Modeling and Analysis of Uber’s Rider Pricing

DOI

10.2991/aebmr.k.191217.127How to use a DOI?

Keywords

sharing economy, ride-hailing systems, dynamic pricing, econometrics, big data analysis

Abstract

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/aebmr.k.191217.127
ID  - Chao2019
ER  -