Ridesharing Price Prediction: Exploring the strategies of Dynamic Pricing

Haolun Huang

doi:10.54097/hbem.v10i.7963

Authors

Haolun Huang

DOI:

https://doi.org/10.54097/hbem.v10i.7963

Keywords:

Dynamic pricing, Rideshare, dynamically-priced labor markets.

Abstract

This paper explores the application of dynamic pricing algorithms in rideshare industries and examines the key variables that influence trip prices by analyzing Uber and Lyft Dataset of Boston in 2018. This dataset contains Uber and Lyft data mainly from November and December 2018. It contains essential variables such like distance and price for rideshare price prediction, together with many other variables which could be included in regression model such as weather and hours in the day. The Ordinary Least Square regression method is used in this paper, and results shows that the most influential and statistically significant variables on price are the distance of trip and surge multiplier which indicates traffic surge. The regression result is in accordance with the intuition that price is positively correlated with distance and price goes up during surge time. The result implies that the dynamic pricing algorithm that adjusts live prices heavily relies on the current traffic condition and distance of the trip, while the price is not determined by hour of the trip during the day and temperature.

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References

Banerjee, Siddhartha, Ramesh Johari, and Carlos Riquelme. 2016. “Dynamic Pricing in Ridesharing Platforms.” ACM SIGecom Exchanges 15 (1): 65–70. Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.

Chen, M Keith, and Michael Sheldon. n.d. “Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform,” 19. Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.

Guo, Suiming, Chao Chen, Jingyuan Wang, Yaxiao Liu, Ke Xu, and Dah Ming Chiu. 2018. “Dynamic Price Prediction in Ride-on-Demand Service with Multi-Source Urban Data.” In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 412–21. MobiQuitous ’18. New York, NY, USA: Association for Computing Machinery

Boer, Arnoud V. den. 2015. “Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions.” Surveys in Operations Research and Management Science 20 (1): 1–18. Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.

Elmaghraby, Wedad, and Pınar Keskinocak. 2003. “Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions.” Management Science 49 (10): 1287–1309. Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.

Mullainathan, Sendhil, and Jann Spiess. 2017. “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives 31 (2): 87–106. Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.

Srinivas, Rishi, B. Ankayarkanni, and R. Sathya Bama Krishna. 2021. “Uber Related Data Analysis Using Machine Learning.” In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 1148–53.

Laptev, Nikolay, Jason Yosinski, Li Erran Li, and Slawek Smyl. n.d. “Time-Series Extreme Event Forecasting with Neural Networks at Uber,” 5.

Hall, Jonathan, Cory Kendrick, and Chris Nosko. n.d. “The Effects of Uber’s Surge Pricing: A Case Study,” 8.

Wang, Xiaolei, Fang He, Hai Yang, and H. Oliver Gao. 2016. “Pricing Strategies for a Taxi-Hailing Platform.” Transportation Research Part E: Logistics and Transportation Review 93 (September): 212–31.

Rogers, Brishen. 2015. “The Social Costs of Uber.” University of Chicago Law Review Dialogue 82: 85–102.

Uber and Lyft Dataset Boston, MA, available on Kaggle, https://www.kaggle.com/datasets/brllrb/uber-and-lyft-dataset-boston-ma