Data to solve Citi Bike's Big Idea. Citi Bike Live Station Feed (JSON) - https://gbfs.citibikenyc.com/gbfs/en/station_status.json

Description

The existing bicycle rental systems in large cities have a system automated collection and return of the vehicle through a network of stations distributed throughout the entire metropolis. With the use of these systems, people can rent a bike in a location and return it in a different one depending on your needs. The data generated by these systems are attractive to researchers due to variables such as the duration of the trip, departure and destination points and travel time. Therefore, exchange systems Bicycles work as a network of sensors that are useful for mobility studies. With In order to improve management, one of these companies needs to anticipate the demand that there will be in a certain range of time depending on factors such as the time zone, the type day (weekday or holiday), the weather, etc.

The objective of this data set is to predict the demand in a series of specific time slots, using the historical data set as the basis to build a linear model.

Data Description

Two data sets will be delivered containing the number of rented bicycles in different time slots:

1. Training data. They will contain the response variable (number of bicycles rented in that strip)
2. Test data. They will not contain the response variable and the response variable must be predicted based on on the historical data of the training set.

The variables present in the 2 data sets are:

• id: time slot identifier (not related to time order)
• year: year (2011 or 2012)
• hour: hour of the day (0 to 23)
• season: 1 = winter, 2 = spring, 3 = summer, 4 = autumn
• holiday: if the day was a holiday
• workingday: if the day was a working day (neither a holiday nor a weekend)
• weather: four categories (1 to 4) ranging from best to worst weather
• temp: temperature in degrees Celsius
• atemp: sensation of temperature in degrees Celsius
• humidity: relative humidity
• windspeed: wind speed (km/h)
• count (only in the training set): total number of rentals in that band

Bicycle counts conducted around New York City at key locations. For the counter locations, please refer to the Bicycle Counters dataset.

The data may have lapses due to transmission issues cause by weather, connection interruptions, equipment malfunctions, vandalism, etc. The data will update as soon as it is feasible. The City makes no presentation as to the accuracy of the content and assumes no liability for omissions or errors in information contains on the website. Time is captured in GMT/UTC timezone.

Blue Bikes (formerly Hubway) is jointly owned and managed by the municipalities of Boston, Arlington, Brookline, Cambridge, Chelsea, Everett, Malden, Medford, Newton, Revere, Salem, Somerville, and Watertown. This external website provides datasets on Blue Bikes usage.

It includes:

• Comprehensive set of trip histories which is updated monthly
• Real time system data, published in open General Bikeshare Feed Specification (GBFS) format - a format recommended by the North American Bike Share Association (NABSA).

This data is provided according to the Blue Bikes Data License Agreement.

Moby is a licensed dockless bike-share scheme within the Dublin region. This page includes an API developed according to the General Bikeshare Feed Specification (GBFS) (e.g.) information about vehicles, stations, pricing, etc. The current location of the vehicles is updated every five minutes. In addition, this page includes historical files of bike location data. Disclaimer - Please note that some of the historical files are empty due to historical data issues.

Bike Share data from LA Metro, pulled from https://bikeshare.metro.net/about/data/

Historical availability of bicycles and docks to return bicycles at the Divvy stations (http://divvybikes.com/). For the current list of stations, see https://data.cityofchicago.org/d/bbyy-e7gq For real-time status of stations in machine-readable format, see https://feeds.divvybikes.com/stations/stations.json. Due to a change in the data source, discussed at http://dev.cityofchicago.org/open%20data/data%20portal/2019/07/10/divvy-datasets-frozen.html, records between 7/7/2019 and 12/9/2019 are missing.

This data shows the location of transportation corridors on state Department of Environmental Conservation lands that are approved for mountain bike use.

The New York City Department of Transportation (NYC DOT) builds and manages bicycle facilities across the five boroughs, alongside other City agencies, New York State, and external partners. This dataset contain records of the current and historic network of designated bicycle routes and facilities, including bicycle facility type and relevant street information represented as line segments. Additional information about NYC DOT's commitment to safe all-ages and abilities bicycling, along with data about the growth of bicycling in NYC and PDF maps of the current network can be found at: https://www.nyc.gov/html/dot/html/bicyclists/bicyclists.shtml

Bicycle Training Data

## Overview

Bicycle Training Data is a dataset for object detection tasks - it contains Bicycles annotations for 127 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Cyclistic Bike-Share Data

This dataset is based on the Cyclistic Bike-Share case study, which is the part of Google Data Analytics Professional Certificate capestone project.

About Cyclistic

A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use the bikes to commute to work each day.

Data Source

Data in this dataset is collected from thedivvy-tripdata.s3.amazonaws.com . The data has been made available by Motivate International Inc. under thislicense. This is public data that we can use to explore how different customer types are using Cyclistic bikes. But note that data-privacy issues prohibit us from using riders’ personally identifiable information.

Bike Paths. Generally crushed stone within rural areas; paved in urban areas. Includes paths that can accommodate biking, but does not include pedestrian paths.

This data layer is used by the Dane County Bicycle Map and Neighborhood Development Plan Resource Viewer applications.Off Street Type (Off_Type): Shared-Use Path (SP) Path at least 8’ wide and/or striped, designed to accommodate bikes and pedestrians. Connecting Path (CP) Not currently in use.Wide Sidewalk (WS) Sidewalk (>8’) that is intended to accommodate bikes. Pedestrian Path with Bikes Allowed (PP) Path or sidewalk not specifically designed for bikes (< 8’) on the bike network. Municipal Lot (ML) Route through parking lot, etc. (not a defined path passing through or adjacent to a lot).Cycletrack – One-way (CTO) Bike lane/path separated or protected from traffic.Cycletrack – Two-way (CTT) Bike lane/path separated or protected from traffic.Cycletrack – Contraflow (CTC) Bike lane/path separated or protected from traffic.Bike Functional Classification (BFuncClass) Primary (P)Secondary (S)None (N)Bike Functional Classification Planned (BFuncClassP) Primary (P)Secondary (S)None (N)Null (NULL)Primary Name (Pri_Name) Primary name of path.Secondary Name (Sec_Name) Secondary name of path.Type Status (Status)PRG: Programmed - Funded, will most likely be built.CONC: Conceptual - Project was suggested and may have merit but hasn’t been given much review yet.EX: Existing - Existing feature.PLF: Planned – Feasible - In the bike plan, project was given a cursory look and determined to be most likely feasible.PLO: Planned – Obstacles -Unlikely to occur due to physical limitation. Generally do not show on maps.UC: Under Construction - Currently under construction.PLT: Platted - Platted for construction. Generally only coded for City of Madison area.Surface (Surface) Paved (P)Unpaved (U)Bike Path Width (BikePaWidth)Signed (Signed) Bike Route signs or wayfinding signsDirectional Indicator (DIR_INDC) Used to filter bike paths that run parallel to each other.Primary (P)Opposite (O)Source (Source) Ortho [YEAR]; Plan Name; etc.External ID (ExtID) Unique ID

This dataset contains 250 million rows of information from the ~500 bike stations of the Barcelona public bicycle sharing service. The data consists in time series information of the electric and mechanical bicycles available every 4 minutes aprox., from March 2019 to March 2024 (latest available csv file, with the idea of being updated with every new month's file). This data could inspire many different use cases, from geographical data analysis to hierarchical ML time series models or Graph Neural Networks among others. Feel free to create a New Notebook from this page to use it and share your ideas with everyone!

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3317928%2F64409b5bd3c220993e05f5e155fd8c25%2Fstations_map_2024.png?generation=1713725887609128&alt=media" alt="">

Every month's information is separated in a different file as {year}_{month}_STATIONS.csv. Then the metadata info of every station has been simplified and compressed in the {year}_INFO.csv files where there is a single entry for every station and day, separated in a different file for every year.

The original data has some different errors, few of them have been already corrected but there are still some missing values, columns with wrong data types and other fewer artifacts or missing data. From time to time I may be manually correcting more of those.

The data is collected from the public BCN Open Data website, which is available for everyone (some resources need from creating a free account and token): - Stations data: https://opendata-ajuntament.barcelona.cat/data/en/dataset/estat-estacions-bicing - Stations info: https://opendata-ajuntament.barcelona.cat/data/en/dataset/informacio-estacions-bicing

You can find more information in them.

Please, consider upvoting this dataset if you find it interesting! 🤗

Some observations:
The historical data for June '19 does not have data for the 20th between 7:40 am and 2:00 pm.
The historical data for July '19 does not have data from the 26th at 1:30 pm until the 29th at 10:40 am.
The historical data for November '19 may not have some data from 10:00 pm on the 26th to 11:00 am on the 27th.
The historical data for August '20 does not have data from the 7th at 2:25 am until the 10th at 10:40 am.
The historical data for November '20 does not have data on the following days/times: 4th from 1:45 am to 11:05 am 20th from 7:50 pm to the 21st at 10:50 am 27th from 2:50 am to the 30th at 9:50 am.
The historical data for August '23 does not have data from the 22nd to the 31st due to a technical incident.
The historical data for September '23 does not have data from the 1st to the 5th due to a technical incident.
The historical data for February '24 does not have data on the 5th between 12:50 pm and 1:05 pm.
Others: Due to COVID-19 measures, the Bicing service was temporarily stopped, reflecting this situation in the historical data.

Field Description:

Array of data for each station:

station_id: Identifier of the station
num_bikes_available: Number of available bikes
num_bikes_available_types: Array of types of available bikes
mechanical: Number of available mechanical bikes
ebike: Number of available electric bikes
num_docks_available: Number of available docks
is_installed: The station is properly installed (0-NO,1-YES)
is_renting: The station is providing bikes correctly
is_returning: The station is docking bikes correctly
last_reported: Timestamp of the station information
is_charging_station: The station has electric bike charging capacity
status: Status of the station (IN_SERVICE=In service, CLOSED=Closed)

A link to the data dictionary for this dataset can be found here.Overview of the DataThe purpose of this document is to describe the data in the Bicycle Facilities layer viewable in the Bike Facilities map published on the City of Rochester’s website (link here). This dataset includes five layers (Bike Features, Bike Rack Locations, Trail points, Bike Marking, and Trails) each with fields containing information about the bicycle infrastructure. These features are updated periodically to reflect current conditions. For more information about cycling in Rochester, please visit https://cityofrochester.gov/bikerochester.Update FrequencyThe Bicycle Facilities dataset will be updated as new infrastructure is installed/removed and is reviewed annually at the end of the construction season to ensure accuracy. Data SourceThis dataset is maintained by This dataset is maintained by the City of Rochester’s Street Design Division of the Department of Environmental Services, with assistance provided by GIS staff.Disclaimer on AccuracyThe City’s investments in bicycle lanes, cycle tracks and multi-use trails are ongoing. As such, some facilities may be added or removed, or lane striping may fade. This map will be updated as improvements are added, but it may not capture all available bike facilities, and thus not be completely accurate.How to Use This DataThis dataset provides spatial information on bicycle infrastructure, including bicycle amenities, bike rack locations, trail points, bike lanes, and trails. The data can be used to support research on urban planning and transportation analysis, and be of use to recreational cyclists looking for trails or other places to park and service their bikes.

A. SUMMARY This dataset contains datapoints on the location of bike share locations across the Bay Area.

B. HOW THE DATASET IS CREATED This dataset is an extract from the MTA feature server.

C. UPDATE PROCESS An extract is pulled from the MTA feature server on a weekly basis. A few fields are added such as data_loaded_at.

D. HOW TO USE THIS DATASET Use this dataset to understand and locate the locations of bike share stations.

This data was provided by the Chattanooga Bicycle Transit System Station Map. This data was collected and then edited and/or checked for accuracy by staff of the UT Chattanooga ARCS. Geospatial data creation was completed 6/4/2012. Updated 0501/2014

Files include data for bike lanes, protected bike lanes, trails, bike routes, shared lane markings, cautionary bike routes, and bridge data from the BikePGH Pittsburgh Bike Map. BikePGH developed this map in 2007 and has been publishing it both on paper and online ever since. See: http://bikepgh.org/maps for more info.

Count data recorded during the 2019 Walk & Bike Count. Includes those walking, biking, riding motorized scooters, and using other active travel modes.

GPS pings collected by study participants who rode conventional and e-bikes at Minute Man National Historic Park between April and September 2022.