Predicting Demand Improves Ridesharing Experience for Drivers, Passengers

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.