{ "info": { "author": "Yiwen Wang", "author_email": "yiwenwang9702@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "# Prediction of Bike Sharing Usage based on Citibike New York Data in 2018\n\nPlease download and open the html files to see the plots. The available plots demonstrate the stations with the highest Departures and Arrivals.\n\nTo install, please do:\n\n```bash\npip install bspred\n```\n\n## Key Variables\nArrivals: number of trips that end at a station per day. \n\nDepartures: number of trips that start at a station per day. \n\nCharacteristic prediction: prediction of bike sharing usage at a specific location, given that there are no bike stations in a vicinity of the location.\n\n## Dependency\n\nkeras\n\n## Description\n\nThis is a project in progress. \n\nThere are two kinds of prediction available. All predictions come with asymptotic 95% confidence intervals.\n\n```python\nimport bspred\nimport numpy as np\nimport pandas as pd\n\ndata = pd.DataFrame({\n'latitude':[40.75058535], \n'longitude':[-73.99468482]\n})\n```\n\n1. Prediction of bike sharing usage without current Citibike stations. \n\nPlease notice that this is not the true bike sharing usage, but rather an order of magnitude analysis.\n\n```python\nbspred.predict(data)\n```\nThe results will be: \n\nArrivals = 826.452942,\n\nDepartures = 839.163513.\n\n2. Prediction of bike sharing usage given the current active Citibike stations.\n\n```python\nbspred.predict_with_Citibike(data)\n```\n\nThe result will be a pandas dataframe of the predicted bike sharing usage at current bike stations and the newly added ones.\n\nThe last row of the dataframe, which is the prediction of bike sharing usage at the new bike station, will be:\n\nArrivals = 441.030067,\n\nDepartures = 474.2624.\n\nIf removing some current Citibike stations is desired, a list of the station names can be passed as remove_list:\n\n```python\nbspred.predict_with_Citibike(data, remove_list = [])\n```\n\nThe names can be extracted from https://member.citibikenyc.com/map/.\n\nOr, to view the current Citibike trip history data, run:\n\n```python\npath = bspred.get_path()\ndf = pd.read_csv(path + '/stations_reshaped.csv', sep=',', engine='python')\ndf\n```\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, 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