Campuses:

idle drivers

Friday, October 5, 2018 - 9:45am - 10:30am
Sharon Di (Columbia University)
Vacant taxi drivers’ cruising behavior to seek the next potential passenger in a road network generates additional vehicle traveled miles, adding congestion and pollution into the road network and the environment. This study aims to employ reinforcement learning to model idle e-hailing drivers’ optimal sequential decisions in passenger-seeking. While there exist a few studies that applied Markov decision process (MDP) to taxi drivers searching behavior, these studies were primarily focused on modeling traditional taxi drivers behavior.
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