Developing a predictive algorithm for selecting matching candidates in dynamic carpool application

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Rideflag is a startup company developing a system for carpooling on demand. Using RideFlag's mobileapplication, riders can request a carpool and the RideFlag system matches riders routes with available driverson these routes enabling both parties to save money and reduce congestion. The system has been actively doingcarpool matches for the past 12 months. But the system is facing a scalability problem as the number of usersincrease which possible route matches increase exponentially.This collaborative project goal is to develop a new algorithm for gross filtering of possible routes to enable thesystem to scale up to a large pool of users and routes. This will allow the system to maintain its performancemetrics while being cost effective. We propose using advanced machine learning coupled with massive data todevelop this new algorithm that can adapt to new environments.