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http://dx.doi.org/10.12815/kits.2020.19.6.118

Dynamic Pricing Based on Reinforcement Learning Reflecting the Relationship between Driver and Passenger Using Matching Matrix  

Park, Jun Hyung (Dept. of Computer Engineering, Univ. of Hongik)
Lee, Chan Jae (Dept. of Artificial Intelligence.Big Data, Univ. of Hongik)
Yoon, Young (Dept. of Computer Engineering, Univ. of Hongik)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.6, 2020 , pp. 118-133 More about this Journal
Abstract
Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.
Keywords
Reinforcement Learning; Dynamic Pricing; Matching Matrix; Relationship of Driver and Passenger; Mobility as a Service;
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1 Bertsimas D. and Perakis G.(2006), Dynamic pricing: A learning approach. In Mathematical and computational models for congestion charging, Springer, Boston, MA, pp.45-79.
2 Campello R. J., Moulavi D., Zimek A. and Sander J.(2015), "Hierarchical density estimates for data clustering, visualization, and outlier detection," ACM Transactions on Knowledge Discovery from Data(TKDD), vol. 10, no. 1, pp.1-51.
3 Castillo J. C., Knoepfle D. and Weyl G.(2017), "Surge pricing solves the wild goose chase," In Proceedings of the 2017 ACM Conference on Economics and Computation, pp.241-242.
4 Ester M., Kriegel H. P., Sander J. and Xu X.(1996), "A density-based algorithm for discovering clusters in large spatial databases with noise," KDD-96 Proceedings, vol. 96, no. 34, pp.226-231.
5 Guo S., Liu Y., Xu K. and Chiu D. M.(2017), "Understanding ride-on-demand service: Demand and dynamic pricing," In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, pp.509-514.
6 Haws K. L. and Bearden W. O.(2006), "Dynamic pricing and consumer fairness perceptions," Journal of Consumer Research, vol. 33, no. 3, pp.304-311.   DOI
7 Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G. and Petersen S.(2015), "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, pp.529-533.   DOI
8 Karypis G., Han E. H. and Kumar V.(1999), "Chameleon: Hierarchical clustering using dynamic modeling," Computer, vol. 32, no. 8, pp.68-75.   DOI
9 Lu A., Frazier P. and Kislev O.(2018), Surge Pricing Moves Uber's Driver Partners, Available at SSRN 3180246.
10 Mnih V., Kavukcuoglu K., Silver D., Graves A., Antonoglou I., Wierstra D. and Riedmiller M.(2013), Playing atari with deep reinforcement learning, arXiv preprint arXiv:1312.5602.
11 Samworth R. J.(2012), "Optimal weighted nearest neighbour classifiers," The Annals of Statistics, vol. 40, no. 5, pp.2733-2763.   DOI
12 Song J., Cho Y. J., Kang M. H. and Hwang K. Y.(2020), "An Application of Reinforced Learning-Based Dynamic Pricing for Improvement of Ridesharing Platform Service in Seoul," Electronics, vol. 9, no. 11, p.1818.   DOI
13 Sutton R. S. and Barto A. G.(2018), Reinforcement learning: An introduction, MIT Press.
14 Wu T., Joseph A. D. and Russell S. J.(2016), Automated pricing agents in the on-demand economy, University of California.
15 Van Otterlo M. and Wiering M.(2012), "Reinforcement learning and markov decision processes," In Reinforcement Learning, Springer, Berlin, Heidelberg, pp.3-42.