Designing A Project Contracted By The Capital Bikeshare

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This project plan details the intentions and requirements of a project contracted by the Capital Bikeshare. The project’s goal is to increase bike rentals by optimally selecting locations for bike rack stations. As contracted consultants, our team’s intent is to provide Capital Bikeshare with an analytic tool that evaluates locations in the Washington DC metropolitan statistical area for their fit as bike stations. The analytic tool includes visuals to supplement the scoring system to assist the Capital Bikeshare operators with their judgments.

The Capital Bikeshare system is jointly owned in the jurisdictions where it operates: the District of Columbia, Arlington County, Alexandria, and Montgomery County. Capital Bikeshare employs a
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If the variance of actual ridership explained by predicted ridership if sufficient, the model will be supported.

PROJECT SCOPE:

The Capital Bikeshare system is jointly owned in the jurisdictions where it operates: the District of Columbia, Arlington County, Alexandria, and Montgomery County. Capital Bikeshare employs a community use model where an individual checks a bike out from one of many locations and returns to the bike to the same or a different location. This project plan details the intentions and requirements of a project contracted by the Capital Bikeshare. The project’s goal is to increase bike rentals by optimally selecting locations for bike rack stations. As contracted consultant, my intent is to provide Capital Bikeshare with an analytic tool that evaluates locations in the Washington DC metropolitan statistical area for their fit as bike stations. The analytic tool includes visuals to supplement the scoring system to assist the Capital Bikeshare operators with their judgments. The success of this model may be evaluated by the statistical significance of the difference of means test between expert-selected sites and randomly-selected sites. My goal is to recommend sites that correlate to known successful rental attracting features. Thus the station sites recommends should have a predicted performance much better than randomly selected sites. While actual ridership at new locations must serve as
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