Deep Q-Network Based Route Scheduling for Transportation Network Company Vehicles
Document Type
Conference Proceeding
Publication Date
1-1-2018
Abstract
The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiencyearnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.
Publication Source (Journal or Book title)
Proceedings - IEEE Global Communications Conference, GLOBECOM
Recommended Citation
Shi, D., Ding, J., Errapotu, S., Yue, H., Xu, W., Zhou, X., & Pan, M. (2018). Deep Q-Network Based Route Scheduling for Transportation Network Company Vehicles. Proceedings - IEEE Global Communications Conference, GLOBECOM https://doi.org/10.1109/GLOCOM.2018.8647546