternate street name * address information * road classification * number of lanes * road surface * speed limit * structure * toll point * blocked passage * route name * route number * jurisdiction * source * underpass * junction

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

Since 2015, on some roads managed by the Metropolitan City of Milan, an infringement detection system (in particular exceeding speed limits) has been put in place, the devices of which remain in operation 7 days a week, 24 hours a day. The devices carry out a systematic and daily count of all vehicles passing on the observed roadway, regardless of the detection of the violation, thus providing the possibility of obtaining a measurement of traffic which, although restricted to some important positions, is worthy of interest for continuity of the relevance of the data over time. The Dataset contains the processing of the counts provided by the equipment. Such data is only valid and meaningful at the point at which it was actually measured. Due to the very nature of the measurement activity, the data cannot be separated from the precise location of the counting section, shown in the table. We are therefore warned that it is not correct to extend the validity of the count to other sections of the road as the presence of a ramp at an interchange, by adding or removing vehicular flows, can change, even significantly, the extent of the flow upstream and downstream. downstream of the measurement section.

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.The color-coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation, and field operations. A color-coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes.The map also includes dynamic traffic incidents showing the location of accidents, construction, closures, and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.

The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.

As part of this dataset, the following files were provided:

• I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X.
• I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes.
• Annotation on Regions.zip, which includes images that visually map lanes (I90_9

Scenario description:

Precondition:

A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.

Actions or events:

1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.

2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.

3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.

Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).

Session description:

Test session with only connected cars, lap of 12,3 km on the highway. The goal is to check all the systems and data management before the next test session with AD cars.

Datasets description:

AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors

This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data

AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during the Highway Pilot sessions

This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.

AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform

This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.

Scenario description:

Precondition:

A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed.

Actions or events:

1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone.

2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.

3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed.

Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).

Session description:

pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management.

Datasets description:

AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors

This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data

AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during the Highway Pilot sessions

This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.

AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform

This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.

Scenario description:

Precondition:

1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.

Actions or events:

1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.

2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.

3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car

4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers

Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).

Session description:

Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway.

Datasets description:

AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors

This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data

AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during platooning sessions

This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.

AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform

This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.

Maximum speeds: The beginning of the maximum speed is indicated with RVV sign A1. The end with RVV sign A2 or F8, even if the maximum speed corresponds to the general rules of conduct. The maximum speed is shown in column DESCRIPT.

Scenario description:

Precondition:

1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.

Actions or events:

1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.

2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.

3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car

4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers

Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).

Session description:

Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.

Datasets description:

AUTOPILOT_Livorno_HighwayPilot_Vehicle_all: Data generated from the vehicle sensors

This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data

AUTOPILOT_Livorno_HighwayPilot_V2X_all: V2V messages during platooning sessions

This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.

AUTOPILOT_Livorno_HighwayPilot_IoT_all: Data extracted from IoT oneM2M platform

This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.