Context: Bike shares are becoming a popular alternative means of transportation. The City of Austin makes data available on >649k bike trips over 2013-2017. Content: This data includes information on bike trip start location, stop location, duration, type of bike share user. Bike station location data is also provided. Dataset Description ***Use this dataset with BigQuery*** You can use Kernels to analyze, share, and discuss this data on Kaggle, but if you’re looking for real-time updates and bigger data, check out the [data on BigQuery, too](https://cloud.google.com/bigquery/public-data/). ***austin_bikeshare_trips.csv*** * bikeid: integer id of bike * checkout_time: HH:MM:SS, see start time for date stamp * duration_minutes: int minutes of trip duration * end_station_id: integer id of end station * end_station_name: string of end station name * month: month, integer * start_station_id: integer id of start station * start_station_name: string of start station name * start_time: YYYY-MM-DD HH:MM:SS * subscriber_type: membership typ e.g. walk up, annual, other bike share, etc * trip_id: unique trip id int * year: year of trip, int ***austin_bikeshare_stations.csv*** * latitude: geospatial latitude, precision to 5 places * location: (lat, long) * longitude: geospatial longitude, precision to 5 places * name: station name, str * station_id: unique station id, int * status: station status (active, closed, moved, ACL-only) Acknowledgements: This dataset is available from [Google Public Data](https://cloud.google.com/bigquery/public-data/ ). Inspiration: * What stations are most popular? At certain times? * What are the average user trip? * Can you predict station usage to improve the ability of bike share employees to supply high-use stations?