{ "data_id": "42713", "name": "Bike_Sharing_Demand", "exact_name": "Bike_Sharing_Demand", "version": 3, "version_label": "1", "description": "**Author**: Hadi Fanaee-T and Joao Gama \r\n**Source**: [original](http:\/\/archive.ics.uci.edu\/ml\/datasets\/Bike+Sharing+Dataset) - 01-01-2013 \r\n**Please cite**: Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15 \r\n\r\nBike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return \r\nback has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return \r\nback at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of \r\nover 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, \r\nenvironmental and health issues. \r\n\r\nApart from interesting real world applications of bike sharing systems, the characteristics of data being generated by\r\nthese systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration\r\nof travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into\r\na virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important\r\nevents in the city could be detected via monitoring these data.\r\n\r\nBike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions,\r\nprecipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to \r\nthe two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is \r\npublicly available in http:\/\/capitalbikeshare.com\/system-data. We aggregated the data on two hourly and daily basis and then \r\nextracted and added the corresponding weather and seasonal information. Weather information are extracted from http:\/\/www.freemeteo.com. \r\n\r\nUse of this dataset in publications must be cited to the following publication:\r\nFanaee-T, Hadi, and Gama, Joao, \"Event labeling combining ensemble detectors and background knowledge\", \r\nProgress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007\/s13748-013-0040-3.\r\n\r\nAttributes: \r\n- season : season (1:spring, 2:summer, 3:fall, 4:winter) \r\n- yr : year (0: 2011, 1:2012) \r\n- mnth : month ( 1 to 12) \r\n- hr : hour (0 to 23) \r\n- holiday : weather day is holiday or not (extracted from http:\/\/dchr.dc.gov\/page\/holiday-schedule) \r\n- weekday : day of the week \r\n- workingday : if day is neither weekend nor holiday is 1, otherwise is 0. \r\n- weathersit: \r\n - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\r\n - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\r\n - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\r\n - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \r\n- temp : Normalized temperature in Celsius. The values are divided to 41 (max) \r\n- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max) \r\n- hum: Normalized humidity. The values are divided to 100 (max) \r\n- windspeed: Normalized wind speed. The values are divided to 67 (max) \r\n- casual: count of casual users \r\n- registered: count of registered users \r\n- cnt: count of total rental bikes including both casual and registered \r\n\r\nThis version was cleaned up up by Joaquin Vanschoren: \r\n- Category labels replaced by category names (season, weathersit, year) \r\n- Turned back normalization for temperature and windspeed for interpretability \r\n- Renamed features for readability \r\n- Note: the correct order of the seasons seems to be ['summer', 'winter', 'spring', 'fall'] ", "format": "arff", "uploader": "Joaquin Vanschoren", "uploader_id": 2, "visibility": "public", "creator": "\"Hadi Fanaee-T and Joao Gama\"", "contributor": null, "date": "2020-10-17 03:23:51", "update_comment": null, "last_update": "2020-10-17 03:23:51", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22044628\/dataset", "default_target_attribute": "count", "row_id_attribute": null, "ignore_attribute": "\"casual\",\"registered\"", "runs": 0, "suggest": { "input": [ "Bike_Sharing_Demand", "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environment " ], "weight": 5 }, "qualities": { "NumberOfInstances": 17379, "NumberOfFeatures": 13, "NumberOfClasses": 0, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 11, "NumberOfSymbolicFeatures": 2, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "PercentageOfMissingValues": 0, "AutoCorrelation": -63.92513522844976, "PercentageOfNumericFeatures": 84.61538461538461, "Dimensionality": 0.0007480292306807066, "PercentageOfSymbolicFeatures": 15.384615384615385, "MajorityClassPercentage": null, "MajorityClassSize": null }, "tags": [], "features": [ { "name": "count", "index": "14", "type": "numeric", "distinct": "869", "missing": "0", "target": "1", "min": "1", "max": "977", "mean": "189", "stdev": "181" }, { "name": "season", "index": "0", "type": "nominal", "distinct": "4", "missing": "0", "distr": [] }, { "name": "year", "index": "1", "type": "numeric", "distinct": "2", "missing": "0", "min": "2011", "max": "2012", "mean": "2012", "stdev": "1" }, { "name": "month", "index": "2", "type": "numeric", "distinct": "12", "missing": "0", "min": "1", "max": "12", "mean": "7", "stdev": "3" }, { "name": "hour", "index": "3", "type": "numeric", "distinct": "24", "missing": "0", "min": "0", "max": "23", "mean": "12", "stdev": "7" }, { "name": "holiday", "index": "4", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "0" }, { "name": "weekday", "index": "5", "type": "numeric", "distinct": "7", "missing": "0", "min": "0", "max": "6", "mean": "3", "stdev": "2" }, { "name": "workingday", "index": "6", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "1", "stdev": "0" }, { "name": "weather", "index": "7", "type": "nominal", "distinct": "4", "missing": "0", "distr": [] }, { "name": "temp", "index": "8", "type": "numeric", "distinct": "50", "missing": "0", "min": "1", "max": "41", "mean": "20", "stdev": "8" }, { "name": "feel_temp", "index": "9", "type": "numeric", "distinct": "65", "missing": "0", "min": "0", "max": "50", "mean": "24", "stdev": "9" }, { "name": "humidity", "index": "10", "type": "numeric", "distinct": "89", "missing": "0", "min": "0", "max": "1", "mean": "1", "stdev": "0" }, { "name": "windspeed", "index": "11", "type": "numeric", "distinct": "30", "missing": "0", "min": "0", "max": "57", "mean": "13", "stdev": "8" }, { "name": "casual", "index": "12", "type": "numeric", "distinct": "322", "missing": "0", "ignore": "1", "min": "0", "max": "367", "mean": "36", "stdev": "49" }, { "name": "registered", "index": "13", "type": "numeric", "distinct": "776", "missing": "0", "ignore": "1", "min": "0", "max": "886", "mean": "154", "stdev": "151" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 2, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 2 }