Reference number : GC-2017-Q3-00564, amendment number : , agreement type : G, recipient legal name : Moussa, Medhat (University of Guelph), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Rideflag is a startup company developing a system for carpooling on demand. Using RideFlag's mobile_x000D_
application, riders can request a carpool and the RideFlag system matches riders routes with available drivers_x000D_
on these routes enabling both parties to save money and reduce congestion. The system has been actively doing_x000D_
carpool matches for the past 12 months. But the system is facing a scalability problem as the number of users_x000D_
increase which possible route matches increase exponentially._x000D_
This collaborative project goal is to develop a new algorithm for gross filtering of possible routes to enable the_x000D_
system to scale up to a large pool of users and routes. This will allow the system to maintain its performance_x000D_
metrics while being cost effective. We propose using advanced machine learning coupled with massive data to_x000D_
develop this new algorithm that can adapt to new environments., program purpose french : Rideflag is a startup company developing a system for carpooling on demand. Using RideFlag's mobile_x000D_
application, riders can request a carpool and the RideFlag system matches riders routes with available drivers_x000D_
on these routes enabling both parties to save money and reduce congestion. The system has been actively doing_x000D_
carpool matches for the past 12 months. But the system is facing a scalability problem as the number of users_x000D_
increase which possible route matches increase exponentially._x000D_
This collaborative project goal is to develop a new algorithm for gross filtering of possible routes to enable the_x000D_
system to scale up to a large pool of users and routes. This will allow the system to maintain its performance_x000D_
metrics while being cost effective. We propose using advanced machine learning coupled with massive data to_x000D_
develop this new algorithm that can adapt to new environments., agreement title english : Developing a predictive algorithm for selecting matching candidates in dynamic carpool application, agreement title french : Developing a predictive algorithm for selecting matching candidates in dynamic carpool application, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-12-13, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada