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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Analysis of Per-Ride Fare Data for Capital Bikeshare</title>
      <link>https://rip.trb.org/View/1416765</link>
      <description><![CDATA[Pricing models for bikesharing systems worldwide typically share a similar structure, consisting of a fixed membership fee that provides access to the system, and a variable usage fee for rides over a certain length of time. Capital Bikeshare stakeholder organizations would like to understand how ridership, membership, and revenue change with conversion to a pricing structure that charges riders per-ride, without a fixed membership charge.

In conjunction with the SafeTrack Metrorail service disruptions, Capital Bikeshare is offering $2 per-ride memberships to the general public. The goal of this study is to analyze the impacts to the Capital Bikeshare system that come from offering this new membership option rather than the current model of membership time periods offering unlimited short trips. 
]]></description>
      <pubDate>Tue, 19 Jul 2016 11:23:25 GMT</pubDate>
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      <title>Real-Time System Prediction &amp; Optimal Rebalancing Strategies for Public Bike Sharing Systems</title>
      <link>https://rip.trb.org/View/1401175</link>
      <description><![CDATA[The primary goal of the proposed study is to develop tools that can be used to enhance the performance of public Bicycle Sharing Systems (BSS), in particular the Capital Bikeshare. Capital Bikeshare is one of the oldest and largest BSS operating in Washington D.C., Virginia, and Maryland. In this context, the objectives of the study are three-fold:
Objective (1): Develop real-time system state prediction models of BSS. These models will be able to predict the number of departures of customers for retrieving bikes and number of arrivals of customers for returning bikes at each station by time-of-day.
Objective (2): Develop heuristic algorithms for managing the expected demand patterns. This includes development of quick heuristic algorithms that can identify optimal rebalancing schedules as demand evolves in real-time
Objective (3): Integrate the predictive models into a geographic information systems (GIS) toolkit with an easy-to-use Graphical User Interface (GUI) that visually depicts the future demand patterns (for BSS customers) and associated optimal rebalancing routes and schedules (for BSS operators).]]></description>
      <pubDate>Tue, 15 Mar 2016 18:21:10 GMT</pubDate>
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