6 datasets found
  1. London Bike-Share Usage Dataset

    • kaggle.com
    Updated Apr 28, 2024
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    Svetlana Kalacheva (2024). London Bike-Share Usage Dataset [Dataset]. https://www.kaggle.com/datasets/kalacheva/london-bike-share-usage-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 28, 2024
    Dataset provided by
    Kaggle
    Authors
    Svetlana Kalacheva
    Area covered
    London
    Description

    Context

    This dataset contains detailed records of 776,527 bicycle journeys from the Transport for London (TfL) Cycle Hire system spanning from August 1 to August 31, 2023. The TfL Cycle Hire initiative provides publicly accessible bicycles for rent across London, promoting sustainable transportation and physical fitness. This comprehensive dataset captures individual trip data, which can be utilized to analyze urban mobility patterns, station performance, and cycling preferences among London's diverse population. This dataset provides a snapshot of cycling activity during the month, including start and end details for each journey, the bicycle used, and the duration of hire.

    Dataset Usage

    The dataset can be used for: - Time Series Forecasting: Predict future bike rental demands based on historical usage patterns. - Geospatial Analysis: Map the start and end locations of trips to identify popular routes and areas with high cycling traffic. - Customer Behavior Analysis: Analyze the duration and frequency of rentals to understand user preferences and habits. - Predictive Maintenance: Use trip duration and frequency data to predict when bikes are likely to require maintenance or replacement. - Multivariate Analysis: Explore relationships between different variables, such as trip durations, station popularity, and time of day, to uncover underlying patterns in bike usage.

    Attribute Information

    The dataset includes the following variables for each ride: - Number: A unique identifier for each trip (Trip ID). - Start Date: The date and time when the trip began. - Start Station Number: The identifier for the starting station. - Start Station: The name of the starting station. - End Date: The date and time when the trip ended. - End Station Number: The identifier for the ending station. - End Station: The name of the ending station. - Bike Number: A unique identifier for the bicycle used. - Bike Model: The model of the bicycle used. - Total Duration: The total time duration of the trip (in a human-readable format). - Total Duration (ms): The total time duration of the trip in milliseconds.

    Source This dataset was sourced directly from the Transport for London's official website, which provides open data to encourage public use and analysis. More details and related datasets can be found at Transport for London (TfL).

    Reference: Transport for London. (August 2023). TfL Cycle Hire Trip Data. Retrieved [Date Retrieved], from https://tfl.gov.uk/info-for/open-data-users/our-open-data.

  2. A

    ‘London bike sharing dataset’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Nov 12, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘London bike sharing dataset’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-london-bike-sharing-dataset-1419/a256f068/?iid=015-106&v=presentation
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    Dataset updated
    Nov 12, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    London
    Description

    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

    These licence terms and conditions apply to TfL's free transport data service and are based on version 2.0 of the Open Government Licence with specific amendments for Transport for London (the "Licence"). TfL may at any time revise this Licence without notice. It is up to you ("You") to regularly review the Licence, which will be available on this website, in case there are any changes. Your continued use of the transport data feeds You have opted to receive ("Information") after a change has been made to the Licence will be treated as Your acceptance of that change.

    Using Information under this Licence TfL grants You a worldwide, royalty-free, perpetual, non-exclusive Licence to use the Information subject to the conditions below (as varied from time to time).

    This Licence does not affect Your freedom under fair dealing or fair use or any other copyright or database right exceptions and limitations.

    This Licence shall apply from the date of registration and shall continue for the period the Information is provided to You or You breach the Licence.

    Rights You are free to:

    Copy, publish, distribute and transmit the Information Adapt the Information and Exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in Your own product or application Requirements You must, where You do any of the above:

    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.

    Exemptions This Licence does not:

    Transfer any intellectual property rights in the Information to You or any third party Include personal data in the Information Provide any rights to use the Information after this Licence has ended Provide any rights to use any other intellectual property rights, including patents, trade marks, and design rights or permit You to: Use data from the Oyster, Congestion Charging and Santander Cycles websites to populate or update any other software or database or Use any automated system, software or process to extract content and/or data, including trawling, data mining and screen scraping in relation to the Oyster, Congestion Charging and Santander Cycles websites, except where expressly permitted under a written licence agreement with TfL. 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.

    Non-endorsement This Licence does not grant You any right to use the Information in a way that suggests any official status or that TfL endorses You or Your use of the Information.

    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 ---

  3. london-bike-sharing-dataset

    • kaggle.com
    Updated Jul 4, 2025
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    서울_김주혜_1340048 (2025). london-bike-sharing-dataset [Dataset]. https://www.kaggle.com/datasets/juhyemi/london-bike-sharing-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    서울_김주혜_1340048
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Area covered
    London
    Description

    Dataset

    This dataset was created by 서울_김주혜_1340048

    Released under Apache 2.0

    Contents

  4. Key players in the bike sharing market of the UK 2022

    • statista.com
    Updated Jul 2, 2025
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    Statista (2025). Key players in the bike sharing market of the UK 2022 [Dataset]. https://www.statista.com/statistics/1405623/bike-sharing-market-united-kingdom-key-players/
    Explore at:
    Dataset updated
    Jul 2, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United Kingdom
    Description

    In 2022, ** percent of the bike sharing market in the United Kingdom (UK) was held by the Estonian mobility company Bolt. Bolt, Lime and Voi, the top three brands in the bike sharing market alone, account for ** percent bike sharing market in the UK.

  5. D

    Smart Bike-Sharing Infrastructure Market Research Report 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jun 28, 2025
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    Dataintelo (2025). Smart Bike-Sharing Infrastructure Market Research Report 2033 [Dataset]. https://dataintelo.com/report/smart-bike-sharing-infrastructure-market
    Explore at:
    pptx, pdf, csvAvailable download formats
    Dataset updated
    Jun 28, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Smart Bike-Sharing Infrastructure Market Outlook



    According to our latest research, the global smart bike-sharing infrastructure market size reached USD 5.27 billion in 2024, driven by the rapid adoption of sustainable urban mobility solutions and technological advancements. The market demonstrated a robust growth trend, registering a CAGR of 14.2% from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 16.23 billion, propelled by increasing urbanization, government initiatives for green transportation, and the proliferation of IoT-enabled bike-sharing systems worldwide. As per our latest research, the sector's expansion is further catalyzed by the integration of smart technologies and the growing emphasis on reducing carbon emissions in urban environments.




    A major growth factor for the smart bike-sharing infrastructure market is the escalating demand for eco-friendly transportation in densely populated urban centers. Governments and city planners across the globe are prioritizing sustainable mobility solutions to combat traffic congestion and air pollution. The integration of smart bike-sharing systems aligns seamlessly with these objectives, offering a low-carbon alternative that also eases the burden on public transit networks. The proliferation of dedicated cycling lanes, public awareness campaigns about environmental conservation, and incentives such as subsidies and tax breaks for using shared bikes are further accelerating market adoption. Additionally, the pandemic-induced shift toward open-air, socially distanced commuting has reinforced the value proposition of bike-sharing schemes, prompting both public and private stakeholders to invest heavily in infrastructure upgrades and network expansion.




    Technological innovation stands out as another pivotal driver in the smart bike-sharing infrastructure market. The convergence of IoT, GPS, AI, and mobile connectivity has revolutionized the user experience, making bike-sharing more accessible, secure, and efficient. Advanced software platforms now enable seamless bike reservations, real-time tracking, dynamic pricing, and predictive maintenance, minimizing operational downtime and maximizing fleet utilization. The integration of digital payment solutions and user-friendly mobile applications has further simplified the process for end-users, eliminating traditional barriers to entry. As cities move toward smart urban ecosystems, the interoperability of bike-sharing platforms with other modes of transport, such as buses, trams, and ride-hailing services, is creating a holistic, multimodal mobility landscape, thereby amplifying the market's growth trajectory.




    Another significant growth catalyst is the increasing investment from both public and private sectors in expanding and upgrading smart bike-sharing infrastructure. Urban authorities are collaborating with technology providers and mobility startups to deploy state-of-the-art docking stations, e-bikes, and data-driven management systems. These partnerships are fostering innovation and driving down operational costs through shared resources and economies of scale. Furthermore, corporate campuses and educational institutions are adopting customized bike-sharing solutions to promote green commuting among employees and students, enhancing their sustainability credentials and supporting broader environmental goals. The influx of venture capital and strategic alliances is accelerating the rollout of next-generation bike-sharing networks, positioning the market for sustained long-term growth.




    Regionally, the Asia Pacific market is leading in terms of both deployment scale and technological innovation, accounting for a significant share of global revenues. Cities like Beijing, Shanghai, and Singapore have emerged as pioneers, leveraging large-scale smart bike-sharing programs to address urban mobility challenges. Europe follows closely, with cities such as Paris, London, and Amsterdam integrating bike-sharing into their public transit frameworks. North America, while relatively nascent, is witnessing rapid growth, particularly in metropolitan areas focused on reducing carbon footprints. The Middle East and Latin America are also investing in pilot projects and public-private partnerships, reflecting the global momentum behind smart bike-sharing infrastructure.



    Component Analysis



    The component segment of the smart bike-sharing infrastructure market is divided into bikes, docking stations, software, a

  6. Bicycle sales in Great Britain 2000-2022

    • statista.com
    Updated Jul 2, 2025
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    Statista (2025). Bicycle sales in Great Britain 2000-2022 [Dataset]. https://www.statista.com/statistics/398178/bicycle-sales-in-great-britain-uk/
    Explore at:
    Dataset updated
    Jul 2, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Great Britain, United Kingdom
    Description

    The number of bicycles sold in Great Britain reached over *********** in 2022. Sales had been decreasing between 2014 and 2018, dropping to *********** that year. From 2018 sales began rising again, peaking at *********** in 2021.

    The end of the pandemic bike boom? As in many countries across Europe, the United Kingdom experienced an increased demand for bicycles during the COVID-19 pandemic, leading to the spike in demand for new bicycles in 2021. As the pandemic waned in 2022, bicycle traffic also began decreasing across the country. This decrease was, however, not nearly as strong as the decrease in bicycle sales. Next to changes in exercise and travel behavior in 2022, supply chain issues, which could lead to months-long waits for the delivery of bicycles, also impacted sales. Bike-sharing increasingly popular Bike sharing schemes have been spreading to cities across the UK in recent years, no longer making ownership of a bike a prerequisite to cycling. Relative to its population size, London has one of the largest shared bike fleets in the country, which is dominated by station-based bicycles. Manchester, however, has a particularly large free-floating shared bike fleet, with ** bikes per 10,000 inhabitants. By 2027, the revenue from the bike sharing sector in the UK is expected to reach ********** euros, a ** percent increase compared to 2022.

  7. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Click to copy link
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Svetlana Kalacheva (2024). London Bike-Share Usage Dataset [Dataset]. https://www.kaggle.com/datasets/kalacheva/london-bike-share-usage-dataset
Organization logo

London Bike-Share Usage Dataset

Bicycle journeys from Transport for London (TfL) for August 2023

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 28, 2024
Dataset provided by
Kaggle
Authors
Svetlana Kalacheva
Area covered
London
Description

Context

This dataset contains detailed records of 776,527 bicycle journeys from the Transport for London (TfL) Cycle Hire system spanning from August 1 to August 31, 2023. The TfL Cycle Hire initiative provides publicly accessible bicycles for rent across London, promoting sustainable transportation and physical fitness. This comprehensive dataset captures individual trip data, which can be utilized to analyze urban mobility patterns, station performance, and cycling preferences among London's diverse population. This dataset provides a snapshot of cycling activity during the month, including start and end details for each journey, the bicycle used, and the duration of hire.

Dataset Usage

The dataset can be used for: - Time Series Forecasting: Predict future bike rental demands based on historical usage patterns. - Geospatial Analysis: Map the start and end locations of trips to identify popular routes and areas with high cycling traffic. - Customer Behavior Analysis: Analyze the duration and frequency of rentals to understand user preferences and habits. - Predictive Maintenance: Use trip duration and frequency data to predict when bikes are likely to require maintenance or replacement. - Multivariate Analysis: Explore relationships between different variables, such as trip durations, station popularity, and time of day, to uncover underlying patterns in bike usage.

Attribute Information

The dataset includes the following variables for each ride: - Number: A unique identifier for each trip (Trip ID). - Start Date: The date and time when the trip began. - Start Station Number: The identifier for the starting station. - Start Station: The name of the starting station. - End Date: The date and time when the trip ended. - End Station Number: The identifier for the ending station. - End Station: The name of the ending station. - Bike Number: A unique identifier for the bicycle used. - Bike Model: The model of the bicycle used. - Total Duration: The total time duration of the trip (in a human-readable format). - Total Duration (ms): The total time duration of the trip in milliseconds.

Source This dataset was sourced directly from the Transport for London's official website, which provides open data to encourage public use and analysis. More details and related datasets can be found at Transport for London (TfL).

Reference: Transport for London. (August 2023). TfL Cycle Hire Trip Data. Retrieved [Date Retrieved], from https://tfl.gov.uk/info-for/open-data-users/our-open-data.

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