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9 results for “bike share”
Data Used in [~Re] Setting Inventory Levels in a Bike Sharing Network
<p>Data used to reproduce the publication "Setting an Inventory Levels in a Bike Sharing Network" by Datner et al.</p> <p>This data correspond to the scenarios generated from the parameters given by the authors.</p>
NYC Bike Sharing Network: Time-Series Enhanced Nodes and Edges Dataset
<p>This dataset presents a comprehensive graph representation of the New York City Bike Sharing system, structured with nodes representing stations and edges delineating trips between these stations. The dataset is distinctive in integrating dynamic properties as time series data, which are meticulously updated using historical records (csv files) and live data feeds (gbfs files) provided by<a href="https://citibikenyc.com/system-data" target="_blank" rel="noopener"> NYC Bike sharing system</a>. </p> <ul> <li> <p><strong>Nodes</strong>:</p> <ul> <li><strong>Source</strong>: Data is collected from the New York City Bike Station Information API.</li> <li><strong>Attributes</strong>: <ul> <li><strong>ID</strong>: Unique identifier for each station.</li> <li><strong>Name</strong>: Name of the station.</li> <li><strong>Capacity</strong>: Number of bikes the station can accommodate.</li> <li><strong>Short ID</strong>: A condensed identifier used internally.</li> </ul> </li> <li><strong>Time Series Data</strong>: <ul> <li>Updated every 5 minutes from the Station Status API.</li> <li>Captures changes in bike availability, recording values only when they differ from previous data points.</li> </ul> </li> </ul> </li> <li> <p><strong>Edges</strong>:</p> <ul> <li><strong>Source</strong>: Compiled from trip data provided in CSV format specific to NYC Bike Sharing.</li> <li><strong>Attributes</strong>: <ul> <li><strong>Trip Counter</strong>: Total number of trips recorded.</li> <li><strong>Bike Type Counter</strong>: Counts trips made with electric versus classic bikes.</li> <li><strong>Trip Type Counter</strong>: Separates trips made by members versus casual riders.</li> <li><strong>Active Trips Tracker</strong>: Tracks the number of active trips at any given moment.</li> </ul> </li> <li><strong>Aggregation</strong>: Trip data between identical start and end points, in the same direction, are aggregated into a single edge, with time-series tracking the frequency of these trips.</li> </ul> </li> </ul>
"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Occupancy history of Seville's bike-sharing network. January-June 2021
<p>Occupancy history of Seville's bike-sharing network and station coordinates. January-June 2021 </p>
Edinburgh Bike Sharing Data
<p>Accompanying datasets for the simulation study found here: <a href="https://doi.org/10.5281/zenodo.3702267">doi</a> / <a href="https://github.com/justinnk/bss-simulation-study">GitHub</a></p> <p>Please note the restricted usecases defined in the LICENSE.</p>
Bike-sharing data Berlin from Nextbike and Call-a-Bike for 2019 and 2022
<p>This includes various data sets used to estimate cycling volume in Berlin. It contains the raw bike-sharing data as well as a routed and cleaned version thereof.</p><p> </p><p>The data is based on free-floating bike-sharing systems and is available in the form of individual trips, for each departure and starting point as well as the respective times at the minute level are known. The bike-sharing data comprises the months of April until December 2019 for the providers Nextbike and Call-a-Bike (provided by City Lab Berlin). Additionally, we web scrap the equivalent data for the months of June until December 2022 from Nextbike (web scraped data).</p><p> </p>
STRIDE Project F3 - Bike Share, Electric-Powered Pedal-Assist Bike Share, and Electric Scooter System Operation
<p>STRIDE Project F3 - Bike Share, Electric-Powered Pedal-Assist Bike Share, and Electric Scooter System Operation Data including:</p> <p>1) Birmingham, AL trip counts</p> <p>2) Mobile, AL trip counts</p> <p>3) Data Tables and GIS attribute table export</p> <p>4) Birmingham, AL maps</p> <p>5) Mobile, AL maps</p> <p>6) Routes</p> <p>7) LTS analysis</p>
Sacramento bike share surveys
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