Evaluation of Dynamic Pricing Practices in Car Rental Companies: A Literature-Based Assessment

Abstract

Dynamic pricing is an increasingly utilized revenue management strategy in service sectors characterized by demand uncertainty and temporal fluctuations. While it has been widely implemented in industries such as airlines, hospitality, and public transportation, its adoption in the car rental sector remains relatively underexplored in academic literature. This study examines the theoretical foundations and practical relevance of dynamic pricing approaches within the context of car rental operations. The research focuses on five distinct model categories commonly discussed in the literature: rule-based models, price elasticity and regression models, optimization models, stochastic pricing models, and Markov decision processes (MDP). The study adopts a conceptual comparative analysis based on existing literature. These models are reviewed and comparatively evaluated based on their assumptions, computational complexity, and operational applicability within the car rental industry. Special emphasis is placed on demand elasticity and regression-based approaches, which appear to be particularly suitable due to their balance of analytical rigor and operational feasibility. This study is primarily a conceptual assessment of dynamic pricing frameworks within the car rental industry; the included quantitative analysis serves as an illustrative numerical example to facilitate methodological clarity, rather than an empirical investigation intended for statistical generalizability.

Keywords

• Car rental industry
• Dynamic pricing
• Revenue management
• Demand elasticity
• Pricing models

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

1. Afshar, R. R., Rhuggenaath, J., Zhang, Y., & Kaymak, U. (2023). An automated deep reinforcement learning pipeline for dynamic pricing. IEEE Transactions on Artificial Intelligence, 4(3), 428–437.
2. Armstrong, M. (2006). Recent developments in the economics of price discrimination. In: R. Blundell, W. K. Newey, & T. Persson (Ed.), Advances in economics and econometrics: Theory and applications. Cambridge University Press, 97–141.
3. Arrieta, A., Díaz-Rodríguez, N., Ser, J., Bennetot, A., Tabik, S., Barbado, A., & et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
4. Bitran, G., & Caldentey, R. (2003). An overview of pricing models for revenue management. Manufacturing & Service Operations Management, 5(3), 203–229.
5. Chaitanya, K., Gonesh, C., Saha, H., Acharya, S., & Singla, M. (2023). The impact of artificial intelligence and machine learning in digital marketing strategies. European Economic Letters, 13(3).
6. Chen, X., Owen, Z., & Pixton, C. , Simchi-Levi D. (2022). A statistical learning approach to personalization in revenue management. Management Science, 68(3), 1923–1937.
7. Dana, J. D. (1999). Equilibrium price dispersion under demand uncertainty: The roles of costly capacity and market structure. The RAND Journal of Economics, 30(4), 632–660.
8. Dazeley, R., Vamplew, P., & Cruz, F. (2023). Explainable reinforcement learning for broad-XAI: A conceptual framework and survey. Neural Computing and Applications, 35, 16893–16916.

9. Den Boer, A. V. (2015). Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys in Operations Research and Management Science, 20(1), 1–18.
10. Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287–1309.
11. Fraiberger, S., & Sundararajan, A. (2015). Peer-to-peer rental markets in the sharing economy. SSRN Electronic Journal.
12. Gajcin, J., & Dusparić, I. (2024). Redefining counterfactual explanations for reinforcement learning: Overview, challenges and opportunities. ACM Computing Surveys, 56(9), 1–33.
13. Gallego, G., & van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 40(8), 999–1020.
14. Guizzardi, A., Pons, F., Angelini, G. & E. Ranieri (2021). Big data from dynamic pricing: A smart approach to tourism demand forecasting. International Journal of Forecasting, 37(3), 1049–1060.
15. Kastius, A., & Schlößer, R. (2021). Dynamic pricing under competition using reinforcement learning. Journal of Revenue and Pricing Management, 21(1), 50–63.
16. Klein, R., Koch, S., & Steinhardt, C. (2020). A review of revenue management: Recent generalizations and advances in industry applications. European Journal of Operational Research, 284(2), 397–412.
17. Liu, C., Wang, Q., & Susilo, Y. O. (2019). Assessing the impacts of collection-delivery points to individual’s activity-travel patterns: A greener last mile alternative? Transportation Research Part E: Logistics and Transportation Review, 121, 84–99.
18. Masiero, L., Viglia, G., & Nieto-García, M. (2020). Strategic consumer behavior in online hotel booking. Annals of Tourism Research, 83, Article 102947.
19. McCloskey, D. N. (1985). The rhetoric of economics. University of Wisconsin Press.
20. Milani, S., Topin, N., Veloso, M., & Fang, F. (2024). Explainable reinforcement learning: A survey and comparative review. ACM Computing Surveys, 56(7), 1–36.
21. Strauss, A., Klein, R., & Steinhardt, C. (2018). A review of choice-based revenue management: Theory and methods. European Journal of Operational Research, 271(2), 375–387.
22. Talluri, K. T., & van Ryzin, G. J. (2004). The theory and practice of revenue management. Kluwer Academic Publishers.
23. Talluri, K., & van Ryzin, G. (2004). Revenue management under a general discrete choice model of consumer behavior. Management Science, 50(1), 15–33.
24. Wang, D., & Nicolau, J. (2017). Price determinants of sharing economy-based accommodation rental: A study of listings from 33 cities on airbnb.com. International Journal of Hospitality Management, 62, 120–131.