research-article

Understanding the Operational Dynamics of Mobility Service Providers: A Case of Uber

Authors:

Satish V. UkkusuriAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 6, Issue 2

Article No.: 12, Pages 1 - 20

https://doi.org/10.1145/3378888

Published: 07 February 2020 Publication History

Abstract

The rise of mobility service providers (MSPs) is reforming the traditional taxi service (TTS) market. MSPs differ from TTS with the core idea of using technology to optimally match riders with drivers, features like ride-sharing and surge pricing, and are not entry-regulated. It is of great significance to understand how MSPs operate and how we can integrate them with TTS for efficient urban mobility. Unfortunately, little is known about MSPs due to limited data revealed by them. In this study, we collect and mine the trajectory data of online drivers who serve Uber (one of the largest MSP) to demystify how Uber drives their drivers. We analyze the trip patterns of different Uber services and reveal their market share, trip metrics, and the spatial distributions of trip origins and destinations. We explore how MSPs improve the driver-rider matching efficiency and empirically validate the enormous efficiency gap between TTS and MSPs. In the end, we debunk the surge price as an instrument to restore driver-rider balance theory and show that drivers choose to chase or avoid the high surge areas depending on various other factors such as traffic congestion, time and location, and availability of alternate travel options as well. The results of this article provide insightful knowledge about the supply side of MSPs and contribute to new ideas on improving TTS and regulating MSPs.

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Cited By

Guo SQian X(2024)Optimal Drive-By Sensing in Urban Road Networks With Large-Scale Ridesourcing VehiclesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339474825:10(14389-14400)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3394748
Alattar M(2024)Spatiotemporal Exploration of Ridesharing Services Ridership through Geovisualization: A Case Study of the New York City RegionThe Professional Geographer10.1080/00330124.2024.2398242(1-13)Online publication date: 7-Oct-2024
https://doi.org/10.1080/00330124.2024.2398242
Chen XLei ZUkkusuri S(2024)Modeling the influence of charging cost on electric ride-hailing vehiclesTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104514160(104514)Online publication date: Mar-2024
https://doi.org/10.1016/j.trc.2024.104514
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Index Terms

Understanding the Operational Dynamics of Mobility Service Providers: A Case of Uber
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 6, Issue 2

June 2020

192 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3375460

Editor:
Walid G. Aref
Purdue University, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2020

Accepted: 01 December 2019

Revised: 01 June 2019

Received: 01 December 2018

Published in TSAS Volume 6, Issue 2

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Cited By

Guo SQian X(2024)Optimal Drive-By Sensing in Urban Road Networks With Large-Scale Ridesourcing VehiclesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339474825:10(14389-14400)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3394748
Alattar M(2024)Spatiotemporal Exploration of Ridesharing Services Ridership through Geovisualization: A Case Study of the New York City RegionThe Professional Geographer10.1080/00330124.2024.2398242(1-13)Online publication date: 7-Oct-2024
https://doi.org/10.1080/00330124.2024.2398242
Chen XLei ZUkkusuri S(2024)Modeling the influence of charging cost on electric ride-hailing vehiclesTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104514160(104514)Online publication date: Mar-2024
https://doi.org/10.1016/j.trc.2024.104514
Ali KSivasubramanian R(2024)Understanding the Nexus Between Techno-Stress, Psychological Well-Being, and the Moderating Role of Job Resources in the Gig EconomyEmployee Responsibilities and Rights Journal10.1007/s10672-024-09505-5Online publication date: 28-May-2024
https://doi.org/10.1007/s10672-024-09505-5
ElDahshan KAlHabshy AAbozeid A(2024)New directions in motion-prediction-based systemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09760-628:13-14(7687-7700)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00500-024-09760-6
Teusch JGremmel JKoetsier CJohora FSester MWoisetschläger DMüller J(2023)A Systematic Literature Review on Machine Learning in Shared MobilityIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33343934(870-899)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3334393
Feng DZhang HSong X(2023)Noise filter method for mobile trajectory dataHandbook of Mobility Data Mining10.1016/B978-0-443-18428-4.00003-7(35-50)Online publication date: 2023
https://doi.org/10.1016/B978-0-443-18428-4.00003-7
Flor MOrtuño AGuirao B(2022)Ride-hailing services: Competition or complement to public transport to reduce accident rates. The case of MadridFrontiers in Psychology10.3389/fpsyg.2022.95125813Online publication date: 27-Jul-2022
https://doi.org/10.3389/fpsyg.2022.951258
Nian GHuang JSun D(2022)Exploring Built Environment Influence on Taxi Vacant Time in Megacities: A Case Study of Chongqing, ChinaJournal of Advanced Transportation10.1155/2022/30969012022(1-14)Online publication date: 21-Jan-2022
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Qian XGuo SAggarwal V(2022)DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioningTransportation Research Part C: Emerging Technologies10.1016/j.trc.2022.103923145(103923)Online publication date: Dec-2022
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