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

RideFLAG是一家初创公司，正在开发一种按需拼车系统。使用RideFlag的移动应用程序，乘客可以申请拼车，RideFlag系统会将乘客路线与这些路线上的可用司机进行匹配，从而使双方都能节省资金并减少拥堵。在过去的12个月里，该系统一直在积极地进行拼车比赛。但随着用户数量的增加，可能匹配的路径呈指数级增长，系统面临着可扩展性问题。这个合作项目的目标是开发一种新的算法，对可能的路径进行粗略过滤，使系统能够扩展到大量的用户和路径。这将使该系统在保持其性能指标的同时具有成本效益。我们建议使用先进的机器学习和海量数据来开发这种能够适应新环境的新算法。

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.