Abstract
Nowadays, ride-hailing services play a significant role in daily transportation. In the ride-hailing system, the temporal and spatial distribution of demand and supply in different regions is different. Therefore, it is necessary to differentiate pricing for regions based on demand and supply. Instead of setting discriminatory prices by simply dividing the whole area into some fixed regions, which failed to take into account the demand and supply dynamics over time, we developed a dynamic region-division based pricing strategy according to demand and supply in different regions, with the goal of maximizing the platform’s long-term profit. Furthermore, we perform comprehensive experiments on a real-world dataset to demonstrate the effectiveness of the proposed algorithm. The experimental results indicate that our algorithm can outperform other typical benchmark approaches.
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References
Chen, M., Shen, W., Tang, P., Zuo, S.: Dispatching through pricing: modeling ride-sharing and designing dynamic prices (2019)
Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)
Rempel, J.: A review of uber, the growing alternative to traditional taxi service. AFB AccessWorld® Maga. 51(6) (2014)
Schröder, M., Storch, D.M., Marszal, P., Timme, M.: Anomalous supply shortages from dynamic pricing in on-demand mobility. Nat. Commun. 11(1), 1–8 (2020)
Shi, B., Cao, Z., Luo, Y.: A deep reinforcement learning based dynamic pricing algorithm in ride-hailing. In: Bhattacharya, A., et al. (eds.) DASFAA 2022. LNCS, vol. 13246, pp. 489–505. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-00126-0_36
Tong, Y., Wang, L., Zhou, Z., Chen, L., Du, B., Ye, J.: Dynamic pricing in spatial crowdsourcing: a matching-based approach. In: Proceedings of the 2018 International Conference on Management of Data, pp. 773–788 (2018)
Yan, C., Zhu, H., Korolko, N., Woodard, D.: Dynamic pricing and matching in ride-hailing platforms. Naval Res. Logist. (NRL) 67(8), 705–724 (2020)
Zhao, Q., Yang, S., Qin, L., Frnti, P.: A grid-growing clustering algorithm for geo-spatial data. Pattern Recogn. Lett. 53(53), 77–84 (2015)
Acknowledgment
This paper was funded by the Humanity and Social Science Youth Research Foundation of Ministry of Education (Grant No. 19YJC790111), the Philosophy and Social Science Post-Foundation of Ministry of Education (Grant No. 18JHQ060) and Shenzhen Fundamental Research Program (Grant No. JCYJ20190809175613332).
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Shi, B., Lu, Y., Cao, Z. (2024). A Dynamic Pricing Strategy in Divided Regions for Ride-Hailing. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14326. Springer, Singapore. https://doi.org/10.1007/978-981-99-7022-3_9
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DOI: https://doi.org/10.1007/978-981-99-7022-3_9
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