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Modelling Passengers' Reaction to Dynamic Prices in Ride-on-demand Services: A Search for the Best Fare

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Dah Ming ChiuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 4

Article No.: 136, Pages 1 - 23

https://doi.org/10.1145/3161194

Published: 08 January 2018 Publication History

Abstract

In emerging ride-on-demand (RoD) services such as Uber and Didi (in China), dynamic prices play an important role in regulating supply and demand, trying to improve the service quality for both drivers and passengers. In this paper, we take a new perspective to study RoD services besides the supply or demand, and focus on passengers' reaction to dynamic prices. Passengers' reaction can be regarded as a process of searching for the best price before getting on a car, and the searching process reflects passengers' demand elasticity -- “how eager they are requesting a ride”. We collect data of passengers' reaction from a real RoD service provider in China, and analyze the patterns of passengers' reaction. The analysis results show that both the dynamic prices and passengers' demand elasticity influence their reaction. We then adopt and extend a previous model for sequential search from a known distribution to understand passengers' reaction, and use our data to obtain the search costs under various circumstances, which could be interpreted as passengers' demand elasticity. Insights on the search cost and other relevant quantities are discussed. Our expectation is that the result of the study should be helpful not only for service providers in designing dynamic pricing algorithms, but also for passengers and policy makers in understanding the effects and implications of dynamic pricing.

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Cited By

Guo SChen CLi ZLiao CLiu YXu KZhang D(2024)Privacy Leakage From Dynamic Prices: Trip Purpose Mining as an ExampleIEEE Transactions on Mobile Computing10.1109/TMC.2024.340841923:12(12378-12395)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3408419
Guo SDeng BChen CKe JWang JLong SXu K(2024)Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.340711911:18(29890-29910)Online publication date: 15-Sep-2024
https://doi.org/10.1109/JIOT.2024.3407119
Zhou ZChen RDeng W(2024)A Delaunay triangulation based dynamic pricing approach to profit‐driven crowdsourced package deliveryIET Intelligent Transport Systems10.1049/itr2.1243618:6(984-1003)Online publication date: 19-Mar-2024
https://doi.org/10.1049/itr2.12436
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Index Terms

Modelling Passengers' Reaction to Dynamic Prices in Ride-on-demand Services: A Search for the Best Fare
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
      1. Location based services

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Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 4

December 2017

1298 pages

EISSN:2474-9567

DOI:10.1145/3178157

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 08 January 2018

Accepted: 01 October 2017

Revised: 01 August 2017

Received: 01 May 2017

Published in IMWUT Volume 1, Issue 4

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Cited By

Guo SChen CLi ZLiao CLiu YXu KZhang D(2024)Privacy Leakage From Dynamic Prices: Trip Purpose Mining as an ExampleIEEE Transactions on Mobile Computing10.1109/TMC.2024.340841923:12(12378-12395)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3408419
Guo SDeng BChen CKe JWang JLong SXu K(2024)Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.340711911:18(29890-29910)Online publication date: 15-Sep-2024
https://doi.org/10.1109/JIOT.2024.3407119
Zhou ZChen RDeng W(2024)A Delaunay triangulation based dynamic pricing approach to profit‐driven crowdsourced package deliveryIET Intelligent Transport Systems10.1049/itr2.1243618:6(984-1003)Online publication date: 19-Mar-2024
https://doi.org/10.1049/itr2.12436
Guo SShen QLiu ZChen CChen CWang JLi ZXu K(2023)Seeking Based on Dynamic Prices: Higher Earnings and Better Strategies in Ride-on-Demand ServicesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.324304524:5(5527-5542)Online publication date: May-2023
https://doi.org/10.1109/TITS.2023.3243045
Deng BGuo SChen C(2023)Optimizing Drivers’ Revenue Efficiency for Ride-On-Demand Services: A Reinforcement Learning Approach with Dynamic Price Prediction2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10448741(1-8)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10448741
Guo SChen CWang JDing YLiu YXu KYu ZZhang D(2022)A Force-Directed Approach to Seeking Route Recommendation in Ride-on-Demand Service Using Multi-Source Urban DataIEEE Transactions on Mobile Computing10.1109/TMC.2020.303327421:6(1909-1926)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TMC.2020.3033274
Wang EDing RYang ZJin HMiao CSu LZhang FQiao CWang X(2022)Joint Charging and Relocation Recommendation for E-Taxi Drivers via Multi-Agent Mean Field Hierarchical Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2020.302217321:4(1274-1290)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TMC.2020.3022173
He SShin K(2022)Spatio-Temporal Capsule-Based Reinforcement Learning for Mobility-on-Demand CoordinationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299256534:3(1446-1461)Online publication date: 1-Mar-2022
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Guo SZhang PShen Q(2022)Dynamic Adjustment Policy of Search Driver Matching Distance via Markov Decision ProcessAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95384-3_21(319-335)Online publication date: 23-Feb-2022
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Dong XGuerra EDaziano R(2021)Impact of TNC on travel behavior and mode choice: a comparative analysis of Boston and PhiladelphiaTransportation10.1007/s11116-021-10220-549:6(1577-1597)Online publication date: 9-Aug-2021
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