Modeling and Analysis of Uber’s Rider Pricing
Available Online 20 December 2019.
- https://doi.org/10.2991/aebmr.k.191217.127How to use a DOI?
- sharing economy, ride-hailing systems, dynamic pricing, econometrics, big data analysis
- A bustling financial center and a diverse cultural cosmopolitan, New York City (NYC)’s transportation system has always been an interesting topic for academics and various industries. The patterns and features of the transportation system, including traditional means of travel such as taxis and subways as well as innovative tools like ride-hailing platforms (Uber, Lyft, etc.), are important research topics in economics, transportation, and operational research fields. Thanks to Uber Developer’s family of APIs, we now have a precious opportunity to acquire real-time operational data (price, ETA etc.) to further our analysis. This project aims to analyze the data of different locations, weathers, times, and dates (intraday and mid-week), using the acquired Uber operational data in New York City and applying time series analysis, statistical regression and prediction in econometrics. By calculating and analyzing the impact of these factors on Uber riders' payment amounts, we obtain conclusions that are instructive and beneficial in practice.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Junzhi Chao PY - 2019 DA - 2019/12/20 TI - Modeling and Analysis of Uber’s Rider Pricing BT - Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019) PB - Atlantis Press SP - 693 EP - 711 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.191217.127 DO - https://doi.org/10.2991/aebmr.k.191217.127 ID - Chao2019 ER -