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Xu, Z., Men, C., Li, P., Jin, B., Li, G., Yang, Y., Liu, C., Wang, B. and Qie, X. (2020) When Recommender Systems Meet Fleet Management: Practical Study in Online Driver Repositioning System. Proceedings of the Web Conference 2020, New York, April 2020, 2220-2229.
https://doi.org/10.1145/3366423.3380287

has been cited by the following article:

• TITLE: Seeking for Passenger under Dynamic Prices: A Markov Decision Process Approach

  AUTHORS: Qianrong Shen

  KEYWORDS: Ride-on-Demand Service, Markov Decision Process, Dynamic Pricing, Taxi Services, Route Recommendation

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.12, December 30, 2021

  ABSTRACT: In recent years, ride-on-demand (RoD) services such as Uber and Didi are becoming increasingly popular. Different from traditional taxi services, RoD services adopt dynamic pricing mechanisms to manipulate the supply and demand on the road, and such mechanisms improve service capacity and quality. Seeking route recommendation has been widely studied in taxi service. In RoD services, the dynamic price is a new and accurate indicator that represents the supply and demand condition, but it is yet rarely studied in providing clues for drivers to seek for passengers. In this paper, we proposed to incorporate the impacts of dynamic prices as a key factor in recommending seeking routes to drivers. We first showed the importance and need to do that by analyzing real service data. We then designed a Markov Decision Process (MDP) model based on passenger order and car GPS trajectories datasets, and took into account dynamic prices in designing rewards. Results show that our model not only guides drivers to locations with higher prices, but also significantly improves driver revenue. Compared with things with the drivers before using the model, the maximum yield after using it can be increased to 28%.