Refuse Auto Pickup Routes Feature Service

The Ames Transit Agency, doing business as CyRide, is the public transportation agency serving the Ames, Iowa metropolitan area, including the campus of Iowa State University. In 2021, CyRide installed automatic passenger counters (APCs) across the fixed-route bus fleet. This allowed the agency to begin collecting per-bus, per-stop rider information, which is assembled in the attached data. Capacity levels on buses were also published via rider apps to allow passengers to select rides on vehicles with lower occupancy. These files could be used to model transit activity in a university community, analyze how transit usage changes as COVID-19 risks subside, or provide a general view of transportation within a small urban community.

Change log:

2023-04-19: data files for July 2022 through March 2023 have been added to the dataset and the title and readme have been updated accordingly.

The National Census of Ferry Operators (NCFO) Routes dataset was collected through December 31, 2020 and compiled on October 16, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Ferry Routes dataset represents all ferry routes from operators that provided responses to the 2020 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the United States Virgin Island, and American Samoa. Each segment in the dataset connects to two terminals from the Ferry Terminals dataset, describing the route ferries travel between them. Route geometries were determined using GPS points from Automatic Identification System data, as well existing government datasets from the Census Bureau, the US Geological Survey, the National Oceanic and Atmospheric Association, and the US Army Corps of Engineers. Other routes were determined using least-cost analysis.

Transit Routes (OCP). This is part of the Transportation Network in the Official Community Plan (OCP).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.

This dataset comprises license plate data collected using Automatic Number Plate Recognition (ANPR) devices as vehicles entered or exited the smart village area of Alpujarra, which includes three towns: Pampaneira, Capileira, and Bubión. The sensor network consists of four Hikvision IP cameras that leverage deep learning-based ANPR technology to ensure complete coverage of vehicular movements. Each camera is equipped with vehicle detection sensors, a 2MP resolution, varifocal lenses (2.8–12 mm), and infrared LEDs with a 50-meter range. In this dataset, the nodes correspond to the four cameras: two at the entrance and exit of Pampaneira (PAM1 and PAM2), one at the entrance of Bubión (BUB), and one at the entrance of Capileira (CAP). Tourist routes are represented as graphs where each route forms a unique graph structure. Data was recorded from February 2022 to December 2024.

The dataset covers 467,773 distinct vehicles and includes the following variables:

num_plate_id: An anonymized license plate number.
route: The sequence of nodes (camera identifiers) representing each tourist visit; possible values are PAM1, PAM2, CAP, and BUB.
times: The travel time in seconds between consecutive nodes.
entry_date: The timestamp when the vehicle was first detected.
exit_date: The timestamp when the vehicle was last detected.
directions: A sequence indicating the travel direction at each camera (1 for entering, 0 for exiting).
num_visits: A label representing the total number of visits by that tourist from February 2022 to December 2024.

With an ever-increasing number of vessels at sea, the modelling, analysis and visualisation of maritime traffic are of paramount importance to support the monitoring tasks of maritime stakeholders. Sensors have been developed in this respect to track vessels and capture the maritime traffic at the global scale. The Automatic Identification System (AIS) is transmitting maritime positional and nominative information at highest frequency rate, making it a valuable source for maritime traffic modelling. From an original AIS dataset covering the area of Brest, France, we extracted a set of 17 maritime routes, connecting ports in this area. Two different representations for the routes are provided: (1) clusters of AIS contacts, and (2) route prototypes, representing the nominal trajectory of the vessels following the route. Additionally, a set of tracklets (built by five consecutive AIS contacts from the same vessel trajectory) has been extracted from the set of routes and the original dataset, and labelled either with the route name to which they belong or as off-route tracklets. This dataset provides thus some ground truth on the routes followed by vessels and is aimed at testing and validating vessel-to-route or track-to-route association algorithms.

The data on this map is automatically generated from a GIS process that identifies route alignment differences between the National Cycle Network as drawn in OpenStreetMap (OSM) and the National Cycle Network as drawn by Sustrans. OpenStreetMap aligns with Sustrans 'reclassifed' routeSustrans reclassified this route in 2020 and no longer considers it part of the National Cycle Network.OpenStreetMap does not align with Sustrans NCN routeThis feature is drawn in OpenStreetMap data but not in Sustrans data. Sustrans NCN does not align with OpenStreetMapThis feature is drawn in Sustrans data but not in OpenStreetMap.Use this web application to identify and correct the following alignment differences between National Cycle Network (NCN) as drawn in OpenStreetMap and the NCN as drawn and managed by Sustrans:

Here are a few use cases for this project:

1. Traffic Flow Analysis: The dataset can be used in machine learning models to analyze traffic flow in cities. It can identify the type of vehicles on the city roads at different times of the day, helping in planning and traffic management.

2. Vehicle Class Based Toll Collection: Toll booths can use this model to automatically classify and charge vehicles based on their type, enabling a more efficient and automated system.

3. Parking Management System: Parking lot owners can use this model to easily classify vehicles as they enter for better space management. Knowing the vehicle type can help assign it to the most suitable parking spot.

4. Traffic Rule Enforcement: The dataset can be used to create a computer vision model to automatically detect any traffic violations like wrong lane driving by different vehicle types, and notify law enforcement agencies.

5. Smart Ambulance Tracking: The system can help in identifying and tracking ambulances and other emergency vehicles, enabling traffic management systems to provide priority routing during emergencies.

Urban cleaning collects household waste every day. This dataset shows per fraction which street is in which route. The base layer is road register_segment, and contains the WS_OIDN, LSTRNMID, LSTRNM, RSTRNMID, RSTRNM of this. The base layer is not systematically updated, but adjusted if necessary (new streets). WS_OIDN can no longer be 100% linked to the most recent road register. Ordinary collection: Groups: REST, HP, PMD, GFTZ (summer), GFTW (winter). If there is no difference in the FTT routes between the seasons, this will only be entered in 1 column. There is no collection on public holidays. The following day, a double collection is organized. New routes were created for this purpose. Groups: REST_FEEST, PK_FEEST, PMD_FEEST, GFTZ_FEEST, GFTW_FEEST SECTOR_FRACTIE_DAY/NIGHT_ROUTENUMMER_WEEKDAY . E.g. M_REST_N_001_MA = sector middle, fraction rest, night, route number 1, Monday. The CURRENT field is an automatically filled-in field based on the REST fraction. The DAGNIGHT field is an automatically filled-in field based on the REST fraction. This dataset only shows which road segments are picked up in one route, but does not say anything about the order of the streets (routing). SR-Planning keeps this information up-to-date on a daily basis via a GeoCortex counter.

Dataset showing main primary gritting routes in Leeds. Includes information of starting and end points and ward locations. Automated update This dataset is updated automatically on the 1st day of each quater.

The Request Data extension for CKAN introduces a mechanism for managing access-controlled datasets. It enables the publication of dataset metadata without directly hosting the data itself. Instead, users request access to the data, triggering a notification to the dataset maintainers, who can then approve or deny the request. This allows for a workflow where data access is granted on a case-by-case basis. Key Features: Request-Based Data Access: Allows users to request access to datasets, rather than directly accessing the data files. This is particularly useful for sensitive or proprietary information. Maintainer Notification System: Automatically notifies dataset maintainers when a user requests access to a dataset, allowing them to review the request. Approval/Denial Workflow: Provides maintainers with the ability to approve or deny data access requests through a dedicated dashboard. Dashboard Statistics: Tracks key statistics related to data requests, such as the number of requests, replies, declined requests, and data shares, providing insights into data access patterns. Organization and System Administration Overviews: Provides overview dashboards for organizations and system administrators to track data requests across multiple datasets. Email Notifications: Sends personalized e-mails to maintainers and requesters (presumably) to notify them about the request statuses. Use Cases: Handling Sensitive Data: Organizations dealing with confidential or regulated data can use this extension to control who has access to specific datasets. Proprietary Information Management: Companies can publish metadata about their proprietary datasets, allowing interested parties to request access under specific terms or conditions. Research Data Access: Researchers can use this extension to manage access to datasets that require specific permissions or agreements before being shared. Technical Integration: The Request Data extension integrates into CKAN by adding a new dataset type and modifying user interfaces to manage requests and approvals. The extension also sends e-mails (when new data request is sent) but allows modifications of the footer links through configuration options. The core logic involves intercepting data access requests and routing them to the appropriate maintainers. Activating the "requestdata" plugin in the CKAN configuration file is necessary for the extension to function correctly. Benefits & Impact: The Request Data extension enhances CKAN's ability to manage access-controlled data. It fosters better oversight over potentially sensitive data while retaining the core strengths of metadata publishing and searching.

Kitchen Scraps and Garbage Collection Schedule routes and information.Sign up on our website for email, text, voicemail or Twitter reminders, or integrate the collection schedule with your icalendar! You can also print your personal Kitchen Scraps and Garbage schedule.Waste Collection | City of VictoriaData 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.

Bike lanes: AAA protected / off-street facility, AAA shared-use street, Painted bike lane, Shared bus & bike lane, Signed bike route. Attributes indicate the type of bike route and the number of lanes.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.

Notice - Replacement of the English and French Web services (WMS and ESRI REST) with a bilingual one. The NRN product is distributed in the form of thirteen provincial or territorial datasets and consists of two linear entities (Road Segment and Ferry Connection Segment) and three punctual entities (Junction, Blocked Passage, Toll Point) with which is associated a series of descriptive attributes such as, among others: First House Number, Last House Number, Street Name Body, Place Name, Functional Road Class, Pavement Status, Number Of Lanes, Structure Type, Route Number, Route Name, Exit Number. The development of the NRN was realized by means of individual meetings and national workshops with interested data providers from the federal, provincial, territorial and municipal governments. In 2005, the NRN edition 2.0 was alternately adopted by members from the Inter-Agency Committee on Geomatics (IACG) and the Canadian Council on Geomatics (CCOG). The NRN content largely conforms to the ISO 14825 from ISO/TC 204.

This is part of the Transportation Network in the Official Community Plan (OCP).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.

Primary and Secondary Truck Routes. This is part of the Transportation Network in the Official Community Plan (OCP).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.

View full metadata https://www.cambridgema.gov/GIS/gisdatadictionary/DPW/DPW_SnowEmergencyTowRoutes

Description The information on this map is for general informational purposes only and the actually SNOW emergency signs posted on street are the legal Traffic Regulations and for updated information please contact the Traffic, Parking and Transportation. City Snow Emergency Tow Routes. This layer is maintained by Cambridge Public Works.

About Edit Dates This data is automatically updated on a set schedule. The Socrata edit date may not reflect the actual edit dates in the data. For more details please see the update date on the full metadata page or view the edit date within the data rows.

Here are a few use cases for this project:

1. Traffic Management: The Malam model can be used by traffic management authorities to automatically recognize the types of vehicles on roads, helping in real-time traffic analysis, such as identifying traffic congestion, planning route diversions, or even detecting unauthorized vehicular presence in restricted zones.

2. Road Infrastructure Planning: Urban planners and civil engineers can use the Malam to analyze vehicle types commonly using certain routes. This data can inform road design upgrades by identifying the need for heavier-duty infrastructure for areas with frequent heavy trucks traffic.

3. Intelligent Surveillance Systems: The Malam model can be integrated into surveillance systems to automatically identify and classify vehicles at night, allowing agencies to monitor vehicle movement, detect suspicious activities, or perform after-crime investigations.

4. Night-time Vehicle Inspection at Borders: Customs or border protection agencies could use Malam to assist in monitoring and recording vehicle types crossing borders during night hours.

5. Commercial Logistic Monitoring: Commercial transportation or logistic companies can use this model to automatically classify types of their vehicles on the roads during night hours for effective fleet management. It could also potentially detect unauthorized use or deviations from specified routes.

Bei der Stadtreinigung wird täglich Hausmüll gesammelt. Dieser Datensatz zeigt pro Fraktion, welche Straße auf welcher Strecke liegt. Die Basisschicht ist road register_segment und enthält die WS_OIDN, LSTRNMID, LSTRNM, RSTRNMID, RSTRNM. Die Basisschicht wird nicht systematisch aktualisiert, sondern bei Bedarf angepasst (neue Straßen). WS_OIDN kann nicht mehr zu 100% mit dem letzten Straßenregister verknüpft werden. Gewöhnliche Sammlung: Gruppen: REST, HP, PMD, GFTZ (Sommer), GFTW (Winter). Wenn zwischen den Jahreszeiten keine Unterschiede zwischen den FTS-Strecken bestehen, wird dies nur in einer Spalte eingetragen. An Feiertagen gibt es keine Abholung. Am nächsten Tag wird eine Doppelsammlung organisiert. Zu diesem Zweck wurden neue Routen geschaffen. Gruppen: REST_FEEST, PK_FEEST, PMD_FEEST, GFTZ_FEEST und GFTW_FEEST SECTOR_FRACTIE_DAY/NIGHT_ROUTENUMMER_WEEKDAY . Z. B. M_REST_N_001_MA = Sektormitte, Fraktionsruhe, Nacht, Strecke Nummer 1, Montag. Das AKTUELLE Feld ist ein automatisch ausgefülltes Feld basierend auf dem REST-Anteil. Das DAGNIGHT-Feld ist ein automatisch ausgefülltes Feld basierend auf dem REST-Anteil. Dieser Datensatz zeigt nur, welche Straßenabschnitte in einer Route erfasst werden, sagt aber nichts über die Reihenfolge der Straßen (Routing) aus. SR-Planning hält diese Informationen täglich über einen GeoCortex-Zähler auf dem neuesten Stand.

ChebTransport Daily GPS Dataset

This dataset contains daily GPS and metadata records of public transport vehicles in Cheboksary, Russia, for the period from 2025-04-22 to 2025-07-03. Each file corresponds to a "transport day" (which may start and end at different times depending on the actual end of public transport service, not at midnight).

Data Source

The data was parsed from the website buscheb.ru, which aggregates public transport data for the city of Cheboksary as a contractor. The original data may be owned by buscheb.ru and/or the Cheboksary city transport authority. Please attribute buscheb.ru as the data source.

Fields

Each row in the CSV files contains the following fields:

Transport Day Definition

A "transport day" is determined automatically for each day based on the longest night break in the data (typically between 00:01 and 03:00). All records after the detected break (or after 03:00 if no break is found) are assigned to the next transport day.

Usage

You can use this dataset for: - Public transport analytics - Spatio-temporal modeling - Urban mobility research - Machine learning on real-world vehicle trajectories

Legal Notice

The data is used in accordance with the standard terms for the use of publicly available information posted on the Internet as open data. User rights to use open data are determined by the Law on Information and the Law on Access to Information: 1) Users are free to search, receive, transmit, and distribute open data.

License

This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This is the most widely used open data license, allowing any use (including commercial), provided the source is attributed. See CC BY 4.0 summary for details.

Датасет GPS-движения общественного транспорта Чебоксар

Этот датасет содержит ежедневные GPS-записи и метаданные движения общественного транспорта в Чебоксарах за период с 22.04.2025 по 03.07.2025. Каждый файл соответствует одному «транспортному дню» (граница дня определяется по фактическому завершению движения транспорта, а не по полуночи).

Источник данных

Данные были спарсены с сайта buscheb.ru, который агрегирует данные о транспорте города Чебоксары как подрядчик. Права на исходные данные могут принадлежать buscheb.ru и/или транспортному управлению города Чебоксары. При использовании указывайте buscheb.ru как источник данных.

Поля

В каждой строке CSV содержатся следующие поля: