Ride Hailing

Ride-hailing research focuses on optimizing the efficiency and fairness of on-demand transportation systems, primarily through improved algorithms for matching riders and drivers, and for dynamically rebalancing vehicle supply across a city. Current research employs machine learning models, including deep reinforcement learning, graph convolutional networks, and transformer networks, to predict demand, optimize vehicle routing and pricing, and enhance fairness for both riders and drivers. These advancements aim to improve system performance metrics such as wait times, driver income, and overall platform revenue while addressing equity concerns related to service accessibility in underserved communities.