Introduction

Bicycle Industry Statistics: The demand in the bicycle industry has witnessed eminent growth over the past few years due to the growing awareness about being health-sensitive among people across the world. People generally use bicycles for activities like sports, hiking, exercise, commuting, and others. Thus, the product manufacturers give a variety of designs for these applications to cover a larger customer base.

Some of the sections given by the manufacturers also involve road, hybrid, and mountain bikes. Factors like growing traffic congestion, growing surrounding problems, and urbanization are driving industrial growth. This article will shed more light on “Bicycle Industry Statistics.â€

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)

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

This statistic represents the results of a Statista survey among Americans in 2017 regarding bicycles. During the survey, some 23 percent of respondents stated they would consult YouTube to gather information prior to the purchase of a new electric bicycle. Multiple responses were possible.

This dataset provides endpoints for dockless mobility providers that are operating within the City of Tempe. The City of Tempe shares these data as provided and the quality of these data are the responsibility of the third-party vendor.For more information see the following Github repository.This endpoint is provided by Lime Bikes and lists the free scooters that are available within the City of Tempe.Additional InformationContact E-Mail (author): data@tempe.gov

The real-time information interface for Taipei City's public bicycle system YouBike is located at https://tcgbusfs.blob.core.windows.net/dotapp/youbike/v2/youbike_immediate.json, with the file format being json.

Comprehensive dataset of 3 Bicycle clubs in Мерсин, Turkey as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The field of cycling analytics has only recently started to develop due to limited access to open data sources. Accordingly, research and data sources are very divergent, with large differences in information used across studies. To improve this, and facilitate further research in the field, we propose the publication of a data set which links thousands of professional race results from the period 2017-2023 to detailed geographic information about the courses ridden, an essential aspect in road cycling analytics. Detailed Data Descriptor currenlty undergoing review process.When using the data set, please refer to: Janssens, B., Pappalardo, L., De Bock, J., Bogaert, M., & Verstockt, S. (2024). Geospatial Road Cycling Race Results Data Set. arXiv preprint arXiv:2410.09055.

The Street Safety: All Traffic Crashes indicator measures the total number of traffic crash incidents each year in the Champaign-Urbana metropolitan planning area (MPA) and Champaign County. The Champaign-Urbana MPA includes the Cities of Champaign and Urbana, and the Villages of Bondville, Mahomet, Savoy, and Tolono. Street safety is an important part of community safety, especially as interest in and use of alternative means of transportation increase.

The number of crashes and injuries involving bicycles and pedestrians decreased overall in both the MPA and Champaign County in 2020 compared to recent years. However, the number of bicyclist and pedestrian crash fatalities increased in 2020, with the number of bicycle fatalities being the highest in 2020 in the measured timeframe. The beginning of the COVID-19 pandemic in 2020 reduced the number of crashes and injuries presumably due to fewer drivers commuting to work, but some of those crashes were more severe since the number of fatalities increased.

This data was sourced from the Champaign County Traffic Crash Dashboard and Champaign-Urbana Urban Area Safety Plan Report Card.

Sources: Champaign County Regional Planning Commission. Champaign County Traffic Crash Dashboard. https://crashdashboard.ccrpc.org/. (Accessed 14 March 2022).; Champaign County Regional Planning Commission. Champaign-Urbana Urban Safety Plan Report Card. https://ccrpc.org/documents/champaign-urbana-safety-plan/urban-safety-plan-report-card/. (Accessed 14 March 2022).; Champaign County Regional Planning Commission. (2019). Champaign-Urbana Urban Area Safety Plan. CUUATS.; Champaign County Regional Planning Commission. (2019). Rural Champaign County Area Safety Plan. CUUATS.; Champaign County Regional Planning Commission. (2015). Traffic Crash Facts for Champaign-Urbana: Selected Crash Intersection Locations (SCIL) 2009-2013. CUUATS.; Champaign County Regional Planning Commission. (2014). Traffic Crash Facts for Champaign-Urbana: Selected Crash Intersection Locations (SCIL) 2007-2011. CUUATS.; Champaign County Regional Planning Commission. (2011). Traffic Crash Facts for Champaign-Urbana: Selected Crash Intersection Locations (SCIL) 2005-2009. CUUATS.; Champaign County Regional Planning Commission. (2009). Traffic Crash Facts for Champaign-Urbana: Selected Crash Intersection Locations (SCIL) 2003-2007. CUUATS.; Champaign County Regional Planning Commission. (2008). Traffic Crash Facts for Champaign-Urbana: Selected Crash Intersection Locations (SCIL) 2001-2005. CUUATS.; Champaign County Regional Planning Commission. (2003). Selected Intersection Crash Analysis for 2003. Draft Final Report. CUUATS.; Champaign County Regional Planning Commission. (2004). Selected Crash Intersection Locations (SCIL) 1998-2002. CUUATS.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2022. It shows selected building attributes including location, construction year, refurbished year, number of floors above ground, predominant space use, bicycle/shower facilities and building accessibility. Building accessibility data is collected to track accessibility for internal City of Melbourne purposes. This data is provided as a community service by the City of Melbourne. It is not and does not purport to be a complete guide. There may be errors or omissions. Data is liable to change. The City of Melbourne accepts no responsibility in respect of any claim arising from use or reliance upon this data.

Analysis of ‘London bike sharing dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hmavrodiev/london-bike-sharing-dataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

License

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Acknowledge TfL as the source of the Information by including the following attribution statement 'Powered by TfL Open Data' Acknowledge that this Information contains Ordnance Survey derived data by including the following attribution statement: 'Contains OS data © Crown copyright and database rights 2016' and Geomni UK Map data © and database rights [2019] Ensure our intellectual property rights, including all logos, design rights, patents and trademarks, are protected by following our design and branding guidelines Limit traffic requests up to a maximum of 300 calls per minute per data feed. TfL reserves the right to throttle or limit access to feeds when it is believed the overall service is being degraded by excessive use and Ensure the information You provide on registration is accurate These are important conditions of this Licence and if You fail to comply with them the rights granted to You under this Licence, or any similar licence granted by TfL, will end automatically.

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Context

The purpose is to try predict the future bike shares.

Content

The data is acquired from 3 sources:
- Https://cycling.data.tfl.gov.uk/ 'Contains OS data © Crown copyright and database rights 2016' and Geomni UK Map data © and database rights [2019] 'Powered by TfL Open Data'
- freemeteo.com - weather data
- https://www.gov.uk/bank-holidays
From 1/1/2015 to 31/12/2016

The data from cycling dataset is grouped by "Start time", this represent the count of new bike shares grouped by hour. The long duration shares are not taken in the count.

Metadata:

"timestamp" - timestamp field for grouping the data
"cnt" - the count of a new bike shares
"t1" - real temperature in C
"t2" - temperature in C "feels like"
"hum" - humidity in percentage
"wind_speed" - wind speed in km/h
"weather_code" - category of the weather
"is_holiday" - boolean field - 1 holiday / 0 non holiday
"is_weekend" - boolean field - 1 if the day is weekend
"season" - category field meteorological seasons: 0-spring ; 1-summer; 2-fall; 3-winter.

"weathe_code" category description:
1 = Clear ; mostly clear but have some values with haze/fog/patches of fog/ fog in vicinity
2 = scattered clouds / few clouds
3 = Broken clouds
4 = Cloudy
7 = Rain/ light Rain shower/ Light rain
10 = rain with thunderstorm
26 = snowfall
94 = Freezing Fog

--- Original source retains full ownership of the source dataset ---

AGOL Hosted Feature Layer which provides access to the MDOT SHA Bike Spine geospatial data product.MDOT SHA Bike Spine data consists of linear geometric features which represent roadways & trails throughout the State of Maryland that are officially designated as routes that meet specific safety criteria to accommodate bicycles. MDOT SHA Bike Spine data was developed to support a variety of MDOT initiatives involving Bicycle & Pedestrian Safety. MDOT SHA Bike Spine data is key to understanding the network of roadways throughout the State of Maryland that are capable of accommodating bicycle traffic safely. This data is used by various transportation business units throughout MDOT, as well as many other Federal, State, and local government agencies.MDOT SHA Bike Spine data is owned and maintained by the MDOT SHA Regional Intermodal Planning Division (RIPD). This data is updated on an As-Needed / Irregular basis, as it does not frequently change. This data was last updated in June 2019.Last Updated: 06/07/2019For additional information, contact the MDOT SHA Geospatial Technologies Team: Email: GIS@mdot.maryland.govFor 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://www.roads.maryland.gov/Home.aspxMDOT SHA Geospatial 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.

The e-bike market is projected to generate around 44.1 billion U.S. dollars in revenue by 2029. In 2024, revenue from the global e-bike market U.S. dollars. Large markets across the globe The Asia-Pacific region is the largest e-bike market, but the European market has picked up steam. In some countries within Europe, e-bikes now make up more than half of all bicycles sold. The leader here is the Netherlands, which reached this tipping point in 2020. Germany is another important market for e-bikes, with 2.1 million units sold in 2023.

Geographical layer describing linear cycling developments in Île-de-France: bike paths, bike strips, meeting areas. #### Given its volume, data in SHAPE format is provided as an attachment (see bottom of page) and not in the export tab. The same provisions specific to the licence ODbL Version Française apply. — Genealogy This data is derived from crowdsourcing performed by contributors to the OpenStreetMap project and is under ODbL license and the attribution statement must be “© OpenStreetMap contributors under ODbL license” in accordance with [http://osm.org/copyright] As part of the identification of cycling facilities by Île-de-France Mobilités, these data have been completed and verified. They were also converted into a format and a data model directly usable in a GIS. Work in partnership with Géovélo and Carto'Cité. More information: Île-de-France Mobilités site | OSM wikiproject site | website portal * * * * Quality The modeling of bike layouts is discussed among OpenStreetMap users. The template is available on this page. The attributes selected are as follows: bi- | bidirectional cycling strip (dual-way bikes materialised by a band are not taken into account) —|— uni-band | Unidirectional cycling strip (double-way bikes materialised by a band are not taken into account) bi | Bi-directional bike paths uni-track | Unidirectional bike paths bi-bus route | Bidirectional bus lane open to cyclists uni-bus route | One-way bus route open to cyclists DSC | Two-way cycle (cyclists can drive both directions of a one-way street) not materialised by either a strip or a cycle path DSC band | Double sense cycling (cyclists can ride both directions of a one-way street) materialised by a bicycle strip DSC track | Two-way cycle (cyclists can ride both directions of a one-way street) materialised by a cycle path unified Greenway | Unidirectional Green Way for Cyclists track uni sidewalk | Bicycle path materialised on sidewalk. Unidirectional for cyclists. unidirectional sidewalk | Unidirectional cycling sidewalk for cyclists path service clean site uni | Track or lane where motor vehicles are prohibited. Unidirectional for cyclists. Chemin dedie uni | Pedestrian and bicycle path where the bike is explicitly allowed. Unidirectional for cyclists. other uni bike path | Pedestrian path where bicycles are allowed, bikes can travel in one direction. Two typologies are also proposed for medium-scale facilitated representation (EPCI) and small scale (IDF): Small Scale (IDF) | Medium Scale (EPCI) —|— Layout on a clean site (having are own axis separate from the vehicle axis) | 1 | 11 ⋆ Bidirectional green path/cycle path 12 | Bicycle path/unidirectional green road Shared lane with motor vehicle (means out of calm traffic area, see code 3) | 2 | 21 ▲ Bidirectional cycling strip 22 | Unidirectional bike strip 23 | Bidirectional shared bus route 24 | Unidirectional shared bus route Track in soothed traffic area | 3 | 31 ⋆ Route in zone 30 (z30) 32 | Streetway 30 (“limit 30”) 33 | Meeting area (“z20”) 34 | Other cycling double-sense Shared lane with pedestrians (means shared with pedestrians only, see code 3) | 4 | 41 ⋆ Shared path with pedestrians (no subcategory) More information on the use of the data: PDF document * * * * Change history 2023 * April 2023: Updating the data * January 2023: Updating the data 2022 * October 2022: Updating the data * August 2022: Updating the data * January 2022: Updating the data 2021 * October 2021: Updating the data * August 2021: Updating the data * March 2021: Update of the data, with the addition of typologies for representation of small and medium-scale developments 2020 * December 2020: Updating the data * September 2020: Updating the data * May 2020: Updating the data * January 2020: Updating the data 2019 * October 2019: Updating the data * June 2019: Update of data + addition of fields “INSEE” (community INSEE code), “NOM_COM” (name of municipality) and “NOM_VOIE” (name of track). * January 2019: Initialisation of the data. * * * *

Context

The bike-share industry is rapidly becoming popular as a primary use of transportation, especially in urban areas and campuses. In the past, people cycled primarily as a form of sport or exercise, but now, households and individuals make use of bicycles to transport themselves.

Due to the pandemic, the bike-share industry experienced a decline in the number of bike-share customers, leading to a huge dip in revenue, but it is now gradually recovering as cities and businesses reopen. Research on bike and scooter rental markets predicts that the revenue for the bike-share industry will increase from $2.5 billion in 2019 to $10.1 billion in 2027, which is due to people’s need for easy commuting and increasing use in the food delivery industry to avoid traffic.

Bearing the high initial capital expenditure and ongoing depreciation expense, the bike sharing companies are facing risks of loss and a problem on how to maximize allocation efficiency to gain profits.

Content

The dataset is inspired by the past Bike Sharing Demand Competition. The data is hourly from 2018.1.1 to 2021.08.31. Data in 2020 April is missing since Capital Bikeshare does not provide the information on its website.

• datetime: Date and hour of the bike share data
• count: Count of total rentals
• holiday: Whether it is a holiday
• workingday: Whether it is a working day (Mon-Fri except holiday)
• temp: Temperature in Celsius
• feels_like: “Feels like” Temperature in Celsius
• temp_min: Lowest temperature
• temp_max: Highest temperature
• pressure: Air pressure
• humidity: Relative humidity
• wind_speed: Wind speed
• wind_deg: Wind direction degree
• rain_1h: Precipitation in the last 1 hour (missing when it did not rain)
• snow_1h: Snow volume for the last 1 hour (missing when it did not snow)
• cloud_all: Cloudiness in percentage
• weather_main: Weather types (Rain, Snow, Extreme etc.)

Acknowledgements

Original dataset: https://www.kaggle.com/c/bike-sharing-demand Capital Bikeshare trip data: http://capitalbikeshare.com/system-data Weather Information: https://openweathermap.org/history Holiday Schedule: http://dchr.dc.gov/page/holiday-schedule

Inspiration

Bearing the high initial capital expenditure and ongoing depreciation expense, the bike sharing companies are facing risks of loss. How can we better predict the bike share demand in order to help the companies maximize allocation efficiency?

Comprehensive dataset of 113 Bicycle clubs in Turkey as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Dataset Description

This dataset, titled "Indian Bike Features and Reviews Dataset," is a comprehensive collection of information pertaining to various bikes available in the Indian market, including both domestic and international brands. It encompasses essential features and customer reviews, making it a valuable resource for individuals, researchers, and businesses interested in the Indian automotive industry.

Content

The dataset consists of the following key information:

Bike make and model

Detailed bike features such as engine capacity, mileage, price, etc. Customer reviews and ratings Source The data was meticulously scraped and curated from BikeDekho, a prominent website specializing in providing in-depth information about bikes available in India. BikeDekho offers a wide array of bike-related details, making it an ideal source for compiling this dataset.

Inspiration

The inspiration behind creating this dataset was to offer a centralized and accessible repository of bike information for enthusiasts, analysts, and data scientists. Understanding the features and public perception of various bikes can aid in market analysis, consumer preferences, and informed decision-making within the automotive industry. This dataset empowers researchers and data enthusiasts to perform insightful analyses and gain valuable insights into the Indian bike market.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Comprehensive dataset of 1 Bicycle clubs in Rhode Island, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.