NSF Abstract

CiBiC (Civic Bicycle Commuting) addresses the spatial mismatch gap by increasing the number of people who are willing to bike to work, using a community-driven group bicycling system. We propose that biking will increase transportation satisfaction, lower transportation costs, and increase flexibility to respond to employment and housing opportunities, while our unique technosocial, cyberphysical approach will enhance recruitment and retention, foster community engagement, and inform future plans for physical bicycling infrastructure. The research is situated in Los Angeles, California, within a pilot study area whose residents are predominantly lower income people of color. It brings together community organizations that support them with bicycling advocates to help design and execute the approach in partnership with university and industry collaborators, a model that will improve engagement within the project and applicability beyond it.

CiBiC will design and deploy a cloud-supported mobile app that engages communities in organizing demand-aware bike "flows", or group commuting corridors. It aims to generate emergent bike-to-work communities of practice that commute together, pair less skilled riders with more experienced ones, and can incorporate eBikes to enhance inclusion. Flows are planned around enjoyment and "bikability" as well as efficiency and connectivity to public transportation. Riders will use the CiBiC app to join these flows in small groups to increase safety and facilitate adoption. Participating bicycle commuters are further invited to co-create the system itself through a participatory data-driven public art component that provides visualization of flows and the activity of pods and riders within them. This promotes participation of community members that will influence both this project and future system designs. The participatory public artwork will be displayed in public spaces, such as transit hubs and community centers, providing entry points for engagement and stimulating collective reflection, evaluation, and co-design. CiBiC combines the day-to-day support of a smartphone app with novel, data-driven create expression to enhance feelings of collective identity, inclusion and ownership and sustain its ridership. Its route planning emphasizes a broader range of metrics than trip efficiency, providing additional degrees of freedom for route planning that could have lasting impact on mapping and navigation tools. With its exploration of potential machine learning support for emergence and tuning of new flows in new locations based on local transportation demand, CiBiC aims to maximize future scalability, transferability, and impact.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Abstract #2133309