Ridesharing pricing models are based on supply and demand. When demand spikes, prices surge, and when demand is low, prices are based on a flat base rate. Drivers enjoy higher profits in popular areas with surging prices, but current pricing models function in real-time, meaning that drivers only see busy areas after the areas are already busy.
As a result, both passengers and drivers lose out. Passengers wait longer and potentially pay more, while drivers try to catch up with changing rider demands. Additionally, when drivers hit the roads without a plan, they end up coasting and looking for riders, which not only wastes time and fuel, but also worsens traffic congestion.
To improve ridesharing services, Sean Qian, an assistant professor in Carnegie Mellon University's Department of Civil and Environmental Engineering and director of the Mobility Data Analytics Center, teamed up with Gridwise, a Pittsburgh startup company founded in 2016 that aims to improve on-demand ridesharing. The group received funding from the Pennsylvania Infrastructure Technology Alliance (PITA) to develop an advanced predictive model that makes ridesharing platforms more efficient. Now the Gridwise mobile application allows drivers to track real-time rider demand, as well as plan ahead of time for upcoming surges in driver demand.
Several years ago, Ryan Green, now the CEO and cofounder of Gridwise, drove for rideshare companies like Uber and Lyft. He grew tired of chasing surges and founded Gridwise in 2016 to help his fellow drivers.