I-Corps: Translation Potential of a Personalized Car-sharing System for Smart Urban Mobility

Summary

The broader impact of this I-Corps project is based on the development of a car-sharing system that not only fosters a culture of communal resource utilization but also addresses key urban challenges. By reducing the number of privately owned vehicles, this initiative can: 1) alleviate parking demands in densely populated areas, freeing up valuable space for alternative uses, 2) mitigate traffic congestion and pollution emissions, and 3) enhance access to affordable and reliable transportation for low-income and underserved communities. Collectively, the system will foster economic empowerment and social inclusion, as individuals can engage in activities that enrich their quality of life. This solution targets customer segments that find vehicle insurance, maintenance, and other ownership expenses burdensome. By paying only for usage time in a shared ownership model, users could reduce transportation costs by up to 65% compared to full ownership. Unlike classic car-sharing platforms, this technology can analyze users' travel behavior, predict and suggest trips, and maximize vehicle availability.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of an innovative approach in using travel behavior data and predictive artificial intelligence (AI) algorithms for optimizing shared mobility systems. By leveraging these technologies, the commercialized product aims to disrupt the way individual travel patterns are analyzed, predicted, and utilized, leading to more efficient and reliable car-sharing services. Particularly, machine learning algorithms are utilized to collect, analyze, and cluster travel patterns, allowing for a deeper understanding of individual travel needs and predictable future trips. Furthermore, optimization algorithms are developed to match users with complimentary travel needs, maximizing system utilization and ultimately enhancing the reliability and quality of services while reducing the cost of car-sharing. By combining these cutting-edge methodologies and applying them in the context of shared mobility systems, the project offers practical solutions to improve efficiency and address drawbacks of existing mobility services.