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Dynamic Demand-Driven Routes

Establish a hybridized model of transport, leveraging emerging tech to blend on-demand ride-sharing with public transit infrastructure.

Photo of Ryan Anderson
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The residents of Grand Rapids face a challenge in making our existing public transit system work for them. The residents with the highest need for public transit - those in underserved and marginalized communities trying to reach critical resources and places of employment - are highly distributed in a variety of locations. Furthermore, while there is significant job growth downtown in the CBD, there is also a great deal of job growth distributed in emerging, increasingly-gentrified neighborhoods in the city where residents who need those jobs may no longer be able to afford to live. 

While The Rapid can get most people from point A to point B, it's historical reliance upon a fixed-route/fixed-schedule system means that it often takes an inordinately long time to do so. Ride sharing services such as Uber and Lyft provide a more dynamic and efficient alternative, their cost is prohibitively high for daily use. The principle behind these private shared-economy services is to optimize the use of underutilized assets through technology, and I'd propose that the Rapid, in combination with its partners, deploy a fleet of city-run ride sharing vehicles whose use patterns will inform a dynamic, demand-generated system of routes and schedules.  Here's are the key steps required to do so:

1. Identify specific neighborhoods that are in most need of affordable, on-demand transportation options to pilot this approach

2. Organize a fleet of city-run vehicles (cars, or potentially bikes or electric scooters) focused on pick-ups in those designated neighborhoods

3. Create a Rapid on-demand app that mirrors the user experience of private ride-sharing apps

4. Promote the use of this app on a pilot basis in designated neighborhoods, with highly-affordable rates that approximates the cost of using Rapid

5. Use aggregated data analytics and machine learning to determine highly concentrated areas of pick-up and drop-off that will guide the creation of new routes and schedules for Rapid buses. These on-demand routes will be understood to constitute only a portion of Rapid's overall routes, and are subject to changes based upon demand.

6. Redeploy the "test fleet" of vehicles in new areas throughout the city for continued route exploration

7. Continue to monitor, through the tracking of those newly-established routes and data provided from the Rapid ride-share app, the effectiveness of the new routes and iteratively adjust them as needed.

In essence, this creates a data-driven approach to enabling better Rapid service by offering the convenience, positive user experiences, and data analytics of private consumer ride-share services.

Describe who will use your solution (1,000 characters)

This solution is designed to serve marginalized neighborhoods by using a data-driven, consumerized approach to community engagement.

Describe your solution's stage of development

  • Initial Design - you are still exploring the idea and have not tested it with users

Tell us about your team or organization (500 characters)

There's no team - just me as a concerned citizen with a history of systems innovation and technology development. It could easily be executed by a task force of City collaborators. The $50k requested below would be for app development, and assumes that the fleet of vehicles could be procured (leased, donated, borrowed) in cooperation with a partner such as Ford.

Size of your team or organization

  • I am submitting as an individual

Funding Request

  • $50,000

Describe how you would pilot your idea (1000 characters)

See above

Describe how you would measure the success of your pilot (1000 characters)

See above

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Photo of Tom Bulten
Team

Hi Ryan: I'm glad you've connected with Matt Beck  and John Vargo . On Monday, the "Propose" phase of the project ends. It would be great if you could add a photo to your proposal before then.

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